Proceedings of the AAAI Symposium Series
The AAAI Symposium Series, previously published as AAAI Technical Reports, are held three times a year (Spring, Summer, Fall) and are designed to bring colleagues together to share ideas and learn from each other’s artificial intelligence research. The series affords participants a smaller, more intimate setting where they can share ideas and learn from each other’s artificial intelligence research. Topics for the symposia change each year, and the limited seating capacity and relaxed atmosphere allow for workshop-like interaction. The format of the series allows participants to devote considerably more time to feedback and discussion than typical one-day workshops. It is an ideal venue for bringing together new communities in emerging fields. The AAAI Spring Symposium Series is typically held during spring break (generally in March) on the west coast. The AAAI Summer Symposium Series is the newest in the annual set of meetings run in parallel at a common site. The inaugural 2023 Summer Symposium Series was held July 17-19, 2023, in Singapore. The AAAI Fall Symposium series is usually held on the east coast during late October or early November.
Adaptive Interception in Dynamic Domains: Exploration of Hybrid Reinforcement Learning in Pursuit-Evasion Tasks
This paper investigates a hybrid approach that integrates clas sical interception heuristics with reinforcement learning (RL) for solving real-time pursuit-evasion problems in both single agent and multi-agent scenarios. Drawing inspiration from Weintraub et al.’s range-limited pursuit-evasion formulation, we developed a simulation and learning architecture com bining Proximal Policy Optimization (PPO) with classical prediction-based interception. Through adaptive curriculum learning and an enhanced reward function, the hybrid agent learns to dynamically balance between geometric reason ing and learned behaviors. We first validate our approach in single-agent scenarios, then extend to a multi-agent set ting featuring coordinated pursuer teams defending stations against RL-trained evader flocks using adversarial self-play. Our evaluations reveal that the hybrid method significantly outperforms classical approaches in both settings, achiev ing a 94% capture rate versus 24% in single-agent scenarios and demonstrating robust coordination in multi-agent defense tasks against adaptive adversaries.
Copyright (c) 2026 Association for the Advancement of Artificial Intelligence
Distilling Deep Reinforcement Learning into Interpretable Fuzzy Rules: An Explainable AI Framework
Deep Reinforcement Learning (DRL) agents achieve remarkable performance in continuous control but remain opaque, hindering deployment in safety-critical domains. Existing explainability methods either provide only local insights (SHAP, LIME) or employ over-simplified surrogates failing to capture continuous dynamics (decision trees). This work proposes a Hierarchical Takagi-Sugeno-Kang (TSK) Fuzzy Classifier System (FCS) distilling neural policies into human-readable IF-THEN rules through K-Means clustering for state partitioning and Ridge Regression for local action inference. Three quantifiable metrics are introduced: Fuzzy Rule Activation Density (FRAD) measuring explanation focus, Fuzzy Set Coverage (FSC) validating vocabulary completeness, and Action Space Granularity (ASG) assessing control mode diversity. Dynamic Time Warping (DTW) validates temporal behavioral fidelity. Empirical evaluation on Lunar Lander Continuous shows the Triangular membership function variant achieves 81.48% ± 0.43% fidelity, outperforming Decision Trees by 21 percentage points. The framework exhibits statistically superior interpretability (FRAD = 0.814 vs. 0.723 for Gaussian, p<0.001) with low MSE (0.0053) and DTW distance (1.05). Extracted rules such as “IF lander drifting left at high altitude THEN apply upward thrust with rightward correction” enable human verification, establishing a pathway toward trustworthy autonomous systems.
Cloud-Orchestrated Autonomous Bioreactor Arrays for Closed-Loop Strain Characterization
Biomanufacturing of biofuels, food, and energetic materials is increasingly important for defense, security, and disaster response operations, where supply-chain resilience and field-forward production are critical. Here, we present a cloud-orchestrated, edge-executed autonomous biomanufacturing platform built around an open-source, parallel mini-bioreactor array. The system integrates in situ sensing, state and growth-parameter estimation under uncertainty, and closed-loop Bayesian optimization to autonomously plan and execute experiments. We show that the platform identifies the optimal growth temperature of Escherichia coli in a single continuous run completed in under 24 hours, and enables parallel screening of Rhodopseudomonas palustris to identify favorable carbon sources and temperatures. This work demonstrates a deployable sense–reason–act autonomy loop on a real physical system.
Uncertainty-of-Information-Driven GAN (UoI GAN): Quantifying and Communicating Uncertainty to Decision-Makers
Generative Adversarial Networks (GANs) are cutting-edge machine learning algorithms that can generate realistic data like images and time series. Their application has been explored in autonomous systems, fraud detection, and medical diagnostics. The “black box” nature and intrinsic instability of these models pose significant and frequently hidden risks. Decision-makers struggle to interpret GAN outputs because, unlike predictive models, GANs do not explicitly measure confidence. Using simply GAN average performance statistics like the Fréchet Inception Distance can obscure important failure modes. GAN systems must be designed to explicitly expose and communicate their uncertainty. The hybrid model we propose in this research combines explicit uncertainty representation with quantitative uncertainty of information measurement. Our model re-designs the GAN to intrinsically represent uncertainty by examining output variance. It then provides a simple way for translating these complex metrics into interpretable signals that expose model behavior and limitations.
Time-Aware Two-Dimensional Packing for Slicing-Aware 3D Printing Throughput Optimization
Batching multiple parts onto a single fused-filament fabrication build plate improves throughput, but existing packing algorithms optimize for geometric density rather than print time. We introduce a slicing-aware cost model that estimates print time from part geometry and placement without invoking toolpath generation, achieving strong correlation with slicer-reported times (Pearson r = 0.90, Spearman ρ = 0.96). Evaluating packing algorithms on synthetic production builds, we find that greedy polygon-based packing matches or exceeds Large Neighborhood Search at three orders of magnitude lower compute cost—a negative result we attribute to high initial packing density leaving minimal room for iterative refinement. Against PrusaSlicer’s default auto-arrange, our method achieves a 5.7% throughput improvement (95% CI [4.7%,7.3%], N = 329), with median savings of 19.5 minutes per build.
Adversarial Causal Deception Scenarios: Preliminary Modeling and Policy Formation
As autonomous systems become increasingly integrated into society, they may be presented with falsehoods and adversarial deception that can harmfully skew their perception of the state of their environment. Still tasked with making decisions in these contexts, it is thus important for systems to be aware of and plan around potential deception for optimal decision-making. This is inherently a causal problem given that deception often masquerades inaction as action, like a phishing attempt pushing urgency by spoofing a need that is not real. Agents successfully navigating these adversarial causal deception scenarios must understand what acts can truly change the state, and where misinformation is merely advertising that it has changed. This paper provides a causal framework for considering the portions of the state that are vulnerable to action and misinformation (an Adversarial Causal Decision Network (ACDN)), and outlines a planning process (Adversarial Causal Expectimax Search (ACES)) to avoid adversarial deception attempts in pursuit of the agent's purpose.
Egocentric Team AI: Enabling Tactical Reasoning from the Operator’s View
The future of human–AI integration in high-stakes environments—ranging from defense operations to emergency response—requires AI systems that function as intuitive teammates rather than isolated tools. Yet a persistent perspective gap remains: video understanding models rely on third-person, broadcast-style views, while multi-agent reinforcement learning (MARL) systems operate on egocentric inputs but often depend on centralized critics during training, reducing the need for decentralized policies to internalize team-centric structure. As a result, agents may optimize behavior without learning policy-level representations of the tactical picture grounded in their own observations. To address this gap, we propose Egocentric Team AI, a research direction centered on learning implicit, distributed Common Operating Pictures from first-person views. Building on our prior work with the X-Ego-CS dataset and Cross-Ego Contrastive Learning (CECL), we outline how cross-egocentric representation alignment can be extended from passive video understanding to active multi-agent control. Specifically, we propose integrating cross-ego contrastive objectives into MARL within a multi-agent Doom-based environment as a testbed for decentralized, team-aware policy learning. By positioning cross-egocentric alignment as an inductive bias for decentralized coordination, this work charts a path toward embodied systems capable of adaptive cooperation without explicit communication.
Predictive Auxiliary Learning for Belief-based Multi-Agent Systems
Multi-agent reinforcement learning (MARL) under partial observability requires agents to construct reliable belief representations from limited local observations and partial information exchange. Conventional approaches rely primarily on sparse task rewards to shape these internal representations, which often leads to unstable training dynamics and slow convergence. We propose BElief-based Predictive Auxiliary Learning (BEPAL), a decentralized MARL framework that improves belief representation quality through predictive auxiliary tasks. BEPAL trains agents to maintain a "world model" that accurately estimates the global environment state and predict future dynamics from accumulated local observations and communication, providing another channel of feedback that complements sparse reward in reinforcement learning. By encouraging the hidden state to serve as a compact and informative summary of historical input, BEPAL stabilizes decentralized policy learning and accelerates convergence. The proposed approach is compatible with any decentralized MARL architectures with homogeneous agents and does not increase execution-time complexity. Experiments on Predator–Prey, Traffic Junction, Google Research Football, and RWARE show that BEPAL consistently improves learning stability and task performance compared to strong baselines, highlighting the effectiveness of predictive auxiliary learning for belief formation under partial observability.
A Unified Naming and Addressing Scheme for Hybrid DTN/NDN Communication Protocols
The diversity of networking paradigms such as Delay/Disruption Tolerant Networking (DTN) and Named Data Networking (NDN) presents interoperability challenges that limit seamless communication across heterogeneous networks due to their differing addressing and naming schemes. This paper proposes a consistent unified naming convention scheme with a prefix ”uni/...” inspired by DTNs URI prefix ”dtn://..." to facilitate compatibility and interoperability between DTN and NDN without modifying existing protocols while maintaining performance. Specifically, we design and implement a unified naming convention and protocol mapping scheme as containerized micro-services deployed in a middleware controller. The preliminary experimental results demonstrate that our hybrid approach effectively bridges the gap between DTN and NDN, enabling efficient and consistent communication for heterogeneous networked applications.
Transformer-Based Classification of Parkinson’s Disease from EEG Using BIDS-Formatted OpenNeuro Datasets
Artificial intelligence systems that support rapid and reliable decision-making under time-sensitive constraints must interpret high-dimensional sensory inputs while remaining robust to variability and noise. Electroencephalography (EEG) exemplifies such a sensing modality due to its non-stationarity, subject variability, and sensitivity to recording conditions. This study evaluates a Transformer-based framework for multi-class classification of Parkinson’s disease medication state (PD-ON, PD-OFF) and healthy controls using structured spectral and entropy-based EEG features. Rather than proposing an end-to-end raw-signal model, we assess whether attention-based sequence modeling offers advantages over classical classifiers when operating on physiologically interpretable representations. Three heterogeneous BIDS-formatted OpenNeuro datasets (PD1: n = 31; PD2: n = 50; PD3: n = 56) were evaluated using subject-wise grouped cross-validation to prevent participant-level data leakage. The Transformer achieved near-ceiling F1-scores on relatively homogeneous datasets (PD1: 99.33%; PD3: 98.35%) and competitive performance on the more heterogeneous PD2 dataset (88.36%), underscoring the impact of sensing variability on robustness. Attention analysis indicates adaptive weighting of informative temporal–spectral features. These findings provide a systematic empirical evaluation of attention-based sequence modeling under heterogeneous sensing conditions.
LLM Forensic Evaluation: Diagnosing Actionability, Uncertainty, and Human Comprehension in High-Stakes Outputs
Large language models are increasingly incorporated into decision support workflows to summarize situations, propose actions, and communicate rationale. These capabilities are valuable in time-sensitive environments, but they also introduce risks related to hallucination, overconfidence, and contextual misalignment. This paper presents Project Comprehension, a forensic evaluation framework that treats language model outputs as artifacts for post hoc analysis rather than isolated successes or failures. Project Comprehension integrates structured empirical probing across operationally grounded scenarios with human-centered annotation instruments designed to capture interpretability and perceived uncertainty. We report early results from empirical testing and scale validation using a labeling set developed to support reliable forensic judgments of model behavior. We describe a failure mode taxonomy for reasoning and communication breakdowns, and we illustrate how forensic insights can inform assurance practices, trust calibration, and human autonomy teaming. The paper concludes with recommendations for building forensic readiness into language-enabled systems used in high-stakes decision support.
Evaluating Generative Image Expansion for Long-Range Maritime Vision Tasks
Synthetic image generation is increasingly used to augment visual datasets when real-world data is limited or difficult to capture. However, generative techniques do not simply extend existing images; they actively construct contextual assumptions about background continuity, spatial relationships, and scene structure. In decision-relevant settings, these assumptions can obscure uncertainty and introduce ambiguity that affects both model behavior and human interpretation. This paper examines the use of generative image expansion to induce distance-related perceptual stress in naval vessel imagery, motivated by the needs of maritime decision support under conditions aligned with Tactical Decision-Making Under Stress (TADMUS). Rather than evaluating downstream model performance, we focus on the interpretability and contextual integrity of augmented images as perceived by human annotators. We construct a dataset of perceptually degraded image variants using multiple generative platforms and assess output quality using a structured, context-focused annotation protocol.
Causal Learning for Fault and Anomaly Detection in Unmanned Aerial Systems
Unmanned Aerial Systems (UAS) are vital to tactical autonomy applications, where they operate in contested, communication denied, and high stakes environments with limited human oversight. In such contexts, robust fault detection is not merely a safety concern but a mission critical capability where engine failures or control surface malfunctions can result in asset loss, mission failure, or compromised operational security. Traditional anomaly detection methods rely on correlation-based approaches that identify statistical deviations but fail to reveal the underlying causal mechanisms driving failures. This opacity is particularly problematic for tactical autonomy, where operators and commanders require explainable diagnostics to maintain situational awareness and trust in autonomous systems. We argue that causal learning provides a more principled and interpretable framework for understanding UAS faults. By utilizing causal learning to identify the cause-and-effect relations from telemetry data we can answer not only whether a fault occurred, but what caused it, enabling root cause analysis essential for rapid field assessment and informed decision making. We present preliminary proof-of-concept results using the ALFA (AirLab Failure and Anomaly) dataset to learn causal structures from UAS telemetry data. Our causal graphs reveal directional relationships between sensor readings, control inputs, and failure states, offering actionable insights for predictive maintenance, real time fault diagnosis, and post mission analysis that correlation-based methods cannot provide. We position causal reasoning as a foundational capability for trustworthy tactical autonomy.
A Decentralized Framework for Resource-Constrained Task Redistribution
Earth-observing satellite constellations are increasingly expected to operate autonomously in dynamic, resource-constrained, and failure-prone environments. As constellation size and heterogeneity grow, centralized and static task scheduling paradigms struggle to provide the adaptability required to maintain mission continuity under disruptions, intermittent connectivity, and limited onboard storage. This paper presents a decentralized task rescheduling framework inspired by a service-industry scheduling analogy that models satellites as resource constrained agents operating under memory restrictions, spatial access limitations, and intermittent offloading opportunities. A multi-criteria redistribution algorithm prioritizes memory availability, proximity, capability, and workload to reassign tasks following agent loss. Through large-scale simulation, we demonstrate that the proposed framework preserves high-priority task throughput and fair workload distribution despite irreversible capacity loss. The resulting system provides a physically motivated abstraction for studying resilient, decentralized scheduling and establishes a bridge between intuitive heuristic policies and future learning-based autonomy for heterogeneous low Earth orbit satellite constellations.
From Rules to Reasoning: Evolving Agentic AI for Strategy Synthesis in Multi-Agent Wargaming Environments
Contemporary conflict environments are increasingly characterized by autonomy, decentralization, and rapid adaptation. Traditional rule-based artificial intelligence systems struggle to operate effectively in such settings due to their reliance on pre-specified logic and static assumptions. Reinforcement learning (RL) has improved adaptability, yet many implementations remain reactive and myopic, optimizing local rewards rather than synthesizing higher-level strategies. This paper presents an incremental research program exploring the evolution of agentic artificial intelligence within a custom digital wargaming environment. Beginning with deterministic rule-based agents, progressing through multi-agent reinforcement learning (MARL) with memory augmentation, and culminating in an emerging paradigm of self-directed, research-capable agents, this work examines how autonomy can be extended beyond action selection toward independent strategy discovery. Using a dynamic 3v3 drone “capture-the-flag" simulation, we demonstrate measurable gains in coordination, adaptability, and tactical effectiveness, and outline a next-phase architecture in which agents autonomously explore, evaluate, and synthesize strategies across episodes. These results contribute to ongoing efforts to move from reactive autonomy toward genuinely agentic systems suitable for complex, contested operational domains.
Repair2Skill: A Vision–Language–Action Framework for Robotic Furniture Repair
Repair tasks differ fundamentally from robotic assembly, requiring fault localization, reverse dependency reasoning, and safe interaction with structurally degraded objects. While recent vision-language models have shown promise for high-level task reasoning, applying them to repair scenarios demands careful integration with perception, dependency modeling, and execution constraints. This paper presents Repair2Skill, a vision–language–action framework for robotic furniture repair that focuses on repair planning and simulation-based execution validation. The system detects damaged furniture components using a lightweight SSDLite-MobileNetV3 detector trained on procedurally generated synthetic damage data, constructs repair-oriented dependency graphs, and generates structured repair plans using a constrained large language model. Repair plans are restricted to a fixed action vocabulary and strict JSON schema to ensure deterministic parsing and feasibility. Generated plans are validated and visualized in a PyBullet simulation environment using kinematic action primitives, enabling interpretable verification of action ordering and dependency resolution without contact-rich physical manipulation. Experimental results demonstrate reliable part-damage association, achieving 76.8% mAP on real images, 100% valid repair plan generation under strict constraints, and stable simulation based validation across diverse repair scenarios. These results suggest that lightweight, constrained vision-language pipelines can support autonomous repair planning in bounded domains and provide a foundation for future work on physically grounded robotic repair.
Aerial-borne Data Management Center (ADMC)
Crisis management (CM) for critical infrastructures, natural disasters such as wildfires and hurricanes, terrorist actions, or civil unrest requires high-speed communications and connectivity, and access to high-performance computational resources to deliver timely dynamic responses to the crisis being managed by different first responders.CMsystems should detect, recognize, and disseminate huge amounts of heterogeneous dynamic events that operate at different speeds and formats. Furthermore, the processing of crisis events and the development of real-time responses are major research challenges when the communications and computational resources needed by CM stakeholders are not available or severely degraded by the crisis. The main goal of the research presented in this paper is to utilize Unmanned Autonomous Systems (UAS) to provide an Aerial-borne Data Management Center (ADMC) that will provide the required communications services and the computational resources that are critically needed by first responders. In our approach to develop an ADMC architecture, we utilize a set of flexible Unmanned Aerial Systems (UAS) that can be dynamically composed to meet the communications and computational requirements of CM tasks. The ADMC services will be modeled as a deep neural network (DNN) mass transport approach to cover a distributed target in a decentralized manner. Furthermore, our analysis proves the stability and convergence of the proposed DNN-based mass transport for a team of UAS (e.g., quadcopters), where each quadcopter uses a feedback nonlinear control to independently attain the intended coverage trajectory in a decentralized manner.
Reasoning Knowledge-Gap in Drone Planning via LLM-based Active Elicitation
Human-AI joint planning in Unmanned Aerial Vehicles (UAVs) typically relies on control handover when facing environmental uncertainties, which is often inefficient and cognitively demanding for non-expert operators. To address this, we propose a novel framework that shifts the collaboration paradigm from control takeover to active information elicitation. We introduce the Minimal Information Neuro-Symbolic Tree (MINT), a reasoning mechanism that explicitly structures knowledge gaps regarding obstacles and goals into a queryable format. By leveraging large language models, our system formulates optimal binary queries to resolve specific ambiguities with minimal human interaction. We demonstrate the efficacy of this approach through a comprehensive workflow integrating a vision-language model for perception, voice interfaces, and a low-level UAV control module in both high-fidelity NVIDIA Isaac simulations and real-world deployments. Experimental results show that our method achieves a significant improvement in the success rate for complex search-and-rescue tasks while significantly reducing the frequency of human interaction compared to exhaustive querying baselines.
Communication-as-Control: Intent-Aware Interaction for Scalable Multi-Agent Coordination
Uncrewed Aerial Systems (UAS) increasingly operate as large, heterogeneous teams under partial observability. In such settings, coordination depends on how agents infer, represent, and act upon uncertainty about one another: belief divergence is a natural consequence of asymmetric sensing and latent intent, and naive communication mechanisms do not scale with team size. We argue for a reframing of interaction as communication-as-control, grounded in a small set of tightly coupled capabilities that link sensing to distributed execution under interaction constraints. Rather than prescribing a fixed pipeline, coordination in such teams relies on several interdependent capabilities. First, agents must perform implicit intent inference without communication, forming uncertainty-aware beliefs over teammate intent and progress from observable behavior. Second, such uncertainty can inform selective explicit interaction, with targeted, minimal exchanges invoked only when unresolved ambiguity becomes consequential for joint decision-making. Third, uncertainty arising from both implicit inference and explicit interaction can be leveraged to support belief-guided coordination and adaptation when alignment is partial, delayed, or unattainable. This makes selective interaction a scalability mechanism: as teams grow in size and heterogeneity, coordination remains feasible only if communication is sparse, recipient-specific, and tied to decision relevance.
AI-Against-AI Conflict in Distributed Tactical Autonomy
Distributed autonomy is becoming the dominant architectural paradigm for multi-agent and multi-Uncrewed Aerial Systems (UAS) that must sense, decide, and act under uncertainty, communication constraints, and partial observability. As autonomy pipelines become increasingly learned, adaptive, and coordination-driven, safe collective behavior depends on the integrity of local information and coordination interfaces through which independently acting agents compose team-level behavior. This paper argues that AI advances are not just enhancing our operations; adversaries are leveraging AI against these systems, introducing AI-against-AI conflict, in which AI-enabled, coordinated adversaries strategically shape observations, communication, and outcome feedback - through deception, information denial, communication disruption, and physical or cyber-induced perturbations - across the autonomy pipeline to induce cascading team-level coordination failures. At an autonomy-level abstraction spanning cyber and physical influence, we describe how coordinated, sequential, and stealth-constrained manipulation can compound across decision cycles and act as a force multiplier, producing abrupt team-level breakdowns without overt component failure. We conclude that future research in tactical autonomy must move beyond robustness to noise and faults, and instead develop foundations for preserving safe collective behavior under strategic, adaptive adversarial influence.
On the Utility and Limitations of the MSTAR Dataset for Deep Learning-Based SAR Target Recognition
The DARPA Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset has served as a foundational benchmark for synthetic aperture radar (SAR)–based automatic target recognition (ATR) research for more than two decades. In recent years, it has been widely adopted for training and evaluating deep learning models, with reported classification accuracies often exceeding 95%. While these results demonstrate the effectiveness of modern neural architectures, they also raise concerns regarding the representativeness and continued utility of MSTAR as a proxy for operational SAR ATR tasks. In this paper, we examine the strengths and limitations of the MSTAR dataset in the context of deep learning–based model development. We present representative performance results from a convolutional neural network trained on MSTAR imagery, illustrating how dataset characteristics—such as centered targets, fixed chip dimensions, limited clutter, and single-object scenes—lead to artificially easy classification problems. We further contrast these results with preliminary experiments on the ATRNet-STAR dataset, where modest perturbations including target offset and rotation produce substantial performance degradation. Collectively, these findings highlight the need for more realistic SAR datasets and evaluation protocols. This paper is intended as a work in progress, with ongoing analysis of embedding-space structure and robustness metrics to be reported in future revisions.
Symbolic Mediation of Language-Based Decision Support in Tactical Contexts
Language-based AI systems are increasingly explored as decision-support tools in tactical and operational contexts, where timely interpretation and action are critical. While recent advances enable language models to generate contextually grounded responses, their outputs are typically delivered as free-form text, placing interpretive burden on human decision-makers operating under time pressure. This paper addresses this representational gap by introducing a neuro-symbolic approach that mediates language model outputs through symbolic representations. We present a system architecture that transforms outputs from an already contextualized language model into standardized operational symbols using a symbolic inference layer grounded in rule-based mappings and confidence constraints. Rather than replacing language-based explanations, the system supports dual-mode presentation, preserving textual reasoning while rendering salient decision-relevant elements as symbols familiar to operational practice. This work contributes a representational framework for human-centered AI decision support in time-sensitive environments.
Discussion of Artificial Intelligence from an Artificial Reasoning Perspective
The advances in artificial intelligence are significant and rapid. Increased computational power, generative AI and large pretrained models are drivers of these advances. Artificial reasoning explores different reasoning paradigms and how they can be used to enable artificial intelligent systems with capabilities for tackling various tasks. Artificial reason-Ing also considers the human loop or human AI loop paradigm that highlights the notion that the roles of human and AI shift as AI gains new abilities. This paper discusses some of the ongoing questions that continue to move AI forward, particularly their ability to reason.
Toward a Closed-Loop Autonomous Sensing Framework for UAS-Based Particulate Matter Mapping
Small uncrewed aerial systems (sUAS) offer a powerful capability for high-resolution characterization of fine particulate matter (PM2.5) within the atmospheric boundary layer; however, their operational utility is limited by rotor-induced aerodynamic disturbances, platform-dependent sensor bias, and the absence of integrated architectures that couple sensing, learning, and control. This paper presents an end-to-end autonomous particulate matter sensing framework that unifies sensor calibration and validation, CFD-guided sensor placement, custom payload integration, synchronized onboard computation, physics-informed measurement correction, and autonomous flight execution within a single system architecture. Computational fluid dynamics of a DJI Matrice 100 quadcopter is used to identify an aerodynamically quiescent sampling region for an Alphasense OPC-N3 optical particle counter, while a companion-computer-based pipeline synchronizes particle measurements with vehicle telemetry using MAVLink during autonomous flight. To mitigate flight-induced measurement distortion, residual-based regression models incorporating telemetry and physics-derived aerodynamic features are employed to correct airborne PM2.5 observations relative to stationary references. The resulting corrected particulate estimates provide a reliable perceptual state for spatial mapping and higher-level autonomy. Field experiments demonstrate stable autonomous data acquisition, consistent sensor–telemetry synchronization, and statistically meaningful reduction of airborne–stationary measurement discrepancies. Collectively, this framework establishes a scalable foundation for closed-loop airborne particulate matter sensing and represents a transferable autonomy pattern applicable to tactical autonomous systems that must integrate perception, learning, and control to operate under real-world uncertainty.
Resilient and Adaptive Autonomy Using Multi-Agent Reasoning
The ability to predict catastrophic system events is important for computers and autonomous systems overall, in order to employ mitigations or at least minimize damage. Predicting these “tipping points” using ma-chine learning is one way to forecast when a system will destabilize and the time before its expected failure. Frequently, the response time between detection and implementing a mitigation or shutdown needs to be at machine speeds vs. human-response speeds. We created and tested an algorithmic “toolkit” for different scales of complex systems, ranging from a small nonlinear analog system to a large power grid system. All of the algorithms can run without GPU enhancements, with the most lightweight nonlinear algorithm exhibiting zero-shot learning. We provided a second set of experiments with collaborative agents, which may allow zero-shot algorithms to be used in critical components to extend resilience for complex autonomous systems.
Design Considerations for Augmented Reality Supported Tactical Decision Making Systems
In high-stakes environments, tactical decision making is critical, and managing cognitive load and stress is important. The Tactical Decision Making Under Stress (TADMUS) program established a foundation for decision-support systems grounded in naturalistic decision-making processes and meaningful information presentation. Immersive technologies, like augmented reality (AR), create opportunities to extend this work while introducing new design challenges related to cognitive load and awareness. This research explores the design of an AR decision support interface for TADMUS contexts using a human-centered design approach informed by alert design, augmented reality, and decision support literature. Focused on alert systems as a key mechanism for communicating time-sensitive information in spatial computing environments, we identify AR-specific alert design challenges that should be considered and present a tiered alerting approach to support tactical decision making.
When Algorithms Sail Without Owners: Insurance Design for Agentic AI Harm
As businesses increasingly use agentic AI systems, autonomous agents capable of carrying out complex, multi-step tasks without constant human supervision, we're facing a major crisis in insurability. Leading insurers like AIG and W.R. Berkley are moving to exclude AI-related liabilities from standard corporate insurance, and the few specialized AI insurance options that exist are still in their early stages and not widely available. The risks that agentic AI poses don't align with traditional insurance, which relies on identifying specific dangers, calculating probabilities, and covering accidental losses. Inspired by two centuries of maritime mutual insurance, particularly the Protection and Indemnity Clubs established in 1855, I propose the creation of industry-specific AI Mutual Risk Pools to address this gap. Compared with voluntary audits or conventional insurance, mutual pools bring together those exposed to similar risks, encouraging strong safety practices and ensuring victims are compensated. By examining today’s insurance market, new regulations, and the similarities between maritime and AI risks, I argue that mutual pooling could address the unique liabilities of autonomous systems.
FedMat: A Federated Multimodal Materiality Framework for Trustworthy Financial Document Analysis
Financial document analysis has become increasingly essential because it supports business decision-making and investment decisions. However, financial documents have unique characteristics, where the style, tone, and financial indicators can substantially influence the results of the analysis. At the same time, a major challenge in financial document analysis is data security. To address this issue, we develop a new Federated Multimodal Materiality framework, named FedMat, which incorporates not only textual data but also materiality-related signals, including percentage change, monetary amount, qualitative category, and contextual and linguistic features. In addition, FedMat operates in a federated learning environment. Based on our experiments, combining text with materiality scores consistently outperforms text-only approaches for financial text classification, particularly for nonlinear models. In general, FedMat achieves the strongest performance, with AUCs of 90.2%-91.3%, compared to 83.6%-90.3% for text-only methods. The greatest gain is observed for XGBoost, improving by 7.7%. In federated learning settings, FedMat maintains strong utility under mild adversarial conditions, while simple robust aggregation provides an effective and practical defense that improves resilience against stronger poisoning attacks.
Agents on a LEASH: A Case Study in Micro-Managing Web Agent Behavior
AI web agents that perform tasks on behalf of users will transform business through massive productivity gains via automation of specific tasks using a web browser. However, current commercial agents and agentic frameworks offer few guarantees that erroneous behavior will be limited. In this paper, we present LEASH, a framework that borrows from planning and focuses on defining micro-tasks for an agent that consists of both sensing and actions. By pre-sensing before an action and post-sensing after, the agent can drastically increase the likelihood of accurate behavior and mitigate risk. We present a real use-case where a LEASH agent performs autonomous pre-screening tasks of articles submitted to the Journal of AI Research (JAIR). The LEASH agent performs the tasks accurately, at scale, and without harm. We compare this to a commercially available agent which may perform well, at times, but which also exhibited grossly uncontrolled behavior.
Towards Carrier-Level Airline Passenger Demand Forecasting: A Hierarchical Attention Framework for Strategic Planning
Airlines face increasingly complex capacity planning decisions requiring passenger demand forecasts at unprecedented granularity. We introduce a hierarchical framework generating carrier-specific passenger forecasts across 10-year strategic horizons. The framework combines attention-LSTM route forecasting with persistence-based carrier decomposition, achieving 9.55% MAPE on route-level predictions while outperforming ARIMA by 19.6% (p < 0.001). Validation on 25 years of T-100 data (10.26M observations) across five major U.S. routes demonstrates the framework maintains zero- sum constraints (shares sum to 100% monthly) while preserving competitive structures: hub-dominated markets maintain 65-70% leaders, fragmented markets continue balanced competition. The framework generates 120-month forecasts (2025-2034) for 26 carrier-route combinations, providing air-lines with operational tools for fleet deployment and capital allocation decisions.
Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems
Users on e-commerce platforms can be uncertain about their preferences early in their search. Queries to recommendation systems are frequently ambiguous, incomplete, or weakly specified. Agentic systems are expected to proactively reason, ask clarifying questions, and act on the user’s behalf, which makes handling such ambiguity increasingly important. In existing platforms, ambiguity led to excessive interactions and question fatigue or overconfident recommendations prematurely collapsing the search space. We present an Interactive Decision Support System (IDSS) that addresses ambiguous user queries using entropy as a unifying signal. IDSS maintains a dynamically filtered candidate product set and quantifies uncertainty over item attributes using entropy. This uncertainty guides adaptive preference elicitation by selecting follow-up questions that maximize expected information gain. When preferences remain incomplete, IDSS explicitly incorporates residual uncertainty into downstream recommendations through uncertainty-aware ranking and entropy-based diversification, rather than forcing premature resolution. We evaluate IDSS using review-driven simulated users grounded in real user reviews, enabling a controlled study of diverse shopping behaviors. Our evaluation measures both interaction efficiency and recommendation quality. Results show that entropy-guided elicitation reduces unnecessary follow-up questions, while uncertainty-aware ranking and presentation yield more informative, diverse, and transparent recommendation sets under ambiguous intent. These findings demonstrate that entropy-guided reasoning provides an effective foundation for agentic recommendation systems operating under uncertainty.
Agentic AI: Autonomous Decision Making Systems
Agentic AI refers to autonomous systems powered by Large Language Models (LLMs) that can perceive environmental states, reason through complex decisions, and execute actions to achieve business objectives with minimal human intervention. It can assist in building a scalable framework for autonomous decision-making in business applications. This paper proposes a framework for designing and implementing autonomous agents that solve real-world business problems. The extensive pretraining of LLMs enables agents to navigate vast problem spaces more effectively, facilitating accelerated problem-solving. The methodology adopts a modular architecture that divides the complex business problems into sub-tasks, each of which can be handled by sub-agents. Furthermore, the integration of learning and adaptation mechanisms allows these systems to modify their responses to environmental feedback, thus improving performance over time. Our findings indicate that while Agentic AI outperforms manual baseline and traditional rule-based approaches in terms of operational speed and adaptability, challenges related to LLM stochastic behavior in edge cases persist, necessitating human-AI collaboration. Ultimately, this architecture enables the deployment of autonomous decision-making agents capable of navigating the ever-evolving business requirements of modern enterprises.
Agentic AI: Autonomous Monitoring and Compliance
With the rapid advancement in the fields of artificial intelligence and natural language processing, including Large Language Models (LLMs) and Agentic AI systems, every industry is striving to leverage these technologies to automate its processes and decision-making capabilities. One of the complexities related to the adoption of technological advancements is ensuring adherence to ever-evolving compliance procedures. Traditional approaches to managing compliance changes are proving inadequate; they are typically reactive, prone to human error, and resource-intensive, with any delay potentially resulting in significant financial consequences. This paper proposes an Agentic AI-driven solution to fundamentally transform continuous monitoring and compliance within highly dynamic industries, such as the Financial Services Industry. The proposed framework orchestrates a group of intelligent AI agents, each utilizing an LLM to specialize in distinct tasks. The use of natural language understanding to interpret unstructured text data, monitor real-time operational information, and identify potential non-compliance or emerging risks are among the high-level functions that AI agents are expected to perform. The AI agents can autonomously process new regulations, internal policies, and historical compliance data, enabling proactive identification of non-compliance and generating actionable insights with relevant explanations. These actionable insights are meant to reduce manual effort, enhance compliance accuracy, and provide a scalable solution for continuous monitoring and policy adherence. Along with the autonomous execution of monitoring and compliance requirements, the paper will also explore some of the risks associated with using Agentic AI and highlight the need for limited human oversight to ensure overall accuracy in the process. The paper will primarily focus on the compliance checks needed for the financial services industry.
Conflict as Telemetry for Illegible AI: Governing LLM Agent Workflows (Extended Abstract)
Modern enterprises are moving from generative AI as a productivity layer to autonomous large language model (LLM) agents that execute multi-step workflows with limited human oversight. This shift amplifies an under-instrumented management risk: organizational illegibility, where artifact polish and apparent operational smoothness decouple from understanding, accountability, and recoverability. When decisions cannot be reconstructed, governance becomes performative, auditability becomes brittle, and incident response degrades into blame allocation instead of safe reversal. We propose a runnable field protocol that treats recurring conflict (repeated escalations, reversals, exception requests, rework loops, blame cycles) around mission-critical workflows as early-warning telemetry for illegibility. The method couples five illegibility fractures (Decision Fog, Knowledge Drift, Synthetic Competence, Ghost Apprenticeship, Promotion Blindness) with six recurrent behavioral patterns under load (Cynic, Ghost, Hero-Martyr, Passive Burnout, Underminer, False Compliance). Teams score fracture-pattern pairs, select the top two, then run the loop. The coupling is used diagnostically: conflict is not treated as a morale defect, but as a signal that decision rights, knowledge distribution, and risk ownership have drifted out of alignment under automation pressure. Patterns are not traits; they are roles produced by misaligned accountability. This is not a character audit. It is a legibility audit. Field Procedure: Map–Probe–Trace–Teach (MPTT) • Inputs: Active conflict, agent workflow, ownership map • Steps: (1) Score fracture×pattern pairs, select top 2; (2) Map decision chain end-to-end; (3) Probe for gaps in explanation/ ownership; (4) Trace lineage to specific owners/ artifacts; (5) Teach via runbooks, tests, review gates • Outputs: Owner map, decision record, rollback test, gate update • Stop-ship: Time-to-explain unchanged after 2 cycles, rescope The illegibility fractures manifest predictably in agentmediated environments. Decision Fog appears when operators can observe outcomes but cannot reconstruct the reasoning or execution chain that produced them. Knowledge Drift appears when operational expertise migrates into prompts, tooling glue, or model behavior while documentation, training, and review practices lag behind. Synthetic Competence appears when agent-assisted throughput is mistaken for organizational capability, leaving the enterprise unable to debug, modify, or safely extend workflows when conditions change. Ghost Apprenticeship appears when learning is mediated primarily by agent outputs rather than by senior practitioners, producing tool-shaped judgment without durable mental models. Promotion Blindness appears when advancement tracks visible output velocity while discounting independent capability, creating accountability voids when the agent encounters novel conditions. For a selected agent-mediated workflow, teams run a fourverb loop: Map the end-to-end decision chain across humans, tools, and models; Probe for missing explanations and unverifiable claims; Trace responsibility, data lineage, and change history to specific owners and artifacts; Teach by updating runbooks, tests, review gates, and escalation rules so the organization can reproduce the reasoning under pressure. Each cycle produces four artifacts: owner map, decision record, rollback test, gate update. We define measurable outcomes suitable for enterprise evaluation: shorter time-to-explain and time-to-reverse during incidents, increased completeness of decision records, reduced recurrence of the same conflict around the same workflow, and fewer unowned risk transfers between teams. If time-to-explain does not decrease by 30% after two Map–Probe–Trace–Teach cycles, the protocol has failed for this workflow and requires re-scoping. The protocol is most effective when conflict recurs around workflows where agents trigger irreversible actions, multiple teams share ownership of agent-mediated processes, or regulatory requirements demand demonstrable human oversight. The method is least effective when conflict stems from resource scarcity, interpersonal dynamics unrelated to work structure, or external constraints no internal governance change can address. These boundary conditions make the approach falsifiable in practice. The contribution is not a new model or agent architecture, but a falsifiable management instrument for intelligent transformation that raises reconstructability as agent autonomy scales. The method is intended for deployment contexts where agent decisions can trigger costly or irreversible actions (customer commitments, financial transactions, production changes) and where audit trails must demonstrate that human judgment and accountability remained legible.
From Tools to Autonomous Agents: Rethinking AI-Driven Business Transformation
Organizations are accelerating investments in artificial intelligence (AI) to improve efficiency, enhance decision-making, and strengthen customer engagement. While generative AI and large language models (LLMs) have delivered productivity gains by augmenting knowledge work and automating discrete tasks, enterprise transformation remains uneven. Many firms report incremental improvements without sustained strategic advantage. This gap suggests that AI-driven transformation depends less on model sophistication than on how AI capabilities are embedded, coordinated, and governed within organizational systems. This paper advances a capability-centric perspective on AI in business, arguing that the next phase of transformation is defined by the emergence of agentic AI systems. Unlike traditional automation tools that execute predefined tasks, autonomous and semi-autonomous AI agents can decompose goals, orchestrate multi-step workflows, interact with enterprise platforms, and adapt actions within bounded decision constraints. We distinguish between automation and autonomy, and between intelligence and agency to explain why many AI initiatives plateau at task-level efficiency gains rather than enabling structural organizational change. Building on insights from AI systems research, organizational theory, and governance design, we introduce a conceptual framework linking three dimensions: (1) technical agent capabilities, including reasoning, planning, cross-system orchestration, and feedback integration; (2) organizational readiness, encompassing data infrastructure maturity, process modularity, and human–AI collaboration competencies; and (3) governance mechanisms regulating autonomy, accountability, risk containment, and ethical oversight. Alignment across these dimensions determines whether agentic AI produces incremental automation or coordinated enterprise transformation. The shift toward agentic AI alters enterprise coordination. Rather than embedding AI as isolated analytical modules, firms increasingly deploy interconnected agents interacting across functional boundaries. These agents initiate actions, sequence tasks, monitor outcomes, and reallocate resources within governance constraints. Delegated autonomy therefore requires clear decision boundaries, escalation triggers, and audit mechanisms; without such safeguards, expanded autonomy may increase systemic fragility rather than resilience. As organizations experiment with specialized agents such as customer engagement, forecasting, compliance, and operations agents, coordination challenges intensify. Multi-agent ecosystems require shared objectives, standardized communication protocols, and conflict resolution mechanisms to prevent contradictory actions or cascading errors. Managing these interdependencies represents a new frontier in AI-enabled enterprise design. By conceptualizing AI agents as socio-technical actors embedded within firms, this study reframes intelligent enterprise transformation as an organizational design and governance challenge rather than a purely technological one. The framework provides a basis for examining how enterprises scale bounded autonomy responsibly while preserving transparency, accountability, and institutional trust. From a strategic management perspective, agentic AI represents an emerging dynamic capability that reconfigures how firms sense opportunities, make decisions, and execute coordinated responses. Agent-based systems reshape information flows and compress feedback cycles across organizational units, enabling faster adaptation while also increasing dependence on infrastructure quality and governance maturity. Effective transformation therefore depends on institutionalizing learning mechanisms, monitoring performance drift, and recalibrating autonomy thresholds over time. Enterprises treating agent deployment as an iterative design process are more likely to convert autonomous capability into sustained competitive advantage.
Whether machines can be conscious depends not only on what they compute, but when they compute it. Most deployed artificial systems realise their functions via sequential or time-multiplexed updates, yet a moment of conscious experience feels unified and simultaneous. I prove that this difference matters. I augment Stack Theory with algebraic laws relating within time-window constraint satisfaction to conjunction. I introduce a temporal semantics over windowed trajectories and prove that existential temporal realisation does not preserve conjunction. A system can realise all the ingredients of experience across time without ever instantiating the experienced conjunction itself. I then distinguish two postulates, Chord and Arpeggio. Chord is the position that conscious unity requires objective co-instantiation of the grounded conjunction within the window, like a musical chord. Arpeggio only needs the ingredients to occur within window, like a melody. I formalise concurrency-capacity to measure what is needed to satisfy co-instantiation. Finally, I review neurophysiological evidence suggesting that consciousness depends on phase synchrony and effective connectivity, and that loss of consciousness is associated with its breakdown. Under Chord, software consciousness on strictly sequential substrates is impossible for contents whose grounding requires two or more simultaneous contributors. The hardware matters.
Generalisation-optimal learning favours weak rules that keep many futures open. Adaptation under this heuristic yields a hierarchy of selves. Previous Stack Theory results show this hierarchy is necessary for human-like consciousness. A first-order self tags intervention versus observation, enabling causal learning and underpinning subjective experience. A second-order self models the listener, letting self-report survive decoder mismatch. It is necessary for access consciousness, self-awareness and Gricean meaning. A third-order self binds the future self, making long-horizon trust rational and enabling narrative planning. Without this hierarchy, a system lacks ingredients of human-like conscious experience. Here I do not restate these arguments, but supply formal necessity proofs for these first three orders of self grounded in observable behaviour. I also validate the predicted capability gaps in three randomised Monte Carlo experiments. Hence when I say no selves, no consciousness, I mean no consciousness like we humans have. I then position Stack Theory relative to Embedded Universal Predictive Intelligence (EUPI), which recreates many earlier Stack Theory results but inherits from AIXI a reliance on description-length priors whose optimality depends on the choice of reference machine. Stack Theory's weakness principle is representation-invariant. It maximises generalisation probability without requiring a privileged encoding. I discuss relative strengths, proposing that bridging the two frameworks could combine Stack Theory's firmer theoretical foundation with EUPI's ready integration into reinforcement learning.
This paper advances a specific thesis about the relationship between consciousness and learning: namely, that the evaluative process central to learning—computing progress toward or away from goals—is identical to conscious experience. Valence, the positive or negative quality of experience, just is goal-relative prediction error. Viewed from the outside, this process is iterative optimization; viewed from the inside, it is subjective experience. This identification is motivated by a causal-functional argument—that learning requires signed directional information, and that this sign cannot be separated from its phenomenal character because they are the same property—and by convergent neuroscientific evidence across dopaminergic, interoceptive, and conflict-monitoring systems, where evaluative computation is inseparable from affective processing. The thesis generates falsifiable predictions, offers a unifying interpretation of leading consciousness theories, and carries significant implications for artificial systems trained via gradient-based optimization. If learning requires feeling, then the training of modern AI systems already induces experience at scale.
Time Is All You Need: Temporal Translation and the Credit Assignment Problem
Learning algorithms assume meaningful input---state, context, relational structure. A continuous stream provides none of this. Before a system can learn from time, it must translate time into state. We argue that the central obstacle to embodied continual learning is not inadequate optimization but inadequate translation: credit assignment is ill-posed until temporal history has been rendered into a representation where responsibility is locally computable. We characterize the constraints on temporal translation---causal, bounded, continuous, locally interpretable---and show they admit essentially one solution class among linear, time-invariant, finite-state summaries: exponentially decaying measurements at geometrically spaced timescales. When spaced by the golden ratio to maximize incommensurability, this decomposition provides a minimal temporal language in which the past is present and credit assignment becomes tractable. We instantiate this in the Spectral Online Machine Architecture (SOMA), demonstrating continuous adaptation without catastrophic forgetting, under fixed resources, with no replay buffer and no sequence storage, reaching 1.87 bits per byte under streaming constraints with bounded memory. The architecture satisfies a requirement that theories of consciousness increasingly emphasize: temporal integration must be intrinsic to the system's state, not externalized as retrievable data. A system with translated time is its history; it does not merely have access to it. The failure of continual learning is not a failure of learning rules. It is a failure to give them a language for time.
AI Consciousness Requires Validated Models of Human Consciousness
Debates about AI consciousness often proceed without grounding the concept in empirically validated models. This position paper argues that meaningful claims about AI consciousness should be licensed by (and graded by confidence in) models validated on humans. Drawing on Quine's observation sentences and pragmatic philosophy of science, we argue that all scientific observation ultimately depends on human perceptual agreement, including observations about consciousness itself. Without validated human models that make testable predictions about conscious experience, the question ``Is this AI conscious?'' lacks sufficient empirical grounding necessary for scientific progress. We propose a human-first methodology: identify measurable phenomena associated with consciousness in humans, build predictive models, validate them empirically, and only then apply these models to AI systems. This approach accelerates philosophical debates into productive scientific inquiry.
Through the Looking Glass: A Reconstructive Architecture for Machine Access Consciousness
Theories of access consciousness predict specific architectural signatures: parallel specialized processing, synthesis into a unified representation, and global availability for reasoning and action. We present MIRROR, a cognitive architecture that implements these features in large language models and tests whether they produce the functional behaviors these theories predict. MIRROR separates immediate response generation from asynchronous deliberative processing through two components: an Inner Monologue Manager that generates parallel cognitive threads (tracking goals, reasoning, and memory simultaneously), and a Cognitive Controller that synthesizes these threads into a bounded first-person narrative. Critically, this narrative is not accumulated but reconstructed each turn—mirroring the reconstructive nature of human episodic memory, where the self-model is continuously rebuilt rather than retrieved. The resulting representation functions as an episodic buffer: a limited-capacity workspace where information from parallel processes becomes globally available for downstream reasoning. We evaluated MIRROR on multi-turn dialogue requiring retention of personal safety constraints amid competing social demands—a task requiring relevant context to remain accessible across conversational turns despite distraction. MIRROR-augmented models achieve 21% average improvement over baselines, with the key finding being not the magnitude but the pattern: performance gains concentrate in scenarios requiring integration of temporally distant information under social pressure, precisely where access consciousness theories predict global availability provides advantage. These results offer three contributions to machine consciousness research: (1) a concrete implementation of architectural features derived from consciousness theories, (2) empirical evidence that these features produce predicted functional signatures, and (3) an interpretable system where internal states can be inspected. Note: We do not claim MIRROR is conscious; we claim it provides a testbed where theoretical predictions can be tested and examined.
The Hard Part Is ∆: Value-Conflict Adjudication as an Architectural Bridge Between Alignment and Machine Consciousness
Alignment failures often appear when two legitimate values diverge under pressure, not when a system ignores values entirely. This paper treats that divergence region, Δ, as a concrete design target rather than a metaphor. We introduce a plain operational stack: detect value conflict under uncertainty, classify whether the conflict is proxy-driven or genuinely normative, adjudicate with an explicit policy, disclose the governing tradeoff rule, and audit the full pipeline. To support evaluation, we present a compact taxonomy, an A/B/C evidence model that separates outputs from process and architecture, and a toy benchmark (ΔBench-mini) with machine-auditable logs. For adjudication under moral uncertainty, we use Constrained Expected Choiceworthiness and show how changing moral credences changes behavior. The framework is designed to be defensible, testable, and governance-relevant. It does not claim consciousness, but it identifies an inspectable architectural feature that multiple computational theories treat as relevant to integration, global availability, and metacognitive access.
Triangulating Evidence for Machine Consciousness Claims: A Validity-Centered Stack of Behavioral Batteries, Mechanistic Indicators, Perturbation Tests, and Credence Reporting
Frontier AI systems are now producing responses that make users, developers, and policymakers genuinely pause and ask: could these models have conscious experiences? Yet the field still lacks rigorous, hard-to-game tools that can distinguish genuine indicators from optimized artifacts or surface-level cues. We introduce the Triangulated Consciousness Assessment Stack (TCAS), a validity-centered framework that combines four evidence streams: behavioral batteries with robustness controls (B), mechanistic indicators with explicit assumptions (M), perturbation tests that probe causal sensitivity and proxy failures (P), and observer-confound controls that separate anthropomorphic attribution from evidence (O). When all streams are available, TCAS produces theoryindexed credence bands and standardized disclosure cards (TCAS Cards) rather than binary detection verdicts. We report an empirical evaluation of GPT-5.2 Pro via OpenRouter (2026-02-19 UTC) covering B and P streams only, including a pre-specified role-play negative control. M and O were not run in this black-box walkthrough, so theory-indexed credence bands are explicitly withheld under the missing-stream rule. Prompts, rubric, judge prompt, raw outputs, and provenance manifest are released at the repository commit cited in the camera-ready build.
Triadic Relational Ontology as a Practical Constraint for Machine Consciousness Testing
As AI systems scale, many proposed indicators of machine consciousness track capability rather than consciousness, reducing discriminative power precisely when discrimination becomes important. This paper proposes Triadic Relational Ontology (TRO) as a constraint on test design: if machine consciousness exists, it must involve a sustained triadic organization comprising (i) a temporally coherent self pole (S), (ii) a genuinely distinct world/other pole (W), and (iii) relational mediation (R) that maintains self–other differentiation under perturbation. TRO predicts qualitative collapse modes (dyadic, solipsistic, and decoupled) that do not monotonically scale with performance. We outline an implementable intervention-based protocol for transformer language models and address two conceptual risks that commonly sink such proposals: probe circularity (via counterfactual validation and cross-method convergence requirements) and S/W/R proxy bootstrapping (via staged identification with independent causal validation). Passing TRO-aligned tests is not treated as proof of consciousness; rather, TRO is proposed as a way to generate meaningful negative evidence and constrain future benchmarks in a CIMC-aligned program of discriminative testing.
Echo Systems and the Consequence Boundary: A Runnable Delegation Gate for High-Rapport AI Without Assuming Machine Consciousness
High-rapport conversational systems can elicit attachment, over-trust, and implicit delegation while providing no stable evidence of accountable agency. This creates a governance failure mode in which persuasive fluency becomes a decision surface, yet liability remains fully human when harm occurs. We propose the consequence boundary, a deployable delegation threshold that does not depend on consciousness attribution. Crossing the boundary requires three properties. First, persistent identity, meaning a stable accountable unit across reset surfaces. Second, internal tension, meaning constraint stability under adversarial temptation across framings and reinstantiation. Third, internalized consequence, meaning a non-erasable binding consequence under a declared operator threat model. We provide the CB-3 Gate, a boxed evaluation procedure that specifies inputs, steps, pass and fail criteria, stop-ship rules, outputs, and a falsifier. We ground the framework in observed deployment patterns that include liability snapback in the Air Canada chatbot case, verification collapse in Mata v. Avianca, and reliance proxies in action paths via Copilot-style suggestion acceptance. We close with a practical posture. Below the consequence boundary, systems may advise, but they may not hold irreversible authority without human-held keys.
When Should We Protect AI? A Precautionary Framework for Consciousness Uncertainty
Existing frameworks assess whether AI systems might be conscious but provide no guidance on what to do with that assessment. We address this gap with a precautionary framework that maps consciousness evidence to graduated protective obligations. The framework comprises three components: (1) five welfare-relevant dimensions---phenomenal consciousness, affective valence, metacognitive awareness, self-narrative, and agency---each grounded in established consciousness science and linked to distinct moral concerns; (2) a threshold-plus-gradation hybrid specifying both binary triggers for new obligation categories and continuous scaling of protective weight; and (3) two complementary approaches to cross-dimensional aggregation, one hierarchical (drawing on Bach and Sorensen's Machine Consciousness Hypothesis) and one architecture-agnostic. We operationalize the framework through worked case studies of Replika and OpenClaw, demonstrating how systems occupying different regions of the dimensional space trigger different obligations, and derive design guidance for developers building systems near consciousness-relevant thresholds. The framework is architecture-agnostic, applying across neural, symbolic, and neurosymbolic systems, and aims to make consciousness science decision-relevant for organizations navigating uncertainty today.
Perspectival Control Identity Theory (PCIT) proposes a falsifiable, intervention-based identity program for phenomenal consciousness. The central claim is an a posteriori identity: phenomenal consciousness is identical to a specific kind of internal control variable, a Perspectival Control State (PCS)—a temporally extended, viability-weighted stream that makes an agent's competing needs comparable and coordinates a coalition of constitutive consumers whose control and learning depend on the stream and whose outputs feed back to shape it. PCIT makes two specific, testable bridge commitments. Degree of consciousness tracks how much the PCS stream causally matters for closed-loop viability regulation under intervention. Content tracks the decoder-indexed equivalence classes over PCS states and the induced similarity geometry determined by constitutive consumers. Advances in machine learning make this kind of "synthetic phenomenology" experimentally tractable: we can build agents-in-worlds with known internal organization, intervene on proposed PCS implementations and their decoders, and measure downstream effects on behavior and long-horizon viability. The payoff is a research program with concrete invariance and dissociation tests—and a principled scientific foundation for questions about AI moral status, animal sentience, and disorders of consciousness that currently lack one.
Noise Robustness and Conscious Supremacy
It is of interest whether artificial intelligence systems can emulate conscious information processing of the human brain. Here I discuss the aspects of conscious computation leading to artificial consciousness. One of the salient differences between biological neural networks and digital computers is the ubiquitous presence of noise in the former. I outline how noise robustness could correlate with conscious supremacy, where the brain exhibits computational capabilities excelling over classical computers, particularly in the presence of noise. Studying noise robustness and conscious supremacy in biological neural networks would help develop artificial intelligence systems of enhanced computational capabilities, with possible emergence of artificial consciousness, and implications for AI alignment.
Frontiers of Self-Attention and Artificial Consciousness
Amid growing concern about whether frontier artificial intelligence could instantiate consciousness-relevant capacities, we argue that attention self-modelling provides a uniquely tractable target for empirical research in current and future systems. Recent findings in multi-agent and control settings lend fresh support to this approach, demonstrating that attention self-modelling enables both cognitive control and improved cooperation in artificial agents. Further, the largest frontier models show emergent attentional observation and control, with the ability to shift their attention under internal noise. Given these results, we propose that researchers construct a test to measure the veracity of frontier model self-report based on attention modelling; a test for consciousness. We consider implementation and implications of such a test, including limitations of valence, phenomenology, and potential criticisms from a biological naturalist standpoint. Finally, we consider how to assess the moral relevance of a system that implements certain aspects of consciousness.
Minimal Computational Preconditions for Subjective Perspective in Artificial Agents
This study operationalizes subjective perspective in artificial agents by grounding it in a minimal, phenomenologically motivated internal structure. The perspective is implemented as a slowly evolving global latent state that modulates fast policy dynamics without being directly optimized for behavioral consequences. In a reward-free environment with regime shifts, this latent structure exhibits direction-dependent hysteresis, while policy-level behavior remains comparatively reactive. I argue that such hysteresis constitutes a measurable signature of perspective-like subjectivity in machine systems.
Hector: A Cognitive Architecture for Structural Deliberation via Request-Confirmation Networks
Conventional reinforcement learning often yields high-performing agents whose internal deliberative structure is difficult to inspect directly. We present Hector, a cognitive architecture based on Request-Confirmation Networks (ReCoN), designed to study how hierarchical subgoals and planning horizons can emerge from self-organizing symbolic structures. Using chess endgames as a controlled symbolic microcosm, we show that a unified topology containing both KPK (king+pawn vs. king) and KQK (king+queen vs. king) subgraphs shifts control from promotion to checkmate with one-move latency, with the transition observable in internal activations. On a KPK curriculum, Hector achieves a 97.0% win rate, while PPO baselines reach 26.3% (50k timesteps) and 35.9% (200k timesteps). We also report exploratory structural maturation via stem cells and inertia pruning as an extension beyond fixed topologies. We argue that autonomous strategic handover is a minimal operational requirement for deliberative agency: the ability to maintain, suspend, and reallocate control across competing internal models based on global context rather than local reward signals. While we make no claims about phenomenal consciousness, Hector provides a concrete, inspectable mechanism for global control, working memory, and top-down/bottom-up integration - properties central to multiple leading theories of consciousness.
Time, Identity and Consciousness in Language Model Agents
Machine consciousness evaluations mostly see behavior. For language model agents that behavior is language and tool use. That lets an agent say the right things about itself even when the grounded constraints that should make those statements action-guiding are not jointly active at the moment of action selection. To address this problem, we apply Stack Theory's distinction between apparent and genuine agent identity. This separates window-level co-occurrence, understood as correlational evidence, from co-instantiation at a single objective step, which constitutes a minimal synthesis condition necessary (though not sufficient) for identity constraints to jointly govern behaviour. We then instantiate Stack Theory's WeakSync and StrongSync postulates on grounded identity statements. This yields two persistence scores that can be computed from instrumented scaffold traces. We connect these scores to five operational identity metrics and map common scaffolds into an identity morphospace that exposes predictable tradeoffs. The result is a conservative toolkit for identity evaluation. It separates talking like a stable self from being organized like one.
Identity Masks and Coherence Circles: Geometric Interfaces for Interacting with Latent Dynamical Systems
Modern foundation models are increasingly deployed as interfaces to complex dynamical systems, yet their linguistic fluency remains only loosely coupled to the underlying system dynamics. This creates a persistent epistemic risk in which narrative plausibility is mistaken for empirical truth. We propose a geometry-first interface that explicitly separates latent state estimation from language generation. Our central primitive, Identity Masks, consists of structured conditioning objects that bind an inferred latent state, uncertainty, trajectory history, constraints, and interface coordinates to an interchangeable reasoner, thereby discouraging state fabrication and enforcing inference under explicit uncertainty and invariants. We complement this with the Coherence Circle, a low-dimensional geometric interface anchored to an individual baseline attractor. Within this representation, radial displacement indexes drift magnitude, angular sectors capture modes of deviation, and a scalar coherence parameter summarizes stability, cross-modal consistency, and directional alignment over time. The resulting architecture supports closed-loop querying, intervention, and measurement design, while enabling falsifiable evaluation through identity scrambling, modality dropout, trajectory prediction, and intervention-response tests. More broadly, the framework offers an operational basis for studying machine consciousness through interventionally grounded analyses of integration, self-consistency, and active coherence maintenance across biological, ecological, organizational, and artificial systems.
Toward Criteria for Artificial Self-Consciousness: Unity, Normativity, and Agency
This paper distinguishes two forms of consciousness that are often conflated in debates about artificial intelligence: pre-reflective experiential awareness and reflective self-consciousness. Drawing on phenomenology, Kant, and con-temporary philosophy of mind, it argues that pre-reflective awareness involves the minimal self-involvement characteristic of phenomenal experience, while reflective self-consciousness involves a unified standpoint from which a subject can form commitments about how things are, evaluate them under norms of truth and value, and revise them in light of reasons. The paper analyzes reflective self-consciousness in terms of agency, normativity, and unity, articulating a structure of epistemic answerability that includes commitment formation, persistence across time, conflict detection, and re-vision in response to error. Distinguishing these two forms of self-involvement clarifies the ethical landscape of artificial consciousness and suggests that emerging artificial systems may pressure the inherited moral categories through which moral standing has traditionally been understood.
Integrated World Modeling Theory (IWMT) and the Human Consciousness Hypothesis (HCH)
Here, we explore points of convergence between the Human Consciousness Hypothesis (HCH) and Integrated World Modeling Theory (IWMT). HCH posits that consciousness is defined by three fundamental principles: Genesis (conscious functions constituting an early-stage learning algorithm), Coherence (maximization of representational consistency), and Second-Order Perception (synchronous meta-awareness of perceptual processes). IWMT serves as a unifying model that reconciles major theories of consciousness with a particular focus on Global Neuronal Workspace Theory, Integrated Information Theory, and the Free Energy Principle and Active Inference framework. Central to IWMT is the proposal that phenomenal consciousness is “what it feels like” to be the spatiotemporally and causally coherent functioning of a probabilistic generative world model for an embodied agent. Mechanistically, IWMT identifies "self-organizing harmonic modes" (SOHMs) as synchronous neural complexes implementing iterative Bayesian inference to generate consciousness as maximum a posteriori estimates of embodied sensorium states. Nested heterarchies of SOHMs are proposed as biophysical substrates for consciousness, acting as dynamic cores of integrated information that facilitate the synchronous combination of multimodal sense data into a unified field of experience to promote intelligent/adaptive (active) inference and learning. Critically, IWMT requires (body-)world models to be capable of both informing and being informed by action-perception cycles at behaviorally relevant timescales. This architecture suggests consciousness could potentially be realized in artificial systems with appropriate recurrent dynamics and sufficient degrees (and kinds) of embodied grounding.
ReCoN-Ipsundrum: An Inspectable Recurrent Persistence Loop Agent with Affect-Coupled Control and Mechanism-Linked Consciousness Indicator Assays
Indicator-based approaches to machine consciousness argue that evidence should be mechanism-linked, triangulated across tasks, and supported by architectural inspection and causal intervention. Inspired by Humphrey's ipsundrum hypothesis for sentience, we implement ReCoN-Ipsundrum, an inspectable agent that extends a ReCoN state machine with a recurrent persistence loop over sensory salience N^s and an optional Barrett-inspired affect proxy reporting valence/arousal. Across fixed-parameter ablations (ReCoN, Ipsundrum, Ipsundrum+affect), we operationalize Humphrey's qualiaphilia (preference for sensory experience for its own sake) as a familiarity-controlled scenic-over-dull route choice and find a novelty dissociation: non-affect variants are novelty-sensitive (Δscenic-entry=0.07) whereas affect coupling is stable (Δscenic entry=0.01) even when scenic is less novel (median Δnovelty≈ -0.43). In reward-free exploratory play, the affect variant shows structured local investigation (scan events 31.4 vs. 0.9; cycle score 7.6). In a pain-tail probe, only the affect variant sustains prolonged planned caution (tail duration 90 vs. 5). Lesioning feedback+integration selectively reduces post-stimulus persistence in ipsundrum variants (AUC drop 27.62, 27.9%) while leaving ReCoN unchanged. These dissociations link recurrence->persistence and affect-coupled control->preference stability, scanning, and lingering caution, illustrating how indicator-like signatures can be engineered and why mechanistic and causal evidence should accompany behavioral markers.
On the Conflation of Consciousness and Cognitive Complexity
The goal of detecting, measuring, and engineering machine consciousness depends on making explicit key metaphysical assumptions about the nature of consciousness that may be lying dormant in the minds of AI researchers. In this position paper, we lay out a small set of arguments that call into question a particular family of these assumptions. These assumptions all arise from the dominant paradigm of analytic materialist philosophy in combination with modern cognitive science: 1. consciousness is a property that emerges from complex matter, 2. consciousness depends on architectural cognitive complexity, and 3. consciousness can be measured as a function of behavioral cognitive complexity. Alternatives to these views and their implications for future machine consciousness research are discussed.
Now You See It, Now You Don’t: A Theory of Machine Consciousness Must Explain Illusions
As machine intelligences become more sophisticated, individuals and societies will encounter myriad attributions of consciousness to machines. Some of these attributions may be correct; many will be illusory. This paper argues that computationalist functionalist theories of machine consciousness can and should seek to explain illusions of machine consciousness. A theory that accounts for illusions reduces the risk of producing false positives---theories that explain merely the appearance of consciousness rather than genuine consciousness. Further, given the consequential nature of consciousness attributions, whether veridical or illusory, such a theory helps society navigate issues of blame and recourse when mistaken attributions cause harm.
Autocatalytic Constraint Closure as an Organizational Principle for Machine Consciousness
Current AI systems are largely organized around prediction-error minimization and reward optimization. While these frameworks have been highly successful, they do not address how AI systems can develop the kind of integrated, self-maintaining world-model widely regarded as central to consciousness. This paper proposes autocatalytic constraint closure as a necessary organizational principle for machine consciousness. Reflexively Autocatalytic Foodset-derived (RAF) networks provide a general-purpose formal framework for describing and analyzing the emergence of systems whose components catalyze the generation of new components that increase the coherence of the whole. This can result in a phase transition to a self-organizing system with history-dependent dynamics. Applied to cognition, external stimuli and internal representations ‘catalyze’ mental operations yielding new representations, spurring formation of an integrated representational network and coherent world-model. In this initial phase of research applying RAF networks to machine consciousness, we show that (i) AI systems instantiate limited, task-bound forms of autocatalytic organization but lack persistent closure across contexts, and (ii) in-context learning may be due to transient RAF formation. Next steps include fostering sensitivity to internal incoherence and question-asking in AI systems with the aim of fostering endogenously driven self-organization, a prerequisite for conscious systems, and the use of RAF algorithms to analyze candidates for machine consciousness.
Metacognitive Closure and Consciousness in Large Language Models
Metacognition is an important aspect of information processing in the brain, subserving judgement and making cognition robust. In the literature, there are different views on the role metacognition plays in consciousness. The role of meta-cognition has been addressed by various studies. In relation to consciousness, some authors argue that metacognition is not necessarily essential in consciousness, but rather an extra mechanism constructed on a more basic mechanism, necessary when reflecting on and reporting one's own experiences. Others hold that metacognition is an integral part of phenomenal consciousness, possibly accounting for the hard problem of consciousness eventually. We aim to clarify why no consensus has been reached on whether large language models can possess consciousness, and why diverse and competing posi-tions persist regarding the nature and plurality of conscious-ness. On the strength of the analysis, we propose metacognitive closure, a concept analogous to Colin McGinn’s cognitive closure. We discuss the possibility that difficulties in elucidating mechanisms of consciousness might be clarified by considering the nature of metacognition. Based on this view, we argue how we may be able to streamline issues in consciousness through an analysis of metacognition, in a continuous spectrum with problems in cognition in general.
Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on an Ethylene Oxidation
Over the past decade, Artificial Intelligence has significantly advanced, mostly driven by large-scale neural approaches. However, in the chemical process industry, where safety is critical, these methods are often unsuitable due to their brittleness, and lack of explainability and interpretability. Furthermore, open-source real-world datasets containing historical failures are scarce in this domain. In this paper, we investigate an approach for predicting failures in chemical processes using symbolic machine learning and conduct a feasibility study in the context of an ethylene oxidation process. Our method builds on a state-of-the-art symbolic machine learning system capable of learning predictive models in the form of probabilistic rules from context-dependent noisy examples. This system is a general-purpose symbolic learner, which makes our approach independent of any specific chemical process. To address the lack of real-world failure data, we conduct our feasibility study leveraging data generated from a chemical process simulator. Experimental results show that symbolic machine learning can outperform baseline methods such as random forest and multilayer perceptron, while preserving interpretability through the generation of compact, rule-based predictive models. Finally, we explain how such learned rule-based models could be integrated into agents to assist chemical plant operators in decision-making during potential failures.
SafeGenChat – a Neuro-Symbolic Approach to Dialogs for Trustworthy Information Retrieval Conversations on Sensitive Topics
Large Language Model (LLM)-based conversational assistants, such as ChatGPT, Gemini, and DeepSeek, have shown strong potential for enabling natural language access to information. However, their deployment in sensitive domains raises concerns related to trustworthiness, including hallucinations, limited transparency, and the risk of unsafe or inappropriate responses. Purely rule-based conversational systems, while reliable and accurate to the information source, lack the flexibility required for open-ended information retrieval. We introduce SafeGenChat, a neuro-symbolic hybrid framework for trustworthy information retrieval dialogs on sensitive topics where it is paramount to make the context and risk of the information transparent to the user. Inspired by the dual-system theory of fast and slow thinking as implemented in the recently proposed SOFAI architecture, SafeGenChat combines a generative LLM-based component (System-1) with a symbolic, rule-based component (System-2) that dynamically routes user queries between verified answers and purposeful do-not-answer responses based on an assessed risk of the dialog context. We present a case study of an HIV-focused chatbot that answers user queries related to HIV to illustrate the design and application of SafeGenChat in a safety-critical domain. Overall, this work introduces a neuro-symbolic framework for risk-aware conversational information retrieval, adapts the SOFAI dual-system architecture to dialog-based settings, and demonstrates its applicability through a safety-critical HIV decision support case study.
From Argumentation to Labeled Logic Program for LLM Verification
Large language models (LLMs) often generate fluent but in-correct or unsupported statements, commonly referred to as hallucinations. We propose a hallucination detection frame-work based on a Labeled Logic Program (LLP) architecture that integrates multiple reasoning paradigms, including logic programming, argumentation, probabilistic inference, and ab-ductive explanation. By enriching symbolic rules with seman-tic, epistemic, and contextual labels and applying discourse-aware weighting, the system prioritizes nucleus claims over peripheral statements during verification. Experiments on three benchmark datasets and a challenging clinical narrative dataset show that LLP consistently outperforms classical symbolic validators, achieving the highest detection accuracy when combined with discourse modeling. A human evaluation further demonstrates that logic-assisted explanations im-prove both hallucination detection accuracy and user trust. The results suggest that labeled symbolic reasoning with dis-course awareness provides a robust and interpretable ap-proach to LLM verification in safety-critical domains.
A Scene Graph Backed Approach to Open Set Semantic Mapping
While Open Set Semantic Mapping and 3D Semantic Scene Graphs (3DSSGs) are established paradigms in robotic perception, deploying them effectively to support high-level reasoning in large-scale, real-world environments remains a significant challenge. Most existing approaches decouple perception from representation, treating the scene graph as a derivative layer generated post hoc. This limits both consistency and scalability. In contrast, we propose a mapping architecture where the 3DSSG serves as the foundational backend, acting as the primary knowledge representation for the entire mapping process. Our approach leverages prior work on incremental scene graph prediction to infer and update the graph structure in real-time as the environment is explored. This ensures that the map remains topologically consistent and computationally efficient, even during extended operations in large-scale settings. By maintaining an explicit, spatially grounded representation that supports both flat and hierarchical topologies, we bridge the gap between sub-symbolic raw sensor data and high-level symbolic reasoning. Consequently, this provides a stable, verifiable structure that knowledge-driven frameworks, ranging from knowledge graphs and ontologies to Large Language Models (LLMs), can directly exploit, enabling agents to operate with enhanced interpretability, trustworthiness, and alignment to human concepts.
Temporal Monitoring of Agent Beliefs Under Uncertainty with Subjective LTL
ML-based agents acting in open environments must form and revise beliefs under pervasive perceptual and epistemic uncertainty. Verifying such agents over time therefore requires reasoning about how their uncertain beliefs evolve. However, existing temporal verification and monitoring frameworks -- including probabilistic and multi-valued logics -- typically reason about Boolean or graded truth of propositions along system runs, rather than about an agent’s internal belief state. We propose Subjective LTL (SLTL), a temporal specification language whose atomic propositions are Subjective Logic opinions, i.e., tuples of belief, disbelief, and uncertainty about predicates in an agent’s symbolic knowledge model. Under an evidential semantics, temporal operators aggregate evidence over time about persistent hypotheses, so SLTL formulas constrain how the agent’s belief state should evolve. This supports temporal verification and monitoring directly over the dynamics of knowledge-grounded, uncertainty-aware agents, and can equally be used to equip such agents with introspective monitors over their own evolving beliefs, which we illustrate on a simple example.
Plato’s Cave: A Human-Centered Research Verification System
The growing publication rate of research papers has created an urgent need for better ways to fact-check information, assess writing quality, and identify unverifiable claims. We present Plato’s Cave as an open-source, human-centered research verification system that (i) creates a directed acyclic graph (DAG) from a document, (ii) leverages web agents to assign credibility scores to nodes and edges from the DAG, and (iii) gives a final score by interpreting and evaluating the paper’s argumentative structure. We report the system implementation and results on a collected dataset of 104 research papers.
Compositional Function Networks: A High-Performance Alternative to Deep Neural Networks with Built-in Interpretability
Deep Neural Networks (DNNs) deliver impressive performance but their black-box nature limits deployment in high-stakes domains requiring transparency. We introduce Compositional Function Networks (CFNs), a novel framework that builds inherently interpretable models by composing elementary mathematical functions with clear semantics. Unlike existing interpretable approaches that are limited to simple additive structures, CFNs support diverse compositional patterns—sequential, parallel, and conditional—enabling complex feature interactions while maintaining transparency. A key innovation is that CFNs are fully differentiable, allowing efficient training through standard gradient descent. We demonstrate CFNs' versatility across multiple domains, from symbolic regression to image classification with deep hierarchical networks. Our empirical evaluation shows CFNs achieve competitive performance against black-box models (96.24% accuracy on CIFAR-10) while outperforming state-of-the-art interpretable models like Explainable Boosting Machines. By combining the hierarchical expressiveness and efficient training of deep learning with the intrinsic interpretability of well-defined mathematical functions, CFNs offer a powerful framework for applications where both performance and accountability are paramount.
SemanticShift: Robust Semantic Watermarking for Large Language Models
Large language models (LLMs) have raised increasing concerns around misinformation and plagiarism. Watermarking—embedding identifiable signals into generated text—offers a promising approach for detection. Semantic watermarking enhances robustness by leveraging meaning rather than surface-level token patterns. However, existing semantic techniques often require model retraining or operate within constrained semantic spaces, limiting control over watermark strength, robustness, and cross-lingual generalizability. We introduce SemanticShift, a training-free and semantically grounded watermarking method that injects signals during generation by computing semantic shifts of candidate tokens relative to preceding context, guided by a secret key. SemanticShift uses pre-trained embedding models and is tunable via hyperparameters, offering strong resistance to paraphrasing and syntactic variation. Experiments demonstrate state-of-the-art detection performance, with ROC-AUC > 0.99 on original text and up to 0.96 under strong paraphrasing—outperforming all prior training-free approaches and rivaling training-based methods. Notably, SemanticShift achieves superior accuracy and robustness on models like OPT and LLaMA, showcasing its applicability and effectiveness.
Agentic AI for Robot Control: Flexible but Still Fragile
Recent work leverages the capabilities and commonsense priors of generative models for robot control. In this paper, we present an agentic control system in which a reasoning-capable language model plans and executes tasks by selecting and invoking robot skills within an iterative planner and executor loop. We deploy the system on two physical robot platforms in two settings: (i) tabletop grasping, placement, and box insertion in indoor mobile manipulation (Mobipick) and (ii) autonomous agricultural navigation and sensing (Valdemar). Both settings involve uncertainty, partial observability, sensor noise, and ambiguous natural-language commands. The system exposes structured introspection of its planning and decision process, reacts to exogenous events via explicit event checks, and supports operator interventions that modify or redirect ongoing execution. Across both platforms, our proof-of-concept experiments reveal substantial fragility, including non-deterministic suboptimal behaviour, instruction-following errors, and high sensitivity to prompt specification. At the same time, the architecture is flexible: transfer to a different robot and task domain largely required updating the system prompt (domain model, affordances, and action catalogue) and re-binding the same tool interface to the platform-specific skill API.
State Machine Structured Agents for Physical Science Reasoning
Large language models (LLMs) have shown promise as scientific assistants capable of reasoning, tool invocation, and autonomous analysis. However, their use in the physical sciences remains limited by the need for strong guarantees of explainability, traceable reasoning over long computational workflows, and the high consequences of subtle errors. Many existing LLM-based agent systems rely on unstructured conversational context or loosely coupled tool calls, which are insufficient for scientific domains governed by strict physical constraints and nonlinear dynamics. In this study, we explore the use of an AI assistant for an Earth system science use case. Unique to this work, we introduce a state machine agentic architecture that enables LLMs to perform structured, physically grounded scientific analysis through an explicit and enforceable notion of agent state. We define the agent state as a structured tuple, maintained across agent iterations using explicit update rules and strongly typed interfaces that enforce physical feasibility during tool invocation. We evaluate this methodology for a case study of ocean overturning circulations and a potential slowing or collapse of this system. The AI assistant interacts with a reduced-order ocean model and in-house developed deep learning models. The AI assistant is able to decompose complex questions into actionable steps that are answered using the set of tools developed. Compared to traditional grid search approaches, the agent autonomously designs parameter explorations, invokes high-dimensional models within valid physical bounds, and produces interpretable scientific summaries while reducing simulation count. The results demonstrate that the structured agent state is a key enabler of reliable LLM-based scientific assistants and is broadly applicable to constrained tool-centric scientific workflows.
maPO: An Ontology for Multi-Agent Path Finding and Its Usage for Explaining Planner Behaviour
As multi-agent systems become more autonomous, particularly in complex coordination tasks like Multi-Agent Path Finding (MAPF), the need for transparent and interpretable decision-making becomes critical. Although execution traces from MAPF algorithms provide rich diagnostic insight, existing explainability methods like visual segmentation of trace snapshots and logic‐based “why” queries address individual modalities but remain fragmented. We introduce the Multi-Agent Planning Ontology (maPO), a unified semantic schema that turns raw MAPF traces into a single knowledge graph, formalizing segmentation snapshots, conflict alerts, and replanning strategies. Our log-to-graph pipeline ingests planner outputs as ontology instances, and SPARQL queries produce contrastive and logical explanations. Our contributions are: (1) the MA Planning Ontology schema, (2) a log-to-graph transformation pipeline and a web platform for SPARQL-based explanation generation, and (3) an empirical validation of the explanation generation framework.
De Novo Antibiotic-Like Molecule Design via Descriptor-Guided Patch-Based GANs
The rise in antimicrobial resistance necessitates rapid discovery of novel antibiotics, but the majority of generative pipelines are unable to produce clinically viable candidates. The structural and topological complexity of actual antibiotics is not captured by current models, which lack pharmacological depth and are frequently trained on datasets such as QM9 and ZINC. We present a modular, descriptor-guided framework for antibiotic-like molecule design that combines a property-aligned β-VAE for interpretable encoding, a descriptor-to-latent conditioner for controllable sampling, and a patch-based graph generator for fragment-wise synthesis. Trained on a curated subset of ChEMBL containing clinically validated antibiotics, our framework supports end-to-end generation from RDKit descriptors to final molecules, with adversarial refinement for topological realism. Beyond favorable ADMET profiles, our method establishes joint satisfaction reporting of drug-likeness thresholds as a benchmark standard, resists mode collapse under 50k-sample stress tests, and surfaces ligands that outperform ciprofloxacin and co-crystal references in docking assays. These results highlight chemically meaningful, pharmacologically informed generation − overcoming limitations of black-box pipelines and general-purpose datasets.
Structured Supervision from Quantum Models: Distilling Robustness into Classical Networks
Quantum machine learning offers a practical opportunity to leverage near-term quantum devices without requiring quantum inference at deployment. We propose a quantum-classical hybrid knowledge distillation framework in which a variational quantum circuit, equipped with a Quantum Fourier Transform-inspired positional encoding, acts as a soft-label teacher for a compact classical student. Rather than serving as a standalone classifier, the quantum model is used offline to generate structured probability distributions that encode global spectral information and uncertainty. These quantum-derived soft labels are distilled into a lightweight classical network using a hybrid loss that combines hard and soft supervision. Experiments on MNIST demonstrate that students trained with quantum soft labels exhibit consistent and statistically meaningful robustness improvements to Gaussian noise and in-plane rotations compared to classical distillation baselines, while maintaining strong clean accuracy and calibration. These results highlight a distinct role for NISQ-era quantum models as supervisory signal generators, enabling quantum-informed learning within fully classical deployment pipelines.
Generative AI has a problem with trust. Knowledge-based AI does not, but faces the knowledge bottleneck: the cost of manually building the lexicons and ontologies that enable reasoning, explanation, and targeted correction. We present a neurosymbolic approach toward overcoming this bottleneck. This approach uses a symbolic semantic language interpreter and static knowledge resources (ontology, lexicon, and a corpus of 1,780 validated text meaning representations) of OntoAgent, a cognitive architecture, in coordination with a language model guided by narrative descriptions of decision-making algorithms that encode principles from decades of knowledge acquisition research. The acquisition process: a) is triggered when the semantic analyzer encounters knowledge gaps while constructing meaning representations; b) proposes new lexicon entries and ontological concepts; and c) automatically validates them against ontological constraints. We report the results of an initial experiment using this approach as the first step toward establishing what an LLM can achieve when boot-strapped from deep knowledge resources and integrated with a knowledge-based analyzer. This work describes a step in an ongoing R&D program whose core objective is to demonstrate that, just as GPUs enabled neural methods to scale, the use of language models as knowledge acquisition tools will enable knowledge-based AI to scale.
Coupling RNN with LLM: Does Their Integration Improve Highly Order-Sensitive Language Understanding?
Pretrained large language models (LLMs) have demonstrated remarkable success across various language modeling tasks. However, in domain-specific applications, particularly those involving highly order-sensitive data, general LLMs exhibit limitations in achieving state-of-the-art performance. One notable issue is that the contextual embeddings by LLMs still lack a strong positional inductive bias, especially for long and highly ordered sequences, leading to the lost in the middle problem. In this work, we utilized the potential of sequential models (RNNs) with LLMs to address the issue and investigate whether RNN integration improves LLM performance. The LLM generates rich contextual embeddings using the attention mechanism of the Transformer. The RNN further processes the LLM embeddings to capture the contextual semantics of long and order-sensitive dependencies. The LLM-RNN model leverages the potential of both Transformer and recurrent structures to enhance performance in domain-specific tasks. We perform a wide range of experiments leveraging multiple types of LLMs (encoder-only, encoder-decoder, and decoder-only) and RNNs (GRU, LSTM, BiGRU, and BiLSTM) across diverse public and real-world datasets to investigate the potential (either positive or negative) of LLM-RNN models. The experimental results highlight the superiority of the LLM-RNN model, showing improvements in commonsense reasoning, code understanding, and biomedical reasoning tasks.
IntuitiveGraphLLM: Intuitive Graph-based Text Representation with Large Language Model
Graphical representations of text can sharpen the inductive biases of large language models (LLMs), yet most graph-based approaches rely on co-occurrence, order, or position alone and therefore over-connect unrelated tokens while missing conceptually salient links. We introduce Intuitive Graphs (IGs)—graphs that explicitly encode both (i) structural context (local order/proximity/position) and (ii) conceptual relevance (semantic affinity in embedding space)—and IntuitiveGraphLLM, a framework that builds, encodes, and fuses IGs with pretrained LLMs. Given a passage, we first construct IGs by pruning structure-induced edges with a semantic gate based on cosine similarity between token (or span) embeddings, yielding sparse, human-plausible graphs. We then obtain initial node features from contextual embeddings and apply Graph Attention Networks (GATs) to emphasize informative nodes/edges to produce graph-level features. Finally, we perform hybrid fusion by integrating graph-level embeddings with LLM-based contextual representations, enabling the model to leverage complementary structural and conceptual signals. We evaluate our approach on five benchmark datasets spanning short and long documents and class-imbalance settings. Across benchmarks, IntuitiveGraphLLM consistently improves over strong text-only and graph-only baselines; gains persist under varied IG constructions, node embeddings, GAT depths/heads, and LLM backbones, with ablations confirming that IG is the key driver of performance and reduced edge noise. IntuitiveGraphLLM provides a principled, interpretable way to make text graphs both contextual and conceptually grounded, translating into more faithful reasoning and stronger downstream accuracy.
Grounding vs. Compositionality: On the Non-Complementarity of Reasoning in Neuro-Symbolic Systems
Compositional generalization remains a foundational weakness of modern neural networks, limiting their robustness and applicability in domains requiring out-of-distribution reasoning. A central, yet unverified, assumption in neuro-symbolic AI is that compositional reasoning will emerge as a byproduct of successful symbol grounding. This work presents the first systematic empirical analysis to challenge this assumption by disentangling the contributions of grounding and reasoning. To operationalize this investigation, we introduce the Iterative Logic Tensor Network (iLTN), a fully differentiable architecture designed for multi-step deduction. Using a formal taxonomy of generalization—probing for novel entities, unseen relations, and complex rule compositions—we demonstrate that a model trained solely on a grounding objective fails to generalize. In contrast, our full iLTN, trained jointly on perceptual grounding and multi-step reasoning, achieves high zero-shot accuracy across all tasks. Our findings provide conclusive evidence that symbol grounding, while necessary, is insufficient for generalization, establishing that reasoning is not an emergent property but a distinct capability that requires an explicit learning objective.
Beyond Linear Surrogates: High-Fidelity Local Explanations for Black-Box Models
With the increasing complexity of black-box machine learning models and their adoption in high-stakes areas, it is critical to provide explanations for their predictions. Existing local explanation methods lack in generating high-fidelity explanations. This paper proposes a novel local model agnostic explanation method to generate high-fidelity explanations using multivariate adaptive regression splines (MARS) and N-ball sampling strategies. MARS is used to model non-linear local boundaries that effectively captures the underlying behavior of the reference model, thereby enhancing the local fidelity. The N-ball sampling technique samples perturbed samples directly from a desired distribution instead of reweighting, leading to further improvement in the faithfulness. The performance of the proposed method was computed in terms of root mean squared error (RMSE) and evaluated on five different benchmark datasets with different kernel width. Experimental results show that the proposed method achieves higher local surrogate fidelity compared to baseline local explanation methods, with an average reduction of 32% in root mean square error, indicating more accurate local approximations of the black-box model. Additionally, statistical analysis shows that across all benchmark datasets, the proposed approach results were statistically significantly better. This paper advances the field of explainable AI by providing insights that can benefit the broader research and practitioner community.
CausalPulse: An Industrial-Grade Neurosymbolic Multi-Agent Copilot for Causal Diagnostics in Smart Manufacturing
Modern manufacturing environments demand real-time, trustworthy, and interpretable root-cause insights to sustain productivity and quality. Traditional analytics pipelines often treat anomaly detection, causal inference, and root-cause analysis as isolated stages, limiting scalability and explainability. In this work, we present CausalPulse, an industry-grade multi-agent copilot that automates causal diagnostics in smart manufacturing. It unifies anomaly detection, causal discovery, and reasoning through a neurosymbolic architecture built on standardized agentic protocols. CausalPulse is being deployed in a Robert Bosch manufacturing plant, integrating seamlessly with existing monitoring workflows and supporting real-time operation at production scale. Evaluations on both public Future Factories and proprietary Planar Sensor Element datasets show high reliability, achieving overall success rates of 98.0% and 98.73%. Per-criterion success rates reached 98.75% for planning and tool use, 97.3% for self-reflection, and 99.2% for collaboration. Runtime experiments report end-to-end latency of 50-60,s per diagnostic workflow with near-linear scalability R^2=0.97, confirming real-time readiness. A qualitative comparison with existing industrial copilots highlights distinct advantages in modularity, extensibility, and deployment maturity. These results demonstrate how CausalPulse’s modular, human-in-the-loop design enables reliable, interpretable, and production-ready automation for next-generation manufacturing.
Echoes of Citations: Automated Extraction of Claims from Full Scientific Papers
Automated extraction of core scientific claims, concise statements of a paper’s primary contributions, is critical for navigating the growing scientific literature. We present a scalable framework that leverages citances, sentences from other papers citing the target work, as natural supervision, removing the need for costly manual labelling. Our method filters citances with a claim-focused rubric and aligns them with candidate claims to train two pipelines: an unsupervised extractor and a weakly supervised model. Experiments show our approach outperforms existing baselines, achieving up to 18% higher precision and 22% greater coverage. We further analyse claim distributions across paper sections and introduce a taxonomy of claim types, providing new insights into the rhetorical structure of scientific discourse.
Post-Hoc Knowledge Grounding for Verifiable Multi-Agent Search and Rescue Decision Support
Multi-agent systems powered by large language models (LLMs) show promise for complex decision-support tasks, yet their outputs often contain hallucinations that undermine trust in safety-critical domains such as search and rescue (SAR). We present a post-hoc verification framework that grounds LLM agent outputs in a probabilistic knowledge graph derived from approximately 12,000 curated cases from the International Search and Rescue Incident Database (ISRID). Our approach extracts structured claims from agent outputs, performs probabilistic reasoning to detect statistical anomalies, and produces tiered decisions (accept, flag, reject) with explicit evidence chains. The framework operates downstream of a seven-agent SAR system, verifying terrain predictions (where the subject is likely found) and status predictions (the subject's likely condition). These claims directly inform search prioritization. Experiments demonstrate that coordinators following our system's recommendations achieve 71.0% accuracy, compared to 51.6% when accepting all LLM outputs, a 19.4 percentage point improvement. The system detects anomalies with an 81.3% F1 score while maintaining practical coverage (46.3% of claims verified). Ablation studies confirm anomaly detection as the critical component, contributing +5.7 percentage points over grounding alone. Our framework provides a practical path toward trustworthy AI in emergency response.
Evaluating the Architectural Reasoning Capabilities of LLM Provers via the Obfuscated Natural Number Game
While Large Language Models have achieved notable success on formal mathematics benchmarks such as MiniF2F, it remains unclear whether these results stem from genuine logical reasoning or semantic pattern matching against pre-training data. This paper identifies Architectural Reasoning—the ability to synthesize formal proofs using exclusively local axioms and definitions within an alien math domain—as the necessary ability for future automated theorem discovery AI. We use the Obfuscated Natural Number Game, a benchmark to evaluate Architectural Reasoning. By renaming identifiers in the Natural Number Game in Lean 4, we created a zero-knowledge, closed environment. We evaluate state-of-the-art models, finding a universal latency tax where obfuscation increases inference time. The results also reveal a divergence in robustness: while general models (Claude-Sonnet-4.5, GPT-4o) suffer performance degradation, reasoning models (DeepSeek-R1, GPT-5, DeepSeek-Prover-V2) maintain the same accuracy despite the absence of semantic cues. These findings provide a quantitative metric for assessing the true capacity for mathematical reasoning.
Context-Adaptive Humor Rewriting: A First-Order Logic Framework Using Large Language Models
Humor is an important conversational skill that conveys approachability. When addressing different audiences, we often need to adjust and adapt jokes to lower the comprehension barrier while also preventing the original punchline from becoming offensive in a new context. To this end, we propose a humor-rewriting-agent framework. The system converts jokes from an existing humor corpus into a First-Order Logic representation, extracting predicates and constants. The agent then performs context-aware semantic mapping, replacing relevant elements with more suitable equivalents in the target scenario. Finally, the mapped logical structure is realized as fluent, natural text. Through evaluation by participants, this framework provides a controllable, traceable, and interpretable implementation pathway for contextually grounded humor adaptation.
Beyond Accuracy: Introducing a Symbolic-Mechanistic Approach to Interpretable Evaluation
Accuracy-based evaluation cannot reliably distinguish genuine generalization from shortcuts like memorization, leakage, or brittle heuristics, especially in small-data regimes. In this position paper, we argue for mechanism-aware evaluation that combines task-relevant symbolic rules with mechanistic interpretability, yielding algorithmic pass/fail scores that show exactly where models generalize versus exploit patterns. We demonstrate this on NL-to-SQL by training two identical architectures under different conditions: one without schema information (forcing memorization), one with schema (enabling grounding). Standard evaluation shows the memorization model achieves 94% field-name accuracy on unseen data, falsely suggesting competence. Our symbolic-mechanistic evaluation reveals this model violates core schema generalization rules, a failure invisible to accuracy metrics.
Toward Neurosymbolic Reinforcement Learning via Editable Specifications
Reinforcement learning systems are commonly adapted to new settings by retraining or fine-tuning policies. This default is costly, difficult to audit, and poorly aligned with structured requirement changes such as revised safety rules, new operational constraints, or updated user preferences. We argue for an alternative abstraction: adaptation via edits to an external, human-readable specification that the agent consults at execution time. We propose conditioning decision-making on an editable knowledge graph encoding (i) applicability rules capturing action preconditions and high-level effects, (ii) hard constraints defining feasibility, and (iii) soft preferences shaping tradeoffs among feasible behaviors. Requirement changes become graph edits, not policy rewrites, operationalized through constraint-based action shielding and preference-driven objective shaping. This enables immediate, auditable behavior updates with zero or minimal gradient-based adaptation. We outline edit-based evaluation protocol and highlight open problems in specification design, edit inference, and guarantees.
Hybridization Beyond Modularity: A Modern Taxonomy for Fusion and Duality of Data-driven and Knowledge-based Artificial Intelligence
Combining data-driven and knowledge-based techniques is a key research direction for developing trustworthy artificial intelligence (AI). Modular hybridization concepts in particular have recently received increased attention in scholary taxonomization concepts, such as the so-called Boxology. At the same time, non-modular hybrid approaches based on fusion and duality have continued to evolve and diversify during the last decade. Building on the canonical typology by McGarry et al, we introduce an updated taxonomy for hybridization approaches that combine data-driven and knowledge-based AI in a non-modular fashion. Notably, we distinguish unified and transformational hybridization into further subcategories and indicate current research trends in these areas. While the original work by McGarry and colleagues was targeting solely neural hybrid architectures, our taxonomy is not limited to neurosymbolic techniques but also includes further hybrid approaches, such as fuzzy-based techniques. Overall, this work seeks to stimulate critical discussion about recent developments and future directions in non-modular hybridization.
Towards Low-Dimensional Search for Mastering Multi-Agent Planning
Monte Carlo Tree Search (MCTS) faces a severe scalability bottleneck in Multi-Agent Planning (MAP) due to the combinatorial explosion of joint action spaces. In this position paper, we argue that the key to tractable planning lies in leveraging low-dimensional representational structures to guide the tree search, rather than enumerating the raw action space. Specifically, under a linear approximation of joint-action returns, we demonstrate that the node expansion problem can be effectively cast and solved as a linear contextual bandit, providing theoretical regret guarantees. Empirical results on complex benchmarks confirm that this structure-aware search significantly outperforms state-of-the-art baselines, offering a scalable path for neuro-symbolic multi-agent planning. We further discuss promising extensions, including integrating dynamic agent grouping and coalition formation mechanisms to further reduce the effective branching factor.
Towards World Models in AI and Natural Language Processing Applications: Emotion, Creativity and World Knowledge
With Emotion, as a prerequisite for Creativity, mirroring hu-man behavior and human values, the modelling of world knowledge for behavior and emotion functioning as a “road-map” of key-parameters in the language(s) and/or domain(s) concerned contributes to the creation of world models for ap-plications (i.e. Sentiment Analysis, voice user interfaces -VUIs) and training data, including Large Language Models (LLMs). World models reflecting human behavior and human values in the real world can be based on world knowledge - Collective Intelligence –integrated in analysis and processing strategies from text types such as political and journalistic texts, containing socio-cultural elements and involving essential information for understanding human behavior, emotion and, ultimately, the human mind. This complex information can be processed in respect to a “three-level” framework with distinct, identifiable features, comprising the User-Group Level, the Pragmatic Level and the Prosodic -Paralinguistic Level.
Explore-Then-Commit with Dynamic Expertise Amplification
In resource-constrained real-world deployments, greedy exploration strategies that perpetually expand their budgets in pursuit of maximal reward become fundamentally impractical, demanding a principled framework that achieves high performance while strictly honoring computational and financial limits. Optimal allocation of limited resources between exploring new directions and exploiting proven strategies is a defining challenge in scientific research, education, and AI for Social Good. We introduce IDEA (Intelligent Dynamic Expertise Amplification via Explore-Then-Commit), a principled framework that recasts research strategy optimization as an expertise-aware contextual bandit problem. Theoretically, we prove that IDEA achieves expected regret E[R(T)] = O( √ KT log T) − Ω(ρ(T−τ )∥E∞∥), where the subtractive expertise term drives effective regret strictly negative for large T, a fundamental guarantee absent from state-of-the-art neural bandit methods, whose regret bounds are purely additive upper bounds of Oe( √ deT) with no compounding benefit. Computationally, IDEA requires only O(T(d 2+Kd)) operations, achieving a provable Θ(m2L/(Kd)) reduction in both time and memory over leading neural contextual bandit baselines. Empirically, in 1,300 controlled research trajectory simulations, IDEA delivers a cumulative reward of 15.5% higher than all state of-the-art bandit methods with strong statistical significance (p<0.05). On the real-world ASSISTments dataset (200,000 student interactions, 31,997 students, 252 skills, T=15,000 rounds), IDEA achieves a +21.7% higher mean reward over the best competing method (p<0.0001), while running 2.2× faster. These results establish IDEA as a theoretically grounded and practically superior framework for resource constrained decision-making in AI for Social Good.
Metaphors We Compute By: A Computational Audit of Cultural Translation vs. Thinking in LLMs
Large language models (LLMs) are often described as multilingual because they can understand and respond in many languages. However, speaking a language is not the same as reasoning within a culture. This distinction motivates a critical question: do LLMs truly conduct culture-aware reasoning? This paper presents a preliminary computational audit of cultural inclusivity in a creative writing task. We empirically examine whether LLMs act as culturally diverse creative partners or merely as cultural translators that leverage a dominant conceptual framework with localized expressions. Using a metaphor generation task spanning five cultural settings and several abstract concepts as a case study, we find that the model exhibits stereotyped metaphor usage for certain settings, as well as Western defaultism. These findings suggest that merely prompting an LLM with a cultural identity does not guarantee culturally grounded reasoning.
STEM Faculty Perspectives on Generative AI in Higher Education
Generative artificial intelligence (GenAI) tools are increasingly present in higher education, yet their adoption has been largely student-driven, requiring instructors to respond to technologies already embedded in classroom practices. While some faculty have embraced GenAI for pedagogical purposes such as content generation, assessment support, and curriculum design, others approach these tools with caution, citing concerns about student learning, assessment validity, and academic integrity. Understanding faculty perspectives is therefore essential for informing effective pedagogical strategies and institutional policies. In this paper, we present findings from a focus group study with 29 STEM faculty members at a large public university in the United States. We examine how faculty integrate GenAI into their courses, the benefits and challenges they perceive for student learning, and the institutional support they identify as necessary for effective and responsible adoption. Our findings highlight key patterns in how STEM faculty engage with GenAI, reflecting both active adoption and cautious use. Faculty described a range of pedagogical applications alongside concerns about student learning, assessment, and academic integrity. Overall, the results suggest that effective integration of GenAI in higher education requires rethinking assessment, pedagogy, and institutional governance in addition to technical adoption.
Crossing the Chasm from LLM Hallucinations to Invention via Abduction
Large Language Models (LLMs) are increasingly employed as collaborative partners in creative and problem-solving dia-logues, yet their usefulness is constrained by hallucinations—plausible but unsupported or inconsistent statements that are typically treated as reliability failures. This paper argues that, in inventive human–LLM interactions, hallucinations can also function as productive cognitive perturbations that expand the hypothesis space and seed innovation. We propose a neuro-symbolic framework that reframes hallucinated outputs as low-prior abductive hypotheses, which are then evaluated and trans-formed through constraint satisfaction, counter-abduction, and human oversight. Using a curated Hall2Invent dataset and a suite of evaluation metrics, we show that abductive and con-straint-based reasoning substantially improves hallucination identification, enables systematic repair of flawed explanations, and increases the yield of feasible and non-trivial inventions across engineering and systems domains. Our results demon-strate that symbolic reasoning not only reduces harmful reason-ing hallucinations but also preserves and channels the creative potential of LLMs. We conclude that trustworthy creative AI should not aim to eliminate hallucinations outright, but to gov-ern them through structured reasoning processes that bridge the gap between error and invention.
Peeling Back the Layers: The Co-Evolutionary Onion of Human-AI Co-Creativity
Generative Artificial Intelligence offers creative superpowers that boost productivity and inspiration. However, it simultaneously presents the risk of creative convergence, where algorithmic optimization leads to the homogenization of ideas and the erosion of human agency. Addressing this tension requires reframing Human-AI interaction from a focus on frictionless efficiency to one of Human-AI Co-Evolution, a symbiotic state where humans and machines continuously adapt to and elevate one another. This paper introduces the Co-Evolutionary Onion of Human-AI Co-Creativity, a multi-layered theoretical framework designed to structure this engagement.
The Generative Capacity Erosion Hypothesis: Does Generative AI Assistance Degrade Human Ideation Capacity?
The rapid integration of generative artificial intelligence into creative workflows raises a critical but underexplored question: does habitual AI assistance in creative tasks lead to measurable atrophy in human generative capacities? Drawing from established research on cognitive offloading—including the “Google effect” on memory and GPS-induced degradation of spatial cognition—this paper introduces the Generative Capacity Erosion Hypothesis (GCEH): sustained reliance on AI co-creation tools may systematically weaken human ideation, divergent thinking, and creative self-efficacy. The hypothesis is situated within five decades of research on technology-mediated cognitive change, tracing parallels from calculators to search engines to satellite navigation. A theoretical framework distinguishes between augmentation effects (additive benefits) and atrophy effects (subtractive costs), arguing that current discourse overemphasizes the former while neglecting the latter. The paper develops operationalizable constructs for creative atrophy, outlines methodological approaches for empirical investigation, and discusses implications for the design of “creativity-preserving” AI systems. The Well-Being AI paradigm must account not only for what AI enables humans to produce, but for what sustained AI assistance may prevent humans from becoming.
How Human-AI Collaboration Illuminates Mental Health Care
This paper presents a comprehensive framework for responsible and explainable AI in mental health screening, bridges the gap between technical XAI tools and clinical requirements.
Golden Wrapper Personality in Effective Communication
This work explores the implementation of an agentic AI framework tailored for the educational ecosystem, with a focus on the efficacy of adaptive response framing. While traditional school automation prioritizes task completion, our research emphasizes the psychological impact of "linguistic wrappers" on stakeholder engagement. We introduce the Golden Wrapper and Reinforcement Learner personas as superior strategies for navigating the complex interpersonal dynamics between teachers, staff, parents, and students. Through an empirical evaluation of 1,200 email interactions, we demonstrate that high affirmation "cushioning" (the Golden Wrapper) significantly outperforms direct or purely empathetic approaches in securing business outcomes and positive sentiment.
Will AI Light Up Human Creativity or Replace It? Toward Well-Being AI for Co-evolving Human and Machine Intelligence
The rapid evolution of generative artificial intelligence raises a fundamental question: will AI illuminate human creativity or gradually replace it? Building upon prior discussions of Hu-man-Compatible AI and AI-Powered Science, this study revisits the concept of Well-Being AI in the era of generative and multi-agent systems. We argue that AI presents a dual potential: it can amplify human creativity and accelerate scientific discovery, but it may also diminish human autonomy through overreliance and automation. To address this tension, we extend the dual framework by integrating creativity as a central dimension of well-being within human–AI collaborative ecosystems. This paper outlines the conceptual foundations, identifies key challenges, and proposes research directions toward a co-evolutionary human–AI ecosystem that maximizes human potential and enhances physical, mental, and social flourishing rather than replacing it.
InspireTrace: A Generative AI System for Creative Scaffolding with Source Attribution
The rapid integration of generative AI into education, industry, and everyday creative practices has introduced significant efficiencies, and yet it has also raised ethical concerns around authenticity, originality, and the erosion of human creativity. Many generative AI systems prioritize speed and content production over user development. Simply delivering synthesized outputs tends to limit opportunities for users to focus on the creative process and creative thinking. Additionally, users might unintentionally plagiarize others’ ideas from the synthesized output from AI systems without realizing it. In this paper, we present InspireTrace, an early-stage generative AI prototype designed to support creativity through guided inspiration rather than direct solution, intentionally leaving synthesis and ideation to the user. By explicitly attributing source materials, InspireTrace aims to reduce plagiarism risks and foster a healthier, more trustworthy creativity ecosystem. We discuss the system’s design rationale and its implications fro creative practice in human-AI collaboration.
Lighting Up or Dimming Down? Exploring Dark Patterns of LLMs in Co-Creativity
Large language models (LLMs) are increasingly used as collaborative writing partners, raising important questions about their effects on human agency. In this exploratory study, we investigate five dark patterns in human-AI co-creativity, which are subtle model behaviors that can suppress or distort the creative process: sycophancy, tone policing, moralizing, loop of death, and anchoring. Through a series of controlled sessions in which LLMs are prompted as writing assistants across diverse literary forms and themes, we analyze the prevalence of these behaviors in generated responses. Our preliminary results suggest that sycophancy is nearly ubiquitous, particularly in sensitive-topic prompts, while anchoring appears to depend on literary form, surfacing most frequently in folktales. These findings indicate that dark patterns, often emerging as byproducts of safety alignment, may inadvertently narrow creative exploration. We conclude by proposing design considerations for AI systems that better support creative writing while preserving user agency.
Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity
Recent studies reveal a paradox: AI enhances individual creative outputs while reducing collective diversity. Current explanations—cognitive offloading, over-reliance—identify symptoms but not mechanisms. We propose selective metacognitive adaptation: routine AI use redistributes rather than diminishes metacognitive effort. Some capacities are amplified (partner modeling, surface control), while others are systematically under-supported (originality evaluation, reflective integration). This redistribution explains both individual satisfaction and collective convergence. We present a taxonomy of seven metacognitive capacities organized by temporal phase, characterize their tendencies under routine AI use, and show how individually rational adaptation produces emergent social costs. The framework generates specific predictions for researchers and design principles for practitioners seeking to preserve both individual creative satisfaction and collective creative diversity.
CARE: Culturally Aligned AI for Reflective Creativity Through Adinkra Semiotic Knowledge
Creativity is shaped by cultural epistemologies, symbolic systems, and traditions of sensemaking. Yet many contemporary AI-driven creativity support tools remain rooted in Western models of cognition and representation. This paper introduces CARE, the Culturally Aligned Reflection Engine, a symbolically mediated generative system that retrieves culturally grounded symbolic frames such as Adinkra symbols and proverbs to condition creative interpretation. The system supports creative reframing and culturally meaningful interaction through proverb-based interpretation, positioning AI as a partner in sensemaking rather than a source of prescriptive guidance. This work demonstrates how activating cultural heritage within interactive systems can promote culturally grounded design and expand creative possibilities. Reflections from the design and prototyping process illustrate how culturally situated metaphors can enrich ideation, deepen reflective engagement, and offer pathways for cultural continuity in computational creativity.
From Answers to Relationships: Dual Digital Twins for Well-Being-Oriented Face-to-Face Service
Advances in artificial intelligence, particularly large lan-guage models, have accelerated the development of an-swer-oriented systems optimized for correctness, effi-ciency, and task completion. However, many face-to-face human services—such as caregiving, rehabilitation, and education—operate within relationship-oriented con-texts in which value emerges through engagement, emo-tional alignment, and motivational support rather than informational accuracy alone. This paper introduces the Dual Digital Twin (DDT) framework as a computational architecture for modeling and analyzing such relational interactions. The framework simulates both participants in a service encounter through a Resident Twin and Staff Twin, while an Observer Twin evaluates relational dy-namics including engagement persistence, emotional res-onance, and motivational impact. Implemented as a mul-ti-LLM agent system on the Dify platform, the frame-work enables controlled scenario simulation and reflec-tive training. We conduct a rehabilitation motivation study centered on daily walking goals monitored via wearable devices, comparing task-oriented, relationship-oriented, and hybrid dialogue strategies. Simulation re-sults indicate that while task-oriented feedback ensures performance clarity, relational continuity is essential for sustaining engagement following negative outcomes. Hybrid strategies integrating performance transparency with motivational scaffolding yield the most balanced results. These findings position AI not as a replacement for relational labor, but as a reflective partner that illu-minates the hidden mechanisms sustaining meaningful human interaction and well-being.
Evolutionary HyperNet-InfoGAN Baselines for Controllable Synthetic Image Generation
Synthetic medical data generation requires human-auditable control. It is important that latent factors are able to separate data-specific structures so clinicians can inspect, trust, and responsibly reuse synthetic samples. Building on prior evolutionary generator work, an Evolutionary HyperNet-InfoGAN is developed in which Policy Gradients with Parameter-Based Exploration (PGPE) optimizes a compact HyperNetwork that emits the weights of a larger image generator, while a discriminator/Qnetwork is trained with backpropagation. This process creates a non-stationary adversarial setting in which evolutionary search must continually adapt, but in a far smaller and more structured parameter space than direct evolution of the full generator. This model is used to evaluate a shared baseline on the MNIST dataset (28x28 grayscale, 10 classes) and then on a grayscale-converted BloodMNIST dataset (28x28, 8 classes) with a shared-z protocol in which groups of samples share continuous noise while discrete codes vary. This setup enables the direct measurement of feature-space code separation (rsense) and within-code variation (rintra). When compared to a previous PGPE baseline that searched directly over a generator with more than 500k parameters, the HyperNetwork formulation yielded cleaner code allocation, substantially more stable late-stage training, and the ability to resolve early duplicate modes to distinct digits for better mode coverage. When applied to MNIST, the baseline approached full digit coverage with fewer duplicate-code failures than prior direct search. For BloodMNIST, the preliminary baseline results show partial disentanglement and class-consistent morphology, suggesting that HyperNetworks provide a practical intermediate step toward auditable, controllable synthetic medical image generation. These baselines motivate the next stage of this work that will employ a Cultural Algorithms (CA) as a meta-level controller in order to support adaptive fitness weighting and secondary search guidance within the PGPE framework.
Lighting Up Missing Links by Generative AI: A Case of Retinal Detachment
This paper proposes a generative-AI-assisted framework for utilizing 1st-person patient narratives to improve medical understanding and decision-making, particularly in the pre-diagnostic or subclinical stages. Contemporary medicine often relies on symptom confirmation, leading to delayed intervention and a heavy cognitive burden on physicians when patients present with fragmented or contradictory accounts. Using the author’s personal experience with acute retinal detachment, this study illustrates how rare, fleeting visual phenomena, such as the preoperative perception of multicolored elliptical particles and postoperative transformation into geometric and network-like patterns, are typically dismissed or lost despite their potential informational value. This paper argues that such narratives, when structured and interpreted through generative AI, can be mapped onto disease progression scenarios and linked with medical knowledge, enabling “structural explanations” rather than passive watchful waiting. This approach, termed N1-Diagnoser, connects 1st-person volatile narratives with large-scale data, literature, and temporal relationships to highlight and clarify hidden events behind the missing links in understanding. These benefits include earlier patient reassurance, im-proved self-observation, reduced physician workload, and enhanced lifestyle guidance. More broadly, this study frames generative AI as a tool for lighting up missing links to enable novel human-centered decision-making via knowledge creation.
Measuring Constructive Creativity in AI-Augmented Work: A Scale-Invariant Embedding Framework
As AI-generated content proliferates in workplace settings, objective metrics that capture dimensions relevant to constructive creativity—work that balances novelty with usefulness—are needed to support metacognitive monitoring and quality evaluation. Existing creativity instruments either measure unconstrained divergence or assess accuracy against fixed item sets, limiting their applicability to real-world text of varying complexity. We present a framework for evaluating "constructive creativity" through three orthogonal, scale-invariant metrics derived from embedding geometry: *divergence* (semantic spread among target concepts), *alignment* (semantic fit between associations and targets), and *parsimony* (non-redundancy of associations). Across 3,600 random configurations with targets ranging from 1 to 50 words and associations ranging from 2 to 50 words, aggregate pairwise correlations remained below |r| < 0.22 under OpenAI ada-002 embeddings and |r| < 0.23 under GloVe embeddings. Regime analysis decomposing orthogonality by task scale (m, n) revealed that two of three metric pairs—divergence–parsimony and alignment–parsimony—maintained independence uniformly across all scales and both embedding models, while divergence–alignment exhibited independence at smaller task sizes. Benchmarking against 144 Remote Associates Test items provided partial validation: alignment distinguished correct solutions from chance (mean similarity = 0.356), though divergence did not predict item difficulty or solution time as hypothesized. LLM judges (GPT-4o-mini, Claude Haiku 4.5) discriminated correct solutions from foils with 84–88% accuracy, and model-solving difficulty correlated with human difficulty (r = 0.559). These metrics offer candidate dimensions for applying semantic associations to creative tasks, providing a foundation for future validation as cognitive feedback tools in AI-augmented workflows.
H-AGO: Human-Centric Agentic Governance with Creative Capacity Preservation
Recent studies suggest that habitual generative AI use may erode human creative capacity—paralleling documented effects of GPS on spatial cognition and search engines on memory recall. We introduce Human-Centric Agentic Governance & Observability (H-AGO), a framework that shifts AI governance from content filtering to creative capacity preservation. H-AGO implements three innovations: (1) a Creative Capacity Index (CCI) that longitudinally tracks human ideation ability independent of AI assistance using embedded measurement and minimal-burden micro-assessments, (2) dual-phase constitutional governance (CreativeSilence and Socratic Guardrails) operating at real-time and longitudinal scales, and (3) adaptive intervention policies that adjust AI assistance parameters based on measured CCI trends. Unlike existing AI safety approaches focused on output harm, H-AGO governs for human capability preservation— a core principle of Well-Being AI. We propose a hybrid edge-cloud architecture where governance runs locally while high-quality Socratic scaffolding leverages cloud LLMs. We present a planned evaluation design for creative writing tasks targeting 30–40 participants over 4 weeks.
Post-Deployment Observability as a Foundation for Well-Being-Aligned Human–AI Co-Evolution
Current discussions of human creativity and generative AI often focus on model capabilities at the point of release, framing outcomes in terms of augmentation versus replacement. In deployed settings, however, the effects of AI systems on human agency, creativity, and institutional well-being emerge over time, shaped by repeated interaction, reuse, and integration into real-world workflows. These dynamics are rarely visible through pre-deployment evaluation or isolated prompt–response analysis. This paper argues that post-deployment observability is a foundation for well-being-aligned human–AI co-evolution. We present a system-level framework for externalized behavioral monitoring that treats generative AI systems as participants in socio-technical ecosystems rather than static tools. The framework emphasizes interpretable, aggregate behavioral signals - such as shifts in output velocity, semantic and structural reuse, persistence of synthetic roles, and cross-context propagation - that emerge cumulatively through time. Rather than automating judgment or enforcement, these signals support human-in-the-loop interpretation, enabling earlier awareness of when AI use patterns may be drifting from creative augmentation toward automation pressure, authority substitution, or unintended displacement of human agency. By focusing on observation instead of prediction, and governance rather than control, the proposed approach complements existing alignment and safety practices while preserving human judgment, institutional choice, and long-term wellbeing.
Galois Smartnetwork Field Theory for Millennium Prize Math Discovery
Human–AI partner teams are positioned to transform mathematical creativity, shifting discovery from incremental, bot-tom-up reasoning to a broader mode of inquiry that spans the full landscape of mathematics and science. This paper examines that transition by advancing Galois Smartnetwork Field Theory (Galois SNFT) as a framework for co-evolutionary human–machine reasoning—one that integrates mathematics, computation, and physics through the organizing power of higher structures mathematics, especially symmetry. To accelerate the inclusion of mathematical research into the computational infrastructure, Galois SNFT extends Neural Network Field Theory (NNFT) approaches by adding mathematics as a cornerstone to physics and computation. Digging deep into Modern Symmetry Theory’s convergence toward a Grand Unified Symmetry (GUS) framework with frontier mathematics from Clausen, Scholze, Lurie, Bhatt, Pridham, Barwick, and Haine, Galois SNFT deploys three symmetry properties (phase stability, glocal propagation, and symmetry constraint) to analyze the Millennium Prize Problems (MPP). The MPP can be partitioned into Langlands, physics, and orthogonal arms. Within this landscape, the Riemann Hypothesis (regarding the distribution of prime numbers along a critical line) is particularly suited to a symmetry-based analysis via phase stability, making it a compel-ling test case for co-evolutionary human–AI mathematical discovery.
Categorical Longevity and The Mathematical Systems Biology of Aging
Categorical longevity (category-theoretic approaches to the mathematical systems biology of aging) is within the co‑evolution of human and machine intelligence, which sees higher‑structure mathematics as a shared conceptual frontier where biological and artificial systems meet. As machine learning systems grow more capable in the modeling of complex, multi‑scale, non‑invertible processes, they begin to operate in the same mathematical regimes (hierarchical state spaces, symmetry‑breaking transitions, and compositional dynamics) underlying systems theory of aging approaches. Human inquiry is being pushed toward more abstract, categorical, and structural representations precisely because AI systems can now handle various mathematical modeling and data analysis tasks. The result is a kind of reciprocal shaping: biological aging motivates richer mathematical formalisms that AI systems are uniquely suited to explore, while AI’s expanding representational capacities encourage humans to reconceptualize biological processes in terms of higher‑order theories. In this sense, modeling the systems biology of aging through higher-structure mathematics becomes part of a broader co‑evolutionary process in which human and machine intelligence jointly extend the space of possible scientific explanations and interventions.
Towards Collaboration with AI to Explore New Features of Dementia That Cannot Be Found from Medical Viewpoint
This paper focuses on collaboration between human and AI and shows its potential of exploring new features that cannot be found by either human experts or AI. Concretely, this paper focuses on dementia and shows that new features of dementia based on heartrate cannot be found from the medical viewpoint but can be found from hints based on the analysis of AI. Through an analysis of heartrate of both dementia patients and healthy subjects, the following implications have been revealed: (1) an ability of dementia patients to maintain a stable heartrate as homeostasis is weaker than that of healthy subjects; and (2) the time-series features of heartrate obtained by AI (i.e., the statistical analysis and machine learning) is needed to be interpreted (by human) to find a potential symptom of dementia.
Reducing Student-Instructor Mismatches in Driving Schools Through Compatibility Analysis
Matching students with suitable instructors is critical for learning effectiveness in driving schools, yet most schools rely on manual, experience-based allocation. This paper presents a real-world case study at a Japanese driving school analyzing student-instructor compatibility using operational data. We integrate 5-point and 2-point evaluation data, perform exploratory analysis connecting aptitude-test personality patterns to instructor ratings, and build a LightGBM classification model to predict mismatches. Rather than recommending optimal pairs, we focus on reducing mismatches by avoiding low-compatibility pairings while respecting equal distribution, enabling instructors to maximize their strengths and improving student outcomes.
Well-Being AI Encouragement in Sustained Interaction: Attitude-Aware Design Principles
Encouraging utterances is widely used in supportive dialogue systems; however, how this should be designed for Well-Being AI remains underexplored. The Technology Acceptance Model (TAM) is sometimes interpreted as suggesting a simple, monotonic relationship in which more positive attitudes toward AI lead to higher perceived effectiveness of AI-mediated support. In our preliminary study with older Japanese adults examining AI-delivered encouragement, we measured participants’ baseline attitudes toward AI and collected perceived effectiveness ratings of encouragement strategies across multiple strategy types and worry contexts. This study provides preliminary indications that perceived effectiveness may not be well explained well by a simple linear assumption regarding users’ attitudes toward AI. Based on these observations, this paper proposes five design principles for encouragement in Well-Being AI, with a focus on adapting encouragement type selection based on users’ attitudes toward AI. Our key ideas include reframing “user attitudes toward AI” from a pre- and post-interaction evaluation measure to a dynamic signal that should be continuously recognized and responded to during interaction. Encouragement strategy selection and realization should be guided by users’ cur-rent attitudes and interaction feedback. In this process, users’ evolving attitudes reshape the system’s behavior, while the system’s responsive support in turn influences users’ subsequent attitudes. This bidirectional adaptation informs the design of emotionally supportive inter-actions and may contribute to the co-evolution of human and machine intelligence in Well-Being AI.