NEvo performs evolutionary search guided by a dynamic voxel-level encoding model to synthesize videos that maximize predicted activity in target brain ROIs, recovering known selectivities and revealing temporal dynamics differences.
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A new diagnostic benchmark decomposes LLM spatial navigation into three cognitive scales and shows that cross-scale aggregation, not single-level deficits, causes failure beyond small mazes.
PERSUASIONTRACE introduces a Bayesian-network simulated target for multi-turn persuasion that matches human belief dynamics (81 vs 80) better than LLM baselines (64) and enables process-level evaluation.
Maximum entropy connectivity constrained by task moments and weight scale reproduces the qualitative and quantitative structure of gradient-trained networks across learning regimes.
AromaGen generates real-time custom aromas from free-form text or visual inputs via multimodal LLM mapping to 12 odorants, matching or exceeding human mixtures after iterative refinement in a 26-person study.
LLMs excel at retrospective mental-state labeling on naturalistic dialogues but mostly fail a context-free prospective probe that maps isolated mental-state profiles to dialogue trajectories, despite expert 100% accuracy.
A deterministic episodic-to-semantic consolidation function with a structural lemma proving identity invariance, demonstrated in synthetic experiments on an embodied service agent.
A driven Duffing ring on a cycle graph produces shape-dependent harmonics through cubic mode mixing and dissipation-broken time-reversal symmetry, captured by observable φ₀ that stays informative down to 0 dB SNR on synthetic signals.
A Bayesian dynamical model reproduces time-order effects in vibrotactile discrimination experiments with few parameters, transforming stimulus space to reveal approximate perceptual symmetries absent in physical coordinates.
A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.
A new probabilistic model integrates leading consciousness theories to assess AI, finding moderate evidence against 2024 LLMs being conscious but weaker evidence than for simpler AI systems.
Language coherence arises from slow contextual integration in default-mode cortex and rapid event-driven reconfiguration in auditory and language areas, captured by LLM-derived signals in single-subject fMRI.
Conscious access is the emergence of a stable bound cloud-function state on a non-Hermitian GNW landscape, occurring only when both well depth and attention exceed critical values that separate the three sensory-processing regimes.
Analysis of 1,223 AI-HCI papers shows declining focus on human epistemic sovereignty and rising optimization of autonomous agents, leading to a proposal for scaffolded cognitive friction via multi-agent systems to preserve human cognitive agency.
Qualitative studies show creatives prefer self-experimentation over structured guidance for GenAI image tools to preserve creative autonomy despite terminology barriers.
Attention heads exhibit negative higher-order synergy (negative triple dividends), allowing pruning of redundant heads that cuts FLOPs by ~18% with only small perplexity increase.
Active inference offers a variational way to phenotype agency in AI systems by measuring empowerment in generative models via a T-maze paradigm.
citing papers explorer
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NEvo: Neural-Guided Evolutionary Video Synthesis for Dynamic Visual Selectivity
NEvo performs evolutionary search guided by a dynamic voxel-level encoding model to synthesize videos that maximize predicted activity in target brain ROIs, recovering known selectivities and revealing temporal dynamics differences.
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Lost in Aggregation: A Multi-Scale Diagnostic Benchmark for LLM Spatial Navigation
A new diagnostic benchmark decomposes LLM spatial navigation into three cognitive scales and shows that cross-scale aggregation, not single-level deficits, causes failure beyond small mazes.
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A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing
PERSUASIONTRACE introduces a Bayesian-network simulated target for multi-turn persuasion that matches human belief dynamics (81 vs 80) better than LLM baselines (64) and enables process-level evaluation.
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Balancing structure and randomness: maximum entropy networks for context-dependent computations
Maximum entropy connectivity constrained by task moments and weight scale reproduces the qualitative and quantitative structure of gradient-trained networks across learning regimes.
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AromaGen: Interactive Generation of Rich Olfactory Experiences with Multimodal Language Models
AromaGen generates real-time custom aromas from free-form text or visual inputs via multimodal LLM mapping to 12 odorants, matching or exceeding human mixtures after iterative refinement in a 26-person study.
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DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories
LLMs excel at retrospective mental-state labeling on naturalistic dialogues but mostly fail a context-free prospective probe that maps isolated mental-state profiles to dialogue trajectories, despite expert 100% accuracy.
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Episodic-to-Semantic Consolidation Without Identity Drift
A deterministic episodic-to-semantic consolidation function with a structural lemma proving identity invariance, demonstrated in synthetic experiments on an embodied service agent.
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Broken-symmetry shape discrimination on a driven Duffing ring
A driven Duffing ring on a cycle graph produces shape-dependent harmonics through cubic mode mixing and dissipation-broken time-reversal symmetry, captured by observable φ₀ that stays informative down to 0 dB SNR on synthetic signals.
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Modelling time-order effects in haptic perception with a Bayesian dynamical framework
A Bayesian dynamical model reproduces time-order effects in vibrotactile discrimination experiments with few parameters, transforming stimulus space to reveal approximate perceptual symmetries absent in physical coordinates.
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Zero-shot World Models Are Developmentally Efficient Learners
A zero-shot visual world model trained on one child's experience achieves broad competence on physical understanding benchmarks while matching developmental behavioral patterns.
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Initial results of the Digital Consciousness Model
A new probabilistic model integrates leading consciousness theories to assess AI, finding moderate evidence against 2024 LLMs being conscious but weaker evidence than for simpler AI systems.
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Coherence in the brain unfolds across separable temporal regimes
Language coherence arises from slow contextual integration in default-mode cortex and rapid event-driven reconfiguration in auditory and language areas, captured by LLM-derived signals in single-subject fMRI.
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A Non-Hermitian Potential Well Formalism for Conscious--Preconscious--Subliminal Processing
Conscious access is the emergence of a stable bound cloud-function state on a non-Hermitian GNW landscape, occurring only when both well depth and attention exceed critical values that separate the three sensory-processing regimes.
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Cognitive Agency Surrender: Defending Epistemic Sovereignty via Scaffolded AI Friction
Analysis of 1,223 AI-HCI papers shows declining focus on human epistemic sovereignty and rising optimization of autonomous agents, leading to a proposal for scaffolded cognitive friction via multi-agent systems to preserve human cognitive agency.
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How Creatives Approach GenAI Image Generation: Tensions Between Structured Guidance, Self-Experimentation, and Creative Autonomy
Qualitative studies show creatives prefer self-experimentation over structured guidance for GenAI image tools to preserve creative autonomy despite terminology barriers.
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A Game Theoretic Free Energy Analysis of Higher Order Synergy in Attention Heads of Large Language Models
Attention heads exhibit negative higher-order synergy (negative triple dividends), allowing pruning of redundant heads that cuts FLOPs by ~18% with only small perplexity increase.
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Active Inference: A method for Phenotyping Agency in AI systems?
Active inference offers a variational way to phenotype agency in AI systems by measuring empowerment in generative models via a T-maze paradigm.