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This paper proposes an expanded formulation of valence and arousal in an active inference model of human driving, conditioning affective estimates on predicted future outcomes and evaluating it in interactive driving scenarios.
This paper trains 14 small language models (135M to 14B parameters) on Psych-101, a dataset of 10.7 million trial-level human choices, finding that small models suffice for in-distribution matching while larger models generalize better out-of-distribution. Diagnostics show that masking stimuli and feedback destroys most learned information, indicating choice history alone is insufficient.
This paper formulates cognitive experiment design as a Bayesian Experimental Design problem, introducing an amortized framework that efficiently identifies maximally informative environments for inferring latent planning parameters, with validated performance on the Mouselab-MDP paradigm.
The article proposes a vision of a persistent, sovereign AI counterpart for each human—a 'Citizen AI' that maintains a private knowledge graph with provenanced memory, internal coalitions, and human sovereignty, rather than being a disposable assistant. It invites criticism on key architectural and ethical questions.
This paper proposes a mathematical formulation of slow thinking and active perception, introducing a theory called 'active lifting' that derives design, training, and inference processes for slow thinking large language models.
This paper models early language acquisition as a search on a graph-based mental lexicon using spreading activation and category exploration, outperforming a shortest path baseline in simulating normative word acquisition across four languages.
Introduces Epi2Diff, a framework that maps LLM reasoning traces into cognitive episodes to predict human item difficulty, outperforming baselines and providing interpretable process evidence.
Introduces trajectory extrapolation error, a measure derived from transformer LM hidden states that predicts human reading times independently of and orthogonally to surprisal, revealing a dissociable component of incremental processing cost.
This paper proposes a structural and dynamical framework for modeling cognitive processes using iterative state transformations and semantic equivalence, integrating dynamical systems, category theory, and feedback mechanisms to model cognition as a process evolving toward stable interpretations.
This paper presents an in silico simulation of the RAMPHO episodic buffer using phonetic entropy from wav2vec 2.0 to dissociate informational and energetic masking in multi-talker environments, revealing a cognitive-acoustic Pareto optimization problem.
HumanLLM presents a framework for benchmarking and improving LLM anthropomorphism by modeling psychological patterns as interacting causal forces, constructing 244 patterns from academic literature and 11,359 multi-pattern scenarios. The approach demonstrates that authentic human alignment requires cognitive modeling rather than shallow behavioral mimicry, with HumanLLM-8B outperforming larger models like Qwen3-32B on multi-pattern dynamics.