arXiv AI
Aug 3
SERUM: State Extraction and Refinement for User Modeling
arXiv:2607. 29181v1 Announce Type: cross Abstract: Agentic assistants capable of proactive, personalized interactions require structured models of user intent and workflow.
Reading Cognition as Decisions Unfold in Words: A Factorized Inverse Decision Model
Inverse decision modeling infers latent properties of decision processes from observed behavior, but existing formulations rely primarily on action trajectories. In verbalized cognitive tasks, task execution also produces response dynamics that action-only formulations leave unmodeled, such as verbal production, interaction, and hesitation.
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arXiv AI
Aug 7
Small Foundation Models of Human Cognition and Behaviour
arXiv:2608. 05224v1 Announce Type: new Abstract: Large language models fine-tuned on human behavioural data have emerged as general-purpose cognitive proxies, but the scale this requires, and whether these models process task structure or exploit statistical shortcuts, remain open questions.
arXiv AI
Jun 3
Decomposing and Measuring Evaluation Awareness
arXiv:2605. 23055v2 Announce Type: replace-cross Abstract: Frontier language models sometimes recognize that they are being evaluated and adjust their behavior, undermining validity of benchmark results.
arXiv AI
Jun 29
Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction
arXiv:2606. 28186v1 Announce Type: cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction.
arXiv Machine Learning
Jun 30
NeuReasoner: Theory-grounded Mapping of Reasoning Elicitation Boundaries
arXiv:2606. 29971v1 Announce Type: new Abstract: A growing body of work suggests that the reasoning capabilities of large language models are largely latent in their base form, with post-training primarily amplifying rather than introducing them.
arXiv AI
Jun 2
On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance
arXiv:2606. 00467v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-internalized priors interact with user-provided instructions.
arXiv Machine Learning
6d ago
Bayesian Distributional Models of Executive Functioning
arXiv:2510. 00387v4 Announce Type: replace Abstract: This study uses controlled simulations with known ground-truth parameters to evaluate how Distributional Latent Variable Models (DLVM) and Bayesian Distributional Active LEarning (DALE) perform in comparison to conventional Independent Maximum Likelihood Estimation (IMLE).
arXiv AI
Jul 9
Large Behavior Model: A Promptable Digital Twin of the Retail Customer
arXiv:2607. 06993v1 Announce Type: new Abstract: Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data.
arXiv Machine Learning
Jun 2
Adaptive Querying with AI Persona Priors
arXiv:2605. 00696v2 Announce Type: replace-cross Abstract: We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within tight query budgets.
arXiv AI
Aug 3
Cognitive World Model for Progressive BDI/E Trajectory Evaluation of Conversational Agents
arXiv:2606. 29495v2 Announce Type: replace Abstract: As LLM-based conversational agents advance toward increasingly open-ended and interaction-intensive scenarios, task completion alone provides an incomplete assessment of their effectiveness.
arXiv AI
Jul 15
MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations
arXiv:2607. 12893v1 Announce Type: new Abstract: Long-term memory has become a foundational capability for LLM-based agents that accompany users across extended, multi-session interactions.
arXiv AI
Jul 28
Reconstructing Item Characteristic Curves using Fine-Tuned Large Language Models
arXiv:2601. 02580v2 Announce Type: replace-cross Abstract: Traditional methods for determining assessment item parameters, such as difficulty and discrimination, rely heavily on expensive field testing to collect student performance data for Item Response Theory (IRT) calibration.