arXiv:2608.09222v2 Announce Type: replace
Abstract: Inverse decision modeling infers latent properties of decision processes from observed behavior, but existing formulations rely primarily on action...
By Jiawen Kang, Dongrui Han, Xixin Wu, Helen Meng
The paper introduces Process-aware Language Cognitive Diagnosis (PLCD), a framework that replaces traditional ID-based embeddings in Cognitive Diagnosis Models with language-derived structures and response records. PLCD employs large language models to build concept schemas and cognitive process graphs, and uses a Language-to-Cognition Mapper with DA-MoE experts and contrastive learning to map textual evidence into a unified cognitive space. Experiments demonstrate that PLCD outperforms conventional baselines in student performance prediction and shows strong cognitive transfer, improving cold-start robustness and cognitive grounding.
By Minghang Liu, Yuanzhuo Wang, Qiang Qiu, Huawei Shen, Xueqi Cheng
The paper introduces a method that represents language models as log‑likelihood vectors over prompt‑response pairs, enabling the construction of model maps that compare conditional distributions. Squared Euclidean distances in this vector space approximate KL divergence, and experiments show that these maps reveal global structure related to model attributes and task performance. The approach also captures systematic shifts from prompt changes, supports additive compositionality for predicting downstream scores, and offers PMI vectors to mitigate unconditional distribution effects, thereby aiding analysis and prediction of input‑dependent behavior.
By Momose Oyama, Yusuke Takase, Hidetoshi Shimodaira
arXiv:2607. 29181v1 Announce Type: cross Abstract: Agentic assistants capable of proactive, personalized interactions require structured models of user intent and workflow.
By Andy J. Phu, James Mooney, Karin de Langis, Khanh Chi Le, Dongyeop Kang
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.
By Nick Oh, Fernand Gobet
The paper introduces CROP, a method for selective on‑policy distillation that prioritizes token‑level supervision based on task relevance. CROP uses paraphrase‑calibrated counterfactual sensitivity to measure how much each response token depends on the semantic content of the input, constructing validated original‑paraphrase‑counterfactual triplets for each prompt. Experiments in two teacher‑student settings show that CROP outperforms other selectors, improving aggregate performance by 1.92 and 2.96 points.
By Enhan Li, Junhao He, Hongyang Du