arXiv AI By Mengtao Zhou, Jianxiao Wang, Xiupeng Chen, Cailiang Liu, Jie Song

Overtaught, Underlearned: Learner-Centered Distillation from Multi-Agent Discussion

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arXiv AI
Jul 28

From Proprietary to Open-Source: Bridging the Distribution Gap via Multi-Agent Protocol Distillation in Agentic Search

arXiv:2607. 24280v1 Announce Type: new Abstract: Agentic search enables large language models to solve knowledge-intensive tasks by interleaving multi-step reasoning with retrieval, yet optimizing this with outcome-based reinforcement learning (RL) provides only sparse supervision.

By Junlin Liu, Jiangwang Chen, Zixin Song, Shuaiyu Zhou, Chunji Lv, Hank Wu, Kailin Jiang, Jinyang Wu, Bohan Yu, Chenxi Zhou
arXiv Computation and Language
Sep 28

Modeling Student Sensemaking with LLMs and Knowledge-Graph-Guided Inference

The study explores whether instruction-tuned large language models (LLMs) can analyze collaborative student sensemaking without task-specific training, and whether adding structured knowledge-state information enhances this analysis. Two mid-size LLMs were evaluated on 23 expert-labeled episodes under various prompting conditions, showing that reasoning-enabled prompts better detect unsuccessful sensemaking and that knowledge-state diagnostics improve agreement with experts. No single configuration outperformed others across all sensemaking dimensions, highlighting the task’s multidimensional nature.

By \"Ozge Alacam, Z\"ubeyde Demet Kirbulut G\"une\c{s}, Funda Ekici, Nurcan Turan-Oluk, Dilay Din\c{c}demir, Hakk{\i} Kaday{\i}f\c{c}{\i}, Sevin\c{c} Nihal Ye\c{s}ilo\u{g}lu, Burcu I\c{s}{\i}k, Halil T\"umay, Sinem Gencer