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cs.CL2026

Can Vision Language Models Be Adaptive in Mathematics Education? A Learner Model-based Rubric Study

Jie Gao, Yongan Yu, Junzhu Su +3

Adaptive learning refers to educational technologies that track learners' learning progress and adapt the instructional process based on individual learners' learning performance.…

cs.CL2026

Contexting as Recommendation: Evolutionary Collaborative Filtering for Context Engineering

Jiachen Zhu, Zhuoying Ou, Congmin Zheng +9

Large Language Models (LLMs) are highly sensitive to their input contexts, motivating the development of automated context engineering. However, existing methods predominantly trea…

cs.CL2026

Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents

Rong Shan, Te Gao, Hang Zheng +6

The implicit policy of maintaining relatively stable acceptance rates at top AI conferences, despite exponentially growing submissions, introduces a critical structural vulnerabili…

cs.CL2026

LogitsCoder: Towards Efficient Chain-of-Thought Path Search via Logits Preference Decoding for Code Generation

Jizheng Chen, Weiming Zhang, Xinyi Dai +6

Code generation remains a challenging task that requires precise and structured reasoning. Existing Test Time Scaling (TTS) methods, including structured tree search, have made pro…

cs.CL2026

ReMiT: RL-Guided Mid-Training for Iterative LLM Evolution

Junjie Huang, Jiarui Qin, Di Yin +4

Standard training pipelines for large language models (LLMs) are typically unidirectional, progressing from pre-training to post-training. However, the potential for a bidirectiona…

cs.CL2025

LoopTool: Closing the Data-Training Loop for Robust LLM Tool Calls

Kangning Zhang, Wenxiang Jiao, Kounianhua Du +4

Augmenting Large Language Models (LLMs) with external tools enables them to execute complex, multi-step tasks. However, tool learning is hampered by the static synthetic data pipel…