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cs.CL2026

PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress

Kevin Qinghong Lin, Siyuan Hu, Pan Lu +14

Autoresearch agents are reshaping the research ecosystem, but they can also let flawed claims enter the literature at scale. Human advisors catch such issues in drafts through care…

cs.CL2026

WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning

Yizhou Chi, Eric Chamoun, Zifeng Ding +1

Forecasting real-world events requires language-model agents to reason under uncertainty from incomplete, time-bounded information. Yet evaluating whether agents genuinely forecast…

cs.CL2026

ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward

Jingpei Wu, Xiao Han, Weixiang Shen +3

Visual question answering increasingly requires multi-step reasoning. Recent post-training with reinforcement learning under verifiable rewards (RLVR) and Group Relative Policy Opt…

cs.CL2026

SciPaths: Forecasting Pathways to Scientific Discovery

Eric Chamoun, Yizhou Chi, Yulong Chen +4

Scientific progress depends on sequences of enabling contributions, yet existing AI4Science benchmarks largely focus on citation prediction, literature retrieval, or idea generatio…

cs.CL2025

Are Large Language Models Good Temporal Graph Learners?

Shenyang Huang, Ali Parviz, Emma Kondrup +5

Large Language Models (LLMs) have recently driven significant advancements in Natural Language Processing and various other applications. While a broad range of literature has expl…