Large language models can describe mechanisms, yet scalable post-training still depends on costly, manually curated biological reasoning traces. Here we show that cellular perturbation atlases can instead become reinforcement-learning environments, where measured gene responses provide computable rewards for biological...
Zhen-Chao Tang, Xiaogang Xu, Tian-Xu Lv et al.· 0 citations
This work introduces ToolCompass, a post-training framework that guides tool trialing by organizing tool-call representations according to shared functions and jointly reduces intra-function variation across domains and increases inter-function separation.
Jun-Lin Fang, Chong-Chong Zhang, Do Nguyen-Thanh et al.· 0 citations
SCOPED-Hiring reveals that balanced final hire rates can mask hidden trajectory unfairness in multi-agent decision trajectories and reduces total layered burden by 72.3% while shifting the hire rate by only 1.86 pp, showing that process diagnosis can guide effective repair.
Yi-Ran Zhao, Lu Zhou, Liming Fang et al.· 0 citations
This work proposes LEAP (Likelihood Elicitation and Aggregation for Probabilistic forecasting), which reorganizes how collected evidence is used in the prediction stage and improves most prediction and calibration metrics across models and remains stronger under controlled comparisons of prior access, inference budget,...
Yu-Fei Chen, Yi-Ran Zhao, Xiao-Gang Xu et al.· 0 citations
Remember-R1 is proposed, a reinforcement learning framework that mitigates long-context visual forgetting by applying process-level supervision directly on the original reasoning trajectory, demonstrating its effectiveness in mitigating long-context visual forgetting.
Jianmin Chen, Jiaqi Tang, Wei Wei et al.· 0 citations
ABE-Ralph is introduced, a reference-anchored auditing framework that represents claims, protocols, required components, baselines, and metrics as structured experimental constraints, guides implementation through an 8-step workflow, and performs quantitative, qualitative, and code-level verification.
Le-Zhi Yu, Xiao-Gang Xu, Yuhong Zhou et al.· 0 citations
This work investigates LLM-based forecasting agents, meaning systems in which a language model contributes to a scored prediction about a future or currently unobserved target, and organizes architectures into three groups.
Xiao-Gang Xu, Jiaqi Tang, Jianmin Chen et al.· 0 citations
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