It is shown that cellular perturbation atlases can instead become reinforcement-learning environments, where measured gene responses provide computable rewards for biological reasoning, and introduces PertMind, which combines trusted-trajectory supervised initialization with gene-, pathway-, and format-level reinforcem...
Zhen-Chao Tang, Xiao-Gang Xu, Jia-Fei Wu 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.· 1 citation
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
The rapid advancement of Auto-Research has surfaced a fundamental evaluation challenge: how can we measure the alignment, logical coherence, and evolutionary completeness of its research trajectory with human research behavior? We propose Auto-Research's Alignment and Completeness, ARAC-Bench: a Researcher-Mimicking Ev...
Jiale Cui, Yue-Yao Yuan, Kai-Xi Zhong et al.· 0 citations
SpectraReward is proposed, a training-free reward function that turns pretrained MLLMs into off-the-shelf reward models for image-generation reinforcement learning, and Self-SpectraReward is introduced, a special case for unified multimodal models where the policy's own understanding branch serves as the reward model f...
Runhu Huang, Qihui Zhang, Zhe Liu et al.· arXiv.org· 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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