Env-Rethink is proposed, a system with 27B post-trained model that supports three main capabilities that adaptively builds Collection Maps and Event Logs to supplement necessary context and evolves environments through virtual event histories that alter environmental states and evidence relationships.
Yu-Kai Wu, Yuan-Jing Yang, Leon Zhou et al.· 0 citations
Topology-Aware Dynamic Reweighting (TAR+), a model-agnostic framework for node classification under strict graph distribution shifts and related robustness stressors, is proposed, supporting the broad applicability of topology-aware reweighting across heterogeneous graph learning conditions.
Jia-Xing Li, Jia-Shuo Liu, Wei-Huang Zheng et al.· IEEE Transactions on Pattern...· 0 citations
ExplorationBench turns the wicked problem of evaluating scientific exploration into a concrete and tractable framework built on verifiable Alien Worlds, and finds that the strongest systems can acquire and apply unfamiliar rules, while performance varies substantially across trajectories and continued exploration can s...
Ming Zhang, Zhen Xiang, Pei-Zhong Gao et al.· 0 citations
Env-Rethink is proposed, a system with 27B post-trained model that supports three main capabilities that adaptively builds Collection Maps and Event Logs to supplement necessary context and evolves environments through virtual event histories that alter environmental states and evidence relationships.
Yu-Kai Wu, Yuan-Jing Yang, Leon Zhou et al.· 0 citations
E-Bench is introduced, a fully synthetic benchmark with 323 state-changing tasks across three product domains: Honor of Kings, QQ Music, and Tencent Meeting, and it shows that multi-step tool use remains challenging: Pass^3 stays below 60% for the strongest models, and even with code execution in the E-Bench-Code exten...
Weihuang Zheng, Tianyuan Zou, Eileen Ye et al.· arXiv.org· 1 citation
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