ForeSci is introduced, a temporally controlled benchmark for evaluating whether LLM agents can make such forward-looking research judgements from historical evidence, and agent-based methods improve traceability over Hybrid RAG, while their gains in future-target alignment over native LLMs are modest and task dependent...
Qiu-Yu Tian, Hao Yin, Hang Su et al.· arXiv.org· 1 citation
LLM agents that write long-form fiction need an explicit memory of the evolving storyworld to keep new events consistent with established facts. Such memory must keep heterogeneous narrative information distinct, integrate story developments across granularities, and recover dependencies that a writing request leaves i...
Qiu-Yu Tian, Xiao-Wen Gu, Hang Su 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
This paper proposes FRESH, a Failure-aware Retrieval framework over Experience-Structured Heterogeneous graphs, which transforms historical successes and failures into structured external experience for tool-using agents and consistently improves task success and tool-use reliability over no-memory agents and represent...
Jia-Xing Li, Lei Song, Rui Dong 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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