Large language model (LLM) agents have been increasingly adopted in scientific research for organizing and invoking specialized computational tools. However, their reliance on predefined tool spaces with static semantics limits their applicability to open-world scientific workflows, where tool requirements, capabilities, and boundaries evolve dynamically. To this end, we propose SciToolAgent-Evo, an ontology-aware self-evolving agent for open-world scientific tool acquisition. Driven by an evolving memory of skills, experiences, and an ontologized tool graph, it distills generalizable knowledge from contrastive trajectories during accumulation, whereas during inference, it formulates active requests and utilizes a LinUCB-based bandit gate to dynamically balance exploration and exploitation. Once a novel tool is acquired, its scientific ontology is completed online for seamless integration into the known graph. Moreover, we introduce OpenSciToolBench, a benchmark containing 900 realistic tasks across four difficulty levels. Extensive evaluations show that SciToolAgent-Evo achieves state-of-the-art performance, validating its robustness and generalization.
Yuqi Tang, Chenyi Zhou, Libin Wang et al.· 0 citations
Reinforcement learning from human feedback (RLHF) has become the cornerstone of aligning large language models (LLMs) with human intent. Yet a fundamental question remains unaddressed: how should training data be scheduled when both the model's capabilities and the utility of data are constantly evolving? Current pipelines rely on fixed or uniform sampling, treating data value as static, an assumption we demonstrate to be fundamentally flawed. We identify and formalize the Distribution-Value Coevolution principle: the training value of data is not intrinsic, but emerges dynamically from the interaction between data characteristics and the model's evolving capability boundary. What is highly informative at one stage may become redundant, or even detrimental, at another. This insight demands a paradigm shift from static to adaptive curriculum design. We operationalize this principle through a unified framework with three components: (1) distribution-level organization that groups training data into coherent distributions; (2) sliding-window influence estimation that continuously tracks each distribution's evolving training value; and (3) bandit-guided scheduling that adaptively allocates resources with provable exploration-exploitation guarantees. Experiments show that this approach yields measurable improvements, with up to a 57.1% relative (or 8.9% absolute) improvement on AIME24 for Llama3.2-3B, and gains also observed for models ranging from 1B to 7B parameters.
Zairun Yang, Yanbo Yang, Chenyi Zhou et al.· Proceedings of the 32nd ACM...· 0 citations
Results validate that KG-guided reasoning serves as a critical mechanism to compensate for parameter disparity, particularly in tasks requiring deep traversal of biological mechanisms.
Tianwen Lyu, Zhuang Xiang, Keyan Ding et al.· Proceedings of the 32nd ACM...· 0 citations