Huajun Chen

SciToolAgent-Evo: An Ontology-Aware Self-Evolving Agent for Open-World Scientific Tool Acquisition

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.

EM^2Mem: Event-Centric Multimodal Memory for Large Language Models

Multimodal memory offers a scalable interface for long-video question answering, but existing methods often retrieve captions, frames, transcripts, summaries, or graph facts as isolated fragments. Although searchable, such fragments are not generation-ready: language models must reconstruct cross-modal and temporal alignments at inference time, when context is limited and attribution is difficult. We propose EM^2Mem, an event-centric multimodal memory framework that binds heterogeneous evidence to event anchors during memory construction. Each event-indexed memory cell aligns multimodal records, temporal context, graph-linked relations, semantic facts, and provenance, enabling compact evidence readout over grounded multimodal events rather than modality-specific fragments. Across three long-video QA benchmarks, EM^2Mem improves average accuracy over the strongest memory baseline by 2.0, 2.4, and 3.7 points, improves strict event-level Top-5 evidence recall by 7.0 points, and reduces per-query latency by 4.67 times and total inference tokens by 63.66% (The code will be integrated into https://github.com/zjunlp/LightMem).

Towards principled knowledge editing methods for large language model reasoning

Distribution-Value Coevolution for Adaptive RLHF Data Scheduling

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.

Aligning Agentic World Models via Knowledgeable Experience Learning

WorldMind is introduced, a framework that autonomously constructs a symbolic World Knowledge Repository by synthesizing environmental feedback that unifies Process Experience to enforce physical feasibility via prediction errors and Goal Experience to guide task optimality through successful trajectories.

OceanGym: A Benchmark Environment for Underwater Embodied Agents

OceanGym is introduced, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding real-world environments, and reveals substantial gaps between state-of-the-art MLLM-driven agents and human experts.

Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence

Mechanist is an agentic system that uses AI as a scientific instrument for the autonomous discovery of mechanisms underlying AI intelligence, and develops a mechanism theory of belief, revealing how models represent world knowledge, form beliefs, infer the beliefs of others, and how these mechanisms emerge during pretraining.