21 papers
Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions
Pengyu Zhu, Lijun Li, Longju Yang +2
Deep Research agents conduct long-horizon investigations by iteratively planning, retrieving evidence, and generating reports. However, it remains unclear whether they can resist a…
SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring
Chunxiao Li, Yuan Xiong, Lijun Li +4
Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse guidance. Existing benchmarks often rely…
An Early Warning of Emerging Biosecurity Risks in Frontier LLMs
Zhida He, Xia Hu, Baichen Le +20
Frontier large language models (LLMs) are increasingly integrated into scientific workflows, yet their growing biological capabilities may outpace current safeguards. To assess the…
Evolutionary Guided Decoding: Iterative Value Refinement for LLMs
Zhenhua Liu, Lijun Li, Ruizhe Chen +5
While guided decoding, especially value-guided methods, has emerged as a cost-effective alternative for controlling language model outputs without re-training models, its effective…
A Unified Framework for the Evaluation of LLM Agentic Capabilities
Pengyu Zhu, Lijun Li, Yaxing Lyu +8
As LLMs are increasingly deployed as agents, reliable assessment of their agentic capabilities has become essential. However, reported benchmark scores often jointly reflect model…
HarmRLVR: Weaponizing Verifiable Rewards for Harmful LLM Alignment
Yuexiao Liu, Lijun Li, Xingjun Wang +1
Recent advancements in Reinforcement Learning with Verifiable Rewards (RLVR) have gained significant attention due to their objective and verifiable reward signals, demonstrating s…