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Boxun Li

7 accepted papers

2026

MARSHAL: Incentivizing Multi-Agent Reasoning via Self-Play with Strategic LLMs

ICLR 2026poster

Developing Large Language Models (LLMs) to cooperate and compete effectively within multi-agent systems (MASs) is a critical step towards more advanced intelligence. While reinforcement learning (RL) has proven effective for enhancing reasoning in single-agent tasks, its extension to multi-turn, mul…

Cited by 13SourcecodeScholar
2025

Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving

ICML 2025poster

Large language models (LLMs) have demonstrated remarkable performance on various medical benchmarks, but their capabilities across different cognitive levels remain underexplored. Inspired by Bloom's Taxonomy, we propose a multi-cognitive-level evaluation framework for assessing LLMs in the medical…

2025

PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference

ACL 2025long

In this work, we introduce the PKU-SafeRLHF dataset, designed to promote research on safety alignment in large language models (LLMs). As a sibling project to SafeRLHF and BeaverTails, we separate annotations of helpfulness and harmlessness for question-answering pairs, providing distinct perspectiv…

2022

Improving Human-Object Interaction Detection via Phrase Learning and Label Composition

AAAI 2022technical

Human-Object Interaction (HOI) detection is a fundamental task in high-level human-centric scene understanding. We propose PhraseHOI, containing a HOI branch and a novel phrase branch, to leverage language prior and improve relation expression. Specifically, the phrase branch is supervised by semant…

Cited by 44SourcePDFScholar
2022

Motion Sensitive Contrastive Learning for Self-Supervised Video Representation

ECCV 2022poster

"Contrastive learning has shown great potential in video representation learning. However, existing approaches fail to sufficiently exploit short-term motion dynamics, which are crucial to various down-stream video understanding tasks. In this paper, we propose Motion Sensitive Contrastive Learning…

Cited by 20SourcePDFScholar
2021

End-to-End Human Object Interaction Detection With HOI Transformer

CVPR 2021poster

We propose HOI Transformer to tackle human object interaction (HOI) detection in an end-to-end manner. Current approaches either decouple HOI task into separated stages of object detection and interaction classification or introduce surrogate interaction problem. In contrast, our method, named HOI T…

Cited by 266PDFcodeScholar