13 citations · 14 across the 10 of their papers we have counts for
12 papers
Divergence-Routed On-Policy Distillation for Language Models
Tiantian Peng, Guannan Lv, Hongyang Li +5
On-policy distillation (OPD) transfers knowledge from a teacher model to a student along student-generated trajectories, typically by minimizing the reverse Kullback-Leibler (RKL)…
LabEvolver: Training-Free Experience Evolution for Safe and Grounded Wet-Lab Agents
Jingya Wang, Yuyang Gao, Liuzhenghao Lv +2
We introduce LabEvolver, a training-free framework that equips safe and grounded wet-lab agents with episodic memory from execution experience. LabEvolver couples a state-grounded…
MatFormBench: A Benchmarking Evaluation Framework for Target-Driven Materials Formulation
Linhan Wu, Chenxi Wang, Chuhan Yang +2
Inverse design of materials has significantly advanced target-driven formulation optimization, yet existing materials machine learning benchmarks remain limited to forward property…
Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals
Shuo Yang, Jinda Lu, Chiyu Ma +8
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a central paradigm for scaling LLM reasoning, yet its optimization often suffers from training instability and…
One-Way Policy Optimization for Self-Evolving LLMs
Shuo Yang, Jinda Lu, Kexin Huang +6
Reinforcement Learning with Verifiable Rewards (RLVR) has become a promising paradigm for scaling reasoning capabilities of Large Language Models (LLMs). However, the sparsity of b…
Reasoning Portability: Guiding Continual Learning for MLLMs in the RLVR Era
Qiuhe Hong, Yuyang Liu, Shuo Yang +3
Vision-Language Models in Continual Learning (VLM-CL) aim to continuously adapt to new multimodal tasks while retaining prior knowledge. The emerging paradigm that couples Multimod…
BioProAgent: Neuro-Symbolic Grounding for Constrained Scientific Planning
Yuyang Liu, Jingya Wang, Liuzhenghao Lv +1
Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical execution in wet-labs. In these…
Stabilizing the Modality Gap: Null-Space Projected Continual Learning for Vision-Language Models
Tiantian Peng, Yuyang Liu, Shuo Yang +2
The modality gap is intrinsic to multimodal alignment, where embeddings from different modalities remain partially separated in a shared representation space. In continual learning…
Look-Back: Implicit Visual Re-focusing in MLLM Reasoning
Shuo Yang, Yuwei Niu, Yuyang Liu +3
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in multimodal reasoning. However, they often excessively rely on textual information during the later sta…
BioProBench: A Corpus and Benchmark for Biological Protocol Reasoning in Autonomous Science
Yuyang Liu, Liuzhenghao Lv, Xiancheng Zhang +2
The realization of autonomous scientific experimentation is currently limited by LLMs' struggle to grasp the strict procedural logic and accuracy required by biological protocols.…
CASA: Class-Agnostic Shared Attributes in Vision-Language Models for Efficient Incremental Object Detection
Mingyi Guo, Yuyang Liu, Zhiyuan Yan +3
Incremental object detection is fundamentally challenged by catastrophic forgetting. A major factor contributing to this issue is background shift, where background categories in s…
ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing
Liuzhenghao Lv, Zongying Lin, Hao Li +5
Recent advances in Protein Language Models (PLMs) have transformed protein engineering, yet unlike their counterparts in Natural Language Processing (NLP), current PLMs exhibit a f…