collaborators

18 papers

cs.AI2026

Evaluating Counterfactual Sensitivity to Patient Information in Medication-Safety Reasoning

Zhitian Hou, Yuhang Liu, Pengkai Wang +8

Applying a valid medication-safety rule when its patient-specific conditions are not met can produce an incorrect decision. Existing medical evaluations largely use isolated and fi…

cs.CL2026

Beyond Captions: Context-Grounded Reconstruction for Biomedical Multimodal Continued Pretraining

Guanghao Zhu, Zeyu Liu, Zhitian Hou +10

Biomedical figures are explained not by captions alone but by body-text passages that discuss them. Yet current multimodal corpora typically reduce figures to isolated image-captio…

cs.CL2026

InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training

Pengkai Wang, Pengwei Liu, Qi Zuo +3

Reinforcement learning (RL) has powered many recent breakthroughs in large language models (LLMs), especially for tasks where rewards can be computed automatically, such as code ge…

cs.LG2026

Access Sets Matter: Budgeting Expert Reads for Scalable Weight-Space Model Merging

Yuanyi Wang, Yanggan Gu, Su Lu +5

Weight-space model merging is usually formulated as an algebraic operation on checkpoints, yet at LLM scale the limiting resource is often the set of expert weights that must be re…

cs.LG2026

Not All Disagreement Is Learnable: Token Teachability in On-Policy Distillation

Yuanyi Wang, Su Lu, Yanggan Gu +6

On-policy distillation (OPD) trains a student on its own rollouts with token-level teacher supervision. Recent selective OPD methods exploit the non-uniformity of OPD signals by pr…

cs.AI2026

Model Merging Scaling Laws in Large Language Models

Yuanyi Wang, Yanggan Gu, Yiming Zhang +6

We study empirical scaling laws for language model merging measured by cross-entropy. Despite its wide practical use, merging lacks a quantitative rule that predicts returns as we…