activity
20242026
most citedQA-LIGN: Aligning LLMs through Constitutionally Decomposed QA

3 citations · 3 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2026

Adaptive Decoding Methods for Token-Efficient Reinforcement Learning

Zhaonan Li, Zelei Cheng, Amritansh Mishra +5

Rollout decoding affects both which trajectories are used for reinforcement learning with verifiable rewards (RLVR) and how many completion tokens are spent collecting them. We stu…

cs.CV2026

VisAnalog: A Diagnostic Suite for Visual Concept Transfer on Natural Images

Zhaonan Li, Kyle R. Chickering, Bangzheng Li +13

A useful test of visual concept learning is not just whether a model can recognize a concept in a single image, but whether it can preserve and manipulate concept-level properties…

cs.CV2025

Unbiased Visual Reasoning with Controlled Visual Inputs

Zhaonan Li, Shijie Lu, Fei Wang +11

End-to-end Vision-language Models (VLMs) often answer visual questions by exploiting spurious correlations instead of causal visual evidence, and can become more shortcut-prone whe…

cs.CL2025

Evaluating Medical LLMs by Levels of Autonomy: A Survey Moving from Benchmarks to Applications

Xiao Ye, Jacob Dineen, Zhaonan Li +11

Medical Large language models achieve strong scores on standard benchmarks; however, the transfer of those results to safe and reliable performance in clinical workflows remains a…

cs.CL2025

CC-LEARN: Cohort-based Consistency Learning

Xiao Ye, Shaswat Shrivastava, Zhaonan Li +6

Large language models excel at many tasks but still struggle with consistent, robust reasoning. We introduce Cohort-based Consistency Learning (CC-Learn), a reinforcement learning…

cs.CL2025★ 3 cited

QA-LIGN: Aligning LLMs through Constitutionally Decomposed QA

Jacob Dineen, Aswin RRV, Qin Liu +8

Alignment of large language models (LLMs) with principles like helpfulness, honesty, and harmlessness typically relies on scalar rewards that obscure which objectives drive the tra…

cs.CL2024

ToW: Thoughts of Words Improve Reasoning in Large Language Models

Zhikun Xu, Ming Shen, Jacob Dineen +6

We introduce thoughts of words (ToW), a novel training-time data-augmentation method for next-word prediction. ToW views next-word prediction as a core reasoning task and injects f…