activity
20232026
most citedEngagement, User Satisfaction, and the Amplification of Divisive Content on Social Media

36 citations · 51 across the 13 of their papers we have counts for

collaborators
Showing 2026Show all

7 papers · 1 filter

cs.CL2026

Pushback in a Haystack: Language Models Respond to Factual Errors Ambiguously and Ineffectively

Li Lucy, Myra Cheng, Svenja Guhr +7

The sycophancy of language models, or their failure to push back against faulty user input, is now widely studied. However, if LMs do push back, is sycophancy solved? We show that…

cs.AI2026

Rethinking the Evaluation of Harness Evolution for Agents

Yike Wang, Huaisheng Zhu, Zhengyu Hu +8

Harness evolution is an iterative search procedure that repeatedly evaluates and revises candidate harnesses used for LLM agents using task feedback. We revisit the evaluation of s…

cs.AI2026

Scaling Participation in Modular AI Systems

Shangbin Feng, Yike Wang, Weijia Shi +3

Humanity is a mosaic of multifaceted talents and needs, and any truly intelligent AI must reflect that richness. Yet the LLMs used by all are built by the few -- a centralized mark…

cs.AI2026

EvoLM: Self-Evolving Language Models through Co-Evolved Discriminative Rubrics

Shuyue Stella Li, Rui Xin, Teng Xiao +8

Language models encode substantial evaluative knowledge from pretraining, yet current post-training methods rely on external supervision (human annotations, proprietary models, or…

cs.CL2026

Small Reward Models via Backward Inference

Yike Wang, Faeze Brahman, Shangbin Feng +3

Reward models (RMs) play a central role throughout the language model (LM) pipeline, particularly in non-verifiable domains. However, the dominant LLM-as-a-Judge paradigm relies on…

cs.CL2026

MentorCollab: Large-to-Small Inference-Time Mentorship for Concise Reasoning in Language Models

Haojin Wang, Yike Wang, Shangbin Feng +2

Large reasoning models (LRMs) have demonstrated impressive reasoning capabilities, but their solutions are often verbose and computationally expensive, and taxing for users to read…

cs.CL2026

MoCo: A One-Stop Shop for Model Collaboration Research

Shangbin Feng, Yuyang Bai, Ziyuan Yang +17

Advancing beyond single monolithic language models (LMs), recent research increasingly recognizes the importance of model collaboration, where multiple LMs collaborate, compose, an…