5 citations · 6 across the 27 of their papers we have counts for
7 papers · 2 filters
Beyond Clean Accuracy: The Practical Challenges and Benefits of Pixel-Based Language Models
Haowen Tang, Wei Xia, Raoyuan Zhao +2
Pixel-based language models process rendered text without relying on a fixed subword vocabulary, offering a promising approach to noisy and multilingual inputs. However, their prac…
EMO: A Closed-Loop Test-Time Computation Framework via Alternating Context Optimization and Logits Steering
Wei Xia, Yehui Tang, Zhi-Hong Deng
Test-time context optimization adapts frozen language models through textual feedback, but ordinary conditional generation provides no separate control over how strongly to reinfor…
PsyScore: A Psychometrically-Aware Framework for Trait-Adaptive Essay Scoring and ZPD-Scaffolded Feedback
Wei Xia, Jin Wu, Haoran Shi +2
Effective Automated Essay Scoring (AES) are expected to support both reliable assessment and actionable instructional feedback. However, existing approaches often treat scoring and…
DLLG: Dynamic Logit-Level Gating of LLM Experts
Bingnan Li, Zhaoyang Zhang, Xiaoze Liu +6
Leveraging multiple specialized LLMs can combine complementary strengths, but existing approaches trade adaptability for stability: routing commits prematurely, heuristic ensemblin…
TeamLLM: A Human-Like Team-Oriented Collaboration Framework for Multi-Step Contextualized Tasks
Xiangyu Wang, Jin Wu, Haoran Shi +3
Recently, multi-Large Language Model (LLM) frameworks have been proposed to solve contextualized tasks. However, these frameworks do not explicitly emulate human team role division…
Asymmetric Actor-Critic for Multi-turn LLM Agents
Shuli Jiang, Zhaoyang Zhang, Yi Zhang +3
Large language models (LLMs) exhibit strong reasoning and conversational abilities, but ensuring reliable behavior in multi-turn interactions remains challenging. In many real-worl…
Learning When to Attend: Conditional Memory Access for Long-Context LLMs
Sakshi Choudhary, Aditya Chattopadhyay, Luca Zancato +4
Language models struggle to generalize beyond pretraining context lengths, limiting long-horizon reasoning and retrieval. Continued pretraining on long-context data can help but is…