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20242026
most citedOneBit: Towards Extremely Low-bit Large Language Models

8 citations · 18 across the 43 of their papers we have counts for

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Showing 2026 · cs.CLShow all

10 papers · 2 filters

cs.CL2026

Inside VLM Chart Reading: Tracing Value Reading from Vertical Bar Charts Across Space and Depth

Tianhao Niu, Qingfu Zhu, Wanxiang Che

Vision--language models (VLMs) can answer chart questions accurately, but output accuracy does not show how they combine the evidence needed to recover an exact value. We study ver…

cs.CL2026

FormalTCS: Benchmarking End-to-End Frontier Formal Theoretical Computer Science Research of Large Language Models

Dingzirui Wang, Xuanliang Zhang, Keyan Xu +2

Large language models (LLMs) have shown growing potential for automated theoretical computer science (TCS) research, yet existing benchmarks remain far from realistic research sett…

cs.CL2026

SDAM: Structure-Difference-Aware Memory Evolution for Complex Text-to-SQL

Keyan Xu, Dingzirui Wang, Xuanliang Zhang +2

Text-to-SQL aims to convert natural language questions into executable SQL queries. While memory-based agent system improves complex SQL generation, existing memory design neglect…

cs.CL2026

CurateEvo: Data-Curation Evolving for Agentic Post-Training

Dingzirui Wang, Xuanliang Zhang, Keyan Xu +2

Large language model (LLM) agents require post-training methods that can improve long-horizon decision making from environment feedback. However, existing agentic post-training pip…

cs.CL2026

What You See Is What You Get: Observation-Aligned Supervision for Chart-to-Code Generation

Tianhao Niu, Qingfu Zhu, Wanxiang Che

Chart-to-code generation is commonly trained through supervised fine-tuning on reference plotting scripts, implicitly treating the gold code as a fully observable target. However,…

cs.CL2026

Scaling Laws for Agent Harnesses via Effective Feedback Compute

Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2

Agent harnesses shape language-model performance by controlling tool use, feedback, verification, memory, and repair. Yet raw test-time expenditure, such as tokens, tool calls, wal…

cs.CL2026

When Does Language Matter? Multilingual Instructions Reveal Step-wise Language Sensitivity in Vision-Language-Action Models

Xuan Dong, Zhe Han, Tianhao Niu +2

Vision-Language-Action (VLA) models have shown strong performance in language-conditioned robotic manipulation, yet their robustness to linguistic variation remains poorly understo…

cs.CL2026

EpiBench: Benchmarking Multi-turn Research Workflows for Multimodal Agents

Xuan Dong, Huanyang Zheng, Tianhao Niu +7

Scientific research follows multi-turn, multi-step workflows that require proactively searching the literature, consulting figures and tables, and integrating evidence across paper…

cs.CL2026

How Do Language Models Understand Tables? A Mechanistic Analysis of Cell Location

Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2

While Large Language Models (LLMs) are increasingly deployed for table-related tasks, the internal mechanisms enabling them to process linearized two-dimensional structured tables…

cs.CL2026

When Does Context Help? Error Dynamics of Contextual Information in Large Language Models

Dingzirui Wang, Xuanliang Zhang, Keyan Xu +3

Contextual information at inference time, such as demonstrations, retrieved knowledge, or interaction history, can substantially improve large language models (LLMs) without parame…