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20232026
most citedLLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition

12 citations · 60 across the 72 of their papers we have counts for

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13 papers · 1 filter

cs.CV2026

State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models

Mingxu Chai, Chenyu Liu, Ziyu Shen +7

Compared with typical vision-language tasks, document parsing places stronger demands on fine-grained visual perception. Existing vision-language model (VLM)-based parsing approach…

cs.LG2026

A Token-Level Analysis of Sampled-Token Reverse-KL On-Policy Distillation

Bing Shao, Jiazheng Zhang, Long Ma +12

On-policy distillation (OPD) supervises a student on its own trajectories with token-level signals from a frozen teacher, yet how a sampled loss allocates updates across tokens rem…

cs.AI2026

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

Zhiheng Xi, Dingwen Yang, Jiaqi Liu +21

Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for rigorous and realistic eval…

cs.CL2026

CR4T: Rewrite-Based Guardrails for Adolescent LLM Safety

Heajun An, Qi Zhang, Vedanth Achanta +1

Large language models (LLMs) are increasingly embedded in adolescent digital environments, mediating information seeking, advice, and emotionally sensitive interactions. Yet existi…

cs.LG2026

From Parameters to Data: A Task-Parameter-Guided Fine-Tuning Pipeline for Efficient LLM Alignment

Hao Chen, Qi Zhang, Liyao Li +7

Adapting Large Language Models (LLMs) to specialized domains typically incurs high data and computational overhead. While prior efficiency efforts have largely treated data selecti…

cs.CL2026

HyperLogic: A Hard, Forward-Authored Chinese Logical Reasoning Benchmark with Execution-Derived Answers

Ming Zhang, Qiyuan Peng, Yinxi Wei +16

Existing logic benchmarks primarily measure models' ability to answer reasoning questions directly. Scalable benchmarks often generate text from formal structures, which makes answ…

cs.LG2026

Entropy Polarity in Reinforcement Fine-Tuning: Direction, Asymmetry, and Control

Jiazheng Zhang, Ziche Fu, Junrui Shen +17

Policy entropy has emerged as a fundamental measure for understanding and controlling exploration in reinforcement learning with verifiable rewards (RLVR) for LLMs. However, existi…

cs.CV2026

Prefix-Adaptive Block Diffusion for Efficient Document Recognition

Mingxu Chai, Ziyu Shen, Chenyu Liu +3

Block Diffusion Models (BDMs) support parallel generation, flexible-length output, and KV caching, making them promising for efficient document parsing. However, existing BDMs bind…

cs.CL2026

FinToolSyn: A forward synthesis Framework for Financial Tool-Use Dialogue Data with Dynamic Tool Retrieval

Caishuang Huang, Yang Qiao, Rongyu Zhang +8

Tool-use capabilities are vital for Large Language Models (LLMs) in finance, a domain characterized by massive investment targets and data-intensive inquiries. However, existing da…

cs.CL2026

CCTU: A Benchmark for Tool Use under Complex Constraints

Junjie Ye, Guoqiang Zhang, Wenjie Fu +4

Solving problems through tool use under explicit constraints constitutes a highly challenging yet unavoidable scenario for large language models (LLMs), requiring capabilities such…

cs.AI2026

Steering LLMs via Scalable Interactive Oversight

Enyu Zhou, Zhiheng Xi, Long Ma +9

As Large Language Models increasingly automate complex, long-horizon tasks such as \emph{vibe coding}, a supervision gap has emerged. While models excel at execution, users often s…

cs.CL2026★ 1 cited

CL-bench: A Benchmark for Context Learning

Shihan Dou, Ming Zhang, Zhangyue Yin +24

Current language models (LMs) excel at reasoning over prompts using pre-trained knowledge. However, real-world tasks are far more complex and context-dependent: models must learn f…

cs.CL2026

OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding

Deming Ding, Shichun Liu, Enhui Yang +12

Modern coding scaffolds turn LLMs into capable software agents, but their ability to follow scaffold-specified instructions remains under-examined, especially when constraints are…