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20242026
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cs.CL2026

A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression

Jincheng Ren, Siwei Wu, Yizhi Li +8

As terminal agents scale to long-horizon, multi-turn workflows, a key bottleneck is not merely limited context length, but the accumulation of noisy terminal observations in the in…

cs.CL2026

Large-Scale Terminal Agentic Trajectory Generation from Dockerized Environments

Siwei Wu, Yizhi Li, Yuyang Song +8

Training agentic models for terminal-based tasks critically depends on high-quality terminal trajectories that capture realistic long-horizon interactions across diverse domains. H…

cs.CL2025

OmniBench: Towards The Future of Universal Omni-Language Models

Yizhi Li, Yinghao Ma, Ge Zhang +20

Recent advancements in multimodal large language models (MLLMs) have aimed to integrate and interpret data across diverse modalities. However, the capacity of these models to concu…

cs.CL2025

ContrastScore: Towards Higher Quality, Less Biased, More Efficient Evaluation Metrics with Contrastive Evaluation

Xiao Wang, Daniil Larionov, Siwei Wu +4

Evaluating the quality of generated text automatically remains a significant challenge. Conventional reference-based metrics have been shown to exhibit relatively weak correlation…

cs.CL2025

LongEval: A Comprehensive Analysis of Long-Text Generation Through a Plan-based Paradigm

Siwei Wu, Yizhi Li, Xingwei Qu +7

Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks, yet their ability to generate long-form content remains poorly understoo…

cs.CL2024

A Comparative Study on Reasoning Patterns of OpenAI's o1 Model

Siwei Wu, Zhongyuan Peng, Xinrun Du +14

Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers. As LLMs continue to evolve, mer…