2 citations · 3 across the 9 of their papers we have counts for
10 papers
Back Up Before You Regret: Measuring Backup Awareness and Automating Backup in Coding Agents
Xu Li, Simon Yu, Minzhou Pan +4
Coding agents now complete tasks autonomously, modifying real-world state such as artifacts, software environments, and configurations along the way. Yet they show limited backup a…
Success Leaves Detours: Learning Executable Walkthroughs for Long-Horizon Agents
Kaijie Chen, Chenyu Fang, Liang Yan +3
Test-time self-evolving agents improve by reusing past experience, yet sparse-reward trajectories contain failures, loops, and detours, while summaries often omit the state conditi…
MineValiCoder: Reliable Code Generation with Test Case Quality Mining and Bipartite Graph-Based Mutual Validation
Zhen Zhao, Qihang Yang, Feifei Dai +2
Large Language Model (LLM)-based Test-Driven Development (TDD) has advanced automated code generation. However, existing approaches depend heavily on human-crafted test cases and c…
SkillOpt-Lite: Better and Faster Agent Self-evolution via One Line of Vibe
Yifei Shen, Bo Li, Xinjie Zhang
While skill optimization for autonomous agents has gained traction, existing methods rely on complex pipelines. This leaves a fundamental question unaddressed: What constitutes a m…
GUI Agents for Continual Game Generation
Yixu Huang, Bo Li, Na Li +8
Generating a game is not the same as making one playable. Existing code-generation approaches often translate a prompt directly into an artifact, leaving interaction-level failures…
The Limits of Long-Context Reasoning in Automated Bug Fixing
Ravi Raju, Mengmeng Ji, Shubhangi Upasani +2
Rapidly increasing context lengths have led to the assumption that large language models (LLMs) can directly reason over entire codebases. Concurrently, recent advances in LLMs hav…
Unsafer in Many Turns: Benchmarking and Defending Multi-Turn Safety Risks in Tool-Using Agents
Xu Li, Simon Yu, Minzhou Pan +5
LLM-based agents are becoming increasingly capable, yet their safety lags behind. This creates a gap between what agents can do and should do. This gap widens as agents engage in m…
UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
Haoming Wang, Haoyang Zou, Huatong Song +109
The development of autonomous agents for graphical user interfaces (GUIs) presents major challenges in artificial intelligence. While recent advances in native agent models have sh…
Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Deliberation
Haoran Zhang, Yafu Li, Xuyang Hu +4
Large language models (LLMs) are increasingly applied in diverse real-world scenarios, each governed by bespoke behavioral and safety specifications (spec) custom-tailored by users…
FullStack Bench: Evaluating LLMs as Full Stack Coders
Bytedance-Seed-Foundation-Code-Team, :, Yao Cheng +53
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing. However, most exist…