10 papers
Asuka-Bench: Benchmarking Code Agents on Underspecified User Intent and Multi-Round Refinement
Xin Wang, Liangtai Sun, Yaoming Zhu +8
Existing code-generation benchmarks score a single mapping from a complete prompt to a one-shot output. However, real web development is different. Users seldom write a full spec a…
SAGE: A Quantitative Evaluation of Socialized Evolution in Agent Ecosystems
Linyue Pan, Yaoming Zhu, Lin Qiu +2
Self-improving language agents are typically evaluated in isolation: an agent attempts a task, receives feedback, and iteratively refines its own behavior. Yet agents increasingly…
SWE-Cycle: Benchmarking Code Agents across the Complete Issue Resolution Cycle
Hao Guan, Lingyue Fu, Shao Zhang +8
As autonomous code agents move toward end-to-end software development, evaluating their practical autonomy becomes critical. Current benchmarks hide friction by testing agents in p…
OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward
Haoyue Yang, Xuanle Zhao, Xuexin Liu +2
The paradigm of programmable diagram generation is evolving rapidly, playing a crucial role in structured visualization. However, most existing studies are confined to a narrow ran…
LongCat-Next: Lexicalizing Modalities as Discrete Tokens
Meituan LongCat Team, Bin Xiao, Chao Wang +86
The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal syste…
LongCat-Flash-Thinking-2601 Technical Report
Meituan LongCat Team, Anchun Gui, Bei Li +162
We introduce LongCat-Flash-Thinking-2601, a 560-billion-parameter open-source Mixture-of-Experts (MoE) reasoning model with superior agentic reasoning capability. LongCat-Flash-Thi…
CATArena: Evaluating Evolutionary Capabilities of Code Agents via Iterative Tournaments
Lingyue Fu, Xin Ding, Linyue Pan +9
Current evaluation for Large Language Model (LLM) code agents predominantly focus on generating functional code in single-turn scenarios, which fails to evaluate the agent's capabi…
Automatically Benchmarking LLM Code Agents through Agent-Driven Annotation and Evaluation
Lingyue Fu, Bolun Zhang, Hao Guan +7
Recent advances in code agents have enabled automated software development at the project level, supported by large language models (LLMs). However, existing benchmarks for code ag…
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks
Lingyue Fu, Hao Guan, Bolun Zhang +10
The evaluation of Large Language Models (LLMs) for software engineering has shifted towards complex, repository-level tasks. However, existing benchmarks predominantly rely on coar…
OIBench: Benchmarking Strong Reasoning Models with Olympiad in Informatics
Yaoming Zhu, Junxin Wang, Yiyang Li +8
As models become increasingly sophisticated, conventional algorithm benchmarks are increasingly saturated, underscoring the need for more challenging benchmarks to guide future imp…