3 papers
cs.AI2026
Decocted Experience Improves Test-Time Inference in LLM Agents
Maohao Shen, Kaiwen Zha, Zexue He +6
There is growing interest in improving LLMs without updating model parameters. One well-established direction is test-time scaling, where increased inference-time computation (e.g.…
cs.CV2026
Composition-Grounded Data Synthesis for Visual Reasoning
Xinyi Gu, Jiayuan Mao, Zhang-Wei Hong +5
Pretrained multi-modal large language models (MLLMs) demonstrate strong performance on diverse multimodal tasks, but remain limited in reasoning capabilities for domains where anno…
cs.LG2026
BOAD: Discovering Hierarchical Software Engineering Agents via Bandit Optimization
Iris Xu, Guangtao Zeng, Zexue He +5
Large language models (LLMs) have shown strong reasoning and coding capabilities, yet they struggle to generalize to real-world software engineering (SWE) problems that are long-ho…