3 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
EnvOpt: Automated Optimization for Agent Environments
Mingxuan Li, Huanzhi Mao, Qiuyang Mang +3
An LLM agent's cost and success depend on its model, its harness, and its environment. In addition to the widely studied directions of parameter tuning and harness optimization, we…
SlackBench: Benchmarking Agents on Collaborative Projects Grounded in Real Code Repositories
Mingxuan Li, Huanzhi Mao, Ruitao Zheng +5
AI Agents are becoming digital coworkers that can browse the web, write code, run experiments, and assist humans over extended horizons. Yet existing benchmarks fall short of real…
I-PTC: Interactive Programmatic Tool Calling for Stateful Tool-Augmented Agents
Huanzhi Mao, Chengkun Cao, Shuo Yuan +1
Large language models are increasingly capable of writing and executing code, suggesting a natural paradigm for tool-augmented agents: expressing tool use as programs rather than i…
OpenDeepThink: Parallel Reasoning via Bradley-Terry Aggregation
Shang Zhou, Wenhao Chai, Kaiyuan Liu +3
Test-time compute scaling is a primary axis for improving LLM reasoning. Existing methods primarily scale depth by extending a single reasoning trace. Scaling breadth by sampling m…
The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks
Alejandro Cuadron, Dacheng Li, Wenjie Ma +13
Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving capabilities, but their effectiveness in interactive environments can be limited. This paper introduces…