12 papers
FDE-Bench: Evaluating End-to-End Delivery from Underspecified Real-World Business Requests
Huaiming Li, Can Huang, Chufan Wu +18
Real-world business requests often leave key requirements unstated because frontline practitioners struggle to articulate rules they know from experience. Forward-deployed engineer…
VidHarness: Evolving Agent Harnesses for Cost-Efficient Long Video Understanding
Susan Liang, Jianmin Wu, Daxiang Dong
Vision-language models (VLMs) can answer questions about hour-long videos, but processing every frame is prohibitively expensive, even though the evidence for a question usually sp…
Janus: An Algorithm-Evaluator Co-Evolution Framework for LLM-Driven Discovery under Expensive Evaluation Budgets
Ximeng Liu, Qianlong Wang, Yingming Mao +6
LLM-driven program discovery relies on rapid evaluator feedback, but many scientific and engineering tasks require high-fidelity simulations, hardware execution, or physical experi…
Beyond Static Evaluation: Co-Evolutionary Mechanisms for LLM-Driven Strategy Evolution in Adversarial Games
Haoran Li, Zengle Ge, Ziyang Zhang +10
Recent advances in LLM-driven code evolution have enabled automated discovery by iteratively generating and improving programs. However, applying these methods to adversarial multi…
WebGameBench: Requirement-to-Application Evaluation for Coding Agents via Browser-Native Games
Wenyu Zhang, Guoliang You, Tianlun +8
Coding agents are increasingly used as application builders, yet many evaluations still focus on source code, repository-level tests, or intermediate traces rather than the deliver…
Rollout Pass-Rate Control: Steering Binary-Reward RL Toward Its Most Informative Regime
Tianshu Zhu, Wenyu Zhang, Xiaoying Zuo +8
Agentic reinforcement learning (RL) for software engineering spends much of its compute on stateful trajectories whose grouped binary rewards are highly skewed and weakly contrasti…
AEM: Adaptive Entropy Modulation for Multi-Turn Agentic Reinforcement Learning
Haotian Zhao, Songlin Zhou, Yuxin Zhang +9
Reinforcement learning (RL) has substantially improved the ability of large language model (LLM) agents to interact with environments and solve multi-turn tasks. However, effective…
SWE-Hub: A Unified Production System for Scalable, Executable Software Engineering Tasks
Yucheng Zeng, Shupeng Li, Daxiang Dong +11
Progress in software-engineering agents is increasingly constrained by the scarcity of executable, scalable, and realistic data for training and evaluation. This scarcity stems fro…
LOGIGEN: Logic-Driven Generation of Verifiable Agentic Tasks
Yucheng Zeng, Weipeng Lu, Linyun Liu +9
The evolution of Large Language Models (LLMs) from static instruction-followers to autonomous agents necessitates operating within complex, stateful environments to achieve precise…
QianfanHuijin Technical Report: A Novel Multi-Stage Training Paradigm for Finance Industrial LLMs
Shupeng Li, Weipeng Lu, Linyun Liu +16
Domain-specific enhancement of Large Language Models (LLMs) within the financial context has long been a focal point of industrial application. While previous models such as Bloomb…
The FM Agent
Annan Li, Chufan Wu, Zengle Ge +19
Large language models (LLMs) are catalyzing the development of autonomous AI research agents for scientific and engineering discovery. We present FM Agent, a novel and general-purp…
Qianfan-VL: Domain-Enhanced Universal Vision-Language Models
Daxiang Dong, Mingming Zheng, Dong Xu +32
We present Qianfan-VL, a series of multimodal large language models ranging from 3B to 70B parameters, achieving state-of-the-art performance through innovative domain enhancement…