9 papers
EchoFuzz: Empowering Smart Contract Fuzzing with Large Language Models
Juanen Li, Peng Qian, Guanyan Li +6
Smart contracts, serving as the cornerstone of decentralized applications, autonomously manage trillion-dollar digital assets, making them attractive targets for attacks. Fuzzing h…
CAFE: Self-Improving Search Agents Need Co-Evolving Feedback
Boyang Liu, Senjie Jin, Peixin Wang +17
Reliable search requires more than acquiring external evidence. An agent must also recognize and recover from errors as its trajectory unfolds. In-trajectory feedback provides a me…
Entropy Is Not Enough: Unlocking Effective Reinforcement Learning for Visual Reasoning via Vision-Anchored Token Selection
Senjie Jin, Peixin Wang, Boyang Liu +8
While token-level entropy is commonly recognized as effective for credit assignment in text-only reinforcement learning with verifiable rewards (RLVR), it remains unclear whether t…
A Formal Framework for Predicting Distributed System Performance under Faults (Extended Version)
Ziwei Zhou, Si Liu, Zhou Zhou +2
Today's distributed systems operate in complex environments that inevitably involve faults and even adversarial behaviors. Predicting their performance under such environments dire…
CHDP: Cooperative Hybrid Diffusion Policies for Reinforcement Learning in Parameterized Action Space
Bingyi Liu, Jinbo He, Haiyong Shi +5
Hybrid action space, which combines discrete choices and continuous parameters, is prevalent in domains such as robot control and game AI. However, efficiently modeling and optimiz…
Quantitative Verification of Omega-regular Properties in Probabilistic Programming
Peixin Wang, Jianhao Bai, Min Zhang +1
Probabilistic programming provides a high-level framework for specifying statistical models as executable programs with built-in randomness and conditioning. Existing inference tec…
Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
AGI Team, Yuxuan Cai, Lu Chen +62
The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incent…
Unifying Qualitative and Quantitative Safety Verification of DNN-Controlled Systems
Dapeng Zhi, Peixin Wang, Si Liu +2
The rapid advance of deep reinforcement learning techniques enables the oversight of safety-critical systems through the utilization of Deep Neural Networks (DNNs). This underscore…
Robustness Verification of Deep Reinforcement Learning Based Control Systems using Reward Martingales
Dapeng Zhi, Peixin Wang, Cheng Chen +1
Deep Reinforcement Learning (DRL) has gained prominence as an effective approach for control systems. However, its practical deployment is impeded by state perturbations that can s…