most citedAgentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices

2 citations · 4 across the 10 of their papers we have counts for

collaborators

12 papers

cs.CL2026

Playing social deduction games with reinforcement fine-tuned large language models

Lingzhe Zhang, Yunpeng Zhai, Tong Jia +7

Reinforcement fine-tuning (RFT) is increasingly used in applications where large language models (LLMs) interact with humans and other agents. Here we use social deduction games to…

cs.CL2026

EAVer: Long-Form Factuality Verification as an End-to-End Agentic Policy

Kening Zheng, Aoying Zheng, Zhigang Chang +21

Long-form factuality verification is commonly implemented as a static decompose-search-verify pipeline, with separately prompted modules processing claims and invoking external sea…

cs.LG2026

RLCSD: Reinforcement Learning with Contrastive On-Policy Self-Distillation

Leyi Pan, Shuchang Tao, Yunpeng Zhai +5

On-policy self-distillation (OPSD) provides dense, token-level supervision for reasoning models by aligning a model's own distribution with that under privileged context, typically…

cs.PF2026

How Much Parallelism Is "Free"? A Principle of Near-Free Parallelism for Parallel Decoding

Minghua He, Lingzhe Zhang, Yuan Liu +2

Parallel decoding improves generation efficiency by processing multiple decode positions within a single decode forward, but reported speedups conflate algorithmic token utilizatio…

cs.SE2026

E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning

Lingzhe Zhang, Yunpeng Zhai, Tong Jia +5

Contemporary microservice systems continue to grow in scale and complexity, leading to increasingly frequent and costly failures. While recent LLM-based auto-remediation approaches…

cs.SE2026

Efficient Failure Management for Multi-Agent Systems with Reasoning Trace Representation

Lingzhe Zhang, Tong Jia, Mingyu Wang +9

Large Language Models (LLM)-based Multi-Agent Systems (MASs) have emerged as a new paradigm in software system design, increasingly demonstrating strong reasoning and collaboration…

cs.SE2026

RuntimeSlicer: Towards Generalizable Unified Runtime State Representation for Failure Management

Lingzhe Zhang, Tong Jia, Weijie Hong +9

Modern software systems operate at unprecedented scale and complexity, where effective failure management is critical yet increasingly challenging. Metrics, traces, and logs provid…

cs.SE2026★ 2 cited

Hypothesize-Then-Verify: Speculative Root Cause Analysis for Microservices with Pathwise Parallelism

Lingzhe Zhang, Tong Jia, Yunpeng Zhai +5

Microservice systems have become the backbone of cloud-native enterprise applications due to their resource elasticity, loosely coupled architecture, and lightweight deployment. Ye…

cs.SE2026★ 2 cited

Agentic Memory Enhanced Recursive Reasoning for Root Cause Localization in Microservices

Lingzhe Zhang, Tong Jia, Yunpeng Zhai +5

As contemporary microservice systems become increasingly popular and complex-often comprising hundreds or even thousands of fine-grained, interdependent subsystems-they are experie…

cs.CL2025

d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models

Leyi Pan, Shuchang Tao, Yunpeng Zhai +8

Reinforcement learning (RL) is pivotal for enhancing the reasoning capabilities of diffusion large language models (dLLMs). However, existing dLLM policy optimization methods suffe…

cs.CL2025

A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models

Lingzhe Zhang, Liancheng Fang, Chiming Duan +8

As text generation has become a core capability of modern Large Language Models (LLMs), it underpins a wide range of downstream applications. However, most existing LLMs rely on au…

cs.CL2025

Omni-SafetyBench: A Benchmark for Safety Evaluation of Audio-Visual Large Language Models

Leyi Pan, Zheyu Fu, Yunpeng Zhai +9

Omni-modal Large Language Models (OLLMs) that integrate visual, auditory, and textual processing face severe safety risks. They exhibit fragile defenses against audio-visual joint…