89 citations · 105 across the 26 of their papers we have counts for
9 papers · 1 filter
GitHarness: Git Init Your Harness Working Memory for Perpetual User Requirements
Zhibang Yang, Xinke Jiang, Yuxuan Liu +9
LLM-based agents increasingly collaborate with users on long-horizon tasks, accumulating evidence, code, and drafts through extensive search, reasoning, and execution. As users ins…
GenMem: Generative Symbolic Memory for Self-Evolving Harness
Xinke Jiang, Tao Feng, Weixuan Xu +7
Long-term memory supports the self-evolution of LLM agents by retaining experience and skills across tasks and enabling their retrieval, reuse, and revision in subsequent long-hori…
Harness-RL: Black-Box Reinforcement Learning with Action-Args Decoupling for Central-Agent Multi-Agent Harnesses
Xinke Jiang, Zhixin Zhang, Zhibang Yang +6
Large language model agents increasingly solve long-horizon tasks through multi-agent harnesses in which a central agent coordinates specialized sub-agents, tools, and environments…
AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing
Xinke Jiang, Yue Fang, Zhibang Yang +12
Retrieval-Augmented Generation (RAG) improves the factuality of large language models (LLMs), yet existing RAG systems often struggle with complex, multi-step reasoning that requir…
LoongReflect: Boosting Long-Horizon Reflection in Search Agents via Global Perspective Distillation
Zhixin Zhang, Xinke Jiang, Zhibang Yang +5
Large language model agents increasingly rely on long-horizon reasoning to solve complex tasks involving planning, tool use, and memory. A critical capability in such settings is r…
The Weakest Link Tells It All: Outcome-Supervised Process Reward Modeling via Learnable Credit Assignment
Tianyu Jia, Yue Fang, Hongxin Ding +6
Process reward models (PRMs) enhance the reasoning capabilities of large language models (LLMs) by providing fine-grained feedback, yet training PRMs typically requires expensive s…
EvoRubrics: Dynamic Rubrics as Rewards via Adversarial Co-Evolution for LLM Reinforcement Learning
Hongxin Ding, Baixiang Huang, Yue Fang +6
Rubric-based rewards offer interpretable and fine-grained optimization signals for reinforcement learning in open-ended tasks where verifiable answers are unavailable. However, pre…
ScaffoldAgent: Utility-Guided Dynamic Outline Optimization for Open-Ended Deep Research
Zhibang Yang, Xinke Jiang, Yuzhen Xiao +9
Open-ended deep research (OEDR) requires systems to acquire knowledge through multi-round retrieval and generate coherent long-form reports. The outline plays a central role as a s…
StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management
Ruizhe Zhang, Xinke Jiang, Zhibang Yang +12
Multi-agent systems based on large language models, particularly centralized architectures, have recently shown strong potential for complex and knowledge-intensive tasks. However,…