19 citations · 65 across the 63 of their papers we have counts for
44 papers · 1 filter
Compiling Autoregressive LLM Control into Policy-Exact Parallel Inference for Hierarchical RAG
Zehao Lin, Qianfeng Shen, Xixuan Hao +8
Large language models increasingly act as controllers in structured decision pipelines, yet their decisions are still executed by autoregressive text decoding. Hierarchical RAG is…
PHASE-Tree: Modeling Character-State Evolution in Long-Horizon Role-Playing Dialogue
Bo Tang, Jianan Yang, Junyi Zhu +7
Long-horizon role-playing demands that characters remain recognizable as they evolve with the narrative. Yet existing work falls short on two fronts: representations are typically…
Metis: Memory Foundation Model
Zeyu Zhang, Ziliang Guo, Yihang Sun +14
Recent advances in AI agents have increasingly internalized native capabilities into their underlying foundation models, giving rise to multimodal foundation models and large reaso…
From Memory to Skills: Evidence-Grounded Co-Evolution Governance for Long-Horizon LLM Agents
Bo Tang, Yang Zhang, Guomian Zhuang +8
Existing memory systems for long-horizon LLM agents often retrieve prior traces as passive context rather than converting them into executable capabilities. In this paper, we propo…
Are We Ready For An Agent-Native Memory System?
Wei Zhou, Xuanhe Zhou, Shaokun Han +5
Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information stora…
SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution
Hongyi Liu, Haoyan Yang, Tao Jiang +4
Long-horizon LLM agents generate traces that could become reusable experience, but raw trajectories are noisy, local, and hard to govern. Agent Skills offer a structured artifact f…
MemReranker: Reasoning-Aware Reranking for Agent Memory Retrieval
Chunyu Li, Mengyuan Zhang, Jingyi Kang +6
In agent memory systems, the reranking model serves as the critical bridge connecting user queries with long-term memory. Most systems adopt the "retrieve-then-rerank" two-stage pa…
MemReader: From Passive to Active Extraction for Long-Term Agent Memory
Jingyi Kang, Chunyu Li, Ding Chen +3
Long-term memory is fundamental for personalized and autonomous agents, yet populating it remains a bottleneck. Existing systems treat memory extraction as a one-shot, passive tran…
MemFactory: Unified Inference & Training Framework for Agent Memory
Ziliang Guo, Ziheng Li, Bo Tang +2
Memory-augmented Large Language Models (LLMs) are essential for developing capable, long-term AI agents. Recently, applying Reinforcement Learning (RL) to optimize memory operation…
MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory
Shengtao Zhang, Jiaqian Wang, Ruiwen Zhou +11
The hallmark of human intelligence is the self-evolving ability to master new skills by learning from past experiences. However, current AI agents struggle to emulate this self-evo…
TAdaRAG: Task Adaptive Retrieval-Augmented Generation via On-the-Fly Knowledge Graph Construction
Jie Zhang, Bo Tang, Wanzi Shao +8
Retrieval-Augmented Generation (RAG) improves large language models by retrieving external knowledge, often truncated into smaller chunks due to the input context window, which lea…
HaluMem: Evaluating Hallucinations in Memory Systems of Agents
Ding Chen, Simin Niu, Kehang Li +6
Memory systems are key components that enable AI systems such as LLMs and AI agents to achieve long-term learning and sustained interaction. However, during memory storage and retr…
Dissecting Role Cognition in Medical LLMs via Neuronal Ablation
Xun Liang, Huayi Lai, Hanyu Wang +5
Large language models (LLMs) have gained significant traction in medical decision support systems, particularly in the context of medical question answering and role-playing simula…
Text2Mem: A Unified Memory Operation Language for Memory Operating System
Yi Wang, Lihai Yang, Boyu Chen +6
Large language model agents increasingly depend on memory to sustain long horizon interaction, but existing frameworks remain limited. Most expose only a few basic primitives such…
MoM: Mixtures of Scenario-Aware Document Memories for Retrieval-Augmented Generation Systems
Jihao Zhao, Zhiyuan Ji, Simin Niu +3
The traditional RAG paradigm, which typically engages in the comprehension of relevant text chunks in response to received queries, inherently restricts both the depth of knowledge…
InfoMosaic-Bench: Evaluating Multi-Source Information Seeking in Tool-Augmented Agents
Yaxin Du, Yuanshuo Zhang, Xiyuan Yang +10
Information seeking is a fundamental requirement for humans. However, existing LLM agents rely heavily on open-web search, which exposes two fundamental weaknesses: online content…
MemOS: A Memory OS for AI System
Zhiyu Li, Chenyang Xi, Chunyu Li +36
Large Language Models (LLMs) have become an essential infrastructure for Artificial General Intelligence (AGI), yet their lack of well-defined memory management systems hinders the…
Text2VectorSQL: Towards a Unified Interface for Vector Search and SQL Queries
Zhengren Wang, Dongwen Yao, Bozhou Li +7
The proliferation of unstructured data poses a fundamental challenge to traditional database interfaces. While Text-to-SQL has democratized access to structured data, it remains in…
GuessArena: Guess Who I Am? A Self-Adaptive Framework for Evaluating LLMs in Domain-Specific Knowledge and Reasoning
Qingchen Yu, Zifan Zheng, Ding Chen +4
The evaluation of large language models (LLMs) has traditionally relied on static benchmarks, a paradigm that poses two major limitations: (1) predefined test sets lack adaptabilit…
MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models
Zhiyu Li, Shichao Song, Hanyu Wang +19
Large Language Models (LLMs) have emerged as foundational infrastructure in the pursuit of Artificial General Intelligence (AGI). Despite their remarkable capabilities in language…
Token-level Accept or Reject: A Micro Alignment Approach for Large Language Models
Yang Zhang, Yu Yu, Bo Tang +8
With the rapid development of Large Language Models (LLMs), aligning these models with human preferences and values is critical to ensuring ethical and safe applications. However,…
xVerify: Efficient Answer Verifier for Reasoning Model Evaluations
Ding Chen, Qingchen Yu, Pengyuan Wang +9
With the release of OpenAI's o1 model, reasoning models that adopt slow-thinking strategies have become increasingly common. Their outputs often contain complex reasoning, intermed…
SEAP: Training-free Sparse Expert Activation Pruning Unlock the Brainpower of Large Language Models
Xun Liang, Hanyu Wang, Huayi Lai +7
Large Language Models have achieved remarkable success across various natural language processing tasks, yet their high computational cost during inference remains a major bottlene…
RARE: Retrieval-Augmented Reasoning Modeling
Zhengren Wang, Jiayang Yu, Dongsheng Ma +8
Domain-specific intelligence demands specialized knowledge and sophisticated reasoning for problem-solving, posing significant challenges for large language models (LLMs) that stru…