The Year in AI Papers
The year/Topics/Agent skills and memory

Research collection

Agent skills and memory

Reusable agent capability: skill acquisition, distillation and libraries, procedural memory, and transferable lessons from trajectories.

Papers
45
Research labs
1
Official code
34

1–45 of 45 papers in this collection

01

Independent research

DecoEvo: Score-Decoupled Co-Evolution of Solver and Rubric-Generator Skills in Text Space

DecoEvo is a text-space optimization framework that co-evolves a solver skill and a rubric-generator skill under decoupled objectives, avoiding the pitfalls of score-coupled co-adaptation. The solver skill is updated using criterion-level feedback from generated rubrics, while the generator skill is revised through two audits: a task-conditioned structural…

Jiangwang Chen, Zixin Song, Junlin Liu, Shuaiyu Zhou, et al.
Published
Jul 2026
Citations
2
Code
Not linked
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02

Independent research

DataFlow-Harness: A Grounded Code-Agent Platform for Constructing Editable LLM Data Pipelines

DataFlow-Harness is a platform that bridges the NL2Pipeline gap, where coding agents produce scripts that are not persistent, editable platform artifacts. It guides an LLM agent to construct platform-native DAGs via typed, incremental mutations, combining DataFlow-Skills for procedural guidance, an MCP layer for live operator registry and pipeline state,…

Runming He, Zhen Hao Wong, Hao Liang, Zimo Meng, et al.
Published
Jul 2026
Citations
0
Code
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03

Independent research

KnowAct-GUIClaw: Know Deeply, Act Perfectly, Personal GUI Assistant with Self-Evolving Memory and Skill

KnowAct-GUIClaw is a personal GUI assistant framework built on the 'Know Deeply, Act Perfectly' paradigm to address OpenClaw's limitations in cross-platform GUI interaction and self-evolution. It uses a Know-Route-Act-Reflect loop: the host agent decomposes tasks and allocates them (Know), a router classifies tasks and transfers data via a blackboard…

Yunxin Li, Jinchao Li, Shibo Su, Zhenran Xu, et al.
Published
Jul 2026
Citations
0
Code
482 stars
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04

Independent research

ABot-AgentOS: A General Robotic Agent OS with Lifelong Multi-modal Memory

ABot-AgentOS is a general robotic Agent Operating System that provides a deliberative layer above low-level controllers, enabling scene-conditioned planning, context-isolated skill execution, multi-stage verification, multi-modal memory, and edge-cloud collaboration. It introduces Universal Multi-modal Graph Memory, a persistent, source-grounded substrate…

Jiayi Tian, Shiao Liu, Yuting Xu, Jia Lu, et al.
Published
Jul 2026
Citations
0
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05

Independent research

ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes

ResearchStudio-Idea is a suite of three skills for evidence-grounded research ideation in machine learning. It includes Paper-Search for multi-source literature grounding, Scoop-Check for prior-art collision checking, and IdeaSpark, an end-to-end skill that composes evidence grounding, pattern-guided generation, collision retrieval, audit, and idea-card…

Qihao Zhao, Yangyu Huang, Yalun Dai, Lingao Xiao, et al.
Published
Jul 2026
Citations
1
Code
2.1K stars
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06

Independent research

RESOURCE2SKILL: Distilling Executable Agent Skills from Human-Created Multimodal Resources

Resource2Skill is a framework that automatically distills multimodal human-created resources—tutorial videos, repositories, articles, and reference artifacts—into executable skills for software agents, organized as a hierarchical multimodal Skill Wiki. Each skill entry combines structured text, code, visual examples, metadata, and provenance, preserving…

Yijia Fan, Zonglin Di, Zimo Wen, Yifan Yang, et al.
Published
Jun 2026
Citations
0
Code
368 stars
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07

Independent research

Are We Ready For An Agent-Native Memory System?

This paper presents a systematic experimental study of agent memory systems for LLM agents, decomposing them into four core modules: representation/storage, extraction, retrieval/routing, and maintenance. The authors evaluate 12 representative memory systems and two baselines across five benchmark workloads (11 datasets), finding that no single…

Wei Zhou, Xuanhe Zhou, Shaokun Han, Hongming Xu, et al.
Published
Jun 2026
Citations
7
Code
129 stars
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08

Independent research

OpenRath: Session-Centered Runtime State for Agent Systems

OpenRath addresses the hidden-runtime-state problem in multi-agent systems by introducing Session, a first-class runtime value that carries conversation chunks, lineage, sandbox placement, tool evidence, memory events, and usage. The programming model, inspired by PyTorch's architecture, defines objects like Agent, Workflow, Tool, Memory, Sandbox, and…

Fukang Wen, Zhijie Wang, Ruilin Xu
Published
Jun 2026
Citations
0
Code
1.1K stars
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09

Independent research

MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision

MemSlides is a hierarchical memory framework for personalized presentation generation that separates long-term memory (user profile memory and tool memory) from working memory. User profile memory stores intent-conditioned preferences for round-0 personalization, working memory carries active preferences and session constraints across revision rounds, and…

Ye Jin, Yangyang Xu, Jun Zhu, Yibo Yang
Published
Jun 2026
Citations
0
Code
1K stars
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10

Independent research

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

This survey paper by Tencent Youtu Lab and academic partners frames the evolution of Large Language Models (LLMs) as a shift from conversational chatbots to persistent, autonomous 'Digital Colleagues.' The authors organize this transition along two dimensions: the cognitive core (from fast, next-token-prediction 'Chatbot' models to 'Thinking LLMs' that use…

Yongheng Zhang, Ziang Liu, Jiaxuan Zhu, Shuai Wang, et al.
Published
Jun 2026
Citations
3
Code
Not linked
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11

Independent research

Data Journalist Agent: Transforming Data into Verifiable Multimodal Stories

The paper introduces Data Journalist Agent (Data2Story), a multi-agent framework that transforms raw data into verifiable, multimodal articles. It orchestrates seven roles (Detective, Analyst, Editor, Designer, Programmer, Auditor, Inspector) into a virtual newsroom. Key innovations are evidence-grounded claims via an Inspector that links each claim to…

Kevin Qinghong Lin, Batu EI, Yuhong Shi, Pan Lu, et al.
Published
Jun 2026
Citations
0
Code
149 stars
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12

Independent research

LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents

LatentSkill is a framework that converts textual agent skills into plug-and-play LoRA adapters via a pretrained hypernetwork, storing skill knowledge in weight space rather than context space. This removes per-step skill tokens from prompts while preserving modular loading, scaling, and composition. On ALFWorld, LatentSkill improves success by 21.4 and…

Aofan Yu, Chenyu Zhou, Tianyi Xu, Zihan Guo, et al.
Published
Jun 2026
Citations
2
Code
27 stars
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13

Independent research

Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents

The paper introduces MRAgent, a framework for LLM agents that treats memory access as an active, multi-step reconstruction process over a structured memory graph, rather than a passive retrieve-then-reason pipeline. MRAgent organizes memory as a Cue–Tag–Content graph, where associative tags link fine-grained cues to memory contents, enabling the LLM to…

Shuo Ji, Yibo Li, Bryan Hooi
Published
Jun 2026
Citations
2
Code
233 stars
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14

Independent research

Evolving Agents in the Dark: Retrospective Harness Optimization via Self-Preference

The paper introduces Retrospective Harness Optimization (RHO), a self-supervised method to improve an AI agent's harness (skills, tools, workflows) using only past trajectories, without ground-truth labels. RHO selects a diverse, challenging coreset of past tasks via a determinantal point process, re-solves them in parallel, and extracts improvement…

Wenbo Pan, Shujie Liu, Chin-Yew Lin, Jingying Zeng, et al.
Published
Jun 2026
Citations
1
Code
48 stars
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15

arXiv.org

COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation

COLLEAGUE.SKILL is an open-source system that automatically distills heterogeneous traces of a person or role (e.g., work documents, chat logs, public interviews) into portable, inspectable, and correctable AI skill packages. The system generates a versioned package with two tracks: a capability track for practices and decision heuristics, and a bounded…

Tianyi Zhou, Dongrui Liu, Leitao Yuan, Jing Shao, et al.
Published
May 2026
Citations
3
Code
21K stars
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16

arXiv.org

SkillOpt: Executive Strategy for Self-Evolving Agent Skills

SkillOpt is a text-space optimizer that treats an agent's skill document as trainable external state, using a separate optimizer model to propose bounded add/delete/replace edits based on scored rollouts. Edits are accepted only if they improve a held-out validation score, with controls like a textual learning-rate budget, rejected-edit buffer, and…

Yifan Yang, Ziyang Gong, Weiquan Huang, Qihao Yang, et al.
Published
May 2026
Citations
29
Code
16K stars
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17

arXiv.org

Code as Agent Harness

This survey introduces the concept of 'code as agent harness,' reframing code from a mere output of large language models (LLMs) to the operational substrate for agentic AI systems. It argues that code serves as an executable, inspectable, and stateful medium through which agents reason, act, and model their environments. The paper organizes the literature…

Xuying Ning, Katherine Tieu, Dongqi Fu, Tianxin Wei, et al.
Published
May 2026
Citations
12
Code
623 stars
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18

arXiv.org

SkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution

SkillsVote is a framework for governing Agent Skills across collection, recommendation, attribution, and evolution. It profiles a corpus of over one million open-source skills for environment requirements, quality, and verifiability. Before task execution, it performs agentic library search to recommend relevant skills and generate usage guidance. After…

Hongyi Liu, Haoyan Yang, Tao Jiang, Bo Tang, et al.
Published
May 2026
Citations
14
Code
Not linked
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19

arXiv.org

MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory

MemEye is a visual-centric evaluation framework for multimodal agent memory, addressing the gap in benchmarks that test whether agents preserve visual evidence for later reasoning. It introduces a two-axis taxonomy: visual evidence granularity (X-axis, from scene-level X1 to pixel-level X4) and memory reasoning depth (Y-axis, from atomic retrieval Y1 to…

Minghao Guo, Qingyue Jiao, Zeru Shi, Yihao Quan, et al.
Published
May 2026
Citations
6
Code
48 stars
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20

arXiv.org

MMSkills: Towards Multimodal Skills for General Visual Agents

MMSkills is a framework for representing, generating, and using reusable multimodal procedural knowledge for visual agents. Each MMSkill package combines a textual procedure, runtime state cards (encoding when-to-use conditions, visible cues, verification cues), and multi-view keyframes (full-frame, focus crop, before/after) to support state-conditioned…

Kangning Zhang, Shuai Shao, Qingyao Li, Jianghao Lin, et al.
Published
May 2026
Citations
3
Code
29 stars
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21

arXiv.org

Do Enterprise Systems Need Learned World Models? The Importance of Context to Infer Dynamics

The paper investigates whether enterprise systems need learned world models, arguing that runtime discovery of configurable transition dynamics is more robust than offline training. It introduces CascadeBench, a benchmark for enterprise cascade prediction, and enterprise discovery agents that retrieve business rules at inference time. Experiments show…

Jishnu Sethumadhavan Nair, Patrice Bechard, Rishabh Maheshwary, Surajit Dasgupta, et al.
Published
May 2026
Citations
0
Code
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22

arXiv.org

MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents

MemPrivacy is a framework for privacy-preserving memory management in edge-cloud LLM agents. It detects privacy-sensitive spans on edge devices, replaces them with type-aware placeholders (e.g., <Health_Info_1>) before cloud processing, and restores original values locally. This decouples privacy protection from semantic destruction, preserving…

Yining Chen, Jihao Zhao, Bo Tang, Haofen Wang, et al.
Published
May 2026
Citations
7
Code
120 stars
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23

arXiv.org

Skill1: Unified Evolution of Skill-Augmented Agents via Reinforcement Learning

The paper introduces Skill1, a framework for training a single policy to co-evolve skill selection, utilization, and distillation in language model agents. Unlike prior methods that optimize these capabilities in isolation or with separate reward sources, Skill1 derives all learning signals from a single task-outcome reward by decomposing it into a…

Yaorui Shi, Yuxin Chen, Zhengxi Lu, Yuchun Miao, et al.
Published
May 2026
Citations
10
Code
108 stars
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24

arXiv.org

From Context to Skills: Can Language Models Learn from Context Skillfully?

The paper introduces Ctx2Skill, a self-evolving framework that autonomously discovers, refines, and selects context-specific skills from complex contexts to improve language models' context learning, without human annotation or external feedback. It uses a multi-agent self-play loop with a Challenger generating tasks and rubrics, a Reasoner solving them,…

Shuzheng Si, Haozhe Zhao, Yu Lei, Qingyi Wang, et al.
Published
Apr 2026
Citations
15
Code
340 stars
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25

arXiv.org

AgentSPEX: An Agent SPecification and EXecution Language

AgentSPEX is a YAML-based specification and execution language for LLM-agent workflows, designed to overcome the limitations of reactive prompting and Python-coupled orchestration frameworks. It provides explicit control flow (typed steps, branching, loops, parallel execution), reusable submodules, and explicit context management. Workflows run in a…

Pengcheng Wang, Jerry Huang, Jiarui Yao, Rui Pan, et al.
Published
Apr 2026
Citations
6
Code
93 stars
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26

arXiv.org

SkillClaw: Let Skills Evolve Collectively with Agentic Evolver

SkillClaw is a framework for collective skill evolution in multi-user LLM agent ecosystems, addressing the problem of static skills that do not improve with experience. It aggregates interaction trajectories from multiple users, groups them by referenced skills, and uses an agentic evolver to refine existing skills, create new ones, or skip updates based…

Ziyu Ma, Shidong Yang, Yuxiang Ji, Xucong Wang, et al.
Published
Apr 2026
Citations
48
Code
2.4K stars
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27

arXiv.org

Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills

Trace2Skill is a framework that distills agent execution trajectories into portable skills by analyzing many traces in parallel and consolidating recurring lessons into a single skill directory. It supports both deepening existing human-written skills and creating skills from weak LLM-generated drafts. The pipeline has three stages: trajectory generation…

Jingwei Ni, Yihao Liu, Xinpeng Liu, Yutao Sun, et al.
Published
Mar 2026
Citations
78
Code
Not linked
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28

arXiv.org

Memento-Skills: Let Agents Design Agents

Memento-Skills is a generalist LLM agent system that functions as an agent-designing agent, autonomously constructing, adapting, and improving task-specific agents through experience. It uses a memory-based reinforcement learning framework with stateful prompts, where reusable skills stored as structured markdown files serve as persistent, evolving memory.…

Huichi Zhou, Siyuan Guo, Anjie Liu, Zhongwei Yu, et al.
Published
Mar 2026
Citations
39
Code
1.5K stars
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29

arXiv.org

SkillNet: Create, Evaluate, and Connect AI Skills

SkillNet is an open infrastructure for creating, evaluating, and organizing AI skills at scale, addressing the lack of systematic skill consolidation in current AI agents. It structures skills within a three-layer ontology (taxonomy, relation graph, package library) and supports automated skill creation from heterogeneous sources like execution…

Yuan Liang, Ruobin Zhong, Haoming Xu, Chen Jiang, et al.
Published
Feb 2026
Citations
34
Code
1.1K stars
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30

arXiv.org

SkillOrchestra: Learning to Route Agents via Skill Transfer

SkillOrchestra is a framework for skill-aware orchestration in compound AI systems, addressing limitations of existing routing approaches: input-level routers make coarse decisions, and RL-trained orchestrators are expensive and prone to routing collapse. Instead of learning a routing policy end-to-end, SkillOrchestra learns a reusable Skill Handbook from…

Jiayu Wang, Yifei Ming, Zixuan Ke, Shafiq Joty, et al.
Published
Feb 2026
Citations
16
Code
71 stars
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31

arXiv.org

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

SKILLSBENCH is a benchmark for evaluating Agent Skills, which are structured packages of procedural knowledge that augment LLM agents at inference time. The benchmark contains 87 tasks across 8 domains, each with curated Skills and deterministic verifiers. In an evaluation of 18 model–harness configurations, curated Skills raised the average pass rate from…

Xiangyi Li, Yimin Liu, Wenbo Chen, Bingran You, et al.
Published
Feb 2026
Citations
174
Code
Not linked
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32

arXiv.org

MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents

MemSkill reframes LLM agent memory operations as learnable, evolvable memory skills. It maintains a shared skill bank where each skill provides structured guidance (purpose, when to use, how to apply, constraints) for extracting, consolidating, or revising memories. A controller, trained with reinforcement learning (PPO), selects a Top-K set of relevant…

Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng, et al.
Published
Feb 2026
Citations
92
Code
556 stars
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33

Anthropic

How AI Impacts Skill Formation

This study examines how AI assistance affects skill formation in software engineering. In a randomized experiment, 52 developers learned a new asynchronous Python library (Trio) with or without an AI assistant. Results show that AI use significantly reduced quiz scores measuring conceptual understanding, code reading, and debugging (17% lower, Cohen's…

Judy Hanwen Shen, Alex Tamkin
Published
Jan 2026
Citations
24
Code
Not linked
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34

Independent research

Computer Environments Elicit General Agentic Intelligence in LLMs

The paper introduces LLM-in-Sandbox, a paradigm that virtualizes a computer as a minimal code sandbox (with bash, file_editor, and finish tools) to elicit general agentic intelligence in LLMs. Without additional training, strong models (e.g., Claude-Sonnet-4.5-Think, GPT-5, DeepSeek-V3.2-Thinking) show consistent gains across mathematics, physics,…

Daixuan Cheng, Shaohan Huang, Yuxian Gu, Huatong Song, et al.
Published
Jan 2026
Citations
5
Code
241 stars
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35

arXiv.org

Toward Efficient Agents: Memory, Tool learning, and Planning

This survey examines efficiency in LLM-based agents, focusing on memory, tool use, and planning. It defines an efficient agent as one that maximizes task success while minimizing resource consumption (tokens, latency, computational cost). The survey reviews methods for efficient memory (construction, management, access, skills, multi-agent), tool use…

Xiaofang Yang, Lijun Li, Heng Zhou, Tong Zhu, et al.
Published
Jan 2026
Citations
9
Code
293 stars
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36

arXiv.org

Evolving Programmatic Skill Networks

The paper introduces the Programmatic Skill Network (PSN), a framework for continual skill acquisition in open-ended embodied environments. In PSN, skills are executable symbolic programs (e.g., JavaScript for Minecraft) that form a compositional network evolving through experience. PSN implements three core mechanisms via LLMs: REFLECT for trace-based…

Haochen Shi, Xingdi Yuan, Bang Liu
Published
Jan 2026
Citations
4
Code
3 stars
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37

arXiv.org

Memory in the Age of AI Agents

This survey provides a comprehensive overview of memory systems for foundation model-based AI agents, addressing the fragmentation in the field by proposing a unified taxonomy based on forms, functions, and dynamics. It defines agent memory, distinguishing it from LLM memory, RAG, and context engineering. The survey categorizes memory forms into…

Yuyang Hu, Shichun Liu, Yanwei Yue, Guibin Zhang, et al.
Published
Dec 2025
Citations
232
Code
2.3K stars
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38

The Web Conference

PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing

PaperDebugger is an in-editor, multi-agent, plugin-based academic writing assistant that integrates directly into Overleaf via a Chrome extension, eliminating copy-paste workflows. It addresses the challenge of external LLM assistants by providing bidirectional synchronization, version control, secure state management, and multi-agent scheduling. The…

Junyi Hou, Andre Lin Huikai, Nuo Chen, Yiwei Gong, et al.
Published
Dec 2025
Citations
9
Code
1.5K stars
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39

arXiv.org

General Agentic Memory Via Deep Research

The paper introduces General Agentic Memory (GAM), a memory framework for AI agents that follows a just-in-time (JIT) compilation principle, contrasting with traditional ahead-of-time (AOT) static memory systems that suffer from information loss. GAM uses a dual-agent design: a Memorizer that compresses key historical information into lightweight memory…

B. Y. Yan, Chaofan Li, Hongjin Qian, Shuqi Lu, et al.
Published
Nov 2025
Citations
34
Code
859 stars
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40

Conference on Empirical Methods in Natural Language Processing

MADD: Multi-Agent Drug Discovery Orchestra

The paper introduces MADD, a multi-agent system for automated hit identification in early drug discovery. MADD uses four specialized agents (Decomposer, Orchestrator, Summarizer, Chat Agent) to process natural language queries, decompose them into subtasks, generate molecules using GAN and transformer-based CVAE models, and predict properties like IC50 and…

Gleb V. Solovev, Alina B. Zhidkovskaya, Anastasia Orlova, Nina Gubina, et al.
Published
Nov 2025
Citations
4
Code
Not linked
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41

arXiv.org

HaluMem: Evaluating Hallucinations in Memory Systems of Agents

HaluMem is the first operation-level benchmark for evaluating hallucinations in memory systems for LLMs and AI agents. It defines three tasks—memory extraction, memory updating, and memory question answering—to localize hallucinations to specific operational stages. The benchmark includes two datasets, HaluMem-Medium and HaluMem-Long, with about 15k memory…

Ding Chen, Simin Niu, Kehang Li, Peng Liu, et al.
Published
Nov 2025
Citations
37
Code
151 stars
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42

arXiv.org

A Survey of Data Agents: Emerging Paradigm or Overstated Hype?

This survey introduces the first systematic hierarchical taxonomy for data agents, inspired by the SAE J3016 standard for driving automation, to address the terminological ambiguity surrounding the term. The taxonomy comprises six levels (L0–L5) that delineate progressive shifts in autonomy, from manual operations (L0) to a vision of generative, fully…

Yizhang Zhu, Liangwei Wang, Chenyu Yang, Xiaotian Lin, et al.
Published
Oct 2025
Citations
36
Code
688 stars
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43

arXiv.org

AI for Service: Proactive Assistance with AI Glasses

The paper introduces AI for Service (AI4Service), a paradigm for proactive, real-time assistance via AI glasses, contrasting with reactive AI that only responds to explicit commands. It proposes Alpha-Service, a framework inspired by the von Neumann architecture, comprising five components: Input Unit (perception via dual MLLMs), Central Processing Unit…

Zichen Wen, Yiyu Wang, Chenfei Liao, Boxue Yang, et al.
Published
Oct 2025
Citations
16
Code
Not linked
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44

arXiv.org

Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

The paper introduces ACE (Agentic Context Engineering), a framework for context adaptation in LLMs that treats contexts as evolving playbooks, accumulating and refining strategies through generation, reflection, and curation. ACE addresses two limitations of prior methods: brevity bias (over-compression of prompts) and context collapse (loss of detail…

Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, et al.
Published
Oct 2025
Citations
236
Code
1.2K stars
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45

arXiv.org

Memento: Fine-tuning LLM Agents without Fine-tuning LLMs

The paper introduces Memento, a learning paradigm for LLM agents that enables continual adaptation without fine-tuning the underlying LLM. It formalizes a Memory-augmented Markov Decision Process (M-MDP) with a neural case-selection policy, storing past experiences in an episodic case bank. The policy is updated via online soft Q-learning, with retrieval…

Huichi Zhou, Yihang Chen, Siyuan Guo, Xue Yan, et al.
Published
Aug 2025
Citations
87
Code
2.6K stars
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