The Year in AI Papers
The year/Topics/Agents

Topic area

Agents

Every collection across agents.

Papers
275
Research labs
10
Official code
214

51–100 of 275 papers in this topic area

51

Independent research

WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces

WeaveBench is a long-horizon, hybrid-interface benchmark for computer-use agents, comprising 114 tasks across 8 real-world work domains. Each task requires agents to combine GUI observations/actions with CLI/code operations within a single trajectory, satisfying three admission criteria: channel non-substitutability, long-horizon execution, and…

Wanli Li, Bowen Zhou, Yunyao Yu, Zhou Xu, et al.
Published
Jun 2026
Citations
2
Code
160 stars
Read summary Original
52

Independent research

SWE-Explore: Benchmarking How Coding Agents Explore Repositories

SWE-Explore is a benchmark that isolates and evaluates the repository exploration capability of coding agents, independent of patch generation. It formalizes exploration as a ranked, line-level context selection task: given an issue and repository, an explorer returns a ranked list of code regions under a fixed line budget. The benchmark includes 848…

Shaoqiu Zhang, Yuhang Wang, Jialiang Liang, Yuling Shi, et al.
Published
Jun 2026
Citations
10
Code
39 stars
Read summary Original
53

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
Read summary Original
54

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
Read summary Original
55

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
Read summary Original
56

Independent research

SoCRATES: Towards Reliable Automated Evaluation of Proactive LLM Mediation across Domains and Socio-cognitive Variations

SoCRATES is a benchmark for evaluating proactive LLM mediators in realistic, multi-domain conflict scenarios. It uses an agentic pipeline to curate scenarios from real conflicts across eight domains, probes mediators along five socio-cognitive axes (strategic posture, party composition, history length, emotional reactivity, cultural identity), and scores…

Taewon Yun, Hyeonseong Park, Jeonghwan Choi, Hayoon Park, et al.
Published
Jun 2026
Citations
0
Code
0 stars
Read summary Original
57

Independent research

Agents' Last Exam

Agents' Last Exam (ALE) is a benchmark introduced by UC Berkeley and collaborators to evaluate AI agents on long-horizon, economically valuable, real-world professional tasks with verifiable outcomes. Developed with 250+ industry experts, ALE covers 55 subfields across 13 industry clusters, grounded in the O*NET/SOC 2018 occupational taxonomy, and includes…

Yiyou Sun, Xinyang Han, Weichen Zhang, Yuanbo Pang, et al.
Published
Jun 2026
Citations
5
Code
936 stars
Read summary Original
58

Independent research

K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts

K-BROWSECOMP is a new benchmark for evaluating web-browsing agents in Korean contexts, consisting of 400 problems. The 300-problem K-BROWSECOMP-VERIFIED subset was manually constructed and validated by native Korean speakers. On this subset, frontier LLMs like GPT-5.5, DeepSeek-V4-Pro, and GLM-5.1 achieve only 30.00–45.67% accuracy, a significant drop from…

Nahyun Lee, Dongkeun Yoon, Guijin Son, Geewook Kim, et al.
Published
Jun 2026
Citations
1
Code
14 stars
Read summary Original
59

Independent research

Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses

Harness-1 is a 20B search agent trained with reinforcement learning inside a stateful search harness. The harness maintains environment-side working memory (candidate pool, importance-tagged curated set, evidence graph, verification records, compressed/deduplicated observations, budget-aware rendering), while the policy retains semantic decisions: what to…

Pengcheng Jiang, Zhiyi Shi, Kelly Hong, Xueqiang Xu, et al.
Published
Jun 2026
Citations
2
Code
941 stars
Read summary Original
60

Independent research

Where Do Deep-Research Agents Go Wrong? Span-Level Error Localization in Agent Trajectories

This paper introduces TELBENCH, a benchmark for span-level error localization in deep-research agent trajectories, and DRIFT, a claim-centric auditing framework. The authors collected 2,790 real trajectories from two agent frameworks (MiroFlow, OAgent), three backbone models (GPT-5, Gemini-2.5-Pro, Claude-Sonnet-4.5), and three benchmarks (GAIA, XBench,…

Jiaming Wang, Ziteng Feng, Jiangtao Wu, Ruihao Li, et al.
Published
Jun 2026
Citations
0
Code
22 stars
Read summary Original
61

Independent research

Masking Stale Observations Helps Search Agents -- Until It Doesn't: A Regime Map and Its Mechanism

This paper investigates when masking stale observations in long-horizon search agents helps or hurts performance. The authors systematically vary backbone models (4B to 284B parameters) and retrievers (BM25, Qwen3-Emb-8B, AgentIR-4B) on offline (BrowseComp-Plus) and live-web (GAIA, xBench-DeepSearch, BrowseComp-ZH) benchmarks. They find that the accuracy…

Haoxiang Zhang, Qixin Xu, Zhuofeng Li, Lei Zhang, et al.
Published
May 2026
Citations
3
Code
22 stars
Read summary Original
62

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
Read summary Original
63

Independent research

ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research

ResearchClawBench (RCBench) is a benchmark for evaluating end-to-end autonomous scientific research, comprising 40 tasks across 10 scientific domains (Astronomy, Chemistry, Earth Science, Energy, Information, Life, Material, Math, Neuroscience, Physics). Each task is derived from a real published paper, provides related literature and raw data, and hides…

Wanghan Xu, Shuo Li, Tianlin Ye, Qinglong Cao, et al.
Published
May 2026
Citations
3
Code
232 stars
Read summary Original
64

arXiv.org

GrepSeek: Training Search Agents for Direct Corpus Interaction

GrepSeek is a Direct Corpus Interaction (DCI) search agent that bypasses traditional retrieval indexes by having a compact LLM issue Unix shell commands (e.g., rg, grep) directly over a raw text corpus. It uses a two-stage training pipeline: first, a cold-start dataset is generated via an answer-aware Tutor (backward chaining with answer-leak masking) and…

Alireza Salemi, Chang Zeng, Atharva Nijasure, Jui-Hui Chung, et al.
Published
May 2026
Citations
5
Code
52 stars
Read summary Original
65

arXiv.org

OmniRetrieval: Unified Retrieval across Heterogeneous Knowledge Sources

OmniRetrieval is a framework for unified retrieval across heterogeneous knowledge sources (unstructured text, relational databases, RDF knowledge graphs, and labeled property graphs). Instead of collapsing sources into a shared representation, it takes a natural-language query, selects relevant sources via a long-context LLM reading all source descriptors,…

Jinheon Baek, Soyeong Jeong, Sangwoo Park, Woongyeong Yeo, et al.
Published
May 2026
Citations
0
Code
33 stars
Read summary Original
66

arXiv.org

A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks

The paper introduces TASTE (Task Synthesis from Tool Sequence Evolution), an automatic method for generating challenging agent benchmarks with broader tool-use coverage. TASTE reverses the traditional task construction process by first sampling diverse tool sequences using an Adaptive Contrastive n-gram model trained on LLM-judged validity signals, then…

Tomer Keren, Nitay Calderon, Asaf Yehudai, Yotam Perlitz, et al.
Published
May 2026
Citations
0
Code
4 stars
Read summary Original
67

MiniMax

The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence

The MiniMax-M2 series is a family of Mixture-of-Experts language models designed to achieve frontier performance with minimal activated parameters. The flagship M2 has 229.9B total parameters but only 9.8B activated per token, using a 62-layer decoder-only Transformer with 256 fine-grained experts, sigmoid gating, full multi-head attention with GQA, a…

Aili Chen, Aonian Li, Baichuan Zhou, Bangwei Gong, et al.
Published
May 2026
Citations
23
Code
Not linked
Read summary Original
68

arXiv.org

MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research

MOBILEGYM is a browser-hosted, lightweight simulation platform for mobile GUI agent research, targeting interaction fidelity without replicating proprietary backends. It enables verifiable outcome signals via deterministic state-based judging over structured JSON state, and scalable online RL through low-cost parallel rollouts. The environment state is…

Dingbang Wu, Rui Hao, Haiyang Wang, Shuzhe Wu, et al.
Published
May 2026
Citations
2
Code
750 stars
Read summary Original
69

arXiv.org

Macaron-A2UI: A Model for Generative UI in Personal Agents

The paper introduces Macaron-A2UI, a model for Generative UI in personal agents, which dynamically synthesizes executable UI actions alongside natural language to improve interaction efficiency. The authors build a large-scale corpus of over 14,000 samples from four dialogue sources (MultiWOZ, SGD, ESConv, AnnoMI) using a hybrid rule-and-LLM pipeline with…

Fancy Kong, Congjie Zheng, Murphy Zhuang, Rio Yang, et al.
Published
May 2026
Citations
1
Code
Not linked
Read summary Original
70

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
Read summary Original
71

arXiv.org

Foundation Protocol: A Coordination Layer for Agentic Society

The Foundation Protocol (FP) is a graph-first coordination layer for an emerging human-AI society, designed to unify heterogeneous entities (agents, tools, humans, institutions, organizations) and support multi-party organization, event-based collaboration, economic primitives (metering, receipts, settlement), and first-class policy, provenance, and audit.…

Bang Liu, Yongfeng Gu, Jiayi Zhang, Zhaoyang Yu, et al.
Published
May 2026
Citations
0
Code
52 stars
Read summary Original
72

arXiv.org

SciAtlas: A Large-Scale Knowledge Graph for Automated Scientific Research

SciAtlas is a large-scale, multi-disciplinary knowledge graph designed to support automated scientific research by organizing over 43 million papers from 26 disciplines into a structured topological network. It contains 157 million entities (including papers, authors, institutions, keywords, topics, and fields) and 3 billion triplets, with 9 entity types…

Shuofei Qiao, Yunxiang Wei, Jiazheng Fan, Bin Wu, et al.
Published
May 2026
Citations
0
Code
137 stars
Read summary Original
73

arXiv.org

OpenComputer: Verifiable Software Worlds for Computer-Use Agents

OpenComputer is a verifier-grounded framework for constructing verifiable software worlds for computer-use agents. It integrates four components: app-specific state verifiers, a self-evolving verification layer, a task-generation pipeline, and an evaluation harness. The framework covers 33 desktop applications and 1,000 finalized tasks. Experiments show…

Jinbiao Wei, Qianran Ma, Yilun Zhao, Xiao Zhou, et al.
Published
May 2026
Citations
6
Code
31 stars
Read summary Original
74

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
Read summary Original
75

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
Read summary Original
76

arXiv.org

CHI-Bench: Can AI Agents Automate End-to-End, Long-Horizon, Policy-Rich Healthcare Workflows?

χ-Bench is a benchmark for evaluating AI agents on long-horizon, policy-rich healthcare workflows across three domains: provider prior authorization, payer utilization management, and care management. It uses a high-fidelity simulator of 20 healthcare apps exposed via 87 MCP tools, guided by a 1,279-document managed-care operations handbook. Across 30…

Haolin Chen, Deon Metelski, Leon Qi, Tao Xia, et al.
Published
May 2026
Citations
4
Code
54 stars
Read summary Original
77

arXiv.org

Self-Distilled Agentic Reinforcement Learning

The paper introduces SDAR (Self-Distilled Agentic Reinforcement Learning), a method for post-training multi-turn LLM agents. It addresses issues with On-Policy Self-Distillation (OPSD), which provides dense token-level guidance but suffers from multi-turn instability and asymmetric trust in privileged guidance (e.g., retrieved skills). SDAR keeps GRPO as…

Zhengxi Lu, Zhiyuan Yao, Zhuowen Han, Zi-Han Wang, et al.
Published
May 2026
Citations
13
Code
328 stars
Read summary Original
78

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
Read summary Original
79

NVIDIA

MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models

MEMLENS is a new benchmark for evaluating multimodal long-term memory in large vision-language models (LVLMs) and memory-augmented agents. It comprises 789 questions across five memory abilities (information extraction, multi-session reasoning, temporal reasoning, knowledge update, and answer refusal) at four context lengths (32K–256K tokens). An…

Xiyu Ren, Zhaowei Wang, Yiming Du, Zhongwei Xie, et al.
Published
May 2026
Citations
0
Code
26 stars
Read summary Original
80

arXiv.org

Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining

The paper introduces Video2GUI, a fully automated framework that extracts grounded GUI interaction trajectories from unlabeled internet videos to address the scarcity of large-scale training data for GUI agents. The pipeline uses a coarse-to-fine filtering strategy: first, metadata-based classification (using a fine-tuned Qwen2.5-7B) reduces 500 million…

Weimin Xiong, Shuhao Gu, Bowen Ye, Zihao Yue, et al.
Published
May 2026
Citations
2
Code
38 stars
Read summary Original
81

arXiv.org

$π$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows

The paper introduces π-BENCH, a benchmark for evaluating proactive assistance in long-horizon personal assistant workflows. It comprises 100 multi-turn tasks across 5 domain-specific user personas (researcher, marketer, law trainee, pharmacist, financier), organized into 20-session episodes with cross-session dependencies. Each task begins with an…

Haoran Zhang, Luxin Xu, Zhilin Wang, Runquan Gui, et al.
Published
May 2026
Citations
1
Code
58 stars
Read summary Original
82

arXiv.org

Auditing Agent Harness Safety

This paper introduces HarnessAudit, a framework for auditing LLM agent execution harnesses, and HarnessAudit-Bench, a benchmark of 210 tasks across 8 domains. The framework evaluates full execution trajectories on three layers: boundary compliance (tool, resource, and information-flow violations), execution fidelity (action validity and task completion),…

Chengzhi Liu, Yichen Guo, Yepeng Liu, Yuzhe Yang, et al.
Published
May 2026
Citations
1
Code
51 stars
Read summary Original
83

arXiv.org

EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents

EVA-Bench is an end-to-end evaluation framework for voice agents that addresses two challenges: generating realistic simulated conversations and measuring quality across voice-specific failure modes. It uses bot-to-bot audio conversations with automatic simulation validation, and introduces two composite metrics: EVA-A (Accuracy) for task completion,…

Tara Bogavelli, Gabrielle Gauthier Melançon, Katrina Stankiewicz, Oluwanifemi Bamgbose, et al.
Published
May 2026
Citations
3
Code
192 stars
Read summary Original
84

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
Read summary Original
85

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
Not linked
Read summary Original
86

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
Read summary Original
87

arXiv.org

MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments

MCP-Cosmos is a framework that integrates generative World Models (WM) into the Model Context Protocol (MCP) ecosystem to enable predictive task automation. It uses a 'Bring Your Own World Model' (BYOWM) strategy, allowing agents to simulate state transitions and refine plans in a latent space before execution. The framework was evaluated on 24 MCP-Bench…

Giridhar Ganapavarapu, Dhaval Patel
Published
May 2026
Citations
1
Code
Not linked
Read summary Original
88

arXiv.org

HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents

HyperEyes is a parallel multimodal search agent that addresses inefficiencies in sequential search agents for multi-entity queries. It introduces a Unified Grounded Search (UGS) action space, fusing visual grounding and retrieval into a single atomic action to enable concurrent search. Training uses a two-stage approach: a Parallel-Amenable Data Synthesis…

Guankai Li, Jiabin Chen, Yi Xu, Xichen Zhang, et al.
Published
May 2026
Citations
4
Code
71 stars
Read summary Original
89

arXiv.org

MiA-Signature: Approximating Global Activation for Long-Context Understanding

The paper introduces Mindscape Activation Signature (MiA-Signature), a compact representation of the global activation pattern induced by a query over a semantic memory space, inspired by cognitive science theories of global ignition and partial access. It is constructed via submodular selection of high-level concepts (session summaries) that cover the…

Yuqing Li, Jiangnan Li, Mo Yu, Zheng Lin, et al.
Published
May 2026
Citations
1
Code
Not linked
Read summary Original
90

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
Read summary Original
91

arXiv.org

OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents

OpenSearch-VL is a fully open-source recipe for training multimodal deep search agents using agentic reinforcement learning. It addresses the lack of open high-quality training data, transparent trajectory synthesis, and detailed training recipes. The recipe includes a data curation pipeline using Wikipedia path sampling, fuzzy entity rewriting, and…

Shuang Chen, Kaituo Feng, Hangting Chen, Wenxuan Huang, et al.
Published
May 2026
Citations
10
Code
262 stars
Read summary Original
92

arXiv.org

OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories

OpenSeeker-v2 is a search agent developed by an academic team at Shanghai Jiao Tong University, demonstrating that a simple supervised fine-tuning (SFT) approach can rival resource-intensive industrial pipelines when trained on high-quality, high-difficulty trajectories. The authors introduce three data synthesis modifications: scaling the knowledge graph…

Yuwen Du, Rui Ye, Shuo Tang, Keduan Huang, et al.
Published
May 2026
Citations
6
Code
766 stars
Read summary Original
93

arXiv.org

Beyond Semantic Similarity: Rethinking Retrieval for Agentic Search via Direct Corpus Interaction

The paper introduces Direct Corpus Interaction (DCI), a retrieval paradigm where agents search raw corpora using terminal tools (grep, bash, file reads) instead of conventional retrievers. DCI bypasses embedding models and vector indexes, enabling fine-grained, compositional search. Evaluated on BrowseComp-Plus, multi-hop QA, and IR benchmarks,…

Zhuofeng Li, Haoxiang Zhang, Cong Wei, Pan Lu, et al.
Published
May 2026
Citations
21
Code
382 stars
Read summary Original
94

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
Read summary Original
95

arXiv.org

ClawGym: A Scalable Framework for Building Effective Claw Agents

ClawGym is a scalable framework for developing Claw-style personal agents, addressing the lack of systematic data synthesis, training, and evaluation in such environments. It introduces ClawGym-SynData, a dataset of 13.5K filtered tasks generated via persona-driven top-down and skill-grounded bottom-up pipelines, with realistic mock workspaces and hybrid…

Fei Bai, Huatong Song, Shuang Sun, Daixuan Cheng, et al.
Published
Apr 2026
Citations
3
Code
Not linked
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96

arXiv.org

From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company

The paper introduces OneManCompany (OMC), a framework that elevates multi-agent systems to an organizational level by decoupling agent capabilities from organizational structure. OMC encapsulates skills, tools, and configurations into portable 'Talents' that run on heterogeneous backends via 'Containers', with six typed organizational interfaces. A…

Zhengxu Yu, Yu Fu, Zhiyuan He, Yuxuan Huang, et al.
Published
Apr 2026
Citations
0
Code
376 stars
Read summary Original
97

arXiv.org

OpenGame: Open Agentic Coding for Games

OpenGame is an open-source agentic framework for end-to-end web game creation from natural-language specifications. It introduces Game Skill, comprising a Template Skill that evolves a library of project skeletons and a Debug Skill that maintains a living protocol of verified fixes, to address cross-file inconsistencies and integration errors. The…

Yilei Jiang, Jinyuan Hu, Qianyin Xiao, Yaozhi Zheng, et al.
Published
Apr 2026
Citations
5
Code
2.8K stars
Read summary Original
98

arXiv.org

Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence

Agent-World is a self-evolving training arena for general-purpose AI agents, developed by Renmin University of China and ByteDance Seed. It addresses two key bottlenecks in agent training: scalable realism in environment synthesis and continuous self-improvement. The system has two main components: (1) Agentic Environment-Task Discovery, which autonomously…

Guanting Dong, Junting Lu, Junjie Huang, Wanjun Zhong, et al.
Published
Apr 2026
Citations
14
Code
Not linked
Read summary Original
99

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
Read summary Original
100

arXiv.org

ClawGUI: A Unified Framework for Training, Evaluating, and Deploying GUI Agents

ClawGUI is an open-source framework that integrates online reinforcement learning (RL) training, standardized evaluation, and real-device deployment for GUI agents. It addresses three gaps: closed training pipelines, misaligned evaluation protocols, and a broken deployment loop. ClawGUI-RL provides the first open-source RL infrastructure supporting…

Fei Tang, Zhiqiong Lu, Boxuan Zhang, Weiming Lu, et al.
Published
Apr 2026
Citations
10
Code
Not linked
Read summary Original