The Year in AI Papers
The year/Topics/Agents

Topic area

Agents

Every collection across agents.

Papers
275
Research labs
10
Official code
214

201–250 of 275 papers in this topic area

201

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
Read summary Original
202

arXiv.org

Guided Self-Evolving LLMs with Minimal Human Supervision

The paper introduces R-FEW, a guided self-evolving framework for large language models (LLMs) that uses minimal human supervision to achieve stable and controllable self-improvement. R-FEW addresses issues like concept drift and diversity collapse in unguided self-play methods (e.g., R-Zero) by integrating a few-shot grounded Challenger and an online…

Wenhao Yu, Zhenwen Liang, Chengsong Huang, Kishan Panaganti, et al.
Published
Dec 2025
Citations
31
Code
Not linked
Read summary Original
203

arXiv.org

How Far Are We from Genuinely Useful Deep Research Agents?

This paper introduces FINDER, a benchmark for evaluating Deep Research Agents (DRAs) on report generation, and DEFT, a failure taxonomy for diagnosing DRA errors. FINDER consists of 100 human-curated research tasks with 419 structured checklist items, refining the DeepResearch Bench by adding detailed prompts and checklists. DEFT, built via grounded theory…

Dingling Zhang, He Zhu, Jincheng Ren, Kangqi Song, et al.
Published
Dec 2025
Citations
9
Code
66 stars
Read summary Original
204

NVIDIA

ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration

The paper introduces ToolOrchestra, a method for training small language models as orchestrators that coordinate diverse tools, including basic tools (web search, code interpreter), specialized LLMs (math, coding), and generalist LLMs (GPT-5, Claude Opus 4.1). The resulting 8B-parameter Orchestrator model is trained end-to-end with reinforcement learning…

Hongjin Su, Shizhe Diao, Ximing Lu, Mingjie Liu, et al.
Published
Nov 2025
Citations
32
Code
752 stars
Read summary Original
205

arXiv.org

DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research

The paper introduces DR Tulu-8B, the first fully open model trained end-to-end for long-form deep research tasks, using a new method called Reinforcement Learning with Evolving Rubrics (RLER). RLER constructs and maintains rubrics that co-evolve with the policy model during training, incorporating newly explored information from search and contrasting…

Rulin Shao, Akari Asai, Shannon Zejiang Shen, Hamish Ivison, et al.
Published
Nov 2025
Citations
75
Code
692 stars
Read summary Original
206

arXiv.org

AutoEnv: Automated Environments for Measuring Cross-Environment Agent Learning

The paper introduces AUTOENV, an automated framework for generating heterogeneous agent environments by treating them as factorizable distributions over transitions, observations, and rewards, with three abstraction layers (BaseEnv, ObsEnv, SkinEnv) and a three-stage verification pipeline. Using AUTOENV, the authors construct AUTOENV-36, a dataset of 36…

Jiayi Zhang, Yiran Peng, Fanqi Kong, Cheng Yang, et al.
Published
Nov 2025
Citations
13
Code
67 stars
Read summary Original
207

arXiv.org

Deep Research: A Systematic Survey

This survey provides a comprehensive overview of Deep Research (DR) systems, which combine large language models with external tools to perform complex, open-ended research tasks. The authors formalize a three-stage roadmap for DR: Agentic Search, Integrated Research, and Full-stack AI Scientist. They identify four key components of DR systems: query…

Zhengliang Shi, Yiqun Chen, Haitao Li, Weiwei Sun, et al.
Published
Nov 2025
Citations
15
Code
322 stars
Read summary Original
208

arXiv.org

From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence

This survey comprehensively examines the lifecycle of code-focused large language models (LLMs), from data curation and pre-training to post-training, alignment, and deployment as autonomous agents. It analyzes both general-purpose LLMs (e.g., GPT-4, Claude, LLaMA) and code-specialized models (e.g., StarCoder, Code LLaMA, DeepSeek-Coder, QwenCoder),…

Jian Yang, Xianglong Liu, Weifeng Lv, Ken Deng, et al.
Published
Nov 2025
Citations
12
Code
Not linked
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209

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
Read summary Original
210

arXiv.org

Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning

Agent0 is a fully autonomous framework that evolves LLM agents from zero external data by combining tool-integrated reasoning with multi-step co-evolution. It initializes two agents from the same base LLM: a curriculum agent trained via GRPO to generate frontier tasks, rewarded by the executor's uncertainty (self-consistency), tool-use frequency, and a…

Peng Xia, Kaide Zeng, Jiaqi Liu, Can Qin, et al.
Published
Nov 2025
Citations
54
Code
1.2K stars
Read summary Original
211

arXiv.org

GeoVista: Web-Augmented Agentic Visual Reasoning for Geolocalization

The paper introduces GeoVista, an agentic multimodal model for geolocalization that integrates image zoom-in and web-search tools within a dynamic reasoning loop. The authors curate GeoBench, a benchmark of 1,142 high-resolution images (photos, panoramas, satellite) from 66 countries, with multi-level labels (country, province, city) and haversine distance…

Yikun Wang, Zuyan Liu, Ziyi Wang, Han Hu, et al.
Published
Nov 2025
Citations
8
Code
277 stars
Read summary Original
212

Meta AI

What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity

This paper investigates the role of ideation diversity in AI research agents' performance on MLE-bench, a benchmark of 75 Kaggle machine learning tasks. The authors analyzed 11,000 agent trajectories from 6 LLM backbones and 3 scaffolds, measuring ideation diversity via Shannon entropy of model architectures in initial drafts. They found that…

Alexis Audran-Reiss, Jordi Armengol-Estapé, Karen Hambardzumyan, Amar Budhiraja, et al.
Published
Nov 2025
Citations
5
Code
Not linked
Read summary Original
213

arXiv.org

Computer-Use Agents as Judges for Generative User Interface

The paper introduces AUI-Gym, a benchmark for automatic GUI development and testing, and a Coder-CUA collaboration framework. AUI-Gym includes 52 applications across six domains (app, landing, game, interactive, tool, utility) with 1,560 tasks synthesized by GPT-5 and human-validated. Each task is paired with a rule-based verifier that programmatically…

Kevin Qinghong Lin, Siyuan Hu, Linjie Li, Zhengyuan Yang, et al.
Published
Nov 2025
Citations
6
Code
45 stars
Read summary Original
214

Conference of the European Chapter of the Association for Computational Linguistics

Multimodal Evaluation of Russian-language Architectures

The paper introduces MERA Multi, the first open multimodal evaluation benchmark for Russian-language architectures, addressing the lack of such benchmarks for Slavic languages. It comprises 18 instruction-based tasks across text, image, audio, and video modalities, built on a unified taxonomy of multimodal abilities. The benchmark includes 11 private…

Artem Chervyakov, Ulyana Isaeva, Anton Emelyanov, Artem Safin, et al.
Published
Nov 2025
Citations
3
Code
Not linked
Read summary Original
215

arXiv.org

MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling

MiroThinker v1.0 is an open-source research agent that introduces interaction scaling as a third performance dimension, alongside model size and context length. It trains models via reinforcement learning to handle deeper and more frequent agent–environment interactions, using a 256K context window and up to 600 tool calls per task. The 72B variant…

MiroMind Team, Song Bai, Lidong Bing, Carson Chen, et al.
Published
Nov 2025
Citations
62
Code
8.4K stars
Read summary Original
216

arXiv.org

Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds

Lumine, developed by ByteDance Seed, is the first open recipe for building generalist agents that complete hours-long missions in real time within 3D open-world environments. Built on Qwen2-VL-7B-Base, it processes raw pixels at 5 Hz and generates keyboard-mouse actions at 30 Hz using action chunking, with a hybrid thinking mode that invokes reasoning only…

Weihao Tan, Xiangyang Li, Yunhao Fang, Heyuan Yao, et al.
Published
Nov 2025
Citations
13
Code
Not linked
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217

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
Read summary Original
218

arXiv.org

Grounding Computer Use Agents on Human Demonstrations

The paper introduces GROUNDCUA, a large-scale desktop grounding dataset built from expert human demonstrations, covering 87 applications across 12 categories with 56K screenshots and over 3.56M human-verified element annotations. The dataset features high-resolution images (0.39–7.0 megapixels), dense annotations (average 64 per screenshot), and small…

Aarash Feizi, Shravan Nayak, Xiangru Jian, Kevin Qinghong Lin, et al.
Published
Nov 2025
Citations
9
Code
134 stars
Read summary Original
219

Independent research

IterResearch: Rethinking Long-Horizon Agents with Interaction Scaling

IterResearch is a novel iterative deep-research paradigm that addresses the limitations of mono-contextual approaches, which accumulate all information in a single expanding context window, leading to context suffocation and noise contamination. It uses an MDP-inspired architecture with strategic workspace reconstruction, maintaining an evolving report as…

Guoxin Chen, Zile Qiao, Xuanzhong Chen, Donglei Yu, et al.
Published
Nov 2025
Citations
14
Code
Not linked
Read summary Original
220

Annual Meeting of the Association for Computational Linguistics

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs

GroupRank is a novel groupwise reranking paradigm for LLM-based passage retrieval that balances the efficiency of pointwise methods with the accuracy of listwise approaches. It partitions candidate documents into small groups for parallel cross-document comparison, avoiding context-window limits and reducing latency. Training uses an answer-free data…

Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, et al.
Published
Nov 2025
Citations
3
Code
259 stars
Read summary Original
221

arXiv.org

Scaling Agent Learning via Experience Synthesis

DreamGym is a unified framework for scaling reinforcement learning (RL) for LLM-based agents by synthesizing diverse experiences, addressing the high costs and limitations of real-environment rollouts. It uses a reasoning-based experience model that operates in an abstract textual state space, generating consistent state transitions and reward signals via…

Zhaorun Chen, Zhuokai Zhao, Kai Zhang, Bo Liu, et al.
Published
Nov 2025
Citations
24
Code
Not linked
Read summary Original
222

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
Read summary Original
223

Independent research

Tongyi DeepResearch Technical Report

Tongyi DeepResearch is an open-source agentic large language model designed for long-horizon, deep information-seeking research tasks. It uses an end-to-end training framework combining agentic mid-training (two-stage continual pre-training) and agentic post-training (SFT and reinforcement learning), supported by a fully automated synthetic data pipeline…

Tongyi DeepResearch Team, Baixuan Li, Bo Zhang, Dingchu Zhang, et al.
Published
Oct 2025
Citations
18
Code
Not linked
Read summary Original
224

arXiv.org

AgentFold: Long-Horizon Web Agents with Proactive Context Management

AgentFold is a new web agent paradigm that addresses the trade-off between context saturation in ReAct-based agents and information loss from fixed summarization. It treats context as a dynamic workspace with Multi-Scale State Summaries and a Latest Interaction, using a 'folding' operation with two modes: Granular Condensation (preserving fine details) and…

Rui Ye, Zhongwang Zhang, Kuan Li, Huifeng Yin, et al.
Published
Oct 2025
Citations
65
Code
Not linked
Read summary Original
225

arXiv.org

InteractComp: Evaluating Search Agents With Ambiguous Queries

The paper introduces INTERACTCOMP, a benchmark for evaluating whether search agents can recognize and resolve ambiguous queries through interaction. It contains 210 expert-curated questions across 9 domains, built using a target-distractor methodology where questions use only shared attributes of a lesser-known target and a popular alternative, making them…

Mingyi Deng, Lijun Huang, Yani Fan, Fanqi Kong, et al.
Published
Oct 2025
Citations
11
Code
22 stars
Read summary Original
226

Annual Meeting of the Association for Computational Linguistics

OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows

This paper introduces OS-Sentinel, a hybrid framework for detecting safety risks in mobile GUI agents powered by Vision-Language Models (VLMs). The authors first construct MobileRisk-Live, a dynamic Android emulator sandbox that records GUI observations, agent actions, and a System State Trace (T_sys) capturing underlying system metadata. From this, they…

Qiushi Sun, Mukai Li, Zhoumianze Liu, Zhihui Xie, et al.
Published
Oct 2025
Citations
15
Code
49 stars
Read summary Original
227

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
Read summary Original
228

arXiv.org

Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents

Game-TARS is a generalist game agent from ByteDance Seed that uses a unified, scalable action space based on native keyboard and mouse inputs, enabling large-scale continual pre-training across OS, web, and simulation games. It was pre-trained on over 500B tokens, including game trajectories, cross-domain agent data, and general multimodal data. Key…

Zihao Wang, Xujing Li, Yining Ye, Junjie Fang, et al.
Published
Oct 2025
Citations
16
Code
Not linked
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229

arXiv.org

ReCode: Unify Plan and Action for Universal Granularity Control

The paper introduces ReCode, a novel paradigm for LLM-based agents that unifies planning and action within a single code representation to achieve universal control of decision granularity. ReCode treats high-level plans as abstract placeholder functions, which are recursively decomposed into finer-grained sub-functions until reaching executable primitive…

Zhaoyang Yu, Jiayi Zhang, Huixue Su, Yufan Zhao, et al.
Published
Oct 2025
Citations
11
Code
561 stars
Read summary Original
230

arXiv.org

JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence

The paper introduces JANUSCODER, a suite of foundational models for multimodal code intelligence, establishing a unified visual-programmatic interface. It addresses the scarcity of high-quality multimodal code data by developing a data synthesis toolkit that leverages synergies between data modalities. This toolkit enables the creation of JANUSCODE-800K,…

Qiushi Sun, Jingyang Gong, Yang Liu, Qiaosheng Chen, et al.
Published
Oct 2025
Citations
7
Code
78 stars
Read summary Original
231

The Web Conference

DeepAgent: A General Reasoning Agent with Scalable Toolsets

DeepAgent is an end-to-end deep reasoning agent that unifies autonomous thinking, tool discovery, and action execution within a single reasoning process, addressing limitations of predefined workflow-based agents. It introduces an autonomous memory folding mechanism that compresses interaction history into structured episodic, working, and tool memories,…

Xiaoxi Li, Wenxiang Jiao, Jiarui Jin, Guanting Dong, et al.
Published
Oct 2025
Citations
63
Code
1.1K stars
Read summary Original
232

Annual Meeting of the Association for Computational Linguistics

Human-Agent Collaborative Paper-to-Page Crafting

AutoPage is a multi-agent system that automatically converts academic papers into interactive project webpages. It uses a coarse-to-fine pipeline with three phases: narrative planning, multimodal content generation, and interactive page rendering. Dedicated 'Checker' agents verify each step against the source paper to prevent hallucination, and optional…

Qianli Ma, Siyu Wang, Yilin Chen, Yinhao Tang, et al.
Published
Oct 2025
Citations
6
Code
169 stars
Read summary Original
233

arXiv.org

DeepAnalyze: Agentic Large Language Models for Autonomous Data Science

DeepAnalyze-8B is the first agentic large language model designed for autonomous data science, capable of completing the entire pipeline from raw data sources to analyst-grade research reports. It addresses the limitations of previous workflow-based agents by enabling autonomous orchestration and adaptive optimization through a curriculum-based agentic…

Shaolei Zhang, Ju Fan, Meihao Fan, Guoliang Li, et al.
Published
Oct 2025
Citations
43
Code
4.4K stars
Read summary Original
234

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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235

arXiv.org

RAG-Anything: All-in-One RAG Framework

RAG-Anything is a unified framework for multimodal Retrieval-Augmented Generation (RAG) that addresses the limitation of existing text-only RAG systems in handling real-world documents containing text, images, tables, and equations. The framework introduces dual-graph construction, which builds a cross-modal knowledge graph for non-textual content and a…

Zirui Guo, Xubin Ren, Lingrui Xu, Jiahao Zhang, et al.
Published
Oct 2025
Citations
18
Code
23K stars
Read summary Original
236

arXiv.org

DITING: A Multi-Agent Evaluation Framework for Benchmarking Web Novel Translation

The paper introduces DITING, the first comprehensive evaluation framework for web novel translation, assessing narrative and cultural fidelity across six dimensions: idiom translation, lexical ambiguity, terminology localization, tense consistency, zero-pronoun resolution, and cultural safety, supported by over 18K expert-annotated Chinese–English sentence…

Enze Zhang, Jiaying Wang, Mengxi Xiao, Jifei Liu, et al.
Published
Oct 2025
Citations
4
Code
12 stars
Read summary Original
237

arXiv.org

Agent Learning via Early Experience

The paper introduces the 'early experience' paradigm for training language agents, which uses the agent's own actions and resulting future states as supervision without external reward signals. This addresses limitations of both supervised fine-tuning (SFT) on expert data, which is costly and generalizes poorly, and reinforcement learning (RL), which is…

Kai Zhang, Xiangchao Chen, Bo Liu, Tianci Xue, et al.
Published
Oct 2025
Citations
57
Code
136 stars
Read summary Original
238

arXiv.org

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

The paper introduces AGENTFLOW, a trainable agentic framework for planning and tool use that coordinates four specialized modules—planner, executor, verifier, and generator—through an evolving memory. Unlike monolithic tool-integrated reasoning models, AGENTFLOW optimizes its planner on-policy within the multi-turn loop. To address long-horizon credit…

Zhuofeng Li, Haoxiang Zhang, Seungju Han, Sheng Liu, et al.
Published
Oct 2025
Citations
51
Code
2K stars
Read summary Original
239

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
Read summary Original
240

OpenAI

GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks

This paper introduces GDPval, a benchmark for evaluating AI models on real-world, economically valuable tasks. It covers 44 occupations across the top 9 U.S. GDP sectors, with tasks created by industry experts averaging 14 years of experience. The benchmark includes 1,320 tasks in the full set and a 220-task gold subset, graded via human expert pairwise…

Tejal Patwardhan, Rachel Dias, Elizabeth Proehl, Grace Kim, et al.
Published
Oct 2025
Citations
108
Code
Not linked
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241

arXiv.org

StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?

STOCKBENCH is a new benchmark for evaluating LLM agents in realistic, multi-month stock trading environments, using data from March to June 2025 to avoid contamination. Agents receive daily prices, fundamentals, and news, and make sequential buy, sell, or hold decisions. Performance is measured by cumulative return, maximum drawdown, and Sortino ratio.…

Yanxu Chen, Zijun Yao, Yantao Liu, Amy Xin, et al.
Published
Oct 2025
Citations
26
Code
177 stars
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242

arXiv.org

GEM: A Gym for Agentic LLMs

GEM (General Experience Maker) is an open-source environment simulator for agentic LLMs, analogous to OpenAI-Gym for traditional RL. It provides a standardized environment-agent interface with reset() and step(), asynchronous vectorized execution, and modular wrappers. GEM includes diverse tasks (Math, Code, Game, QA, ReasoningGym, Terminal) and tools…

Zichen Liu, Anya Sims, Keyu Duan, Changyu Chen, et al.
Published
Oct 2025
Citations
11
Code
504 stars
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243

arXiv.org

Fathom-DeepResearch: Unlocking Long Horizon Information Retrieval and Synthesis for SLMs

Fathom-DeepResearch is an agentic system for long-horizon information retrieval and synthesis, composed of two 4B-parameter models built on Qwen3-4B. Fathom-Search-4B handles evidence-based investigation via live web search and targeted page querying, trained with DUETQA (a ~5K-sample dataset generated via multi-agent self-play ensuring strict web-search…

Shreyas Singh, Kunal Singh, Pradeep Moturi
Published
Sep 2025
Citations
4
Code
63 stars
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244

arXiv.org

MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use

MCPMark is a benchmark designed to evaluate LLM agents' use of the Model Context Protocol (MCP) in realistic, comprehensive workflows. It comprises 127 tasks across five MCP servers (Filesystem, Notion, Playwright, GitHub, PostgreSQL), each with curated initial states and programmatic verification scripts. Tasks require diverse CRUD operations and average…

Zijian Wu, Xiangyan Liu, Xinyuan Zhang, Lingjun Chen, et al.
Published
Sep 2025
Citations
25
Code
456 stars
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245

arXiv.org

LIMI: Less is More for Agency

The paper introduces LIMI (Less Is More for Intelligent Agency), which challenges the assumption that more training data yields better agentic AI. LIMI demonstrates that sophisticated agentic intelligence can emerge from minimal, strategically curated demonstrations. Using only 78 carefully designed training samples focused on vibe coding and research…

Yang Xiao, Mohan Jiang, Jie Sun, Keyu Li, et al.
Published
Sep 2025
Citations
17
Code
162 stars
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246

arXiv.org

RPG: A Repository Planning Graph for Unified and Scalable Codebase Generation

The paper introduces the Repository Planning Graph (RPG), a structured representation that unifies proposal-level and implementation-level planning for generating complete software repositories from high-level specifications. RPG encodes capabilities, file structures, data flows, and functions as nodes and edges, replacing ambiguous natural language plans.…

Jane Luo, Xin Zhang, Steven Liu, Jie Wu, et al.
Published
Sep 2025
Citations
12
Code
Not linked
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247

arXiv.org

ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform Data

ScaleCUA introduces a large-scale, cross-platform dataset and model family for computer use agents (CUAs), addressing data scarcity via a dual-loop pipeline combining automated agents and human experts across six platforms (Windows, macOS, Linux, Android, iOS, Web). The dataset includes 471K GUI understanding examples, 17.1M grounding annotations, and 19K…

Zhaoyang Liu, Jingjing Xie, Zichen Ding, Zehao Li, et al.
Published
Sep 2025
Citations
46
Code
1.1K stars
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248

arXiv.org

ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization

ReSum is a plug-and-play paradigm that enables LLM-based web agents to perform unbounded exploration by periodically invoking an external summarization tool to condense interaction histories into compact summaries, addressing the conflict between extensive exploration and limited context windows. The authors developed ReSumTool-30B, a specialized summary…

Xixi Wu, Kuan Li, Yida Zhao, Liwen Zhang, et al.
Published
Sep 2025
Citations
95
Code
Not linked
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249

arXiv.org

WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research

WebWeaver is a dual-agent framework for open-ended deep research (OEDR), addressing limitations of static pipelines and monolithic generation. It comprises a planner that iteratively interleaves evidence acquisition with outline optimization, producing a citation-grounded outline linked to a memory bank, and a writer that performs hierarchical,…

Zijian Li, Xin Guan, Bo Zhang, Shen Huang, et al.
Published
Sep 2025
Citations
39
Code
Not linked
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250

Annual Meeting of the Association for Computational Linguistics

Towards General Agentic Intelligence via Environment Scaling

This paper introduces AgentScaler, a family of models trained to advance general agentic intelligence through systematic environment scaling. The authors propose a two-stage pipeline: first, they automatically construct diverse, fully simulated environments by collecting over 30,000 APIs, organizing them into domains via community detection, and…

Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, et al.
Published
Sep 2025
Citations
48
Code
Not linked
Read summary Original