The Year in AI Papers
The year/Topics/Agents

Topic area

Agents

Every collection across agents.

Papers
275
Research labs
10
Official code
214

101–150 of 275 papers in this topic area

101

Qwen

OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation

OCCUBENCH is a benchmark for evaluating AI agents on real-world professional tasks across 100 scenarios, 65 domains, and 10 industry categories. It uses Language Environment Simulators (LESs), where an LLM simulates domain-specific tool responses, enabling evaluation in domains without public environments. A multi-agent pipeline synthesizes 382 solvable,…

Xiaomeng Hu, Yinger Zhang, Fei Huang, Jianhong Tu, et al.
Published
Apr 2026
Citations
1
Code
21 stars
Read summary Original
102

arXiv.org

ClawBench: Can AI Agents Complete Everyday Online Tasks?

ClawBench is a benchmark of 153 everyday online tasks across 144 live websites and 15 categories, focusing on write-heavy, state-changing workflows like purchases, reservations, and applications. Unlike sandboxed benchmarks, it evaluates agents on production sites with dynamic content and anti-bot defenses, using final-request interception to block…

Yuxuan Zhang, Yubo Wang, Yipeng Zhu, Penghui Du, et al.
Published
Apr 2026
Citations
19
Code
550 stars
Read summary Original
103

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
Read summary Original
104

arXiv.org

GameWorld: Towards Standardized and Verifiable Evaluation of Multimodal Game Agents

GameWorld is a benchmark for evaluating multimodal large language models (MLLMs) as game agents in browser environments. It includes 34 diverse games across five genres (Runner, Arcade, Platformer, Puzzle, Simulation) and 170 tasks, each with outcome-based, state-verifiable metrics computed from serialized gameAPI state. The benchmark supports two agent…

Mingyu Ouyang, Siyuan Hu, Kevin Qinghong Lin, Hwee Tou Ng, et al.
Published
Apr 2026
Citations
10
Code
216 stars
Read summary Original
105

Independent research

Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents

Claw-Eval is an end-to-end evaluation suite for LLM-based autonomous agents, addressing gaps in trajectory-opaque grading, underspecified safety/robustness, and narrow task coverage. It comprises 300 human-verified tasks across 9 categories (General, Multimodal, Multi-turn Dialogue) with 2,159 rubric items. Each run is audited via three evidence channels:…

Bowen Ye, Rang Li, Qibin Yang, Yuanxin Liu, et al.
Published
Apr 2026
Citations
38
Code
741 stars
Read summary Original
106

arXiv.org

Memory Intelligence Agent

The paper introduces the Memory Intelligence Agent (MIA), a framework for deep research agents that combines a Manager-Planner-Executor architecture with dual memory systems. The Memory Manager stores compressed historical trajectories as non-parametric memory, while the Planner, trained via alternating reinforcement learning, serves as parametric memory.…

Jingyang Qiao, Weicheng Meng, Yu Cheng, Zhihang Lin, et al.
Published
Apr 2026
Citations
4
Code
792 stars
Read summary Original
107

arXiv.org

InCoder-32B-Thinking: Industrial Code World Model for Thinking

The paper introduces InCoder-32B-Thinking, a 32B-parameter code model trained to reason about industrial software development (chip design, GPU optimization, embedded systems, 3D modeling) by integrating an Error-driven Chain-of-Thought (ECoT) synthesis framework and an Industrial Code World Model (ICWM). ECoT generates reasoning traces by modeling…

Jian Yang, Wei Zhang, Jiajun Wu, Junhang Cheng, et al.
Published
Apr 2026
Citations
3
Code
Not linked
Read summary Original
108

arXiv.org

SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization

SKILL0 is a reinforcement learning framework that internalizes agent skills into model parameters, eliminating the need for inference-time skill retrieval. It uses in-context RL during training, where skills are provided as visual context and progressively withdrawn via a Dynamic Curriculum that evaluates each skill's on-policy helpfulness. Experiments on…

Zhengxi Lu, Zhiyuan Yao, Jinyang Wu, Chengcheng Han, et al.
Published
Apr 2026
Citations
47
Code
360 stars
Read summary Original
109

arXiv.org

CORAL: Towards Autonomous Multi-Agent Evolution for Open-Ended Discovery

CORAL is a framework for autonomous multi-agent evolution on open-ended problems, replacing fixed evolutionary search heuristics with long-running LLM agents that control retrieval, proposal, evaluation, and knowledge accumulation. It uses shared persistent memory (attempts, notes, skills), asynchronous multi-agent execution, and heartbeat-based…

Ao Qu, Han Zheng, Zijian Zhou, Yihao Yan, et al.
Published
Apr 2026
Citations
29
Code
873 stars
Read summary Original
110

arXiv.org

Embarrassingly Simple Self-Distillation Improves Code Generation

The paper introduces Simple Self-Distillation (SSD), a method that improves LLM code generation by sampling solutions from the model itself with specific temperature and truncation settings, then fine-tuning on those raw, unverified outputs via standard supervised fine-tuning. SSD requires no verifier, teacher model, or reinforcement learning. It improves…

Ruixiang Zhang, Richard He Bai, Huangjie Zheng, Navdeep Jaitly, et al.
Published
Apr 2026
Citations
28
Code
798 stars
Read summary Original
111

arXiv.org

Terminal Agents Suffice for Enterprise Automation

The paper argues that minimal terminal-based coding agents, which interact directly with platform APIs via a terminal and filesystem, can match or outperform more complex agent architectures for enterprise automation. The authors introduce StarShell, a terminal agent, and compare it against GUI-driven web agents and MCP-based tool-augmented agents across…

Patrice Bechard, Orlando Marquez Ayala, Emily Chen, Jordan Skelton, et al.
Published
Mar 2026
Citations
5
Code
Not linked
Read summary Original
112

arXiv.org

Gen-Searcher: Reinforcing Agentic Search for Image Generation

Gen-Searcher is the first trained multimodal deep search agent for image generation, addressing the limitation of frozen internal knowledge in text-to-image models. It performs multi-hop web search and reasoning to collect textual knowledge and reference images for grounded generation. The authors built a data pipeline producing two training datasets…

Kaituo Feng, Manyuan Zhang, Shuang Chen, Yunlong Lin, et al.
Published
Mar 2026
Citations
16
Code
381 stars
Read summary Original
113

Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval

Learning to Retrieve from Agent Trajectories

This paper introduces LRAT (Learning to Retrieve from Agent Trajectories), a framework for training retrieval models directly from the interaction data of LLM-powered search agents. The authors argue that traditional human-centric retrieval training is misaligned with agentic search, where agents issue intermediate queries and consume results in multi-turn…

Yuqi Zhou, Sunhao Dai, Changle Qu, Liang Pang, et al.
Published
Mar 2026
Citations
2
Code
56 stars
Read summary Original
114

arXiv.org

MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome

MiroEval is a benchmark and evaluation framework for deep research systems, comprising 100 tasks (70 text-only, 30 multimodal) grounded in real user needs and built via a dual-path pipeline supporting periodic updates. The evaluation suite assesses systems along three complementary dimensions: adaptive synthesis quality with task-specific rubrics, agentic…

Fangda Ye, Yuxin Hu, Pengxiang Zhu, Yibo Li, et al.
Published
Mar 2026
Citations
4
Code
46 stars
Read summary Original
115

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
Read summary Original
116

arXiv.org

QuanBench+: A Unified Multi-Framework Benchmark for LLM-Based Quantum Code Generation

QuanBench+ is a unified benchmark for evaluating LLMs on quantum code generation across Qiskit, PennyLane, and Cirq, holding task intent constant while varying the framework. It includes 42 tasks spanning quantum algorithms, gate decomposition, and state preparation, and uses executable functional tests with Pass@1, Pass@5, and KL-divergence-based…

Ali Slim, Haydar Hamieh, Jawad Kotaich, Yehya Ghosn, et al.
Published
Mar 2026
Citations
1
Code
6 stars
Read summary Original
117

arXiv.org

CUA-Suite: Massive Human-annotated Video Demonstrations for Computer-Use Agents

CUA-SUITE is a large-scale ecosystem of human-annotated video demonstrations for training and evaluating desktop computer-use agents (CUAs). It addresses the scarcity of continuous, high-quality human demonstration videos, which recent work identifies as critical for scaling agents. The core resource, VIDEOCUA, provides approximately 10,000…

Xiangru Jian, Shravan Nayak, Kevin Qinghong Lin, Aarash Feizi, et al.
Published
Mar 2026
Citations
3
Code
Not linked
Read summary Original
118

arXiv.org

ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watchers

ClawKeeper is a comprehensive security framework for OpenClaw, an open-source autonomous agent runtime, addressing critical vulnerabilities such as sensitive data leakage, privilege escalation, and malicious skill execution. It integrates three complementary protection layers: skill-based protection at the instruction level, plugin-based runtime…

Songyang Liu, Chaozhuo Li, Chenxu Wang, Jinyu Hou, et al.
Published
Mar 2026
Citations
18
Code
1K stars
Read summary Original
119

arXiv.org

From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents

This survey reviews methods for optimizing workflows in large language model (LLM)-based agentic systems, which are modeled as agentic computation graphs (ACGs). The authors propose a taxonomy based on when workflow structure is determined, distinguishing static methods (fixed reusable templates optimized offline) from dynamic methods (structure selected,…

Ling Yue, Kushal Raj Bhandari, Ching-Yun Ko, Dhaval Patel, et al.
Published
Mar 2026
Citations
14
Code
73 stars
Read summary Original
120

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
Read summary Original
121

arXiv.org

MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild

MetaClaw is a continual meta-learning framework that enables deployed LLM agents to evolve through two complementary mechanisms: skill-driven fast adaptation and opportunistic policy optimization. Skill-driven fast adaptation analyzes failure trajectories to synthesize new behavioral instructions, which are immediately injected into the agent's prompt…

Peng Xia, Jianwen Chen, Xinyu Yang, Haoqin Tu, et al.
Published
Mar 2026
Citations
20
Code
3.5K stars
Read summary Original
122

arXiv.org

OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis

OpenResearcher is a fully open pipeline for synthesizing long-horizon deep research trajectories. It decouples one-time corpus bootstrapping from multi-turn trajectory synthesis, running the search-and-browse loop entirely offline over a 15M-document corpus using three browser primitives: search, open, and find. Using GPT-OSS-120B as the teacher, it…

Zhuofeng Li, Dongfu Jiang, Xueguang Ma, Haoxiang Zhang, et al.
Published
Mar 2026
Citations
20
Code
1.1K stars
Read summary Original
123

arXiv.org

SocialOmni: Benchmarking Audio-Visual Social Interactivity in Omni Models

SocialOmni is a new benchmark for evaluating social interactivity in omni-modal large language models (OLMs), addressing the gap left by static, accuracy-centric benchmarks. It evaluates three dimensions: who is speaking (speaker identification), when to interject (turn-taking timing), and how to phrase interruptions (response generation). The benchmark…

Tianyu Xie, Jinfa Huang, Yuexiao Ma, Rongfang Luo, et al.
Published
Mar 2026
Citations
5
Code
46 stars
Read summary Original
124

arXiv.org

Online Experiential Learning for Language Models

The paper introduces Online Experiential Learning (OEL), a framework for large language models to continuously improve from their own deployment experience without human annotations, reward models, or access to user-side environments. OEL operates in two iterated stages: first, transferable experiential knowledge is extracted and accumulated from user-side…

Tianzhu Ye, Li Dong, Qingxiu Dong, Xun Wu, et al.
Published
Mar 2026
Citations
20
Code
Not linked
Read summary Original
125

arXiv.org

InCoder-32B: Code Foundation Model for Industrial Scenarios

InCoder-32B is a 32B-parameter code foundation model designed to unify code intelligence across general programming and industrial domains such as chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. It is trained from scratch using a three-stage Code-Flow pipeline: pre-training with curated industrial data,…

Jian Yang, Wei Zhang, Jiajun Wu, Junhang Cheng, et al.
Published
Mar 2026
Citations
6
Code
Not linked
Read summary Original
126

arXiv.org

MiroThinker-1.7 & H1: Towards Heavy-Duty Research Agents via Verification

MiroMind Team introduces MiroThinker-1.7, a research agent for complex long-horizon reasoning, and MiroThinker-H1, which adds verification-centric reasoning. MiroThinker-1.7 improves step-level reliability through agentic mid-training that emphasizes planning, reasoning, and tool interaction. MiroThinker-H1 integrates local verification (evaluating…

MiroMind Team, S. Bai, L. Bing, L. Lei, et al.
Published
Mar 2026
Citations
20
Code
Not linked
Read summary Original
127

arXiv.org

OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data

OpenSeeker is the first fully open-source search agent (model and data) achieving frontier-level performance, developed by an academic team at Shanghai Jiao Tong University. It uses two core innovations: fact-grounded scalable controllable QA synthesis, which reverse-engineers the web graph via topological expansion and entity obfuscation to generate…

Yuwen Du, Rui Ye, Shuo Tang, Xinyu Zhu, et al.
Published
Mar 2026
Citations
21
Code
766 stars
Read summary Original
128

arXiv.org

EnterpriseOps-Gym: Environments and Evaluations for Stateful Agentic Planning and Tool Use in Enterprise Settings

ENTERPRISEOPS-GYM is a benchmark for evaluating LLM agents in realistic enterprise settings, featuring a containerized sandbox with 164 database tables and 512 tools across eight domains (CSM, HR, ITSM, Email, Calendar, Teams, Drive, Hybrid). It includes 1,150 expert-curated tasks, 30 of which are infeasible to test refusal behavior, with SQL-based…

Shiva Krishna Reddy Malay, Shravan Nayak, Jishnu Sethumadhavan Nair, Sagar Davasam, et al.
Published
Mar 2026
Citations
6
Code
115 stars
Read summary Original
129

arXiv.org

LMEB: Long-horizon Memory Embedding Benchmark

The paper introduces LMEB, a benchmark for evaluating embedding models on long-horizon memory retrieval tasks, which are underexplored in existing benchmarks like MTEB. LMEB comprises 22 datasets and 193 zero-shot retrieval tasks across four memory types: episodic, dialogue, semantic, and procedural. The authors evaluate 15 embedding models, ranging from…

Xinping Zhao, Xinshuo Hu, Jiaxin Xu, Danyu Tang, et al.
Published
Mar 2026
Citations
7
Code
14 stars
Read summary Original
130

arXiv.org

Strategic Navigation or Stochastic Search? How Agents and Humans Reason Over Document Collections

The paper introduces MADQA, a benchmark of 2,250 human-authored questions over 800 heterogeneous PDF documents, designed to evaluate multimodal agentic systems. It formalizes Agentic Document Collection VQA with six properties: extractive answers, multi-hop reasoning, closed-world assumption, grounded attribution, agentic retrieval, and visual…

Łukasz Borchmann, Jordy Van Landeghem, Michał Turski, Shreyansh Padarha, et al.
Published
Mar 2026
Citations
1
Code
39 stars
Read summary Original
131

arXiv.org

OpenClaw-RL: Train Any Agent Simply by Talking

OpenClaw-RL is a framework that enables online reinforcement learning for LLM-based agents by extracting training signals from the next-state signals that follow each agent action (e.g., user replies, tool outputs, GUI changes). It uses a server–client architecture where the RL server hosts the policy behind an inference API and user terminals stream…

Yinjie Wang, Xuyang Chen, Xiaolong Jin, Mengdi Wang, et al.
Published
Mar 2026
Citations
66
Code
5.6K stars
Read summary Original
132

Annual Meeting of the Association for Computational Linguistics

Lost in Stories: Consistency Bugs in Long Story Generation by LLMs

The paper introduces ConStory-Bench, a benchmark for evaluating narrative consistency in long-form story generation by LLMs, and CONSTORY-CHECKER, an automated pipeline that detects contradictions with textual evidence. The benchmark includes 2,000 prompts across four task scenarios and a taxonomy of five error categories with 19 subtypes. Evaluation of…

Junjie Li, Xinrui Guo, Yuhao Wu, Roy Ka-Wei Lee, et al.
Published
Mar 2026
Citations
3
Code
192 stars
Read summary Original
133

arXiv.org

DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval

The paper introduces DARE (Distribution-Aware Retrieval Embedding), a lightweight retrieval model that incorporates data distribution information into function representations for retrieving R packages, addressing the underuse of rigorous statistical methods in R by LLM agents. The authors construct RPKB, a curated knowledge base from 8,191 CRAN packages,…

Maojun Sun, Yue Wu, Yifei Xie, Ruijian Han, et al.
Published
Mar 2026
Citations
3
Code
15 stars
Read summary Original
134

arXiv.org

MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier

The paper introduces MOOSE-Star, a framework for tractable training of LLMs for scientific discovery by directly modeling P(hypothesis|background). The authors argue that end-to-end training is intractable due to combinatorial complexity O(N^k) from retrieving k inspirations from a large knowledge base. MOOSE-Star decomposes this into sequential subtasks:…

Zonglin Yang, Lidong Bing
Published
Mar 2026
Citations
3
Code
90 stars
Read summary Original
135

arXiv.org

BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?

BeyondSWE is a 500-instance benchmark from 246 real GitHub repositories that evaluates code agents beyond single-repository bug fixing, covering four settings: cross-repository issue resolution (CrossRepo), domain-specific issue resolution (DomainFix), dependency-driven migration (DepMigrate), and document-to-repository generation (Doc2Repo). These tasks…

Guoxin Chen, Fanzhe Meng, Jiale Zhao, Minghao Li, et al.
Published
Mar 2026
Citations
11
Code
47 stars
Read summary Original
136

Annual Meeting of the Association for Computational Linguistics

RubricBench: Aligning Model-Generated Rubrics with Human Standards

RubricBench is a new benchmark with 1,147 pairwise comparisons designed to assess rubric-guided evaluation in reward models. It uses a multi-dimensional filtration pipeline to select hard samples with input complexity, output surface bias, and process failures, each annotated with human-derived atomic rubrics. Experiments show that while rubric-aware…

Qiyuan Zhang, Junyi Zhou, Yufei Wang, Fuyuan Lyu, et al.
Published
Mar 2026
Citations
10
Code
31 stars
Read summary Original
137

Qwen

Qwen3-Coder-Next Technical Report

Qwen3-Coder-Next is an 80-billion-parameter open-weight language model with only 3 billion active parameters per forward pass, designed for coding agents. It is built on Qwen3-Next with hybrid attention and Mixture-of-Experts, and trained via a staged pipeline: continued pretraining on code and agent-centric data, supervised fine-tuning, and reinforcement…

Ruisheng Cao, Mouxiang Chen, Jiawei Chen, Zeyu Cui, et al.
Published
Feb 2026
Citations
54
Code
17K stars
Read summary Original
138

arXiv.org

SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale

SWE-rebench V2 introduces a language-agnostic automated pipeline for harvesting executable real-world software engineering (SWE) tasks at scale, addressing the scarcity of large-scale training data for reinforcement learning (RL). The pipeline mines pull request histories, synthesizes repository-specific installation and test procedures via an interactive…

Ibragim Badertdinov, Maksim Nekrashevich, Anton Shevtsov, Alexander Golubev
Published
Feb 2026
Citations
11
Code
76 stars
Read summary Original
139

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
Read summary Original
140

arXiv.org

MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios

MobilityBench is a scalable benchmark for evaluating LLM-based route-planning agents in real-world mobility scenarios, built from 100,000 anonymized queries from Amap across 22 countries and over 350 cities. It covers 11 task scenarios in four families: Basic Information Retrieval, Route-Dependent Information Retrieval, Basic Route Planning, and…

Zhiheng Song, Jingshuai Zhang, Chuan Qin, Chao Wang, et al.
Published
Feb 2026
Citations
6
Code
157 stars
Read summary Original
141

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
Read summary Original
142

Z.ai / GLM

GLM-5: from Vibe Coding to Agentic Engineering

GLM-5, developed by Zhipu AI and Tsinghua University, is a next-generation foundation model that shifts from vibe coding to agentic engineering. It builds on the ARC (agentic, reasoning, coding) capabilities of its predecessor, GLM-4.7, and introduces DeepSeek Sparse Attention (DSA) to reduce training and inference costs while maintaining long-context…

GLM-5-Team, :, Aohan Zeng, Xin Lv, et al.
Published
Feb 2026
Citations
295
Code
6.9K stars
Read summary Original
143

Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval

SQuTR: A Robustness Benchmark for Spoken Query to Text Retrieval under Acoustic Noise

SQuTR is a benchmark for evaluating spoken query to text retrieval under controlled acoustic noise. It aggregates 37,317 unique queries from six English and Chinese text retrieval datasets (FiQA, HotpotQA, Natural Questions, MedicalRetrieval, DuRetrieval, T2Retrieval), synthesizing speech with CosyVoice-3 using 200 speakers. Four acoustic conditions…

Yuejie Li, Ke Yang, Yueying Hua, Berlin Chen, et al.
Published
Feb 2026
Citations
1
Code
10 stars
Read summary Original
144

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
Read summary Original
145

arXiv.org

DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories

DeepImageSearch introduces a new paradigm for image retrieval, reformulating it as an agentic exploration task over visual histories. The authors construct DISBench, a benchmark with 122 queries across 57 users and 109,467 photos, built via a human-model collaborative pipeline that mines latent spatiotemporal associations. Queries are categorized into…

Chenlong Deng, Mengjie Deng, Junjie Wu, Dun Zeng, et al.
Published
Feb 2026
Citations
3
Code
87 stars
Read summary Original
146

arXiv.org

Code2World: A GUI World Model via Renderable Code Generation

Code2World is a vision-language coder that predicts the next GUI state by generating renderable HTML code, combining high visual fidelity with structural controllability. To address data scarcity, the authors constructed AndroidCode, a corpus of over 80K screen-action pairs, by translating GUI trajectories from AndroidControl into HTML using GPT-5 and…

Yuhao Zheng, Li'an Zhong, Yi Wang, Rui Dai, et al.
Published
Feb 2026
Citations
14
Code
323 stars
Read summary Original
147

arXiv.org

UI-Venus-1.5 Technical Report

UI-Venus-1.5 is a unified, end-to-end GUI agent family (2B, 8B, and 30B-A3B variants) built on Qwen3-VL, designed for robust real-world applications. It introduces three key advances over UI-Venus-1.0: a Mid-Training stage using 10B tokens from 30+ datasets to establish foundational GUI semantics; Online Reinforcement Learning with full-trajectory rollouts…

Venus Team, Changlong Gao, Zhangxuan Gu, Yulin Liu, et al.
Published
Feb 2026
Citations
27
Code
1K stars
Read summary Original
148

arXiv.org

SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning

SKILLRL is a framework for LLM agents that improves policy learning by distilling raw interaction trajectories into a hierarchical skill library (SKILLBANK) and co-evolving it with the agent's policy during reinforcement learning. It uses a teacher model to extract general and task-specific skills from both successful and failed episodes, achieving 10-20x…

Peng Xia, Jianwen Chen, Hanyang Wang, Jiaqi Liu, et al.
Published
Feb 2026
Citations
150
Code
928 stars
Read summary Original
149

arXiv.org

TermiGen: High-Fidelity Environment and Robust Trajectory Synthesis for Terminal Agents

TermiGen is an end-to-end pipeline for synthesizing verifiable terminal environments and robust expert trajectories to train open-weight LLMs for terminal tasks. It addresses two key challenges: scarcity of high-fidelity executable environments and exposure bias from standard expert trajectories. Phase I uses a multi-agent system to generate diverse tasks…

Kaijie Zhu, Yuzhou Nie, Yijiang Li, Yiming Huang, et al.
Published
Feb 2026
Citations
13
Code
73 stars
Read summary Original
150

arXiv.org

Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Making

Baichuan-M3 is a medical-enhanced large language model designed to shift from passive question-answering to active, clinical-grade decision support. It addresses limitations in open-ended consultations by modeling the systematic workflow of a physician, with key capabilities including proactive information acquisition, long-horizon reasoning, and adaptive…

Baichuan-M3 Team, :, Chengfeng Dou, Fan Yang, et al.
Published
Feb 2026
Citations
8
Code
254 stars
Read summary Original