The Year in AI Papers
The year/Topics/Agents

Topic area

Agents

Every collection across agents.

Papers
275
Research labs
10
Official code
214

151–200 of 275 papers in this topic area

151

arXiv.org

QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining

QuantaAlpha is an evolutionary alpha mining framework that treats each end-to-end mining run as a trajectory and improves factors via trajectory-level mutation and crossover. It addresses limitations in existing agentic systems, such as fragile controllability, limited trustworthiness, and constrained exploration, by localizing suboptimal steps for…

Jun Han, Shuo Zhang, Wei Li, Yifan Dong, et al.
Published
Feb 2026
Citations
7
Code
1.4K stars
Read summary Original
152

arXiv.org

OdysseyArena: Benchmarking Large Language Models For Long-Horizon, Active and Inductive Interactions

ODYSSEYARENA is a benchmark suite for evaluating large language models (LLMs) on long-horizon, active, and inductive interactions, where agents must infer latent transition rules from experience rather than follow explicit instructions. The paper formalizes four structural primitives—discrete symbolic rules, continuous stochastic dynamics, periodic…

Hang Yan, Fangzhi Xu, Qiushi Sun, Jinyang Wu, et al.
Published
Feb 2026
Citations
6
Code
33 stars
Read summary Original
153

arXiv.org

Spider-Sense: Intrinsic Risk Sensing for Efficient Agent Defense with Hierarchical Adaptive Screening

The paper introduces SPIDER-SENSE, a framework for defending LLM-based autonomous agents against security threats. It argues that existing mandatory, stage-wise security checks are inefficient and proposes Intrinsic Risk Sensing (IRS), which embeds risk awareness into the agent's execution flow, triggering defenses only when a risk is perceived. Once…

Zhenxiong Yu, Zhi Yang, Zhiheng Jin, Shuhe Wang, et al.
Published
Feb 2026
Citations
1
Code
21 stars
Read summary Original
154

arXiv.org

WideSeek-R1: Exploring Width Scaling for Broad Information Seeking via Multi-Agent Reinforcement Learning

WIDESEEK-R1 explores width scaling for broad information seeking using a lead-agent–subagent framework trained via multi-agent reinforcement learning (MARL). The system uses a shared LLM with isolated contexts and specialized tools, where the lead agent decomposes tasks and delegates subtasks to parallel subagents. Training on a curated 20k dataset of…

Zelai Xu, Zhexuan Xu, Ruize Zhang, Chunyang Zhu, et al.
Published
Feb 2026
Citations
6
Code
4.5K stars
Read summary Original
155

arXiv.org

AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration

AORCHESTRA is an agentic framework that automates sub-agent creation for complex, long-horizon tasks. It introduces a unified four-tuple abstraction (Instruction, Context, Tools, Model) to model any agent, enabling on-demand specialization. A central orchestrator decomposes tasks, curates context, selects tools and models, and delegates execution to…

Jianhao Ruan, Zhihao Xu, Yiran Peng, Fashen Ren, et al.
Published
Feb 2026
Citations
18
Code
153 stars
Read summary Original
156

Google DeepMind

MARS: Modular Agent with Reflective Search for Automated AI Research

MARS (Modular Agent with Reflective Search) is a framework for automating AI research, specifically addressing the bottleneck of complex machine learning engineering (MLE) tasks. It uses three pillars: Budget-Aware Planning via cost-constrained Monte Carlo Tree Search (MCTS) to balance performance with execution cost, Modular Construction through a…

Jiefeng Chen, Bhavana Dalvi Mishra, Jaehyun Nam, Rui Meng, et al.
Published
Feb 2026
Citations
12
Code
39 stars
Read summary Original
157

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
Read summary Original
158

Qwen

SWE-Universe: Scale Real-World Verifiable Environments to Millions

SWE-Universe is a framework for automatically constructing real-world software engineering (SWE) verifiable environments from GitHub pull requests (PRs) at a million scale. It addresses challenges of low production yield, weak verifiers, and prohibitive cost using a building agent powered by a custom-trained MoE model (Qwen-Next-80B-A3B). The agent uses…

Mouxiang Chen, Lei Zhang, Yunlong Feng, Xuwu Wang, et al.
Published
Feb 2026
Citations
7
Code
Not linked
Read summary Original
159

Moonshot AI

Kimi K2.5: Visual Agentic Intelligence

Kimi K2.5 is an open-source multimodal agentic model that jointly optimizes text and vision through techniques including joint pre-training, zero-vision SFT, and joint reinforcement learning. It introduces Agent Swarm, a parallel agent orchestration framework using Parallel-Agent Reinforcement Learning (PARL) to decompose tasks into heterogeneous…

Kimi Team, Tongtong Bai, Yifan Bai, Yiping Bao, et al.
Published
Feb 2026
Citations
313
Code
2.3K stars
Read summary Original
160

arXiv.org

Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models

The paper introduces VDR-Bench, a benchmark of 2,000 VQA instances designed to evaluate Vision-DeepResearch systems under realistic conditions. It identifies two flaws in existing benchmarks: they are not visual-search-centric (answers can be inferred from text cues or model priors) and rely on idealized retrieval (near-exact whole-image matching).…

Yu Zeng, Wenxuan Huang, Zhen Fang, Shuang Chen, et al.
Published
Feb 2026
Citations
21
Code
Not linked
Read summary Original
161

Open MIND

Closing the Loop: Universal Repository Representation with RPG-Encoder

RPG-Encoder generalizes the Repository Planning Graph (RPG) into a unified, high-fidelity representation for repository reasoning, closing the loop between comprehension and generation. It addresses the disconnect caused by fragmented representations (API documentation lacks structure, dependency graphs lack semantics) by encoding raw code into a dual-view…

Jane Luo, Chengyu Yin, Xin Zhang, Qingtao Li, et al.
Published
Feb 2026
Citations
0
Code
588 stars
Read summary Original
162

arXiv.org

daVinci-Agency: Unlocking Long-Horizon Agency Data-Efficiently

The paper introduces daVinci-Agency, a data synthesis paradigm for training LLM agents on long-horizon tasks. It mines chains of semantically linked GitHub Pull Requests (PRs) to create training trajectories that capture task decomposition, long-term consistency, and iterative refinement. The method constructs task chains from up to five PRs, yielding…

Mohan Jiang, Dayuan Fu, Junhao Shi, Ji Zeng, et al.
Published
Feb 2026
Citations
3
Code
38 stars
Read summary Original
163

arXiv.org

Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models

Vision-DeepResearch introduces a new paradigm for multimodal deep research, addressing the hit-rate problem in image search and the limited reasoning depth and search breadth of existing methods. The approach enables multi-turn, multi-entity, and multi-scale visual and textual search, supporting dozens of reasoning steps and hundreds of engine…

Wenxuan Huang, Yu Zeng, Qiuchen Wang, Zhen Fang, et al.
Published
Jan 2026
Citations
25
Code
669 stars
Read summary Original
164

Volume 1

CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty

CAR-bench is a benchmark for evaluating LLM agents in real-world, user-facing applications, specifically in-car voice assistants. It addresses limitations of existing benchmarks that overlook reliability under uncertainty. The environment includes an LLM-simulated user, 19 domain policies, 58 interconnected tools, and dynamic states/databases. Beyond…

Johannes Kirmayr, Lukas Stappen, Elisabeth André
Published
Jan 2026
Citations
6
Code
33 stars
Read summary Original
165

arXiv.org

ASTRA: Automated Synthesis of agentic Trajectories and Reinforcement Arenas

ASTRA is a fully automated, end-to-end framework for training tool-augmented language model agents. It combines a trajectory synthesis pipeline that uses the static topology of tool-call graphs to generate diverse, multi-turn tool-use trajectories for supervised fine-tuning (SFT), with an environment synthesis framework that converts decomposed…

Xiaoyu Tian, Haotian Wang, Shuaiting Chen, Hao Zhou, et al.
Published
Jan 2026
Citations
3
Code
151 stars
Read summary Original
166

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
Read summary Original
167

arXiv.org

daVinci-Dev: Agent-native Mid-training for Software Engineering

The paper introduces daVinci-Dev, a training recipe for agentic software engineering that uses agent-native mid-training data to bridge the gap between static training corpora and dynamic, feedback-rich development environments. The authors construct two complementary trajectory types: contextually-native trajectories (68.6B tokens) derived from GitHub…

Ji Zeng, Dayuan Fu, Tiantian Mi, Yumin Zhuang, et al.
Published
Jan 2026
Citations
10
Code
73 stars
Read summary Original
168

arXiv.org

SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

SWE-Pruner is a self-adaptive context pruning framework for coding agents, addressing the high API costs and latency from long interaction contexts. It uses a lightweight 0.6B neural skimmer, trained on 61K synthetic samples, to perform task-aware, line-level pruning based on a goal hint provided by the agent. Evaluated on multi-turn tasks (SWE-Bench…

Yuhang Wang, Yuling Shi, Mo Yang, Rongrui Zhang, et al.
Published
Jan 2026
Citations
26
Code
304 stars
Read summary Original
169

arXiv.org

LongCat-Flash-Thinking-2601 Technical Report

LongCat-Flash-Thinking-2601 is a 560B-parameter open-source Mixture-of-Experts (MoE) reasoning model with 27B activated parameters, achieving state-of-the-art performance among open-source models on agentic benchmarks. Its training combines a unified framework with domain-parallel expert training, environment scaling, and a robust RL pipeline. Key…

Meituan LongCat Team, Anchun Gui, Bei Li, Bingyang Tao, et al.
Published
Jan 2026
Citations
23
Code
259 stars
Read summary Original
170

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
Read summary Original
171

arXiv.org

Stable-DiffCoder: Pushing the Frontier of Code Diffusion Large Language Model

Stable-DiffCoder is a diffusion-based language model for code that reuses the Seed-Coder architecture, data, and training pipeline but replaces autoregressive (AR) training with a block diffusion continual pretraining (CPT) stage. The authors introduce a tailored warmup and a block-wise clipped noise schedule to stabilize training and improve knowledge…

Chenghao Fan, Wen Heng, Bo Li, Sichen Liu, et al.
Published
Jan 2026
Citations
13
Code
84 stars
Read summary Original
172

arXiv.org

EvoCUA: Evolving Computer Use Agents via Learning from Scalable Synthetic Experience

EvoCUA is a native computer-use agent that replaces static imitation with a self-sustaining evolutionary cycle integrating verifiable data synthesis, scalable interaction infrastructure, and iterative policy optimization. A synthesis engine generates diverse tasks with executable validators, while a high-throughput sandbox platform orchestrates tens of…

Taofeng Xue, Chong Peng, Mianqiu Huang, Linsen Guo, et al.
Published
Jan 2026
Citations
31
Code
336 stars
Read summary Original
173

Together AI

DSGym: A Holistic Framework for Evaluating and Training Data Science Agents

DSGym is a standardized framework for evaluating and training data science agents in isolated, stateful execution environments. It addresses limitations in existing benchmarks, which often allow tasks to be solved without accessing data files (shortcut solvability), lack cross-benchmark standardization, and have narrow domain coverage. DSGym provides a…

Fan Nie, Junlin Wang, Harper Hua, Federico Bianchi, et al.
Published
Jan 2026
Citations
12
Code
58 stars
Read summary Original
174

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
Read summary Original
175

arXiv.org

Agentic Reasoning for Large Language Models

This survey introduces agentic reasoning, a paradigm shift where large language models (LLMs) act as autonomous agents that plan, act, and learn through continual interaction with their environment, rather than passively generating sequences. The authors organize agentic reasoning along three complementary dimensions: foundational agentic reasoning (core…

Tianxin Wei, Ting-Wei Li, Zhining Liu, Xuying Ning, et al.
Published
Jan 2026
Citations
34
Code
1.3K stars
Read summary Original
176

Annual Meeting of the Association for Computational Linguistics

ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development

ABC-Bench is a benchmark for evaluating LLM-based agents on full-lifecycle backend development tasks, covering repository exploration, code editing, environment configuration, deployment, and end-to-end API testing. It includes 224 tasks from 127 MIT-licensed GitHub repositories, spanning 8 languages and 19 frameworks. The ABC-Pipeline automates task…

Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, et al.
Published
Jan 2026
Citations
3
Code
33 stars
Read summary Original
177

arXiv.org

Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey

This paper presents a systematic survey of LLM-based issue resolution in software engineering, a task formalized by benchmarks like SWE-bench. The survey reviews 175 papers, organizing the field into a taxonomy covering data, methods, and analysis. Data is categorized into evaluation and training datasets, with construction via automated collection or…

Caihua Li, Lianghong Guo, Yanlin Wang, Daya Guo, et al.
Published
Jan 2026
Citations
7
Code
86 stars
Read summary Original
178

arXiv.org

DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluation

DeepResearchEval is an automated framework for constructing deep research tasks and evaluating deep research systems. It addresses limitations in existing benchmarks: annotation-intensive task construction, static evaluation dimensions, and incomplete fact verification. The framework uses a persona-driven pipeline to generate realistic, complex tasks…

Yibo Wang, Lei Wang, Yue Deng, Keming Wu, et al.
Published
Jan 2026
Citations
12
Code
142 stars
Read summary Original
179

arXiv.org

Collaborative Multi-Agent Test-Time Reinforcement Learning for Reasoning

MATTRL is a framework that improves multi-agent LLM reasoning at test time by injecting structured textual experience into multi-agent deliberation, avoiding costly weight updates. It forms a team of specialist agents, retrieves relevant experiences from a pool, and reaches consensus. The experience pool is built by scoring utterances with individual and…

Zhiyuan Hu, Yunhai Hu, Juncheng Liu, Shuyue Stella Li, et al.
Published
Jan 2026
Citations
3
Code
Not linked
Read summary Original
180

arXiv.org

$A^3$-Bench: Benchmarking Memory-Driven Scientific Reasoning via Anchor and Attractor Activation

The paper introduces A3-Bench, a benchmark for evaluating memory-driven scientific reasoning in large language models (LLMs). It is grounded in the concepts of anchors (foundational knowledge units) and attractors (experience-based templates), which are activated during reasoning. The authors annotate 2,198 science problems across math, physics, and…

Jian Zhang, Yu He, Zhiyuan Wang, Zhangqi Wang, et al.
Published
Jan 2026
Citations
1
Code
Not linked
Read summary Original
181

arXiv.org

User-Oriented Multi-Turn Dialogue Generation with Tool Use at scale

This paper introduces a user-oriented simulation framework for generating multi-turn tool-use dialogue data at scale, addressing limitations of static toolsets and single-shot trajectories. The authors first developed a task-oriented pipeline using an LRM-based simulator to dynamically synthesize tools and tasks, but found it produced minimal-interaction,…

Jungho Cho, Minbyul Jeong, Sungrae Park
Published
Jan 2026
Citations
1
Code
Not linked
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182

arXiv.org

Controlled Self-Evolution for Algorithmic Code Optimization

The paper introduces Controlled Self-Evolution (CSE), a framework to improve the exploration efficiency of self-evolution methods for algorithmic code optimization. CSE addresses three bottlenecks: initialization bias, uncontrolled stochastic evolution, and insufficient experience utilization. It comprises three components: Diversified Planning…

Tu Hu, Ronghao Chen, Shuo Zhang, Jianghao Yin, et al.
Published
Jan 2026
Citations
11
Code
131 stars
Read summary Original
183

arXiv.org

Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning

The paper introduces VideoDR, the first benchmark for video deep research, which requires models to extract multi-frame visual anchors from videos, perform interactive web searches, and conduct multi-hop reasoning over combined video-web evidence to answer open-domain factoid questions. The benchmark comprises 500 samples across six semantic domains, with…

Chengwen Liu, Xiaomin Yu, Zhuoyue Chang, Zhe Huang, et al.
Published
Jan 2026
Citations
6
Code
160 stars
Read summary Original
184

arXiv.org

MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences

MemGovern is a framework that transforms raw GitHub issue-tracking data into structured, agent-friendly experiential memory to enhance autonomous software engineering (SWE) agents. It addresses the 'closed-world' limitation of agents that ignore historical human debugging experience. MemGovern uses experience governance to filter, standardize, and…

Qihao Wang, Ziming Cheng, Shuo Zhang, Fan Liu, et al.
Published
Jan 2026
Citations
8
Code
Not linked
Read summary Original
185

Annual Meeting of the Association for Computational Linguistics

Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization

The paper introduces Thinking with Map, a map-augmented agent for image geolocalization that equips a large vision-language model (LVLM) with map tools (POI search, static/satellite map queries, image zoom) to iteratively propose and verify location hypotheses. The method uses a two-stage optimization: agentic reinforcement learning (GRPO) to improve…

Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, et al.
Published
Jan 2026
Citations
8
Code
177 stars
Read summary Original
186

Annual Meeting of the Association for Computational Linguistics

KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions

KnowMe-Bench is a benchmark for evaluating person understanding in lifelong digital companions, built from long-form autobiographical narratives rather than sparse chat logs. It addresses two gaps in existing benchmarks: evaluation misalignment (retrieval proxies vs. true person understanding) and data substrate misalignment (low-density, decontextualized…

Tingyu Wu, Zhisheng Chen, Ziyan Weng, Shuhe Wang, et al.
Published
Jan 2026
Citations
8
Code
133 stars
Read summary Original
187

Qwen

Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking

The report introduces Qwen3-VL-Embedding and Qwen3-VL-Reranker, a unified framework for multimodal retrieval built on the Qwen3-VL foundation model. The embedding model uses a multi-stage training pipeline (contrastive pre-training, multi-task contrastive learning, and reranker distillation) to produce high-dimensional vectors, supporting Matryoshka…

Mingxin Li, Yanzhao Zhang, Dingkun Long, Keqin Chen, et al.
Published
Jan 2026
Citations
167
Code
1.3K stars
Read summary Original
188

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
Read summary Original
189

Qwen

Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem

This technical report introduces the Agentic Learning Ecosystem (ALE), a full-stack infrastructure for developing agentic LLMs, and ROME, an open-source agent model trained within it. ALE comprises three components: ROLL, a scalable RL training framework; ROCK, a sandboxed environment execution engine; and iFlow CLI, an agent framework for context…

Weixun Wang, XiaoXiao Xu, Wanhe An, Fangwen Dai, et al.
Published
Dec 2025
Citations
23
Code
3.4K stars
Read summary Original
190

Independent research

Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models

Youtu-LLM is a 1.96B-parameter language model pre-trained from scratch to achieve native agentic intelligence, balancing efficiency with strong reasoning and planning. It uses a dense Multi-Latent Attention (MLA) architecture with a 128k context window and a STEM-oriented tokenizer. Training follows a 'Commonsense-STEM-Agent' curriculum over 10.84T tokens,…

Junru Lu, Jiarui Qin, Lingfeng Qiao, Yinghui Li, et al.
Published
Dec 2025
Citations
5
Code
593 stars
Read summary Original
191

arXiv.org

Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization

Youtu-Agent is a modular framework addressing high configuration costs and static capabilities in LLM agents. It decouples environments, toolkits, and agents via a YAML-based system, enabling automated generation through a deterministic Workflow mode and a flexible Meta-Agent mode that synthesizes tools, prompts, and configurations. For continuous…

Yuchen Shi, Yuzheng Cai, Siqi Cai, Zihan Xu, et al.
Published
Dec 2025
Citations
5
Code
4.6K stars
Read summary Original
192

Independent research

HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling

HGMEM is a hypergraph-based working memory system for multi-step retrieval-augmented generation (RAG) that improves complex relational modeling in long contexts. Unlike existing memory mechanisms that passively store isolated facts, HGMEM represents memory as a hypergraph where hyperedges serve as memory points, enabling the progressive formation of…

Chulun Zhou, Chunkang Zhang, Guoxin Yu, Fandong Meng, et al.
Published
Dec 2025
Citations
4
Code
131 stars
Read summary Original
193

arXiv.org

Step-DeepResearch Technical Report

Step-DeepResearch is a 32B-parameter, end-to-end Deep Research agent model developed by StepFun. It uses a data synthesis strategy based on atomic capabilities (planning, information seeking, reflection, and report writing) and a progressive training pipeline (agentic mid-training, SFT, RL) with a Checklist-style Judger reward. The model achieves a score…

Chen Hu, Haikuo Du, Heng Wang, Lin Lin, et al.
Published
Dec 2025
Citations
11
Code
569 stars
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194

arXiv.org

Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understanding

The paper introduces Mindscape-Aware RAG (MiA-RAG), a framework that equips LLM-based RAG systems with a global semantic representation, called a mindscape, to improve long-context understanding. The mindscape is built via hierarchical summarization of a document. MiA-RAG conditions both retrieval and generation on this mindscape: a retriever (MiA-Emb)…

Yuqing Li, Jiangnan Li, Zheng Lin, Ziyan Zhou, et al.
Published
Dec 2025
Citations
6
Code
Not linked
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195

arXiv.org

Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills

This survey examines how agentic AI systems are adapted after pretraining, organizing the field into a four-paradigm framework based on what is optimized (the agent or its tools) and the source of the adaptation signal (tool execution or agent output). The paradigms are: A1 (tool-execution-signaled agent adaptation), A2 (agent-output-signaled agent…

Pengcheng Jiang, Jiacheng Lin, Zhiyi Shi, Zifeng Wang, et al.
Published
Dec 2025
Citations
15
Code
679 stars
Read summary Original
196

arXiv.org

Step-GUI Technical Report

This technical report introduces Step-GUI, a family of multimodal GUI agent models (4B/8B) built on Qwen3-VL, achieving state-of-the-art performance across benchmarks (8B: 80.2% AndroidWorld, 48.5% OSWorld, 62.6% ScreenShot-Pro). The core innovation is the Calibrated Step Reward System (CSRS), a self-evolving training pipeline that converts model-generated…

Haolong Yan, Jia Wang, Xin Huang, Yeqing Shen, et al.
Published
Dec 2025
Citations
27
Code
Not linked
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197

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
Read summary Original
198

Annual Meeting of the Association for Computational Linguistics

Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows

FINCH (FinWorkBench) is a benchmark for evaluating AI agents on real-world, enterprise-grade finance and accounting workflows. It is built from authentic enterprise data, including Enron emails and spreadsheets, EUSES, and financial reports from institutions like the World Bank, covering 2000–2025. The dataset comprises 172 composite workflows with 384…

Haoyu Dong, Pengkun Zhang, Yan Gao, Xuanyu Dong, et al.
Published
Dec 2025
Citations
5
Code
13 stars
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199

arXiv.org

Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction

The paper introduces Nex-N1, a series of agentic models trained using a unified ecosystem (NexAU, NexA4A, NexGAP) for large-scale environment construction. NexAU is a modular runtime for scalable agent frameworks, NexA4A automatically generates diverse agent hierarchies from natural language, and NexGAP generates end-to-end agentic trajectories using real…

Nex-AGI Team, :, Yuxuan Cai, Lu Chen, et al.
Published
Dec 2025
Citations
15
Code
Not linked
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200

arXiv.org

DAComp: Benchmarking Data Agents across the Full Data Intelligence Lifecycle

DAComp is a benchmark of 210 tasks evaluating LLM-based agents on enterprise data intelligence, covering repository-level data engineering (DE) and open-ended data analysis (DA). DE tasks include architecture, implementation, and evolution, requiring multi-stage SQL pipelines on schemas averaging 412 columns and over 2,000 lines of code. DA tasks involve…

Fangyu Lei, Jinxiang Meng, Yiming Huang, Junjie Zhao, et al.
Published
Dec 2025
Citations
11
Code
434 stars
Read summary Original