Agents

Dongrui Liu, Yu Li, Zhonghao Yang, Peng Wang, Guanxu Chen, Yuejin Xie, Qinghua Mao, Wanying Qu, Yanxu Zhu, Tianyi Zhou, Leitao Yuan, Zhijie Zheng, Qihao Lin, Yimin Wang, Haoyu Luo, Shuai Shao, Chen Qian, Qingyu Liu, Ling Tang, Ruiyang Qin, Qihan Ren, Junxiao Yang, Kun Wang, Zhiheng Xi, Linfeng Zhang, Ranjie Duan, Bo Zhang, Wenjie Wang, Wen Shen, Qiaosheng Zhang, Yan Teng, Chaochao Lu, Rui Mei, Man Li, Jialing Tao, Xi Lin, Tianhang Zheng, Yong Liu, Quanshi Zhang, Lei Zhu, Xingjun Ma, Junhua Liu, Hui Xue, Xiaoxiang Zuo, Xiangnan He, Chao Shen, Xianglong Liu, Minlie Huang, Jing Shao, Xia Hu

A framework for training small guardrail models (0.8B to 8B parameters) that audit an AI agent's full execution trajectory and label it safe or unsafe, plus diagnose the risk source, failure mode, and real-world harm. It combines a taxonomy-guided synthetic data engine, influence-function-based data selection to shrink training to about 1k samples, and two-stage SFT plus RL (using a per-dimension variant of GRPO called GDPO).

Xiangchen Cheng, Yunwei Jiang, Jianwen Sun, Zizhen Li, Chuanhao Li, Xiangcheng Cao, Yihao Liu, Fanrui Zhang, Li Jin, Kaipeng Zhang

The paper introduces AgenticSTS, an LLM agent for the deck-building game Slay the Spire 2 that replaces the usual growing chat transcript with a bounded memory contract: every decision prompt is rebuilt fresh from five typed slots (protocol, state schema, game rules, episodic summaries, and triggered strategic skills) via typed retrieval, so no raw cross-decision history is appended. It also ships a testbed of 298 tagged trajectories, frozen memory snapshots, and analysis scripts so individual memory layers can be ablated in isolation.

Wei Zhou, Xuanhe Zhou, Shaokun Han, Hongming Xu, Guoliang Li, Zhiyu Li, Feiyu Xiong, Fan Wu

A benchmark study that decomposes 12 LLM agent memory systems (MemGPT/Letta, Mem0, Zep, A-MEM, MemOS, MemoryOS, Cognee, etc.) into four modules (representation/storage, extraction, retrieval/routing, and maintenance) and evaluates them from a data management angle. It runs end-to-end and ablation experiments across five workloads spanning 11 datasets, measuring not just task accuracy but retrieval fidelity, update robustness, long-horizon stability, and operational cost (index build time, query latency).

Xuying Ning, Katherine Tieu, Dongqi Fu, Tianxin Wei, Zihao Li, Yuanchen Bei, Jiaru Zou, Mengting Ai, Zhining Liu, Ting-Wei Li, Lingjie Chen, Yanjun Zhao, Ke Yang, Bingxuan Li, Cheng Qian, Gaotang Li, Xiao Lin, Zhichen Zeng, Ruizhong Qiu, Sirui Chen, Yifan Sun, Xiyuan Yang, Ruida Wang, Rui Pan, Chenyuan Yang, Dylan Zhang, Liri Fang, Zikun Cui, Yang Cao, Pan Chen, Dorothy Sun, Ren Chen, Mahesh Srinivasan, Nipun Mathur, Yinglong Xia, Hong Li, Hong Yan, Pan Lu, Lingming Zhang, Tong Zhang, Hanghang Tong, Jingrui He

A survey that frames code as the 'harness' for LLM agents: the executable, inspectable, and stateful layer through which agents reason, act, model their environment, and coordinate. It organizes existing work into three layers (harness interface, harness mechanisms like planning/memory/tool-use/feedback control, and multi-agent scaling) and catalogs methods across coding assistants, GUI/OS automation, embodied agents, and scientific discovery.

Tianyi Zhou, Dongrui Liu, Leitao Yuan, Jing Shao, Xia Hu

COLLEAGUE.SKILL is a pipeline that takes heterogeneous traces from a person or role (chat logs, docs, review comments, interviews) and distills them into a versioned skill package compatible with the Agent Skills standard (a folder centered on SKILL.md). Each package splits into a capability track (procedures, review criteria, decision heuristics) and a bounded behavior track (communication style, interaction rules, correction log), and supports inspection, natural-language correction, rollback, and installation across agent hosts.

Jincheng Zhong, Weizhi Wang, Che Jiang, Kai Tian, Zhenzhao Yuan, Junlin Yang, Dianqiao Lei, Kaiyan Zhang

EnterpriseClawBench is a benchmark built by an automated pipeline that converts real internal workplace agent sessions into 852 reproducible tasks, each with recovered input fixtures, rewritten single-turn prompts, role/skill taxonomy labels, hard delivery rules, and semantic rubrics. It evaluates harness-model combinations (Claude Code, Codex, DeepAgents, Hermes, OpenClaw paired with various models) on producing usable business artifacts, scoring both objective delivery rules and LLM-judged quality across five dimensions.

Jundong Xu, Qingchuan Li, Jiaying Wu, Yihuai Lan, Shuyue Stella Li, Huichi Zhou, Bowen Jiang, Lei Wang, Jun Wang, Anh Tuan Luu, Caiming Xiong, Hae Won Park, Bryan Hooi, Zhiyuan Hu

EvoArena is a benchmark suite that turns static agent benchmarks (terminal workflows, software repos, user preferences) into version chains where the same environment evolves across releases, and the agent must adapt to changes while preserving still-valid prior behavior. The paper also proposes EvoMem, a git-like memory layer that logs non-additive memory updates as append-only patches (storing before/after state, rationale, and triggering evidence) and retrieves relevant patches alongside the latest memory at inference.

Zhilin Wang, Han Song, Runzhe Zhan, Jusen Du, Jiacheng Chen, Tianle Li, Qingyu Yin, Yulun Wu, Zhennan Shen, Tong Zhu, Yanshu Li, Guanjie Chen, Derek F. Wong, Yafu Li, Yu Cheng, Yang Yang

EvoPolicyGym is a benchmark that measures how well a coding agent (a language model plus its tool-using harness) can iteratively rewrite an executable decision policy for a reinforcement learning environment, using feedback from sandboxed rollouts under a fixed budget of 128 episodes. Agents edit a Python policy that exposes reset and act(obs), submit it for scored train rollouts, and get judged on held-out return of the checkpoint the server picks via hidden validation.

Alireza Salemi, Chang Zeng, Atharva Nijasure, Jui-Hui Chung, Razieh Rahimi, Fernando Diaz, Hamed Zamani

GrepSeek trains a compact LLM (Qwen3.5-9B) to answer questions by issuing Unix shell commands (rg, grep, head, awk, etc.) directly against a raw text corpus instead of querying a precomputed retrieval index. It uses a two-stage pipeline: supervised fine-tuning on a synthetic cold-start dataset built by an answer-aware Tutor and answer-blind Planner, followed by GRPO reinforcement learning, plus a sharded-parallel execution engine that runs shell pipelines across corpus shards while preserving byte-exact output.

Pengcheng Jiang, Zhiyi Shi, Kelly Hong, Xueqiang Xu, Jiashuo Sun, Jimeng Sun, Hammad Bashir, Jiawei Han

Harness-1 is a 20B search agent (built on gpt-oss-20b) trained with reinforcement learning inside a stateful harness that moves search bookkeeping out of the model and into the environment. The environment maintains a candidate pool, an importance-tagged curated set, an evidence graph, verification records, and deduplicated/compressed observations, while the policy only decides what to search, which documents to keep, what to verify, and when to stop.

Nahyun Lee, Dongkeun Yoon, Guijin Son, Geewook Kim, Dayoon Ko, Jeonghun Park, Haneul Yoo, Jaewon Cho, Junghun Park, Changyoon Lee, Kyochul Jang, Jaeyeon Kim, Eunsu Kim, Woojin Cho, Seungone Kim

K-BrowseComp is a web-browsing agent benchmark of 400 problems grounded in Korean web contexts: a 300-question human-verified subset built by native Korean annotators, plus a 100-question synthetic split generated by a browsing agent (Claude Code) that constructs questions backwards from seed pages and filters them against a taxonomy of failure modes. Each question has a single stable answer requiring multi-hop or parallel-constraint retrieval across multiple Korean websites, and models are evaluated pass@1 under a fixed 10-call search budget.

Kwai Keye Team, Bin Wen, Changyi Liu, Chengru Song, Chongling Rao, Guowang Zhang, Han Li, Haonan Fan, Hengrui Ju, Jiankang Chen, Jiapeng Chen, Jiawei Yuan, Kaixuan Yang, Kaiyu Jiang, Kun Gai, Lingzhi Zhou, Na Nie, Sen Na, Tianke Zhang, Tingting Gao, Xuanyu Zheng, Yulong Chen, Fan Yang, Haixuan Gao, Lele Yang, Mingqiao Liu, Muxi Diao, Qi Zhang, Qile Su, Wei Chen, Wentao Hong, Xingyu Lu, Yancheng Long, Yankai Yang, Yingxin Li, Yiyang Fan, Yu Xia, Yuzhe Chen, Ziliang Lai, Chuan Yi, Haonan Jia, Tianming Liang, Weixin Xu, Xiaoxiao Ma, Yang Tian, Yufei Han, Feng Han, Hang Li, Jing Wang, Jinghui Jia, Junmin Chen, Junyu Shi, Ruilin Zhang

A technical report on Kwai Keye-VL-2.0-30B-A3B, an open-source Mixture-of-Experts multimodal model (30B total, 3B active) that adapts DeepSeek Sparse Attention (DSA) to a GQA-based backbone so it can process 256K-token contexts, aimed at hour-long video understanding. It also describes a post-training recipe combining Cross-Modal Multi-Teacher On-Policy Distillation (MOPD) with Context-RL and Video-RL to add agent, code, and search skills without wiping out base reasoning.

Ye Jin, Yangyang Xu, Jun Zhu, Yibo Yang

MemSlides is an agent framework for generating and editing presentation slides that splits memory into three parts: long-term user profile memory (persistent style/layout preferences per user and intent), tool memory (reusable execution traces for editing tools), and session-scoped working memory (temporary constraints across revision turns). It pairs this with a Plan-Act-Guard revision pipeline that scopes edits to the smallest affected slide region rather than regenerating the whole deck on each feedback turn.

Yuru Wang, Lejun Cheng, Yuxin Zuo, Sihang Zeng, Bingxiang He, Che Jiang, Junlin Yang, Yuchong Wang, Kaikai Zhao, Weifeng Huang, Kai Tian, Zhenzhao Yuan, Jincheng Zhong, Weizhi Wang, Ning Ding, Bowen Zhou, Kaiyan Zhang

NatureBench is a benchmark of 90 tasks distilled from Nature-family science papers (2022-2025), built by an automated pipeline (NatureGym) that turns each paper into a containerized package with a task brief, data, a hidden test set, and an automated evaluator, while removing the original method so agents must find their own solution. It scores coding agents against each paper's published state of the art using a SOTA-normalized relative gap metric, with a post-hoc judge that flags shortcuts like output fabrication and feedback gaming.

Shuang Chen, Kaituo Feng, Hangting Chen, Wenxuan Huang, Dasen Dai, Quanxin Shou, Yunlong Lin, Xiangyu Yue, Shenghua Gao, Tianyu Pang

OpenSearch-VL is a fully open recipe (data, code, models) for training multimodal search agents that interleave reasoning with tool calls over images and web search. It combines a Wikipedia-graph-based data synthesis pipeline, a tool environment (text/image search, OCR, crop, sharpen, super-resolution, perspective correction), and a modified GRPO reinforcement learning algorithm with fatal-aware token masking and one-sided advantage clamping to salvage useful reasoning from trajectories that hit cascading tool failures.

Yuwen Du, Rui Ye, Shuo Tang, Keduan Huang, Xinyu Zhu, Yuzhu Cai, Siheng Chen

The paper trains a 30B search agent (OpenSeeker-v2, based on Qwen3-30B-A3B-Thinking) using only supervised fine-tuning on synthetic ReAct trajectories, skipping the usual continual pre-training and reinforcement learning stages. The main contribution is three tweaks to the data synthesis pipeline: expanding the knowledge graph size used to generate questions, enlarging the tool set, and filtering out any trajectory solvable in too few tool-call steps to enforce a difficulty floor.

Yuxin Zuo, Zikai Xiao, Li Sheng, Fei Huang, Jianhong Tu, Yuxuan Liu, Tianyi Tang, Xiaomeng Hu, Yang Su, Qingfeng Lan, Yantao Liu, Qin Zhu, Yinger Zhang, Bowen Yu, Haiquan Zhao, Haiyang Xu, Jianxin Yang, Jiayang Cheng, Junyang Wang, Lianghao Deng, Mingfeng Xue, Tianyi Bai, Yang Fan, Yubo Ma, Yucheng Li, Zeyu Cui, Zhihai Wang, Zhihui Xie, Zhuorui Ye, An Yang, Dayiheng Liu, Jingren Zhou, Ning Ding

Qwen-AgentWorld is a language model trained to act as a world model: given interaction history and an agent's action, it predicts the next environment observation (tool response, terminal output, UI accessibility tree, etc.) across seven domains via chain-of-thought reasoning. It is built through a three-stage pipeline (continual pre-training, supervised fine-tuning, and RL with combined rubric-and-rule rewards) and evaluated on AgentWorldBench, a benchmark of real trajectories from frontier agents.

Lei Bai, Zongsheng Cao, Yang Chen, Zhiyao Cui, Shangheng Du, Yue Fan, Shiyang Feng, Zijie Guo, Haonan He, Liang He, Xiaohan He, Shuyue Hu, Yusong Hu, Songtao Huang, Yichen Jiang, Hao Li, Xin Li, Dahua Lin, Weihao Lin, Fenghua Ling, Dongrui Liu, Zhuo Liu, Runmin Ma, Chunjiang Mu, Haoyang Peng, Tianshuo Peng, Jinxin Shi, Luohe Shi, Boyuan Sun, Zelin Tan, Shengji Tang, Qianyi Wang, Yiming Wu, Yi Xie, Xiangchao Yan, Jingqi Ye, Peng Ye, Fangchen Yu, Jiakang Yuan, Bihao Zhan, Bo Zhang, Chen Zhang, Shufei Zhang, Shuaiyu Zhang, Wenlong Zhang, Yiqun Zhang, Junpeng Zhao, Zhijie Zhong, Bowen Zhou, Yuhao Zhou

A recipe for training a 35B mixture-of-experts agent (Agents-A1) to handle long-horizon agentic tasks by scaling trajectory length rather than parameters. The method has three stages: full-domain supervised fine-tuning on ~100K long trajectories (average 45K tokens), training domain-specific teacher models, then consolidating them into one student via domain-routed on-policy distillation with salient vocabulary alignment (SVA), which aligns student and teacher on the teacher's top-k token support instead of just the sampled token.

Zhengxi Lu, Zhiyuan Yao, Zhuowen Han, Zi-Han Wang, Jinyang Wu, Qi Gu, Xunliang Cai, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen

SDAR trains multi-turn LLM agents by combining GRPO reinforcement learning with an auxiliary on-policy self-distillation loss, where a teacher branch is the same policy given privileged training-only context (retrieved skills). The distillation signal is filtered through a per-token sigmoid gate that strengthens supervision on tokens the teacher endorses (positive teacher-student log-probability gap) and softly attenuates tokens where the teacher disagrees.

Yifan Yang, Ziyang Gong, Weiquan Huang, Qihao Yang, Ziwei Zhou, Zisu Huang, Yan Li, Xuemei Gao, Qi Dai, Bei Liu, Kai Qiu, Yuqing Yang, Dongdong Chen, Xue Yang, Chong Luo

SkillOpt is a text-space optimizer that treats an agent's natural-language skill document as a trainable object, borrowing deep-learning controls like batches, a textual learning rate (bounded number of edits per step), a held-out validation gate, and epoch-wise slow/meta updates. A frontier optimizer model reads execution trajectories, proposes structured add/delete/replace edits to the skill, and only accepts edits that improve validation performance, exporting a compact best_skill.md (300 to 2,000 tokens) while the target model and harness stay frozen.

Hongyi Liu, Haoyan Yang, Tao Jiang, Bo Tang, Feiyu Xiong, Yuyu Luo, Zhiyu Li

SkillsVote is a lifecycle framework for managing libraries of Agent Skills (structured folders of instructions, scripts, and applicability conditions used by LLM coding agents). It does three things: performs agentic search over skill folders to pick relevant skills before a task, decomposes execution traces into skill-linked subtasks after a task, and gates library updates so only successful, reusable discoveries edit or create skills.

Seokju Cho, Ryo Hachiuma, Abhishek Badki, Hang Su, Byung-Kwan Lee, Chan Hee Song, Sifei Liu, Subhashree Radhakrishnan, Seungryong Kim, Yu-Chiang Frank Wang, Min-Hung Chen

SpatialClaw is a training-free agent framework that gives a vision-language model a persistent Python kernel (preloaded with input frames, perception tools like SAM3 and Depth Anything 3, and libraries like NumPy/SciPy) and lets it write and run one code cell per step, inspecting intermediate results before writing the next cell. This 'code as the action interface' approach replaces two prior designs: single-pass code (commit to a full program before seeing any output) and structured JSON/XML tool-calls (fixed API menu).

Jiajie Jin, Yuyang Hu, Kai Qiu, Qi Dai, Chong Luo, Guanting Dong, Xiaoxi Li, Tong Zhao, Xiaolong Ma, Gongrui Zhang, Zhirong Wu, Bei Liu, Zhengyuan Yang, Linjie Li, Lijuan Wang, Hongjin Qian, Yutao Zhu, Zhicheng Dou

Arbor is a framework for autonomous research (which the authors call Autonomous Optimization) where an LLM agent iteratively improves a code artifact against a fixed objective without step-level human supervision. Its core mechanism is Hypothesis Tree Refinement (HTR): a persistent tree whose nodes bind a hypothesis, a git-worktree artifact version, evaluation evidence, and a distilled insight, managed by a long-lived coordinator that dispatches short-lived executors and only promotes changes that pass a held-out test evaluator.