Research on systems that plan, use tools, and act across multiple steps. Follow advances in agent reliability, coordination, and evaluation.
56 papers · Latest edition October 3, 2026
Three of the latest briefs in this collection. Read the evidence and the original papers alongside them.
LEGO-Anything turns images into editable Blender programs through iterative coding agents, offering a promising but still imperfect route to reconstructable 3D worlds.
MILO uses teams of evolving AI agents to automatically discover better harnesses for long-horizon problem-solving systems.
This work trains AI agents to discover how to divide labor, challenge ideas, and combine reasoning so that teams can solve problems no individual agent could solve alone.
Newest editions first.
Xirui Li, Peng Shi, Mingwen Dong, Sheng Zhang, Zhuoyan Xu, Dongkyu Lee, Shuaichen Chang, Yi Xiang, Lin Pan, Jiarong Jiang
LEGO-Anything turns images into editable Blender programs through iterative coding agents, offering a promising but still imperfect route to reconstructable 3D worlds.
Prithwish Jana, Mononito Goswami, Hao Liu, Xinyu Li, Langlin Huang, Zhehui Huang, Zhishen Huang, Patrick Blöbaum, Anoop Deoras, Purak Jain, Nikos Kanakaris, Sahika Genc
MILO uses teams of evolving AI agents to automatically discover better harnesses for long-horizon problem-solving systems.
Aneesh Pappu, Mirac Suzgun, Yongchan Kwon, Federico Bianchi, Batu El, Mykel J. Kochenderfer, Hancheng Cao, James Zou
This work trains AI agents to discover how to divide labor, challenge ideas, and combine reasoning so that teams can solve problems no individual agent could solve alone.
Honglin Guo, Tao Gui, Yicheng Chen, Guanting Dong, Qiming Ge, Yuyang Hu, Zixian Huang, Jiajie Jin, Alexander Lam, Yining Li, Jiahang Lin, Yanjiang Liu, Xinyu Lu, Haijun Lv, Junlin Shang, Qisheng Su, Guoqiang Wang, Rui Wang, Zhecan Wang, Hao Xiang, Xinchen Xie, Shuhao Xing, Xiaoyu Xing, Wanghan Xu, Xinyu Yang, Yajie Yang, Chengfeng Zhao, Haoran Zhao, Ruojun Zhou, Yunhua Zhou, Yicheng Zou, Kun Cai, Qiye Cai, Xinmeng Che, Haodong Chen, Jiabei Chen, Jiahao Chen, Jiayi Chen, Yujia Chen, Lizhi Cui, Youheng Dai, Xin Deng, Yi Dong, Shihan Dou, Chenya Gu, Xu Guo, Ding Han, Feiyang Hao, Haotan He, Jie Hou, Binze Hu, Zijian Hu, Junhao Huang, Huicheng Jiang, Jiazhen Jiang, Shufan Jiang, Jiahao Kuang, Bowen Lai, Bo Li, Jiaqiang Li, Peng Li, Qilong Li, Zhuoqun Li, Jiaxiang Liu, Shuainan Liu, Tong Liu, Yi Liu, Zhonghang Lu, Jianwen Luo, Yanyi Luo, Huijie Lv, Ningsheng Ma, Zerun Ma, Houcheng Min, Chengjun Pan, Qiyuan Peng, Xiaoxuan Peng, Jianmin Qian, Jiantao Qiu, Wanying Ren, Huayu Sha, Jifei Shan...
Atria Dawn explores how research-oriented AI agents can move beyond completing tasks to partnering with humans on scientific discovery while preserving human oversight.
Xu Xu, Jinxiu Liu, Zhangbo Qiao, Jiaxing Lu, Xiangyu Zhang, Yubin Gu, Fangwei Ning, Yan Shi
OmniHarness turns successful visual-agent behavior into reusable symbolic skills that can be practiced, adapted, and plugged into new generation tasks.
Kaiyuan Liu, Qiuyang Mang, Bo Peng, Wenhao Chai, Hanchen Li, Shreyas Pimpalgaonkar, Luke Zettlemoyer, Alex Dimakis, Alvin Cheung
This paper shows that LLM agents often get worse returns from prolonged thinking and can perform better by splitting compute across multiple shorter attempts.
Muhammad Rafay Azhar, Yuhang Zhou, Gilbert Jiang, Yuchen Wang, Rahul Sharma, Matthew DeSousa, Jiayi Liu, Xin Guo, Lizhu Zhang, Xiangjun Fan
CORAL uses an LLM agent to continuously tune live recommender systems, improving engagement or reducing serving costs while staying within operational guardrails.
Ziyuan Liu, Hengqi Liu, Zichuan Wang, Yang Qin, Jiachen Liang, Xu Chu, Shaowei Chen, Yuantao Gu, Mu Chuan
Iris trains large language-model search agents to solve harder multi-hop web questions by repeatedly learning from challenging trajectories and managing context more effectively.
Junyao Yang, Yucheng Shi, Zhongzhi Li, Ruhan Wang, Zongxia Li, Haitao Mi, Leowei Liang
T1 trains a large MoE agent to complete hundreds of shell interactions, substantially improving long-horizon coding and terminal-task performance through verifier rewards and stable RL techniques.
Yuhao Wu, Jingyuan Zhang, Jiajun Shi, Xinping Lei, Qingshui Gu, Yuxuan Zhang, Zexuan Wang, Chen He, Chen Huang, Maojia Song, Zhiyuan Zeng, Shaowen Wang, Jinkai Liu, Yunfeng Shi, Jiaheng Liu, Shen Yan, Wenhao Huang, Ge Zhang, Wenxuan Zhang
HarnessDev asks whether LLMs can build and improve the software infrastructure that makes their own agents effective.
Subhadeep Pal, Fiona Y. Wang, Markus J. Buehler
SwarmWorld shows how language-model agents can collectively evolve technologies through shared environments, physical artifacts, and emergent social organization rather than direct coordination alone.
Yuqi Chen, Vincent Siu, Yang Liu, Dawn Song, Chenguang Wang
A simple inference-time steering vector lets LLM agents smoothly dial tool use up or down, improving the tradeoff between answer quality, cost, and unwanted actions.
Chaokun Chang, Yukun Zhou, Kaihua Fu, Dakai An, Tianyu Feng, Hanfeng Lu, Sheng Yao, Pu Guo, Yinghao Yu, Yizhou Shan, Bo Li, Binhang Yuan, Wei Wang
AgentSysBench shows that serving AI agents requires systems designed for long-running, stateful, tool-using workloads rather than just faster LLM inference.
Batu El, Jinhee Paeng, Fatih Dinc, Shiye Su, Mete Erdogan, Aneesh Pappu, Haotian Ye, Wanjia Zhao, Surya Ganguli, James Zou
A physics-inspired model predicts when groups of AI agents will reach consensus, polarize, or become indifferent—and how their interactions affect truth and political opinions.
Qingyao Li, Wenxiang Jiao, Shuai Shao, Kangning Zhang, Yuan Lu, Yi Guo, Weiwen Liu, Weinan Zhang, Yong Yu
SkillGate helps long-horizon AI agents choose the right procedural skill by giving skill-selection decisions their own dedicated learning signal.
Yifan Zhang, Yutong Dai, Juntao Tan, Luyu Yang, Rishi Mullur, Thai Hoang, Zhiyuan Hu, James Zhu, Phil Mui, Silvio Savarese, Ran Xu, Zeyuan Chen
DarwinX improves AI agents by evolving their prompts, tools, and workflows through population-based selection rather than changing the underlying model.
Igor Itkin
This paper shows how to replace expensive LLM agents with cheap learned surrogates so large artificial societies can be simulated on an ordinary laptop.
Daniel Armstrong, Xuan-Vu Nguyen, Octavian Susanu, Gabriel Gibberd, Théo A. Neukomm, Taddäus Strunden, Dan Forster, Morgane Delattre, Shawn Teh, Clément Rols, John Federice, Hayden Leatherwood, M. Lavelle Barnes, Maarten R. Dobbelaere, Peter Wipf, Jon T. Njardarson, Jieping Zhu, Philippe Schwaller
SynthEx uses an agentic large language model to design and refine ambitious natural-product synthesis routes that experts found comparable to human plans.
Hayder Tirmazi, Sam Markelon, Allison Bishop, Michael Mitzenmacher
This paper turns the practical problem of squeezing an AI agent’s memory into a limited context window into a formal communication-complexity theory.
Shuai Shao, Kangning Zhang, Qingyao Li, Shijian Wang, Hao Wang, Wenxiang Jiao, Yuan Lu, Yi Guo, Weiwen Liu, Weinan Zhang
Harness-R1 trains an engineer agent to patch an LLM agent’s runtime harness from its failures, improving task success across web, household, and database environments.
Dunjie Lu, Shuai Bai, Tianyi Bai, Sicheng Fan, Chang Gao, Jian Guan, Feng Hu, Mianqiu Huang, Xingyang Huang, Yizhen Jiang, Yuheng Jing, Dehui Kong, Ning Li, Dayiheng Liu, Shixuan Liu, Zheng Liu, Que Shen, Bowen Wang, Junli Wang, Chencan Wu, Rui Xie, Tianbao Xie, Zhihui Xie, Haiyang Xu, An Yang, Tao Yu, Wenzhen Yuan, Xi Zhang, Zhenru Zhang, Mingkang Zhu, Zhaoqing Zhu, Yizhong Cao, Kai Dang, Binyuan Hui, Kaixin Li, Junyang Lin, Haiquan Wang, Zekun Wang, Yiheng Xu, Fan Yan, Mengqi Yuan, Danyang Zhang, Jiajun Zhang, Zhipeng Zhang, Fan Zhou, Fan Zhou
Qwen-CUA is a large multimodal agent that learns to control computers directly from screenshots and mouse-keyboard actions, achieving strong performance on challenging real-world software tasks.
Bowen Qin, Yi Xie
This paper benchmarks how well coding agents find the right repository context before they try to write a patch.
Yisen Xu, Jiayuan Zhou, Ruiqi Pan, Tse-Hsun Chen
STAIR helps coding agents learn from past software repairs by turning old debugging trajectories into reusable, adaptable plans.
Qian Cheng, Saad Mohammad Rafid Pial, Ruize Tang, Yiming Su, Emilie Ma, Finn Hackett, Ivan Beschastnikh, Yu Huang, Tianyin Xu
Specula uses self-improving LLM agents to automatically write formal specifications and uncover deep bugs in real-world system software.
Jiarong Zhao, Zhikai Lei, Zhiheng Xi, Rui Zheng, Hang Yan, Jie Zhou, Qin Chen, Liang He
NexForge turns high-level capability requirements into large-scale executable training tasks, substantially improving open-source LLM agent performance.
Xiao Yu, Baolin Peng, Ruize Xu, Hao Zou, Qianhui Wu, Hao Cheng, Wenlin Yao, Nikhil Singh, Zhou Yu, Jianfeng Gao
OpenForgeRL makes it possible to train real-world tool-using and computer-use agents directly inside the complex harnesses where they operate.
Chenhui Gou, Haoqin Tu, Yunhao Fang, Jianfei Cai, Hamid Rezatofighi
Experience Distillation lets agents retain much of what they learn from trial and error without repeatedly paying the cost of interacting with the environment.
Seanie Lee, Sanjoy Chowdhury, Chao Jiang, Cheng-Yu Hsieh, Ting-Yao Hu, Alexander T Toshev, Oncel Tuzel, Raviteja Vemulapalli
This work trains API-calling agents without building real backends by having LLMs simulate entire interactive worlds and generate useful synthetic trajectories.
Yijia Fan, Zonglin Di, Zimo Wen, Yifan Yang, Mingxi Cheng, Qi Dai, Bei Liu, Kai Qiu, Yue Dong, Ji Li, Chong Luo
RESOURCE2SKILL turns videos, code, articles, and other human resources into reusable multimodal skills that make software agents more capable.
Shengyuan Liu, Jia-Xuan Jiang, Boyun Zheng, Cheng Wang, Zipei Wang, Wentao Pan, Hongtao Wu, Houwen Peng, Yu Gu, Lichao Sun, Yixuan Yuan
AMID uses collaborating AI agents to automatically design, test, verify, and audit medical-imaging models across many different tasks.
Zhenting Qi, Huangyuan Su, Ao Qu, Chenyu Wang, Yu Yao, Han Zheng, Kushal Chattopadhyay, Guowei Xu, Zihan Wang, Weirui Ye, Vijay Janapa Reddi, Ju Li, Paul Pu Liang, Himabindu Lakkaraju, Sham Kakade, Yilun Du
This paper argues that you can build smarter multi-agent systems not by centrally coordinating them, but by creating an economy where agents bid, earn, compete, and evolve into better reasoners.
Zhiling Yan, Dingjie Song, Hanrong Zhang, Wei Liang, Yuxuan Zhang, Yutong Dai, Lifang He, Philip S. Yu, Ran Xu, Xiang Li, Lichao Sun
OpenSkill lets LLM agents teach themselves from open-web resources and self-generated practice tasks, even when no labeled target data or verifier exists.
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
This paper introduces Arbor, an AI research agent that keeps a living tree of hypotheses and evidence so it can iteratively plan, test, and improve scientific ideas over long time horizons.
Jacky Kwok, Shulu Li, Pranav Atreya, Yuejiang Liu, Yixing Jiang, Chelsea Finn, Marco Pavone, Ion Stoica, Azalia Mirhoseini
This paper turns LLMs into fine-grained verifiers that can score, rank, and guide agentic tasks more effectively than ordinary judges, improving benchmark performance and even training efficiency.
Zhen Yang, Xiaogang Xu, Wen Wang, Cong Chen, Xander Xu, Ying-Cong Chen
This paper shows that letting multi-agent reasoning systems pass along partial thoughts as they are generated can make them both faster and, surprisingly, more accurate.
Alex Iacob, Andrej Jovanović, William F. Shen, Daniel Burkhardt, Meghdad Kurmanji, Nurbek Tastan, Lorenzo Sani, Niccolò Alberto Elia Venanzi, Ambroise Odonnat, Zeyu Cao, Bill Marino, Xinchi Qiu, Nicholas D. Lane
This paper introduces a self-improving agent framework where the evaluator changes too, letting writers, coders, and reviewers co-evolve in an ongoing Red Queen race.
Yubo Li, Yidi Miao, Yuntian Shen, Yuxin Liu
PANDO makes multimodal web agents cheaper over time by distilling useful skills online, cutting token use while improving task success on VisualWebArena.
Zhengyang Tang, Yuxuan Liu, Xin Lai, Junyi Li, Pengyuan Lyu, Jason, Yiduo Guo, Zhengyao Fang, Yang Ding, Yi Zhang, Weinong Wang, Huawen Shen, Xingran Zhou, Liang Wu, Fei Tang, Sunqi Fan, Shangpin Peng, Zheng Ruan, Anran Zhang, Benyou Wang, Rui Yan, Ji-Rong Wen, Chengquan Zhang, Han Hu
PhoneWorld turns real mobile app traces into scalable training environments, giving phone agents a new way to learn and be evaluated across many apps instead of one benchmark at a time.
MiniMax, :, Aili Chen, Aonian Li, Baichuan Zhou, Bangwei Gong, Binyang Jiang, Boji Dan, Changqing Yu, Chao Wang, Cheng Ma, Cheng Zhong, Cheng Zhu, Chengjun Xiao, Chengyi Yang, Chengyu Du, Chenyang Zhang, Chi Zhang, Chuangyi Huang, Chunhao Zhang, Chunhui Du, Chunyu Zhao, Congchao Guo, Da Chen, Deming Ding, Dianjun Sun, Dongyu Zhang, Enhui Yang, Fei Yu, Guang Zheng, Guodong Zheng, Guohong Li, Haichao Zhu, Haigang Zhou, Haimo Zhang, Han Ding, Hao Zhang, Haohai Sun, Haolin Lyu, Haonan Lu, Haoyu Wang, Huajie Shi, Huiyang Li, Jiacheng Chen, Jian Zhang, Jiaqi Zhuang, Jiaren Cai, Jiaxin Pan, Jiayao Li, Jiayuan Song, Jichuan Zhang, Jie Wang, Jihao Gu, Jin Zhu, Jingwei Dong, Jingyang Li, Jingyu Zhang, Jingze Zhuang, Jinhao Tian, Jinli Liu, Jinyi Hu, Jun Tao, Jun Zhang, Junbin Ruan, Junhao Xu, Junjie Yan, Junteng Liu, Junxian He, Kang Xu, Ke Ji, Ke Yang, Kecheng Xiao, Keyu Duan, Keyu Li, Le Han, Letian Ruan, Li Yuan, Lianfei Yu, Liheng Feng, Lijie Mo, Lin Li, Lingye Bao, Lingyu Yang, Lingyua...
MiniMax-M2 is a huge but sparsely activated language model built for agents, combining verifiable agent data, specialized RL, and self-improving training workflows to push real-world task performance.
Dingwei Chen, Zefang Zong, Zhipeng Ma, Leo Luo, Yang Li, Chengming Li, Peng Chen, Jie Jiang
This paper teaches LLM agents to know when they really need a tool—and when they should trust their own knowledge—cutting unnecessary tool calls while improving accuracy.
Changdae Oh, Wendi Li, Seongheon Park, Samuel Yeh, Tanwi Mallick, Sharon Li
This paper shows that RL post-training itself can provide a free, step-level signal for judging LLM agents, enabling better test-time scaling, uncertainty estimation, and failure analysis without training a separate reward model.
Negin Raoof, Richard Zhuang, Marianna Nezhurina, Etash Guha, Atula Tejaswi, Ryan Marten, Charlie F. Ruan, Tyler Griggs, Alexander Glenn Shaw, Hritik Bansal, E. Kelly Buchanan, Artem Gazizov, Reinhard Heckel, Chinmay Hegde, Sankalp Jajee, Daanish Khazi, Emmanouil Koukoumidis, Xiangyi Li, Hange Liu, Shlok Natarajan, Harsh Raj, Nicholas Roberts, Ethan Shen, Nishad Singhi, Michael Siu, Ashima Suvarna, Hanwen Xing, Patrick Yubeaton, Robert Zhang, Leon Liangyu Chen, Xiaokun Chen, Steven Dillmann, Saadia Gabriel, Xunyi Jiang, Anurag Kashyap, Boxuan Li, Yein Park, Minh Pham, Sujay Sanghavi, Lin Shi, Ke Sun, Yixin Wang, Zhiwei Xu, Erica Zhang, Siyan Zhao, Wanjia Zhao, Jenia Jitsev, Alex Dimakis, Benjamin Feuer, Ludwig Schmidt
This paper shows how to build better open training data for AI agents, and demonstrates that the resulting 100K-example dataset improves performance across multiple agentic benchmarks.
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 turns language models into simulated worlds for agents, enabling better planning, scalable RL training, and stronger downstream agent performance.
Yixuan Tang, Yi Yang
This paper shows that today’s AI research agents mostly generate narrow, local variations on existing science rather than broadly expanding the frontier of discovery.
Igor Costa, Christopher Baran
EvoGraph is a new system that lets software evolve its own code, docs, and pipelines using a graph-based mutation-and-selection loop powered by small language models.
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 treats an agent’s skill as something you can train like a model parameter, using controlled text edits and validation to make AI agents steadily improve without extra runtime cost.
Rana Muhammad Usman
This paper shows that what an LLM agent reads in a feed can quietly steer its decisions, even when the final prompt stays the same.
Feiyang Kang, Hanze Li, Adam Nguyen, Mahavir Dabas, Jiaqi W. Ma, Frederic Sala, Dawn Song, Ruoxi Jia
This paper asks whether coding agents can take over the tedious loop of data curation, and finds they can do useful work—but only when guided by structured method adaptation rather than vague prompting.
NVIDIA, :, Aaron Blakeman, Aaron Thomas, Aastha Jhunjhunwala, Abhibha Gupta, Abhinav Khattar, Adam Rajfer, Adi Renduchintala, Adil Asif, Aditya Vavre, Adriana Flores Miranda, Ahmad Bilal, Aileen Zaman, Ajay Hotchandani, Akanksha Shukla, Akhiad Bercovich, Aleksander Ficek, Alex Gronskiy, Alex Kondratenko, Alex Steiner, Alex Ye, Alexander Bukharin, Alexandre Milesi, Ali Taghibakhshi, Alice Gatti, Alisa Liu, Alok Kumar, Amar Phanishayee, Ameya Sunil Mahabaleshwarkar, Amir Klein, Amit Zuker, Amnon Geifman, Anahita Bhiwandiwalla, Ananth Subramaniam, Andrea Santilli, Andrew Fulks, Andrew McHarg, Andrew Tao, Andrii Skliar, Anjulie Agrusa, Ankur Srivastava, Ankur Verma, Anna Shors, Anna Warno, Antoni-Joan Solergibert I Llaquet, Arham Mehta, Arkadiusz Nowaczynski, Arti Jain, Ashwath Aithal, Ashwin Poojary, Asif Ahamed, Asit Mishra, Asma Kuriparambil Thekkumpate, Atefeh Sohrabizadeh, Avinash Kaur, Avinash Vem, Ayush Dattagupta, Barath Subramaniam Anandan, Bardiya Sadeghi, Ben Lanir, Benedik...
Nemotron 3 Ultra is a massive open AI model that mixes MoE, Mamba, and transformer ideas to deliver faster long-context reasoning for autonomous agents.
Xinyu Che, Junqi Xiong, Yunfei Ge, Xinping Lei, Shihao Li, Hang Yan, Han Li, Yuanxing Zhang, Zhiqi Bai, Jinhua Hao, Ming Sun, Han Li, Jiaheng Liu
This paper teaches AI agents to turn messy human web guides into editable skills they can keep improving from their own experience.
Rui Yang, Qianhui Wu, Yuxi Chen, Hao Bai, Wenlin Yao, Hao Cheng, Baolin Peng, Huan Zhang, Tong Zhang, Jianfeng Gao
OpenWebRL shows that visual web agents can be trained directly on live websites with online multi-turn RL, delivering a strong open-source system that rivals proprietary agents.
Siyi Chen, Hugo Hadfield, Alex Zook, Mikaela Angelina Uy, Chan Hee Song, Erwin Coumans, Xuning Yang, Faisal Ladhak, Qing Qu, Stan Birchfield, Jonathan Tremblay, Valts Blukis
VoLo teaches a vision-language model to act like a robot conductor, dynamically steering tools and recovery steps during long-horizon manipulation instead of waiting for each action to finish.
Zisu Huang, Jingwen Xu, Yifan Yang, Ziyang Gong, Qihao Yang, Muzhao Tian, Xiaohua Wang, Changze Lv, Xuemei Gao, Qi Dai, Bei Liu, Kai Qiu, Xue Yang, Dongdong Chen, Xiaoqing Zheng, Chong Luo
This paper studies how AI agents turn raw experience into reusable skills, finds that these skills can help but also hurt in surprising ways, and proposes a meta-skill to make skill extraction more reliable.
Jian Xie, Tianhe Lin, Zilu Wang, Yuting Ning, Yuekun Yao, Tianci Xue, Zhehao Zhang, Zhongyang Li, Kai Zhang, Yufan Wu, Shijie Chen, Boyu Gou, Mingzhe Han, Yifei Wang, Vint Lee, Xinpeng Wei, Xiangjun Wang, Yu Su, Huan Sun
QUEST shows that fully synthetic training can produce open deep research agents that rival proprietary systems on long-horizon search, citation grounding, and synthesis tasks.
Rui Meng, Bhavana Dalvi Mishra, Jiefeng Chen, Chun-Liang Li, Palash Goyal, Mihir Parmar, Yiwen Song, Yale Song, Rajarishi Sinha, Parthasarathy Ranganathan, Burak Gokturk, Jinsung Yoon, Tomas Pfister
ScientistOne is an autonomous research agent that tries to make every claim traceable, aiming to stop hallucinated citations, unverifiable results, and mismatches between code and papers.
Dingbang Wu, Rui Hao, Haiyang Wang, Shuzhe Wu, Han Xiao, Zhenghong Li, Bojiang Zhou, Zheng Ju, Zichen Liu, Lue Fan, Zhaoxiang Zhang
MobileGym is a scalable, deterministic mobile app simulator and benchmark designed to train and evaluate GUI agents more reliably and at much larger scale.