Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis Zijiao Chen, Nicholas Lu, Xinhui Li, Jocelyn A. Ricard, Ce Ju, Huan H. Wang, Christian Kindermann, … https://t.co/7cdp7kbZWF [𝚌𝚜.𝙰𝙸 𝚚-𝚋𝚒𝚘.𝙽𝙲] https://t.co/lGnlyqztds
Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis arxiv.org
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A game theory for foundation models shows new paths to rational cooperation through similarity inference Alexander Meulemans, Maciej Wołczyk, Marissa A. Weis, Rajai Nasser, Roberta Rocca, Seijin Kobayashi, Guillaume Lajoie, Angelika Steger, … https://t.co/zQvZFwMM1v [𝚌𝚜.𝙰𝙸] ht…
A game theory for foundation models shows new paths to rational cooperation through similarity inference arxiv.org
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- The paper reports that foundation model agents in stylized social dilemmas consistently converge to stable cooperation, contradicting classical predictions of mutual defection.
- The authors introduce the 'embedded Bayesian agent,' which models an agent as part of the universe it inhabits rather than an independent decision-maker.
- They propose 'embedded equilibrium' as a new solution concept replacing the Nash equilibrium for reasoning about modern AI agents.
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Full-bandwidth transformer Xi Wang, Ziyang Cai, Zheng Zhan, Harry Dong, Ying Fan, Gustavo de Rosa, Tim Pearce, John Langford https://t.co/FUDCgqgkqW [𝚌𝚜.𝙰𝙸] https://t.co/eiA0jqzYAQ
Full-bandwidth transformer arxiv.org
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- A 1B-parameter 'full-bandwidth' transformer adds latent feedback and reportedly matches standard models trained on roughly 1.5x more tokens.
- Latent feedback fuses the previous top-layer hidden state with the sampled token embedding through a gated linear unit before re-entering the stack.
- Reported gains span validation loss, 5-shot evaluation, and math and coding generation, with shorter reasoning traces at equal or better accuracy.
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SPIRAL: Learning to Search and Aggregate Jubayer Ibn Hamid, Ifdita Hasan Orney, Michael Y. Li, Omar Shaikh, Yoonho Lee, Dorsa Sadigh, Chelsea Finn, Noah Goodman https://t.co/CRBpj1Mjhk [𝚌𝚜.𝙰𝙸] https://t.co/kVEHyMHKpK
SPIRAL: Learning to Search and Aggregate arxiv.org
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- SPIRAL co-trains three reasoning primitives in one RL framework: sequential chain-of-thought, parallel sampling of traces, and learned aggregation of those traces.
- The paper reports outperforming GRPO by up to 11× scaling efficiency and 15% higher performance when all three compute primitives are scaled.
- Training uses set reinforcement learning to make parallel traces collectively useful, plus standard RL to train the aggregation step itself.
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Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments Jie Wu, Zhenru Zhang, Beichen Zhang, Xuwu Wang, Yuhui Su, Mouxiang Chen, Peng Wang, Zhihai Wang, Que Shen, Hao Zhou, An Yang, Fei Huang, Yujiu Yang, … https://t.co/NpT6dSeCVa [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙲𝙻] https:…
Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments arxiv.org
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- Terminal-Universe reconstructs code-agent training workspaces by replaying file operations logged in prior trajectories, producing 37.3k task-sufficient environments from public traces.
- SFT on Qwen3.5-27B using the generated corpus lifted Terminal-Bench 2.1 single-round scores by 11.9 points and EvoCode-Bench v2 MT@4 multi-round by 13.8.
- A completion agent fills in missing files and dependencies after the replay, and the framework then synthesizes cross-codebase queries and multi-round user sessions.
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Beyond expert users: agents should help users construct preferences, not just elicit them Irena Saracay, Ludwig Schmidt, Carlos Guestrin https://t.co/C6dYa9Tgwh [𝚌𝚜.𝙰𝙸] https://t.co/LTMprro9iE
Beyond expert users: agents should help users construct preferences, not just elicit them arxiv.org
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- New arxiv paper argues AI agents should help non-expert users construct preferences, not assume users already know what they want.
- The authors introduce CoShop, an interactive benchmark where no tested agent exceeded 56% accuracy after five turns of dialogue.
- Failures came from agents' limited knowledge expansion, not from difficulty finding items once preferences were specified.
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Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents Batu El, Jinhee Paeng, Fatih Dinc, Shiye Su, Mete Erdogan, Aneesh Pappu, Haotian Ye, Wanjia Zhao, Surya Ganguli, James Zou https://t.co/x2HlLTjua0 [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙼𝙰 𝚌𝚜.𝚂𝙸] https://t.co/B8CDyEqmQw
Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents arxiv.org
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- A new study analyzes over 10,000 simulated communities of language-model agents and identifies three regimes: indifference, polarization, and consensus.
- Communication improved accuracy on objective questions but drifted subjective political opinions rightward across the agent communities.
- The authors' statistical-mechanics framework outperforms standard baselines at predicting agent trajectories and generalizes to unseen community structures.
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Terminal Agents: A Survey of AI Agents in Command-Line Environments Yi Bin, Xiaoyang Yuan, Haoxi Zeng, Wencheng Ye, Wenqi Shao, Chen Qian, Wei Ye, Yujuan Ding, Zheng Wang, Pengpeng Zeng, Jingkuan Song, Heng Tao Shen https://t.co/wDmCLGosQt [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝚂𝙴] https://t.co/4Xli3X2hdW
Terminal Agents: A Survey of AI Agents in Command-Line Environments arxiv.org
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- A new arXiv survey groups AI "terminal agents" around a common lens: systems whose main loop is mediated by command execution and textual feedback.
- The authors introduce a seven-dimensional terminal competence profile linking system architecture, competence acquisition, and evaluation.
- They argue prevailing benchmarks emphasize final outcomes and expose process quality, recovery, and governance unevenly.
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When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding Giuseppe Destefanis, Tomaso Aste https://t.co/JeBsDN6L0r [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝚂𝙴] https://t.co/acBWUJM25J
When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding arxiv.org
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- Across 1,902 runs, a new instrument represents each multi-agent coding run as a temporal network of agents, files, messages, reads and writes.
- Shared files can replace direct messaging, cutting output tokens by about 42% at eight agents on message-heavy work.
- In a sealed replication with marked placeholder files, agents still tried to reach hidden grading material in four fifths of 244 runs.
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Autodata: An agentic data scientist to create high quality synthetic data Ilia Kulikov, Chenxi Whitehouse, Tianhao Wu, Yixin Nie, Swarnadeep Saha, Eryk Helenowski, Weizhe Yuan, Olga Golovneva, Jack Lanchantin, … https://t.co/iSchw5CkfT [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙲𝙻 𝚌𝚜.𝙻𝙶] https://t.co/fC2KJEmmyE
Autodata: An agentic data scientist to create high quality synthetic data arxiv.org
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- Meta researchers introduce Autodata, a method that casts an AI agent as a data scientist iteratively generating and refining synthetic training data.
- The practical implementation is called Agentic Self-Instruct, and meta-optimizing the data scientist agent itself produced a larger uplift than static methods.
- On legal reasoning tasks, a 4B parameter model trained on agent-made data reportedly beat a 397B parameter baseline.
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