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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
AI Weekly's analysis
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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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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
AI Weekly's analysis
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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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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
AI Weekly's analysis
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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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Bayesian control for coding agents Theodore Papamarkou, Vladislav Smirnov, Viktor Mazanov, Artem Vazhentsev, Preslav Nakov, Timothy Baldwin, Artem Shelmanov https://t.co/1EUIZ7fmTy [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙲𝙻] https://t.co/5sFFzguwnn
Bayesian control for coding agents arxiv.org
AI Weekly's analysis
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- A new arxiv paper recasts coding-agent orchestration as cost-sensitive sequential hypothesis testing managed by a Bayesian controller.
- The controller decides dynamically whether to gather more evidence, refine the solution, run a verifier, or stop the run.
- Authors report the approach is most valuable when verification is costly and critics are informative but imperfect, across six generators and nine benchmarks.
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auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation Ben Prystawski, Kushin Mukherjee, Daniel Wurgaft, Linas Nasvytis, Michael Y. Li, Noah D. Goodman, Michael C. Frank https://t.co/U0Bv3bc9yi [𝚌𝚜.𝙰𝙸] https://t.co/S9vC7tD3F3
auto-psych: Automating the science of mind using agent-driven theory discovery and experimentation arxiv.org
AI Weekly's analysis
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- Auto-psych uses nested loops: an inner loop generates probabilistic cognitive models, an outer loop designs and runs online human experiments.
- In three independent human experiments, the system's discovered theories fit the data better than theories drawn from the scientific literature.
- The benchmark task was a classic cognitive psychology problem about how people perceive randomness in coin flip sequences.
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PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning Chunji Lv, Yangguang Wei, Junlin Liu, Yang Gao, Ming Liu, Xinming Wang, Jinyang Wu, Guoren Wang, Changsheng Li https://t.co/6jiDd4Z8rc [𝚌𝚜.𝙰𝙸] https://t.co/xeEMMkcMyU
PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning arxiv.org
AI Weekly's analysis
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- PCSD combines dense token-level teacher supervision with sparse environmental rewards, jointly optimized with GRPO, addressing credit assignment in agentic reinforcement learning.
- On ALFWorld the method reportedly exceeds GRPO by 15.6-13.3 points and beats a baseline called SDAR by 6.2-5.5 points.
- The unseen ALFWorld split shows a 15.8 point gain over GRPO, while WebShop results remain competitive with existing baselines.
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Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification Yunhao Feng, Ruixiao Lin, Ming Wen, Qinqin He, Yanming Guo, Yifan Ding, Yutao Wu, Jialuo Chen, Yunhao Chen, Xiaohu Du, Jianan Ma, Zixing Chen, … https://t.co/zQzYXScMTq [𝚌𝚜.𝙰𝙸] https://t.…
Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification arxiv.org
AI Weekly's analysis
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- The Vera framework reports average attack success rates of 93.9% against four production agent frameworks under multi-channel attacks.
- Vera-Bench ships 1,600 executable safety cases spanning 124 risk categories, covering OpenClaw, Hermes, Codex, and Claude Code.
- Verifiers judge outcomes using environment state and tool-call evidence rather than the agent's own self-report of what happened.
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Subliminal Clocks: Latent Time Modelling in Diffusion Language Models Maximo Rulli, Thomas Fontanari, Simone Petruzzi, Federico Alvetreti, Giorgio Strano, Donato Crisostomi, Giorgos Nikolaou, Tommaso Mencattini, Andrea Santilli, … https://t.co/jliEcX8tuE [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙲𝙻] https://…
Subliminal Clocks: Latent Time Modelling in Diffusion Language Models arxiv.org
AI Weekly's analysis
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- Diffusion language models lack explicit timestep conditioning yet still encode denoising progress in their residual streams, decodable by probes across layers.
- Steering the model along a low-dimensional subspace tied to the inferred timestep produces predictable shifts in output confidence and entropy.
- The latent time representation shows structured, interpretable geometry in activation space, per researchers at Sapienza University of Rome and EPFL.
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Discrete Diffusion Language Models for Interactive Radiology Report Drafting Max Van Puyvelde, Halil Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert https://t.co/sCm225Db0x [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙻𝙶] https://t.co/Uag6VCLTZv
Discrete Diffusion Language Models for Interactive Radiology Report Drafting arxiv.org
AI Weekly's analysis
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- DiffusionGemma-26B matches or exceeds its same-size autoregressive sibling Gemma-4-26B on every medical VQA dataset the authors tested.
- Decoding is reported at 3.5-4.4x faster than the AR baseline, with 3.8B active parameters after LoRA fine-tuning of the MoE model.
- Bidirectional denoising gives the model any-order infill, so a radiologist can fix report fragments and have the model fill between them.
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Theoria: Rewrite-Acceptability Verification over Informal Reasoning States Ben Slivinski, Michael Saldivar https://t.co/QZpIBtXkjY [𝚌𝚜.𝙰𝙸 𝚌𝚜.𝙲𝙻 𝚌𝚜.𝙻𝙶 𝚌𝚜.𝙻𝙾 𝚌𝚜.𝚂𝙴] https://t.co/WwyjNWGtOs
Theoria: Rewrite-Acceptability Verification over Informal Reasoning States arxiv.org
AI Weekly's analysis
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Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination Subhadeep Pal, Shashwat Sourav, Tirthankar Ghosal, Markus J. Buehler https://t.co/iLYMDMaIY4 [𝚌𝚜.𝙰𝙸 𝚌𝚘𝚗𝚍-𝚖𝚊𝚝.𝚖𝚝𝚛𝚕-𝚜𝚌𝚒 𝚌𝚜.𝙲𝙻 𝚌𝚜.𝙻𝙶] https://t.co/SMlyxB2nbf
Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination arxiv.org
AI Weekly's analysis
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- Graph-PRefLexOR uses Group Relative Policy Optimization to split reasoning into mechanism exploration, graph construction, pattern extraction, and hypothesis synthesis.
- On 100 open-ended materials science and mechanics questions, the system reports 40-65% improvements over base models, with the largest gains in reasoning traceability.
- Output embeddings show roughly 2-3x greater semantic diversity than baselines, which the authors credit to long-range recombination inside a bounded semantic space.
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Two AI Metrics Diverged: Will it Make All the Difference? Alex Fogelson, Zachary A. Brown, Hans Gundlach, Jayson Lynch, Neil Thompson https://t.co/lV6uJDlT4w [𝚌𝚜.𝙰𝙸] https://t.co/Qef4NNwKJB
Two AI Metrics Diverged: Will it Make All the Difference? arxiv.org
AI Weekly's analysis
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- A new arXiv paper argues whether small AI models eventually catch up to frontier systems depends entirely on which performance metric you pick.
- The authors show validation loss gaps shrink, but on other unbounded metrics frontier models grow their lead forever with more compute.
- Bounded and unbounded metrics can suggest opposing policy responses for capabilities like software engineering, synthetic biology, or rhetorical persuasiveness.
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