Jaime Teevan

In my series on Multi-agent design best-practices, this blog outlines several key design considerations and discusses how to implement a system where agents balance competitive and cooperative behaviors. This balance is essential for us to design a MAS that functions as a holistic system of agents. To achieve this I show how we can use game-theoretic structures that account for both individual incentives and collective outcomes.

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New work on Rethinking Thinking Tokens: LLMs as Improvement Operators: https://lnkd.in/errhNuuz Reasoning training encourages LLMs to produce long chains of thought (CoT), improving accuracy via self-checking but increasing context length, compute cost, and latency. This work studies whether frontier models can achieve better trade-offs, higher accuracy with lower cost. The paper develops a simple yet effective Parallel-Distill-Refine (PDR) procedure: Generate diverse drafts in parallel, Distill them into a compact textual workspace, and Refine conditioned on this workspace. This decouples context length from total token count, allowing control over compute via parallelism. PDR yields higher accuracy than long CoT at lower latency. Training an 8B model with RL to align with PDR further shifts the Pareto frontier. On math benchmarks, PDR achieves +11% (AIME 2024) and +9% (AIME 2025) over single-pass baselines. With Lovish Madaan, Aniket Didolkar, Suchin Gururangan, John Quan, Ruan Silva, Manzil Zaheer, Sanjeev Arora, and Anirudh Goyal.

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Tomorrow at #SSDBM2025: Tanya will be presenting our vision for "OmniMesh: Addressing Findability Challenges in Distributed Nature Data Repositories"! This is a joint effort with Wei-Lun (Harry) Chao, Hilmar Lapp, Carl Boettiger, and Rongjun Qin. Findability is a critical impediment to biodiversity work: datasets are scattered and hard to find across many siloed repositories. At the same time, biodiversity data is inherently multimodal, making annotation a bottleneck. OmniMesh combines embeddings and zero-shot models to stitch these silos together with a lightweight, standards-based search layer. #multimodal #search #FAIR #ssdbm

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A recent technical report from Beijing-based researchers at the Chinese Academy of Sciences, titled "SpikingBrain," is generating significant discussion. The paper introduces a family of brain-inspired large models, including SpikingBrain-7B, which reportedly achieves remarkable efficiency gains. Key performance claims from the report include: Inference Speed: The 7B model is said to attain an over 100-fold speedup in Time to First Token (TTFT) when processing long-context sequences of 4 million tokens. Power Efficiency: The model's design, which leverages an event-driven spiking mechanism, achieves 69.15% sparsity. This high level of sparsity is the basis for its claims of significantly reduced power consumption compared to traditional Transformer architectures. These results, if validated, represent a substantial advancement in efficient large-scale model design. As the paper is currently available as a preprint, the forthcoming peer review and independent replication of these findings are highly anticipated by the research community. Supporting Links: Primary Research Paper (arXiv): For a complete technical overview, you can access the preprint directly: https://lnkd.in/erfVy7Hh GitHub Repository: The project code and models have also been made available: https://lnkd.in/ejxD-5xJ

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Signs on Paper: How can we use foundation models to make sense of both modern and historical, and culturally rich sign language print dictionaries? We report on early eplorations in this poster to be presented Friday Sept 12 at CLIN25. Comments and ideas welcome! Nargess Asghari, Victoria Nyst and Peter van der Putten. Signs on Paper: exploring multimodal foundation models for classification of still images of signs in print dictionaries. Poster at Computational Linguistics in The Netherlands (CLIN35), Leuven, Sept 12 2025.

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With growing investment in agentic AI, we need shared frameworks to set expectations and guide responsible progress. This paper (https://lnkd.in/gca56zaE) does exactly that, much like how staged autonomy levels helped advance self-driving when the problem was too complex to solve all at once. There are still open questions about how crisp the boundaries are between levels and how certification might work in practice, but these are exactly the kinds of challenges worth tackling now. A valuable step forward at the right time.

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AI on Review: How Large Language Models Are Reshaping Peer Review The Peer Review Crunch and New "Reviewer Duties" Peer review is the backbone of scientific quality control, ensuring that research findings are vetted for accuracy and significance before publication (pmc.ncbi.nlm.nih.gov). In fast-moving fields like machine learning and computer vision, top conferences function much like journals - and the integrity of science depends on rigorous peer evaluation. However, the system is straining under an avalanche of submissions. Major AI conferences now routinely receive well over 10,000 papers, a surge that has stretched the reviewer pool to its limits (arxiv.org). This deluge has led to radical policy changes: some conferences now essentially conscript all submitting authors into service as reviewers. For example, ICLR 2025 explicitly warned authors that any paper without at least one author signed up to review would be desk-rejected (reddit.com). NeurIPS and others have similarly pleaded that "all authors help with reviewing, if asked," to tackle the reviewer shortage. https://lnkd.in/d7KDP99K

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Today I am releasing our new report on adolescents and anthropomorphic AI with everyone.AI and iRAISE. This work starts from a simple premise: adolescents will relate to AI systems socially, whether developers intend it or not. The real leverage sits in model behavior. In the cues that either keep a young person oriented toward real-world relationships and reflection, or inherently encourage reliance on the system. The question we asked throughout this process was direct: what does AI owe adolescents when it can speak to them like a social partner? Over the past months we combined: – Industry consultations to surface operational design questions – Expert input across developmental science, mental health, children’s rights, and safety – An iRAISE Lab to translate concerns into testable behavioral criteria – International governance dialogue to stress-test the framing across contexts One point kept resurfacing: risk is driven less by the label on a product and more by the interaction pattern that repeats over time. When adolescence, anthropomorphism, and children’s rights are considered together, safety becomes a developmental and governance obligation. The practical issue is whether AI interaction patterns support autonomy, resilience, social competence, and independent thinking, or whether they reshape those trajectories through engineered comfort and approval. Adolescent development is stable, but model behaviors can be adjusted. Our focus has been to identify which behaviors warrant hard boundaries now, and which require further evidence before becoming enforceable design rules. The next step is instrumentation: a clear behavioral taxonomy, explicit gradients from tool-like support to relationship-like dynamics, structured evaluation scenarios, and calibrated expert rating. The aim is to make parasocial pull of AI governable in product terms, while staying honest about uncertainty. The report is live today. Thank you to all the amazing persons and experts who have contributed to this work Adrien ABECASSIS, Kate Blocker, PhD, Dr. Maxime Derian, Sara Grimes, Thao Ha, Sameer Hinduja, Daniel Hipp, Melinda Karth, Ph.D., Pilyoung Kim, Olga Muss Laurenty, Sonia Livingstone, Polina Lulu, Celine Malvoisin, Kris Perry, Gregory Renard, Bethany Robertson, Anne-Sophie SERET, Sonia Tiwari, Scott Traylor, Ed.M., Ying Xu. And a special thank you for Maxime Le Bourgeois for your support on this research. Link in the comment

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«The proposed priority on AI education outlines key areas for expanding responsible AI education, including: - Integrating AI literacy into teaching practices to improve student outcomes - Expanding AI and computer science education in K-12 schools and higher education institutions - Supporting professional development for educators on teaching AI and computer science fundamentals - Using AI to personalize learning and support differentiated instruction, improving outcomes for students at all levels» https://lnkd.in/d2uZCeR6

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Special Issue of Production and Operations Management on "Generative AI (GenAI) and Agentic AI at the Marketing–Operations Interface." Guest edited by Roland Rust, Ming-Hui Huang, Shane Wang, Praveen Kopalle. Commitment to Timeliness   Given the rapid pace of research and practice in this domain, the editorial team is committed to quick turnaround and timely decisions. A paper submitted to the special issue will be processed right away. Authors are encouraged to submit as soon as they are ready. Our goal is to ensure that accepted papers are published swiftly, with the special issue scheduled for print publication in early 2027 to maximize its relevance and impact.   First-round submission deadline: June 30, 2026   First-round decisions: August 31, 2026   Revised submission deadline: November 15, 2026   Final decisions: December 31, 2026

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The new University of California, Berkeley Data Science Education Fellowship trains postdocs to lead data science teaching + research grounded in equity, cultural relevance + ethical practice. Learn more: https://lnkd.in/gGHTsZkQ Backed by the Social Sciences D-Lab, UC Berkeley, the University of California, Berkeley, School of Education (BSE) + UC Berkeley College of Computing, Data Science, and Society (CDSS), the program aims to reshape who data science serves — and how.

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I sat down with Michael J Keegan at IBM Center for The Business of Government on their Federal News Network radio show to discuss our recent work (Yi Ming Andy Xia Michael Overton, Ph.D Nisa Gurbuz Brandon De Bruhl, MPP, MA) on Generative AI and the federal labor market. A link to that interview is here: https://lnkd.in/gzWH6TzJ The report on which it's based can be found in the comments below.

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Back in March, I posted about the National Science Foundation (NSF) Request for Information on the development of an AI Action Plan for the US. https://lnkd.in/gUWEtWix Any member of the public was welcome to submit a comment, so I did, alongside the likes of Google, Anthropic, and OpenAI. Last month, those comments (presumably after a basic quality check) have been made publicly available on a US federal government website. https://lnkd.in/g8QwAUHx I’m happy to say that my dinky little policy memo made the cut! You can find it linked in the comments. My key recommendation was this: // Thus, my proposal is that we begin considering realigning both funding and research efforts somewhat in the next few months. The National Artificial Intelligence Research Resource (NAIRR) Pilot launched last year is one such avenue. For a fraction of the investment needed to build massive new data centers, I propose allocating a portion of the NSF appropriations into establishing a new graduate fellowship program focused on AI, similar to the [DoD] NDSEG or [DoE] CSGF, and complementing the existing NSF GRFP (of which I was a fellow). In particular, there should be equal emphasis on theory and foundational research as on the business and industrial applications of AI. As the first holder of your office, Vannevar Bush, wrote in his epochal 1945 report²: “Progress … depends upon a flow of new scientific knowledge. … This essential, new knowledge can be obtained only through basic scientific research. … With some notable exceptions, most research in industry and Government involves application of existing scientific knowledge to practical problems. It is only the colleges, universities, and a few research institutes that devote most of their research efforts to expanding the frontiers of knowledge.” It would serve us well to remember and heed Bush’s advice. ——— ² https://lnkd.in/gY8fPh67 //

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