They were basically asking for it. This is sensible on part of the U.S. government. If you keep doing fear-mongering, you should be taken at your word.
Who's Who of AI
Shubhendu Trivedi
Why they matter
Directory member with public evidence across Models & releases.
- AI signals
- 18 past 30d
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- 4 distinct domains
- Discussions
- 42 past 30d
- Latest signal
- 2h ago
Interests on bsky: ML research, applied math, and general mathematical and engineering miscellany. Also: Uncertainty, symmetry in ML, reliable deployment; applications in LLMs, computational chemistry/physics, and healthcare.
https://shubhendu-trivedi.org
What they're sharing
Statement on the US government directive to suspend access to Fable 5 and Mythos 5 anthropic.com
When AI builds itself anthropic.com
DiffusionGemma: 4x faster text generation blog.google
math/overview.pdf at main · openai/math github.com
programmablemutter.com
The Siren Call of Silicon Leviathan: Reflections on blowup and Aufklärungsdämmerung arxiv.org
[2605.22763v1] Advancing Mathematics Research with AI-Driven Formal Proof Search arxiv.org
Mechanism Design for Alignment and Control arxiv.org
Price of Anarchy of First-Price Auctions in Agentic Markets arxiv.org
The Emergent Symbolic Structure of Artificial Neural Networks arxiv.org
ATLAS: Active Theory Learning for Automated Science arxiv.org
Conformal Prediction Through the Lens of Hypothesis Testing: Universality, Impossibility, and Optimality arxiv.org
Articles & links
One could read most points here cynically. But could also take them at their word and see what could be done. Given the sort of equilibrium we have been post GPT-2, the sort of pause they are advocating is simply not going to happen. You are talking of powerful international a…
Diffusion Gemma seems quite cool. Going to look into it during the weekend (so another exercise in harness design). It's funny and nice to see Google releasing open models one after the other, with a focus on the small end (quite a significant part of the enterprise ecosystem)…
AI Weekly's analysis
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- DiffusionGemma generates 256 tokens per forward pass using bidirectional attention, reaching 1,000+ tokens/sec on a single H100 GPU.
- With only 3.8B active parameters during inference and an 18GB VRAM footprint when quantized, it runs on consumer hardware without server-grade resources.
- Google recommends DiffusionGemma only for speed-critical workloads like in-line editing and code infilling, not for applications requiring maximum quality.
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Jokes aside, seems like they have partial results for three millennium problems. UGC I don't care about. It seems like they went through a laundry list of problems (Erdos reciprocal level), of the sorts that we know can be attacked with these models. Kinda boring, tbqh github.…
AI Weekly's analysis
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Good article. I don't know and don't care who's Prince. But I like a good Drucker defense. www.programmablemutter.com/p/ai-isnt-ma...
AI Weekly's analysis
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- Cloudflare CEO Matthew Prince laid off more than 20% of his workforce while citing Peter Drucker's 1954 management framework to explain the cuts.
- Prince labeled laid-off workers 'measurers' — a Drucker category he applied to middle management, finance, legal, and internal auditing.
- Henry Farrell argues Prince inverted Drucker's intent: Drucker used measurement to develop managers, not to identify who to eliminate.
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This is a great essay by A. Gamburd. Funnily I had been thinking about Schmitt (and in fact, specifically The Leviathan in the State Theory of Thomas Hobbes) and Panofsky in these very contexts; they had become part of my mental vocabulary on how I thought of the situation, bu…
AI Weekly's analysis
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- Mathematician Alexander Gamburd describes OpenAI's output as 166 pages, 616,000 lines of Lean, 88 hours, 10,000 software agents.
- At the time of writing, the paper reports, no human being had read the proof in full.
- Gamburd calls accepting the certificate without the demonstration a Hobbesian bargain and asks for a covenant requiring human understanding.
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"Our most capable agent autonomously resolved 9 of 353 open Erdős problems at the per-problem cost of a few hundred dollars, proved 44/492 OEIS conjectures, and is being deployed in combinatorics, optimization, graph theory, algebraic geometry, and quantum optics research." Ni…
Not the easiest read, and perhaps too stylized, but a really cool paper: arxiv.org/abs/2609.01595
AI Weekly's analysis
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- Three economists frame AI alignment as a mechanism-design problem where both the agent's preferences and its capabilities are unknown to the principal.
- The key assumption is a 'one-sided imitation structure' in which an AI agent can hide capabilities but cannot fabricate ones it lacks.
- The framework is applied to sandbagging, an alignment–interpretability trade-off, peer scoring, reward coupling and scalable oversight.
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Check out our paper on whether first-price auctions can efficiently allocate queries among strategic AI agents (in a decentralized serving-time market): arxiv.org/abs/2610.20587
AI Weekly's analysis
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- In agentic markets where multiple agents value a prompt above a competitive floor, first-price auction welfare loss can be driven toward zero.
- When value variation hinges on prompt difficulty, efficiency approaches 1 − 1/e² of optimal, tighter than the general 1 − 1/e price-of-anarchy bound.
- The 40-page paper validates its bounds with simulated strategic bidders on the EmbedLLM prompt routing dataset, not production traffic.
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Shubhendu Trivedi reposted
Paper: arxiv.org/abs/2608.29530 We consider a variety of neural networks that perform seemingly-symbolic tasks, from small-scale models trained on simple list-manipulation tasks to LLMs performing tasks in math, logic, coding, and language. 2/n
AI Weekly's analysis
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- A new preprint reports that neural networks' vector representations can be closely approximated by closed-form symbolic equations with behavior 'largely unchanged.'
- The substitution was tested on small networks trained to manipulate lists and on LLMs across arithmetic, logic, computer code, and language.
- The authors say the symbolic approximation also lets them steer an LLM's behavior via targeted interventions on its internal representations.
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Shubhendu Trivedi reposted
14/14 By linking data-driven hypothesis generation with hypothesis-driven experiment selection, ATLAS shows real potential to accelerate the discovery of interpretable insights in cognitive science and beyond. Read the full paper here: arxiv.org/abs/2606.12386
AI Weekly's analysis
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- ATLAS combines sparse neural networks with active learning to generate and test mechanistic hypotheses automatically.
- The system achieved 5-10x better sample efficiency than random experimentation across all evaluation metrics.
- Validation compared ATLAS-designed experiments against expert-designed ones from published cognitive science literature.
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Shubhendu Trivedi reposted
arxiv.org/abs/2608.273... 'Conformal Prediction Through the Lens of Hypothesis Testing: Universality, Impossibility, and Optimality' - Ryan J. Tibshirani, Rina Foygel Barber, Aaditya Ramdas good stuff if you find insight in test-based interpretations of statistical procedures …
AI Weekly's analysis
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- Tibshirani, Barber and Ramdas recast conformal prediction as the inversion of a permutation test for exchangeability of the n+1 joint sample.
- The authors show foundational universality and impossibility results in conformal prediction can be reproduced using classical hypothesis testing theory from Neyman, Lehmann, Scheffé, Kraft and Le Cam.
- For any joint distribution of X,Y and any sample size, they prove the optimal conformal score is the inverse conditional density of Y given X.
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Their own posts
Recent commentary
The phenomenon of Twitter AI anons (why anons, it's very general) converting Terence Tao into a literal hate figure is emblematic of so many things. It's almost industrial scale resentment. Quite incredible to watch, tbqh.
Godfather of AI, Godmother of AI, Luca Brasi of AI, Commission of AI, Crime Familes of AI
The dot com era mega IPOs have a very different character than the 2026 AI ones. Back then you had many companies that were capital-starved before their IPOs and capital-rich after. The IPO itself was quite often a major financing event. Basically, public markets funded the next stage of growth.
Many aspects of the AI twitter peanut gallery seem to have spontaneously emerged on bluesky as well. Many of the opinion microstates and occupancy boxes are thinly traded given the natural constraints of bluesky, but you can see the broader contours.
One thing that has become clear to me just very recently from dozens of conversations with folk at all the frontier labs: Many really do believe that once "AGI happens" it will "make everything easier, from robotics, to manufacturing." Supply chain constraints, ecosystem and labour development
For some reason (ok I partly know the reason) I get a lot of email from kids in middle/high school, and off late I've been unable to do justice to them beyond replying (my routine is messed up). But this was by someone younger, clearly wrote everything himself, even has an AI declaration at the end.
I really feel for graduate students and undergraduates in this climate. Worst macro situation globally in 20 years, the spectre (complete with fear-mongering, doom and gloom) of AI, tough funding situation and research climate. Just too much uncertainty to deal with for those starting out.
It was easy to guess what "this model is too dangerous too release" meant: we don't have enough computational resources to serve. It was easy, in hindsight, to guess what "we are nearing RSI.. pause AI" meant.
I will not lie that I find the genre of TCS person "AGI confessions" very funny: I did not believe two weeks ago (was asleep for ten years, though), but now I do, you should too. This has happened again and again and again, and they all become zealots.
Due to concerns with AI safety I am delayed with a project. I also keep getting writer's block (and writer's thought-train-disruption by LLM) which doesn't help. Humanity won't survive this way.
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