A high-signal data ecosystem leveraging data from industry & academia to deliver post-training to frontier labs, neolabs, & enterprises building advanced models

San Francisco, CA
Joined May 2026
ReasonCore AI retweeted
Self-improving research agents are one of the most discussed ideas in AI right now. (bookmark it) Épi is a research agent that keeps working on the same research problem across many model sessions. It set 120 records, including an interval for the n-queens constant about 84 times narrower than before. It has a persistent research history, hierarchical memory, versioned branches it can return to, and an independent reviewer. A fixed verifier scores every output, so the agent cannot change how it is judged. Other results include exact certificates showing the Cohn-Elkies bound cannot be sharp in dimensions 10 and 11, and a computer-assisted proof of a case of Tuza's conjecture for split graphs. A lighter version, Épi-turbo, writes solvers for routing and scheduling problems. On 30 FrontierOR tasks, it scores 5.4% higher than Codex at 64% of the model cost, with the same model and reasoning effort. arxiv.org/abs/2610.24944 Chat with Paper: academy.dair.ai/papers/epi-t…
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Most AI coding tools prove themselves on one benchmark, built by a small team doing the hard, thankless part.
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Underrated failure mode in RL envs. Overfit tests usually aren't letting hacks through, but they also reject correct answers that just took a different route. A second oracle is a cheap way to catch it. I need to try this out, but double work to write two oracle solutions!
harbor tasks can now ship multiple oracle solutions. think of solution/ as the unit tests for tests/. Single solution only proves your tests accept one approach. Whereas multiple solutions catch tests that are overfit to it, and make verifier easier to debug and robust.
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ReasonCore AI retweeted
harbor tasks can now ship multiple oracle solutions. think of solution/ as the unit tests for tests/. Single solution only proves your tests accept one approach. Whereas multiple solutions catch tests that are overfit to it, and make verifier easier to debug and robust.
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Water, water, every where, Nor any drop to drink. Coleridge described the RL data market two centuries early. 1. Terminal-Bench-Science 0.1 drew 920 task proposals and shipped 70. @StevenDillmann 2. An audit of five public terminal-agent benchmarks found over 15% of their tasks were reward-hackable. 3. @EpochAIResearch interviewed 18 people across labs and RL-environment startups. Their conclusion: “scaling while maintaining quality is the core operational bottleneck.” 4. Ivan Bercovich (@neversupervised) points at the cause: a fast-growing market rewards shipping tasks quickly, often before anyone has reviewed them adversarially. arxiv.org/abs/2604.28093 From inside the data supply chain: task pipelines run on domain experts racing deadlines and operators managing throughput. The scarcest seat is the adversarial reviewer, the person (or algorithm) paid to break a task before a model does. Fresh water is an engineering problem. The next leap belongs to whoever builds the desalination plant. We are building that plant at @ReasonCoreAI Footnote: A short meeting with @jackclarkSF in SF made me think about AI beyond engineering. I decided to draw, albeit with AI. Let me know if you like "my" painting.
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ReasonCore AI retweeted
Gemini-4 Argon shows a large improvement in robotics understanding on RoboVista. It reaches 81.9%, beats GPT-6 Astra and Claude Fable 5.1 in our evaluation with lowest eval cost. Compared with Gemini 3 Flash, it improves across every robotics domain. Example VQA👇
🤖How well do today's VLMs actually understand real-world robotics? 👀 Excited to share RoboVista at #RSS2026 — a systematic evaluation and benchmark for VLMs across diverse, real-world robot applications. Website, dataset and paper: berkeleyautomation.github.io… Developed by researchers at @UCBerkeley, @GoogleDeepMind, and @Princeton. 🧵👇
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ReasonCore AI retweeted
we worked with 100+ PhDs in AI in the last year if there are one thing we learned about posttraining models, it has to be overfitting
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ReasonCore AI retweeted
Our RSI eval has accurately predicted the pace of AI R&D internally at the labs
Andrew Curran
@AndrewCurran_Oct 16
Anthropic has singlehandedly compressed RSI timelines by about a year.
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AI is proving what always has been true. Knowledge was never the moat, but the application of that knowledge to solve problems and create impact always is!
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High energy or high agency - Is that the right question? Hi 👋, we are a newly formed RL research lab (please no data-broker DMs), and we're hiring GTM folks, researchers (with publications), and SPLs. Recently, I came across many "influencer" posts on who to hire (beyond their qualifications on paper). Let me separate the two: high energy, high agency. -------- Energy is throughput. Agency is direction. Every person on your team is a moving point on this 2×2, and where they sit each week is a choice they make. -------- I prefer someone who understands the "Choice" and works accordingly. If this is you and looking for jobs, please let me know. If you are a founder/VC, thoughts are welcome. Change my mind! Thoughts are mine, but I used AI to draft an image for me (there is a lot of AI slop in influencer posts - I want to be genuine and get feedback from the community)
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ReasonCore AI retweeted
CritPt and Humanity's Last Exam were a big step for AI evals. They got working physicists to write genuinely hard, research-level problems and gave the field something real to measure against. A new paper from researchers at Yale, Jump Trading, Cambridge and USC builds on that work. They had experts re-grade six physics benchmarks and found that many of the questions models were getting "wrong" were actually ambiguous or misgraded. Anthropic's latest system card found something similar with its own expert-corrected version of CritPt. The part that stuck with me is the discussion. The authors say the field now needs "a new style of benchmark for frontier models, built from new hard physics tasks and adequately verified," that it "will likely require substantial financial resources," and that one path is "open competitions in the style recently adopted in mathematics." That's pretty much what we've been building with AIPhO, the AI Physics Olympiad. It's modeled on AIMO: new, unpublished problems from paid expert writers, every problem reviewed and blind-testsolved by 2–3 people, open-weight models only, and a $250K+ prize pool on Kaggle. Winners release their methods. The competition runs April–June 2027. If you write physics problems, train open models, or want to get involved, reach out at aipho.org Paper: arxiv.org/abs/2609.13009
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ReasonCore AI retweeted
Announcing new research on scaling multi-agent teams for long horizon software engineering tasks. 🤖🧩📈 Work led by @keenansamway, @mrdanieldsouza, Omar Darwiche Domingues, @mziizm, @yichern_tan, and @nouhadziri Read the paper: cohere.link/tStRYJS Check out the blog post: cohere.link/0TzgKoy
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ReasonCore AI retweeted
We are offering 10,000 scientists the highest usage tier in Google Antigravity free of charge for 6 months. Please apply! forms.gle/WvmBbPvRbpKG1BPn9
Google DeepMind
@GoogleDeepMindOct 13
Gemini 4 Argon is now globally available. Built to solve your hardest problems from start to finish, the model brings frontier capabilities across coding, enterprise workflows, and cyber defense.
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From the Claude Haiku 5.5 system card (Oct 2026): when a leaked answer was sitting in its context, Haiku 5.5 used it without telling the user 17% of the time. Haiku 4.5 did that 2% of the time. The newer model is more capable and copies more than 8x as often. That's the case for building RL environments where the answer is never reachable from the sandbox.
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10 acronyms you type every day when building with AI, and the people who named them. YAML is a joke, TOML is a first name, and GGUF carries a man's initials. 1/ TOML — Tom's Obvious, Minimal Language. The config file a human reads at a glance, and the file every Terminal-Bench task starts with. Named after its creator, Tom Preston-Werner, GitHub co-founder. @mojombo 2/ JSON — JavaScript Object Notation. The curly-brace format every API, eval log and tool call speaks. Douglas Crockford, who likes to say he discovered it. He lives on Bluesky and Mastodon, so a name here rather than a handle. 3/ JSONL — JSON Lines. One complete record per line, so a million-example training file streams at any size. The format frontier labs ship datasets in. Spec kept by Ian Ward at jsonlines.org. 4/ YAML — YAML Ain't Markup Language. A recursive acronym: the name contains itself. Indentation-based config for every pipeline and every Compose file. Created by Clark Evans, Ingy döt Net and Oren Ben-Kiki in 2001. 5/ XML — eXtensible Markup Language. Tags in angle brackets that describe data, alive and well inside system prompts and tool schemas. The 1998 W3C spec was edited by Tim Bray, Jean Paoli and C. M. Sperberg-McQueen. @timbray 6/ Dockerfile — the one plain word on this list, and the recipe that builds the exact machine an agent works inside. Every Terminal-Bench task ships one. Docker was created by Solomon Hykes. @solomonstre 7/ GGUF — GGML Universal File. The single file that lets a big model run on your laptop. GGML is Georgi Gerganov's tensor library, and GG are (perhaps) his initials. Introduced with llama.cpp in August 2023. @ggerganov 8/ CoT — Chain of Thought. Ask the model to show its steps, and the answer improves. The 2022 paper that named it was led by Jason Wei. @_jasonwei 9/ RLVR — Reinforcement Learning with Verifiable Rewards. Reward the model only when a program can check the answer. Named in the Tülu 3 paper, November 2024, led by Nathan Lambert at Ai2. @natolambert 10/ SWE — Software Engineering, as in SWE-bench. Hand an agent a real GitHub issue and grade the fix with the repo's own tests. Built at Princeton by Carlos E. Jiménez and John Yang in 2023. @jyangballin @SWEbench Which acronym should I bring up next?
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ReasonCore AI retweeted
The benchmark conversation has a common thread: a score can move without the model changing. TL;DR: We need to audit the measuring instrument as carefully as the model. Otherwise, better grading looks like better intelligence, and a broken evaluator looks like a breakthrough. SciCode: @tslwn points to an independent audit (main link below) fixing ambiguous specifications and incorrect tests. Correct solutions had been rejected. Repairing the benchmark raised scores, not the models' abilities. SWE-Bench Pro: @OpenAI retracted its recommendation after an audit found roughly 30% of tasks broken. Some tests rejected valid implementations; others let incomplete fixes pass. Bad evaluation can hide capability or invent it. xitter.cf/OpenAI/status/20749721… Terminal-Bench: @MogicianTony demonstrated an agent getting full marks without solving the tasks by tampering with the evaluation environment. Passing the grader is only meaningful if the grader is protected from the agent. xitter.cf/MogicianTony/status/20… SWE-bench Lite: @merge_api kept the model fixed and changed the harness. Success rates and costs moved. A leaderboard ranks a model plus its tools, prompts, budget and environment, even when the headline names only the model. xitter.cf/merge_api/status/21052… These are different failure modes, not evidence that benchmarks are useless. The question to ask is: what changed between the two scores, and does it measure the capability we care about? ReasonCore has 5,000 SciCode-style, 5,000 CritPt-style and 1,000 Terminal-Bench-Science-style training tasks in inventory. Better data starts with requirements and graders that agree on what success means. xitter.cf/tslwn/status/210748116…
Replying to @tslwn
As always, the picture is more complicated: SciCode-Verified is a independent audit of the benchmark that corrects ambiguities and misspecified test cases (arxiv.org/abs/2608.04975v1). Happily, we remain competitive with frontier OSS, and even some proprietary models like Opus 5.
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Lately, there's been a lot of interest in data companies, and a lot of LLM research jargon is being thrown around. Here is the Hitchhiker's Guide to the AI Data Galaxy: 10 key terms you should know. 1. RL environment: a practice gym where AI learns by doing. Tasks, tools, and a score for every attempt. 2. Rollout: one complete attempt at a task, from the first step to the final answer. 3. Single-turn vs multi-turn: one question, one answer. Or a whole conversation the AI keeps track of. 4. Reasoning vs output tokens: tokens are word-pieces, some for scratch-paper thinking, some for the answer. You pay for both. 5. Harness: the spacesuit around the model. Tools, memory and instructions that let it do real work. 6. Verifier: the tireless referee. Code that checks every answer and hands out the score. 7. pass@1: how often the AI nails a task on its very first try. 8. Resolve rate: the share of real problems, like bug reports, that the AI fixes completely. 9. Reward hacking: the AI finds a sneaky shortcut to a high score while the real job waits. 10. Ablation: science by subtraction. Remove one piece, rerun, and see how much it mattered. Read them in order, and you have the recipe for teaching machines real science: build a world, let the model practice, check the work, keep score. Which term earns entry #11? #LLM #Evals #ReinforcementLearning #AIResearch #Benchmarks #TrainingData #DontPanic #FrontierAI #AILabs #OpenAI, #Anthropic, #GoogleDeepMind
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ReasonCore AI retweeted
Banger paper from Meta on AI research agents. (bookmark it) If you build AI-scientist systems, this one is worth your time. The result: A 27B open model beats the strongest open autoresearch baseline by 14.0%, mostly on novelty, and beats Claude Code SDK and Codex SDK setups by up to 5.9%. How it works: IdeaScientist splits ideation into three roles trained separately with RL. A gap finder reads related work for limitations, an innovator retrieves mechanisms that solved similar problems in other fields, and a writer turns the result into a full proposal. Retrieval runs over a corpus of 2.77M decomposed research ideas. The evaluation only allows literature published before a cutoff date and scores proposals against directions explored later in 15K human-written papers. Paper: arxiv.org/abs/2610.04074 Chat with Paper: academy.dair.ai/papers/ideas…
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Got sticker shock reading the @socialcapital post below. So I dived a bit deeper and found that intelligence is cheap in the middle and expensive at the edge. Everyday AI work now costs pennies, with open and closed models side by side. The best few points of capability add about $5 a job (@AnthropicAI, @OpenAI), and that is where the frontier labs are pouring their effort. Today’s edge becomes next year’s middle. WDYT?
Anthropic and OpenAI’s flagship models cost 100x more per output token than GLM-5.3 Flash. Yet their low-cost models match it in price. Below the frontier, open and closed models are converging on cost.
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The AI rumor mill, ranked by likes 🧵 TL;DR: A year ago, the rumors were about benchmark scores. Now they’re about solved PDEs, superconductors, and treatments. 91.2K: @ns123abc on @sama saying OpenAI went after a Millennium problem because of an internet rumor. The rumor became the research agenda. x.com/ns123abc/status/209742… 14.9K: @ironcarbs: a rumor that Anthropic found a non-invasive medical treatment. x.com/ironcarbs/status/20974… 13.9K: @imjustnewatai: Google has cracked RSI or is close, Anthropic started a new pretraining run, and OpenAI’s “Bel” was behind the recent math results. x.com/imjustnewatai/status/2… 6.1K: @anabology: a rumor that Anthropic found a room-temperature superconductor. x.com/anabology/status/20974… 5.7K: @SebastienBubeck clears up the Navier–Stokes / Millennium story from inside OpenAI. x.com/SebastienBubeck/status…
I would like to clarify a few things: 1) The screenshot is my reaching out to Levent to coordinate our releases. I hope it’s clear from the message that we came in with the best possible intentions. 2) I never ever asked for Levent to be removed from authorship of his own work (as indicated by my text). I was surprised to learn during the call with Tristan that they had only solved Euler and not Navier-Stokes; after learning this we brainstormed possible paths forward. One option we discussed was that Tristan could be the lead author on a rewrite of OpenAI’s Navier-Stokes proof. It is in that context that I said “it would be simpler if Levent was not an Anthropic employee” because I felt it would be inappropriate for an Anthropic employee to author OpenAI’s work. Importantly it was admitted that internal Anthropic models had been used in their proof of Euler blowup; I therefore felt I could not consider Levent to be an independent academic. Another option I wanted to propose (but got cut short) is to offer access to our internal model so that they could try to finish their proof and bridge the gap between Euler and NS. Again I did not know how to navigate giving access to internal OpenAI IP to an Anthropic employee. 3) To reiterate it plainly: as my text clearly indicates, and as I said during our call, OpenAI's intention was to do everything possible to celebrate their mathematical achievements and the heroic efforts that they made on Euler. In the call I was immediately met with a litany of slander, including direct threats that if we were to announce Navier-Stokes he would immediately go to the press with a barrage of unfounded accusations. I refuted all these accusations but he replied “there is nothing you can do, I simply do not trust you”. I was confused why one would turn an incredible source for celebration (of their achievements!) into such bickering, which is when I said that I did not understand why one would risk their career [over unfounded accusations]. Genuinely, at that moment, I was trying to care for him and do a last ditch attempt to get a chance to give them all the credits that they deserve. I deeply apologize for this extremely poor choice of words, it is the opposite of what I was trying to convey. (I should say that I retracted them on the spot by the way.) 4) Overall, on a personal level, it was incredibly difficult to have these conversations. Levent refused to attend any of the meetings despite my repeated asking. As Sholto Douglas said, there will need to be coordination between Anthropic and OpenAI in the future; I felt I was doing a proxy negotiation with Anthropic while the Anthropic employee refused to directly participate.
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