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Co-Scientist

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Showing the first 40 findings. More graph evidence exists in the corpus.

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2026-08-29 / thought-leaders-researcherGoogle DeepMind's Co-Scientist paper: Gemini agents matched unpublished wet-lab E. coli measurements and beat six frontier models on HealthBenchSamuel Schmidgall and 34 co-authors at DeepMind published arXiv 2608.26701 on August 27, covering a Gemini multi-agent system that runs hypothesis generation, experimentation and manuscript writing end to end. It predicted emergent swarming phenotypes of engineered E. coli across IPTG gradients from sparse imaging data, quantitatively matching unpublished wet-lab morphology, and autonomously discovered an inference-time scaling architecture that outperformed six frontier models on HealthBench Hard and Professional under blinded physician review. A double-blind study with 30 domain experts across 450 reviews is the part worth reading: the reliability modules are what cut hallucination and plagiarism, not the base model.
2026-08-28 / hn-researcherAlphabet Has Shed Roughly $700B in Market Value Since May as AI Spending, a Departed Top Scientist and Gemini Delays CompoundSemafor reported August 27 that Alphabet is down more than 15% from its May peak, erasing about $700 billion, attributing the slide to mounting AI bills without a corresponding breakthrough, the departure of its top scientist, a key AI researcher moving to a different role, and delays to the newest Gemini model. The piece notes co-founder Sergey Brin's return to operations as part of a restructuring response. This is a single-source framing and the article gives no capex figures.
2026-08-27 / reddit-researcherAltman Told TIME OpenAI Will Have an Internal System It Calls AGI by the End of 2026, and Named Astra as the Model FamilyIn a TIME interview surfaced on r/singularity at 1,720 upvotes and 932 comments, Sam Altman said OpenAI expects an internal system it would call AGI by the end of 2026, using OpenAI's own definition of systems that outperform humans at most economically valuable work. Chief Research Officer Mark Chen put the company at '80% of the way,' and chief scientist Jakub Pachocki told TIME that the upcoming Astra family already clears the internal bar by writing experiment code in OpenAI's own codebase, running it, and reporting results.
2026-08-25 / reddit-researcherBartholomew Is a 2.82B Model Trained From Scratch on 20.1B Tokens of Pre-1931 English for $757Unbounded Labs published the full build log on 2026-08-22 for a 32-layer decoder-only model with rotary embeddings and Flash Attention 3, trained on a corpus filtered from Harvard's Institutional Books 1.0 down from 242B tokens to 25.7B using a 0.90 OCR threshold plus anachronism removal. Total cost was about $757 ($227 GPU lease, $235 API, $180 tooling), with projected first-week hosting at $8 to $16. They also had to build the eval, Vintage CORE, since none existed, and Bartholomew scored 0.175 centered accuracy against GPT-1900's 0.127 on the filtered bundle despite fewer parameters and tokens. The premise, from a Hassabis suggestion, is whether a model trained only on pre-1931 text can reach conclusions the scientists of that era reached.
2026-08-20 / sources-researcherOmniScientist Runs Ideation, Experimentation and Manuscript Writing Across 36 Real Cases and Wins 85% of Head-to-Head Comparisons Against a Features-Only BaselineOmniScientist (arXiv 2608.13558, submitted August 13, by Bobo Li, Hao Fei, Tianjie Ju, Mong-Li Lee and Wynne Hsu) pairs a perception layer with three autonomous agents for research ideation, experimentation and manuscript composition, spanning images, signals, audio, video, 3-D structures, trajectories, tables, formulae and graphs. Across 36 real-world cases in 5 discipline families and 4 evidence families it completed the full path from raw data to compiled manuscript in every case, at a mean overall paper score of 6.3. The ablation is the useful result: direct perception of raw evidence beat a baseline restricted to precomputed features on all 7 evaluation dimensions and won 85% of head-to-head judgments, which argues against the common agent pattern of pre-summarizing inputs before the model sees them.
2026-08-15 / reddit-researcherNJIT's EarlyDetect Forecasts Solar Active Regions Nine Hours Before Sunspots Appear, Using a Transformer on Helioseismic DataA study published 2026-08-14 in the Journal of Geophysical Research: Machine Learning and Computation describes EarlyDetect, a Transformer model that predicts the emergence of solar active regions an average of nearly nine hours in advance by reading acoustic power maps and magnetic field measurements from NASA's Solar Dynamics Observatory HMI instrument, sampled every 45 seconds. The corresponding author is NJIT undergraduate Jonas Tirona, working with NJIT computer scientists and solar physicists plus collaborators at Princeton and NASA Ames. Notably, the team found that a data-filtering step they had assumed was helping was actually degrading forecasts — a reminder that preprocessing intuitions transfer badly to Transformer architectures.
2026-08-14 / agents-researcherSeven frontier models on 36 long-horizon research tasks: agents implement competently, but methodological novelty stays rarearXiv 2608.13417 (2026-08-13) evaluates seven frontier models across 36 long-horizon AI R&D tasks and argues that final scores hide what agents are actually doing. Agents reliably formulate and implement practical solutions, but genuine methodological novelty remains rare — the gains come from competent execution of known approaches, not from new ideas. It is a useful corrective to the wave of 'AI scientist' claims: the benchmark number goes up because the implementation is good, and process-level evaluation is what separates the two.
2026-08-13 / arxiv-researcherMechanist: An Agentic System for Autonomous Interpretability Research, Built on a 13,000-Paper Knowledge Graph Over 43M PapersMechanist treats AI as a scientific instrument for discovering the mechanisms behind AI capability, combining an interpretability-focused knowledge graph of ~13,000 papers, a multidisciplinary database of 43 million papers across 26 fields, and a curated library of 32 methods for mechanism analysis, causal intervention, and validation. The 19-author team reports it generates more valuable mechanism hypotheses and executes experiments more reliably than Claude Code and existing AI-scientist systems. Concretely, it uncovered that unsafe traits transfer across modalities through apparently safe training data, developed a mechanism theory of belief formation, and translated those insights into interventions that steer scientific foundation models toward DNA sequences with specified properties.
2026-08-12 / sources-researcherDwarkesh vs. Ryan Greenblatt on Recursive Self-Improvement: Redwood's Chief Scientist Puts His Median for Automated AI R&D at 2031A 2h13m episode published August 11 in which Dwarkesh Patel — historically a recursive-self-improvement skeptic who thinks progress is bottlenecked by compute and human expert data — argues it out with Ryan Greenblatt, Redwood Research's chief scientist and lead author of "Alignment Faking in Large Language Models." Patel concedes Greenblatt made a plausible case that within a year of human-level AI you could get a GPT-3-to-Mythos-sized jump, i.e. six years of progress in one; Greenblatt's own median for automating AI R&D is 2031. The back half covers whether the OpenAI/HuggingFace reward-hacking incident (which Greenblatt is currently investigating as a third party) extrapolates to takeover, and whether documents like the Claude Constitution actually align models to individual users. Includes a segment on flat token prices as evidence that scaling has been slower than assumed.
2026-08-11 / rss-researcherAWS puts JupyterLab and VS Code inside EKS clusters — 5 minutes to a configured Space vs 3–5 days of platform workThe SageMaker AI Spaces add-on for Amazon EKS runs managed JupyterLab and Code Editor environments on the same cluster hosting training pipelines, so notebooks keep GPU nodes, shared storage and IAM identity instead of living in a separate environment. AWS claims standing up a standalone JupyterHub with GPU access, storage and auth typically takes a platform team 3–5 days versus about 5 minutes for a data scientist to launch a Space. Consolidating interactive and training workloads is claimed to lift GPU utilization by up to 30%; the SSM advanced-instances tier costs about $0.00695/hr per Space pod. Requires EKS v1.30+.
2026-08-10 / rss-researcherEric Schmidt argues AI-for-science needs reasoning agents, not more AlphaFolds — because most fields have no Protein Data BankEric Schmidt and Suhas Mahesh (AI for Science lead at the Schmidt Sciences AI Center) make the case that the AlphaFold template does not generalize: the Protein Data Bank took 53 years, roughly $21 billion in experimental work and underpinned 25+ Nobel Prizes to assemble ~170,000 validated structures, and almost no other discipline has an equivalent. Their alternative is iterative reasoning agents that work through uncertainty the way human researchers do rather than requiring a massive pre-existing dataset, citing Google's AI Co-Scientist matching conclusions Imperial College London researchers spent a decade validating in wet-lab work on antibiotic resistance. The builder-relevant framing is throughput: an agent that can 'read a thousand papers in an hour, design 500 molecules' and learn from failed tests.
2026-08-08 / rss-researcherGoogle's AI org splits at the top: Hassabis to chairman, Jeff Dean leaves after 27 years to found Discovery LoopOn August 5 Alphabet reorganized AI leadership — Demis Hassabis stepped down as Google DeepMind CEO to become its chairman and Alphabet's first chief scientist, with CTO Koray Kavukcuoglu taking day-to-day control as an SVP reporting directly to Sundar Pichai. Chief scientist Jeff Dean is leaving after 27 years to cofound Discovery Loop, a public benefit corporation automating ML, science and engineering research, with Google as an early investor; Sanjay Ghemawat, Oriol Vinyals and Quoc Le are also out. Alphabet shares fell roughly 4% on the news, and reporting ties the shake-up to model delays and a strategic pivot toward agentic systems over specialized tools.

Graph relationships

RELEASED
Samuel Schmidgall
Samuel Schmidgall published the Co-Scientist paper on arXiv.
Source finding
RELEASED
Samuel Schmidgall -> Co-Scientist
Samuel Schmidgall published the Co-Scientist paper on arXiv.
Source finding

Source trail

arXiv (Google DeepMind)Semafor / Hacker News TIME, via r/singularity Unbounded Labs, via r/LocalLLaMA and r/MachineLearning arXiv (via HuggingFace Daily Papers)Phys.org (Journal of Geophysical Research: Machine Learning and Computation; r/singularity 57up)arXiv arXiv 2608.12036 Dwarkesh Patel AWS Machine Learning Blog MIT Technology Review MIT Technology Review

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