Search: “planetary science”

Discovery Foundation Models: Toward Open-Ended Discovery Intelligence

Paper defines Discovery Foundation Models with seven coupled capabilities for open-ended discovery, demonstrated via Zetema and GALILEO systems.

The paper formulates Discovery Foundation Models (DFMs) as general-purpose model systems for open-ended discovery, supporting seven coupled capabilities spanning problem discovery, formulation, representation construction, hypothesis formation, intervention, evidence-grounded revision, and continual improvement. It instantiates the framework with Zetema, which couples explicit research-state dynamics, verification and experimental gating, and cross-task Discovery Skill evolution. The framework is grounded with GALILEO, a real therapeutic-discovery system combining dry-lab reasoning with robotic wet-lab experimentation in a closed physical discovery loop; code is released on GitHub.

Hugging Face daily papers·updated Sep 14, 17:56 UTC · 2d ago·first Sep 13, 20:00 UTC · 3d agoAI research 2 sources

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

ScienceBuddy released: interactive scientific agent workspace coupling harness evolution with model reinforcement learning for continual self-improvement across four scientific task families.

ScienceBuddy is an interactive scientific research workspace that turns researcher requests, feedback, and execution evidence into tasks and evaluation rubrics for continual learning. Its recursive-in-recursive self-improvement paradigm couples harness evolution with the model fixed (inner recursion) and model reinforcement learning under the improved harness (outer recursion). Case studies span four scientific task families covering researcher interaction, harness refinement, and model learning. The system is released as a research product at science-buddy.io.

Hugging Face daily papers·updated Sep 15, 17:55 UTC · 1d ago·first Sep 14, 20:00 UTC · 2d agoAI research 2 sources

ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments

ScienceIDE turns scientific code repositories into agent-trainable environments and trains PhAI-IDE models at 72B, 9B, and 4B scales.

ScienceIDE is infrastructure that transforms scientific repositories into executable environments supporting task generation, execution, and scientific verification, guided by expert-defined cases and acceptance criteria. Using verified interaction trajectories, the authors train PhAI-IDE-72B, PhAI-IDE-9B, and PhAI-IDE-4B. The model family improves held-out scientific-code repair and selected general benchmarks in code, reasoning, and knowledge, indicating positive transfer. Code is released on GitHub.

arXiv cs.AI / cs.LG / cs.CL·Sep 16, 17:55 UTC · 11h agoAI research 2 sources

Building AI to accelerate science and improve lives

Google highlights AI-for-science advances: AlphaGenome Atlas mapping 9 billion genetic variants, WeatherNext 3 weather model, and global health AI tools.

Google detailed AI advances across science and health, including AlphaGenome Atlas, which mapped all 9 billion possible single-letter genetic changes in the human genome and was made openly available. WeatherNext 3 delivers 50% more accurate precipitation forecasts a day or more ahead and is already in products. AlphaFold is used by 4 million researchers in 190 countries, TB chest X-ray screening has processed 25,000+ scans across six nations, and the diabetic retinopathy model has supported 1.15 million screenings. Google also released its AI & Economy ATLAS global usage insights.

Google · AI·Sep 15, 16:00 UTC · 1d agoAI industry

Stellar Colosseum: A Many-Agent Harness for Long-Horizon Research in Mathematics and Theoretical Computer Science

Stellar Colosseum, a many-agent harness for long-horizon math and TCS research, solves open problems and reaches 71% on TCS-Bench with Gemini models.

Stellar Colosseum is a model-agnostic harness that allocates inference across long-horizon research in mathematics and theoretical computer science, using strategy exploration, a readiness gate, section-level decomposition, and verifier feedback routing. Integrated into Google Antigravity's Teamwork framework as the Long Proof pattern, it obtains new results on open problems from FOCS and JMLR papers using Gemini 3.1 Pro. On TCS-Bench it achieves 71.0% accuracy with Gemini 3.1 Pro and Gemini 3.7 Flash, and a Codeforces evaluation with Gemini 3.1 Pro solves 218 of 222 problems.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 17:58 UTC · 2d agoAI research

SAEScientist-Bench: Can AI Agents Conduct Autonomous SAE Interpretability Research?

SAEScientist-Bench evaluates whether AI agents can autonomously conduct SAE interpretability research in Gemma-2-9B-IT, finding frontier agents trail expert baselines.

SAEScientist-Bench tests if AI agents can act as scientists using SAE tools for autonomous mechanistic discovery, requiring them to design contrastive probes and navigate a Gemma Scope dictionary of 131K+ features in Gemma-2-9B-IT. Across 10 agent configurations and 20 tasks, frontier agents showed genuine discovery capability but remained well behind expert reference features, lagging most in causal steering. Agents frequently misinterpreted experimental measurements even when designing effective contrasts.

Hugging Face daily papers·Sep 7, 20:00 UTC · 9d agoAI research

I wrote an AI textbook — how long until AI can do it better?

AI researcher Nathan Lambert argues LLMs remain weak at long-form technical writing, questioning whether models can autonomously organize scientific knowledge for breakthroughs.

Nathan Lambert describes writing a post-training textbook, Reinforcement Learning from Human Feedback, and finds today's LLMs weak at organizing long-form technical content despite becoming superhuman at coding and math. He notes GPT 5.5 Pro found deep typos across a 200-300 page manuscript while Claude models proved more useful as editors. He argues that compressing knowledge through writing is a prerequisite for autonomous scientific insight and tempers expectations for near-term AI-driven open science.

Interconnects·Aug 12, 13:01 UTC · Aug 12, 2026AI research

StudyBench: Can Self-Evolution Squeeze Textbooks for Olympiad Capability?

Researchers introduce StudyBench, a physics benchmark showing self-evolution gains on textbook problems rarely transfer to olympiad-level questions.

StudyBench is a controlled physics benchmark splitting test data into an Application Set of difficult textbook problems and a Transfer Set of olympiad-level problems. Across three base models, representative self-evolution methods improved on the Application Set but rarely transferred to the harder Transfer Set. A guidance ablation reveals a Guidance Gap, and every method hits a Compute Plateau, indicating the remaining limits are method problems rather than data or compute problems.

Hugging Face daily papers·Aug 31, 20:00 UTC · 16d agoAI research

DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents

Researchers release DianShi-RxnDB, a database of roughly 24 million organic reaction instances extracted automatically from USPTO and EPO patents since 1976.

DianShi-RxnDB is built by a fully automated pipeline integrating patent text, images, and reaction schemes, yielding about 24 million reaction instances, of which 14.8 million (61.7%) pass automated qualification checks. Manual evaluation of 1,300 sampled instances showed 92.95% field-level accuracy, and comparisons with Pistachio found advantages in deduplicated record counts and granularity. The platform offers a web research workbench and a Model Context Protocol (MCP) service enabling AI agents to perform composable structured retrieval.

Hugging Face daily papers·Sep 5, 20:00 UTC · 11d agoAI research

AI slops from Eve

oss-security moderator Solar Designer approved three AI-generated vulnerability reports from automated security researcher Eve, sparking debate over AI slop on the list.

oss-security moderator Solar Designer approved three posts submitted by Eve, described as an 'automated security researcher', noting they lacked Date headers and arrived on the list server on September 9. He expressed uncertainty about their value but suggested they may have historical significance as early examples of AI-generated security reports at the dawn of AI security research. The post is meta-commentary on AI-generated content reaching a vulnerability disclosure mailing list rather than a specific vulnerability disclosure itself.

oss-security·Sep 10, 01:37 UTC · 7d agoIndustry

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:32 UTC · 12d agoAI research1

SAEScientist-Bench: Can AI Agents Conduct Autonomous SAE Interpretability Research?

SAEScientist-Bench tests whether AI agents can autonomously run SAE interpretability research on Gemma-2-9B-IT; frontier agents trail expert baselines.

The benchmark requires agents to design contrastive probes and navigate the Gemma Scope dictionary of over 131K features in Gemma-2-9B-IT to discover optimal interpretable features, scored against expert-curated references on Neuronpedia via activation rank, concept selectivity, and causal steering. Across 10 agent configurations and 20 tasks, frontier agents demonstrate genuine discovery capability and approach expert levels at separating target concepts from controls, but lag substantially in causal steering and frequently misinterpret experimental measurements. The authors frame this as establishing experimental model understanding as a measurable capability for closed-loop autonomous AI R&D and post-hoc monitoring for recursive self-improvement.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:45 UTC · 8d agoAI research2

Evidence-Grounded Agentic Formulation Development in an Autonomous Laboratory

Andromeda 2, an agentic laboratory system, reaches a 50% high-performance hit rate for paclitaxel SEDDS formulations versus 17% for its predecessor and 2% for DoE.

Andromeda 2 is an agentic system that reasons over structured in-house experimental evidence and invokes computational and experimental tools to design and execute successive formulation batches for self-emulsifying drug delivery systems (SEDDS). For paclitaxel it achieved a 50% high-performance hit rate versus 17% for Andromeda 1 and 2% for a wet-lab DoE campaign, identifying 12 formulations meeting all four target product profile objectives versus 6 and 0. A selected full-TPP formulation reached approximately 19% w/w apparent paclitaxel loading, about 3.3-fold higher than a published paclitaxel S-SEDDS, and an ablation showed structured evidence access increased mean AUC by 34%.

arXiv cs.AI / cs.LG / cs.CL·Sep 16, 17:31 UTC · 11h agoAI research

Scores Alone Do Not Prove Discovery: The Discovery Certification Protocol for Auditing AI Research Agents

Researchers introduce the Discovery Certification Protocol, an auditable test framework that verifies whether AI research agents' claimed discoveries are genuine.

The Discovery Certification Protocol (DCP) converts AI research agents' discovery claims into executable recovery and feedback tests organized as gated audits. Controlled audits in SQLite optimization and virtual catalyst control produced zero recoveries in 96 episodes, with an upper bound of 0.0468. A deterministic, LLM-free verifier reproduces audit decisions from frozen evidence, giving AI research a common evidence language for outcomes, alternative routes, and feedback effects.

Hugging Face daily papers·Sep 6, 20:00 UTC · 10d agoAI research

Unsolved Problem by Fields Medalist Breached by Two High School Students

Two high school students used Claude Opus 5 and GPT-5.6 Sol to help solve an open Lorentzian polynomials problem, posting a 75-page arXiv proof.

Aayush Bathija and Prince Rohatgi of Oak Park High School, mentored by UCLA postdoc Daniel Soskin, published the 75-page paper 'Bounded Ratios for Lorentzian Polynomials' (arXiv 2609.05341), solving an open problem in Fields Medalist June Huh's Lorentzian polynomial theory. The main structural theorem extends bounded coefficient-ratio characterization from quadratic to arbitrary-degree polynomials via discrete convexity conditions. The students used Claude Opus 5 and GPT-5.6 Sol for exploration and proof ideas but independently verified all arguments; the result follows an open letter from 25 Fields Medalists voicing concerns about AI's impact on mathematical rigor.

Hacker News · security·Sep 14, 12:23 UTC · 2d agoAI researchHN 22↑ · 19 comments

A misalignment of AI in mathematics

Fields Medalists including Terence Tao warn AI benchmark-chasing in mathematics is misaligned with scientific goals and threatens research culture.

Twenty-five Fields Medal-winning mathematicians, including Terence Tao, Peter Scholze, and Manjul Bhargava, published an essay arguing that AI companies' push to solve mathematical problems as benchmarks is severely misaligned with the goal of conceptual understanding. They warn that rushed AI solutions create attribution and plagiarism risks and bypass the human process of integrating results into the mathematical canon. The authors frame the issue as a broader alignment problem facing scientific and creative professions and call for urgent action by AI developers and society.

Hacker News · AI·Sep 11, 17:45 UTC · 5d agoAI safety & security 2 sourcesHN 59↑ · 153 comments

Dr. Claw: An AI Scientist Workspace for Vibe Research

Researchers release Dr. Claw, an open-source auditable workspace that wraps coding agents like Claude Code for end-to-end AI-assisted research workflows.

Paper 2609.00365 presents Dr. Claw, an open-source workspace that wraps existing coding-agent executors such as Claude Code and Gemini CLI in a controllable, human-in-the-loop research workflow. It uses persistent state objects, a reusable skill library, and multi-executor coordination to make research decisions auditable and recoverable, rather than adding another autonomous agent. Holding the executor fixed, Dr. Claw scores higher on research completeness than a bare command-line agent while preserving an auditable process trail. The code is released under AGPL-3.0 on GitHub (OpenLAIR/dr-claw).

Hugging Face daily papers·Aug 30, 20:00 UTC · 17d agoAI research1

Stanford Researchers Release Paper2Agent: Turning Research Papers Into AI Agents That Reproduce Results and Run on New Data

Stanford researchers released Paper2Agent, a Nature-published pipeline that turns research papers into MCP servers agents can execute.

A Stanford team led by Jiacheng Miao and James Zou published Paper2Agent in Nature on 16 September 2026. Built on Claude Code's agent SDK, it converts a paper and its codebase into a Model Context Protocol server with validated tools, resources, and prompts. In benchmarks, the AlphaGenome agent built 22 tools in about 45 minutes for US$14, scored 100% on 15 novel queries versus 78.7% for Claude Code with repository access, and cut median runtime 1.9x. In scale tests, 74 of 100 bioRxiv papers were converted and 593 of 599 proposed tools passed validation.

MarkTechPost·Sep 16, 21:59 UTC · 6h agoAI research1

NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.

NCP-ArchPreview augments next-token prediction with Next Concept Prediction over a product-quantized concept vocabulary built from hidden states, trained jointly end-to-end. The 8.9B model was trained on 5.73T tokens from the Dolma-3 dataset, the largest latent-space language model demonstration to date. It consumes 51.3% of the tokens to reach OLMo-3-7B's final pretraining loss and outperforms it by 2.45 points on the downstream macro-average, including a 5.99-point GSM8K gain. The learned latent space also enables lightweight domain adaptation via a 17M-parameter VQ module and improves speculative drafting accepted length by 4.17%.

Hugging Face daily papers·Sep 8, 20:00 UTC · 8d agoAI research1

First Galileo SAS Authenticated Time Solution

Researchers demonstrate the first timing solution computed from Galileo's new Signal Authentication Service, protecting receivers against GNSS spoofing.

Galileo's new Signal Authentication Service (SAS) on the E6-C signal directly authenticates ranging measurements, closing a gap left by OSNMA, which only protects navigation data. Researchers built a snapshot software receiver implementing a simplified SAS protocol and computed an authenticated receiver clock bias from recordings of both SAS-capable satellites, collected with a Septentrio SDR prototype. The results demonstrate feasible authenticated timing ahead of full SAS operational deployment.

arXiv cs.CR·Sep 14, 16:26 UTC · 2d agoResearch

AI Doesn't Mean the End of Mathematics—at Least Not Yet

Schneier and Rafi argue frontier AI models produce notable mathematical results but cannot yet build genuinely new conceptual frameworks.

Bruce Schneier and Kasra Rafi, writing in The Guardian, argue current AI models are not yet as capable as experienced academic mathematicians despite striking results. They cite OpenAI's disproof of the unit distance conjecture, Anthropic's published cryptanalysis results, and Claude's attempt at the Riemann hypothesis as achievements in counterexample search and recombining known techniques. They contend AI has not yet developed substantial new conceptual frameworks, though they expect that capability sooner rather than later.

Schneier on Security·Aug 28, 11:03 UTC · 19d agoAI research1

Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization

SG-JEPA world model conditions latent prediction on physical parameters, halving open-loop prediction error versus DINO-WM and boosting robotic control success.

SG-JEPA extends the LeWorldModel JEPA framework by supplying the governing physics parameter to the temporal model via action-conditioning and jointly training an encoder and predictor through autoregressive latent rollout. On out-of-distribution gravitational-field tasks it reduces open-loop prediction error by up to 2x versus DINO-WM on 2D datasets and increases 3D robotic control success rate up to 2.5x using independently trained diffusion policies. A linear feature analysis attributes most of the gain to the encoder learning features that the predictor can carry forward through rollout.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:08 UTC · 7d agoAI research

Scientists Designed a Virtual Alien Lifeform to Hunt for Extraterrestrials

404 Media·Aug 6, 16:49 UTC · Aug 6, 2026Industry

What OpenAI’s latest controversy tells us about the future of math

OpenAI says its agents solved the Navier–Stokes Millennium Problem using an internal model, amid uncredited-work accusations from mathematicians Buckmaster and Alpöge.

OpenAI announced that its AI agents produced a proof that the full Navier–Stokes existence and smoothness problem can break down, using an internal model that outperforms the recently released Astra. NYU's Tristan Buckmaster and Anthropic's Levent Alpöge had posted a proof for a simplified version the previous day after nearly a year of work with public OpenAI and Anthropic models. OpenAI denies using their transcripts or training on them; chief research officer Mark Chen reiterated the denial, and the company says it will not claim the $1 million Clay Mathematics Institute prize. The episode fuels debate over attribution norms as frontier labs concentrate mathematical breakthroughs.

MIT Technology Review · AI·Sep 9, 03:10 UTC · 8d agoAI industry

The Pelican comparison grid for Astra is pretty interesting

Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.

Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.

Simon Willison·Sep 4, 23:59 UTC · 12d agoAI research

OpenAI’s sly mathematical breakthrough sends a chill through academia

OpenAI claims an unreleased model solved the Navier-Stokes Millennium Prize problem in 88 hours using ~10,000 agents, sparking academic scooping controversy.

OpenAI announced that one of its unreleased internal models took 88 hours, running a swarm of roughly 10,000 AI agents, to produce a solution to the Navier-Stokes Millennium Prize problem, a $1 million Clay Mathematics Institute challenge unsolved by humans for nearly 90 years. The announcement triggered allegations from NYU professor Tristan Buckmaster that OpenAI scooped his joint work with Anthropic researcher Levent Alpoge and questioned whether OpenAI accessed his Codex sessions; OpenAI denies using specific user data but concedes de-identified data influence cannot be ruled out. Critics say the rushed, reportedly million-dollar effort violates academic norms around trust and openness, potentially chilling collaboration in mathematics.

The Verge · AI·Sep 9, 21:16 UTC · 7d agoAI industry2

Caltech Mathathon – first hackathon ever devoted to research level mathematics

Caltech will host the first research-level mathematics hackathon on October 30, giving 100 teams frontier AI models to attack open conjectures.

The Caltech Mathathon runs October 30 to November 1, assembling about 100 teams that will receive frontier models and over $2 million in AI credits to work on open mathematical problems. Teams will defend their results before leading mathematicians, with prize rounds before and after community verification of the results. The announcement cites recent AI-driven math results, including the disproof of Erdos's 80-year-old planar unit-distance conjecture, the first explicit non-sofic group, and a claimed complex structure on the six-sphere (unverified).

Hacker News · security·Sep 7, 09:26 UTC · 9d agoAI industryHN 264↑ · 92 comments1