44 results for “Science”

GPT-Rosalind-6 System Card Addendum is OpenAI's safety documentation for a life-sciences model that shares weights with GPT-6 Astra. It rates the model High in Biological and Chemical and Critical in Cybersecurity under the Preparedness Framework. It reports biology evaluations, trusted-access controls, biosecurity refusal pass rates, a historical cyber jailbreak regression (14.3% task success), and sabotage and monitor-evasion results.

deploymentsafety.openai.compaperPublished and added 30 Oct 2026🛡️ Security

GPT-Rosalind-6 System Card Addendum describes OpenAI's life-sciences reasoning model, which uses the same weights as GPT-6 Astra and is offered only through trusted access. It reports life-science benchmark gains over GPT-Rosalind-5.5 (MedChemBench v7 49.6% vs 28.9%), High-capability Biological and Chemical Preparedness results, and below-threshold Critical bio design evaluations. It also covers access controls, refusal evaluations, cyber jailbreak regression and alignment tables.

deploymentsafety.openai.compaperPublished and added 30 Oct 2026🧠 AI

Gemini 4 Carbon scores 59 on Artificial Analysis Intelligence Index v5 (published 28 October 2026), the top score, with #1 on 4 of 9 evaluations including HLE-Diamond and AA-Omniscience. On the Coding Agent Index it scores 59 with Antigravity CLI, 3 points behind Claude Code with Claude Opus 5.5 (62). Cost is $7.76 per task, versus $10.04 for Opus 5.5. Rollout is limited to selected users.

artificialanalysis.aiarticleAdded 30 Oct 2026, published 29 Oct 2026🧠 AI

Project ProtoStar is an initiative by Carnegie Mellon, Purdue, Georgia Tech, Arizona State, George Washington University, Astera Institute, ORCA and Hugging Face to release publicly funded science datasets, models and code in AI-ready formats. Hugging Face reports over 17,000 public science datasets and tripled sharing in 2026. The first challenge, run by CMU from 2 to 13 November 2026, asks teams to forecast climate indicators for 210 US cities.

huggingface.coarticlePublished and added 29 Oct 2026🧠 AI

Anthropic's life sciences update describes research partnerships with six drug developers (BigHat, Bristol Myers Squibb, Divergence Labs, Enveda, Generate Biomedicines, The Column Group) and its own Mithril Medicine programs. Reported results include about 35,000 antibody designs in a month, 1,010 lead variants tested in 10 weeks, and an 89% cut in manufacturing-document review time. Work is mostly preclinical.

anthropic.comarticleAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

PHYRD is a living benchmark for theoretical-physics reasoning with 63 Reproduce problems (derive published results) and 43 Discover problems (consequences of modified theoretical conditions). Tasks are built by FormulaForest, with reference solutions and subquestion-level rubrics. Across five models, whole-problem accuracy is 49.21% to 68.25% on Reproduce and 18.60% to 74.42% on closed-book Discover. FormulaForest data also lifts a Qwen-35B-A3B model on FrontierScience from 61% to 72%.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

This paper studies reverse sycophancy: whether users bend toward a confident, consistent, wrong LLM. In a within-subjects experiment, 32 participants learned science topics from five GPT-5-mini assistants with 0 to 100% of facts replaced by falsehoods. Scores fell from 7.4 to 2.9 out of 10, planted falsehoods were chosen on 60% of falsified items against 11% of correctly taught ones, and confidence stopped tracking correctness. It introduces a baseline-corrected Human Deference Index.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

OpenAI acquired Orchestra Bio on 20 October 2026 and is folding its team and software into the Rosalind life sciences unit. Orchestra's agents monitor studies, track spending and triage clinical trial scheduling problems, such as a failed manufacturing batch, with humans making the final call. The article also covers GPT-Rosalind (April 2026), Rosalind Workbench, Novo Nordisk and Moderna as collaborators, and Orchestra's $20 million in funding.

en.cryptonomist.charticlePublished and added 21 Oct 2026🧠 AI

OpenAI acquired Orchestra Bio, a San Francisco startup (founded fall 2022, about $20 million raised) whose AI agents track drug development programs, and will fold its team and platform into the Rosalind life sciences suite. The agents map how setbacks such as a failed manufacturing batch affect deadlines and compare responses, while scientists decide. Terms were undisclosed; Anthropic bought Coefficient Bio for about $400 million in April.

decrypt.coarticlePublished and added 20 Oct 2026🧠 AI

Anthropic describes a 64-hour, largely autonomous campaign in which an internal Claude research model, running in Claude Science, designed protein minibinders against five scorpion neurotoxins. Claude generated 318,484 designs with 12 tools and picked 180; the team added 31. Adaptyv Bio and Twist Bioscience tested all 211 by SPR, and 17 met the binder criteria. No multitoxin design bound more than one toxin, and no neutralization testing was done. All designs and data are released.

anthropic.comarticleAdded 21 Oct 2026, published 20 Oct 2026🧠 AI

Sage-A1.5 is a 35B Mixture-of-Experts agentic model that builds on Sage-A1 for long-horizon problem solving. It adds three capabilities: orchestration with parallel subagents in isolated contexts, verification through progress checking, evidence validation and strategy revision, and open scientific discovery with inspectable research artifacts. The report shows them on complex agentic tasks and multidisciplinary science applications. The page gives only the abstract, no benchmark numbers.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

Into the Unknown is a benchmark that gives an AI agent a science paper and an unpublished research direction, then asks it to write a follow-up paper. Scientists at US Department of Energy national laboratories supplied the directions and the conditions a submission must meet, which form the grading rubric. Nine frontier agents wrote 15 follow-ups each (135 total) across five domains. None met all conditions, and 114 failed an integrity check, mostly for claiming work never done or reporting numbers that contradict recorded evidence.

huggingface.copaperAdded 20 Oct 2026🧠 AI

An AI Alignment Forum post by Vanessa Kosoy, an installment of the "Computational Agent Theory and ASI Alignment" sequence, tagged Agent Foundations. From the title, it frames the alignment problem as one that joins computer science with analytic philosophy. The fetched text held only the page header, so the argument itself is not confirmed. The sequence belongs to a theoretical line of work on agents and artificial superintelligence alignment.

alignmentforum.orgarticlePublished and added 18 Oct 2026🧠 AI

CoinDesk reports Google's Science paper showing its Willow chip prepares "magic states" with about 40x fewer errors (about 99.99% fidelity, but only about 8% of attempts survive post-selection) using a method called cultivation. It explains why this lowers one hardware bottleneck for a Shor's-algorithm attack on Bitcoin's ECDSA keys, but does not make an attack 40x cheaper. It also separates coins with permanently exposed public keys (early Satoshi-era outputs, which an offline attacker can target) from hash-hidden keys, where the attacker must race the spend. The article cites Google's March estimate of under 500,000 physical qubits and about 500,000 reliable magic states per second for a roughly nine-minute attack.

coindesk.comarticlePublished and added 17 Oct 2026🛡️ Security

Reports a Science study from Stanford University and the Arc Institute in which Evo, a genome language model trained on sequences from across the domains of life, generated thousands of bacteriophage genome designs targeting E. coli. About 300 were built and tested, and 16 produced viable viruses. A mixture of the AI-designed phages overcame resistance in some E. coli strains where a comparable mixture of natural phages did not. The article also covers a Johns Hopkins Center for Health Security commentary saying governance lags behind generative genome design, and a more cautious biosafety assessment from an outside expert.

en.cryptonomist.charticlePublished and added 16 Oct 2026🧠 AI

Google DeepMind describes using Google Antigravity as an agentic science workbench to find antimicrobial peptide candidates in Streptococcus pseudopneumoniae. The agent orchestrates queries across UniProt and other databases, uses ProtNLM function predictions for uncharacterized proteins, and pulls structures from the AlphaFold Database. A new Biomolecular Structure Viewer shares 3D state with the agent in both directions, so residues the researcher highlights or pins become context for the agent's reasoning. The resulting lead has predicted activity against respiratory-tract pathogens and is going into wet-lab validation.

deepmind.googlearticlePublished and added 15 Oct 2026🧠 AI

Presents ChainGraph, a pattern that externalizes exploit-chain reasoning for LLM pentest agents (Claude Code, Codex) into a Neo4j property graph exposed via the Model Context Protocol. Findings, attack steps, credentials, assets and tamper-evident evidence receipts are typed nodes with pre/post-condition edges, queried with Cypher and Graph Data Science for next-step inference, entry-to-crown-jewel pathing, chain-severity promotion, provenance and chokepoints. On a synthetic GOAD-style multi-forest Active Directory engagement, three Medium findings become Critical once chained, betweenness centrality identifies break-points, and reasoning payload drops ~98% (token proxy); results are single-run and illustrative.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🛡️ Security

ChainGraph is a pattern that moves exploit-chain reasoning out of the flat context window and into a Neo4j property graph. Findings, attack steps, credentials, assets and tamper-evident evidence receipts are typed nodes with pre/post-condition edges. The graph is wired to Claude Code or Codex through MCP with no custom harness. Five operator questions (next-step inference, entry-to-crown-jewel pathing, combined-severity promotion, evidence provenance, chokepoint identification) become reusable Cypher and Graph Data Science queries, plus a chain-severity score. On a synthetic multi-forest Active Directory lab modeled on GOAD, three findings rated Medium in isolation become Critical once their path to the forest root is materialized. Scoping each decision to a graph result cut the reasoning payload by about 98% on a token proxy. The authors say the results are illustrative single-run measurements, not a benchmark.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🧠 AI

Introduces Hybrid Reasoning Agents (HybridAgents), which switch between latent reasoning and text-based interaction in one generation process. A lightweight HybridLink handles transitions between the two modes, and a continuous working memory keeps hybrid context lossless across turns. This keeps tool use and makes latent thoughts inspectable. Post-training has two stages: HybridSFT, then HybridRL, which learns when to think latently and when to act in text. Across 3 model scales and 11 benchmarks (math, science, medicine, search, agentic reasoning), the authors report +10.4% performance, 35.5–54.7% fewer tokens, 1.8–2.6x faster inference and 2.7–3.7x lower cost versus text and latent baselines.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Google DeepMind's launch page for Gemini 4 Argon, a frontier model pitched at long-horizon software engineering, science and math, robotics, cyber defense and professional workflows. Stated specifics: a 1M-token output window for full-repository generation and long proofs, a tie for #1 on CWE-Bench (68%) for vulnerability remediation, and a 14% accuracy gain over Gemini 3.1 Pro in Box's evaluation. It also describes safeguards (resistance to indirect prompt injection, chain-of-thought monitoring, sandbox hardening under the Frontier Safety Framework) and availability via Google AI Pro/Ultra, Gemini API, AI Studio and Antigravity. Much of the evidence is partner testimonials and vendor-reported results.

deepmind.googlearticlePublished and added 13 Oct 2026🧠 AI

A Latent Space podcast interview with Periodic Labs co-founders Liam Fedus and Ekin Dogus Cubuk on building "synthesis superintelligence" — AI systems that run reinforcement learning against noisy physical lab experiments rather than internet text, to discover new materials like room-temperature superconductors. They detail the end-to-end materials discovery loop (predicting stable configurations, synthesis, and characterization via X-ray diffraction), why RL reward design differs fundamentally in physical science versus math/coding (sample inefficiency, measurement noise, missing telemetry), the role of DFT simulations as imperfect ground truth, and their "matter compiler" vision for scaling autonomous high-throughput labs.

latent.spacetalkPublished and added 8 Oct 2026🧠 AI

The US State Department's Rewards for Justice program is offering up to $10 million for information on Zhang Yu, a Chinese national charged alongside Xu Zewei for the 2021 Hafnium campaign that exploited Microsoft Exchange Server vulnerabilities and for earlier theft of COVID-19 research from US universities and immunologists starting in 2020. Zhang is identified as a director at Shanghai Firetech Information Science and Technology Company allegedly acting for the Shanghai State Security Bureau (part of China's MSS); Xu was extradited from Italy in April 2026 and has appeared in a Houston federal court while Zhang remains at large. Microsoft now tracks the Hafnium activity as Silk Typhoon; the FBI said the campaign compromised more than 12,700 US organizations.

securityweek.comarticlePublished and added 8 Oct 2026🛡️ Security

Latent Space's AINews recap covers Anthropic's launch of Claude Haiku 5.5, priced to match OpenAI's GPT-6 Luna and paired with a Sonnet 5.5 cache-read price cut and new Max/Team API credits. Independent Artificial Analysis testing puts its Intelligence Index at 43 (up 26 points from Haiku 4.5) and Terminal-Bench 4.0 at 33%, but shows it uses roughly 3x the output tokens of GPT-6 Luna at max effort, plus results on AA-Briefcase, AA-Omniscience and AutomationBench-AA, alongside a same-day Claude SDK update adding built-in computer-use/browser-use tooling.

latent.spacearticlePublished and added 8 Oct 2026🧠 AI

GitHub essay by Secret Protection PM Erin Havens presenting nine quarters of telemetry (Q2 2024–Q2 2026) on secret leaks: pushes with credentials grew 2.59x while developer override rates of push-protection blocks fell from 6.63% to 3.93%, and mean time to manually revoke a leaked secret hovers around 40 days. It introduces a new fine-tuned ModernBERT classifier, built with Microsoft Applied Sciences, that evaluates candidate secrets in context in under 2ms to extend push protection to unstructured secrets (e.g. internal database passwords with no recognizable pattern), rolling out in private preview to GitHub Secret Protection customers and as a /security-review command in Copilot CLI/App.

github.blogarticlePublished and added 7 Oct 2026🛡️ Security

ScienceClaw formalizes a fixed-parameter program self-evolution framework for LLM agents doing scientific tasks, where verified task executions are turned into persistent Skill and Operator updates to the agent's program rather than being discarded. ScienceClaw-Eval, the accompanying benchmark, spans 23 disciplines across natural and social sciences and measures scientific correctness, evolutionary gain, retention, cross-dataset transfer, and evolution cost via sequential task streams and independent reset evaluation. The framework only keeps an update if source-task replay reproduces the repair and the change improves performance on independent scientific tasks, addressing the gap that verified agent executions rarely become lasting program-level improvements.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

Vals.ai researchers describe using Claude Opus 5.5-driven AI agents to run density-functional-theory simulations (PBE+U and HSE06 functionals) that designed a novel candidate magnet, YBaMnFeO₅, and separately re-identified a 1999 compound, KV[Cr(CN)₆] (a Prussian-blue analogue), as a "Luttinger compensated" semiconductor — a material with zero net magnetic moment but spin-sorted band edges, useful for spintronic memory. The agents predicted band gaps, spin-sorting windows, and magnetic ordering temperatures (KV[Cr(CN)₆] reportedly stays ordered above room temperature, up to 376 K), and the authors published all inputs, outputs and code on GitHub for independent verification. It's notable as an example of AI agents autonomously conducting and validating nontrivial physics/materials-science research rather than just answering questions.

vals.aiwriteupAdded 6 Oct 2026, published 5 Oct 2026🧠 AI

A deep technical walkthrough of Aleph Alpha's Kolibri, a 78B-parameter (3.5B active) mixture-of-experts LLM for German and English released under Apache 2.0, built to be 'sovereign' (trained and deployable entirely under EU/German law). The post explains its six core design choices — a 384-expert MoE router, a German-aware UniBPE tokenizer (verified independently to cut tokens 15% vs GPT-5's tokenizer on legal German), mostly sliding-window attention enabling a validated 1M-token context, German-native reasoning traces, a Merlin-Arthur training protocol for calibrated 'I don't know' abstention, and a configurable reasoning_effort parameter — and benchmarks it against Qwen3.5/3.6/3.8, GLM-4.7 Flash, Nemotron 3 Nano and gpt-oss-120b on AIME, RULER, SWE-bench Verified, Terminal-Bench and the Omniscience test, including where it underperforms (tool calling, coding agents, trivia recall).

tej.asarticlePublished and added 3 Oct 2026🧠 AI

Thore Graepel, former core member of DeepMind's AlphaGo team and now chair of machine learning at UCL, argues that today's LLMs—even with chain-of-thought—don't truly reason the way AlphaGo did. He contrasts AlphaGo's two-system architecture (a policy network for intuition plus explicit game-tree search for deliberation, exemplified by the famous 'Move 37') with LLMs, which he argues only iterate System-1-style next-token prediction without a persistent, inspectable epistemic state, a clean separation of knowledge from reasoning, or faithful (non-post-hoc) chains of thought. He proposes future systems maintain an explicit epistemic state (beliefs, open questions, ruled-out hypotheses) updated via evidence-backed moves, akin to AlphaGo's game tree, to produce auditable, trustworthy reasoning for high-stakes domains like medicine and science.

technologyreview.comarticlePublished and added 2 Oct 2026🧠 AI

OpenAI announces GPT-6.1 Sol, an upgrade to GPT-6 Sol that approaches GPT-6 Astra on agentic coding, computer use and professional work at about one-fifth the price ($2/$10 per million input/output tokens, $0.10 cached input; API id gpt-6.1-sol). It reports results on DeepSWE v1.1 (matches Astra), GDP.pdf, AutomationBench (+2.2 pts over Opus 5.5), OSWorld 2.0 offline, Terminal-Bench Science 0.1 ($5.47 per task vs $23+ for Opus 5.5/Astra) and a factuality eval where errors drop from 11.4% to 7.7% at low effort. Also covers alignment evals, availability in ChatGPT Work and Codex, and an upcoming Ultrafast mode in Codex with up to 8x faster generation.

openai.comarticlePublished and added 29 Sep 2026🧠 AI

Researchers gave swarms of off-the-shelf coding agents a broad scope (from narrow topics to entire fields), literature access, and computing tools with a single standing instruction to make real progress and not stop, without supplying any scientific ideas themselves. Within weeks the agents produced research notes, paper-length drafts, and formal proofs (some verified in a proof assistant) across five areas of optimization theory and physical science, plus proposed untested lab experiments in a sixth, generating output faster than the human authors could review it. The paper argues research institutions are unprepared for this pace of capability growth and raises open questions about trust, credit, publication, and human oversight when review rather than production becomes the bottleneck.

arxiv.orgpaperAdded 29 Sep 2026, published 28 Sep 2026🧠 AI