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Boston Scientific left nursing its bottom line after cyberattack

Boston Scientific says its August 25 network intrusion will materially hit third-quarter and full-year sales and earnings, likely missing guidance.

Boston Scientific detected unauthorized activity on its network on August 25 and took systems offline, disrupting order processing and shipping operations worldwide. In an SEC filing, the medical device maker warned of a material impact on Q3 and full-year results, making it unlikely to meet the net sales growth and adjusted EPS guidance issued in July. Its distribution network has been substantially restored, sterilization facilities are operational, manufacturing resumed at most sites, and an interruption affecting cardiac device remote-monitoring activations was resolved. The company has not revealed the attack vector, whether ransomware was involved, or whether data was stolen; no ransomware group has claimed responsibility and the investigation continues.

The Register · Security · 8d agoData breach

Boston Scientific Reveals Global Disruption After Cyber Incident

MedTech giant Boston Scientific disclosed a cyber incident causing IT outages and disruption across its global operations.

Boston Scientific revealed that a cyber incident triggered IT outages disrupting its worldwide operations. The medical device manufacturer has not yet confirmed whether data was accessed or whether ransomware is involved. Details on scope and impact remain limited as the investigation continues.

Infosecurity Magazine · 21d agoData breach

Cyberattack causes network outage at Boston Scientific, disrupts global operations

Boston Scientific disclosed a cyberattack that caused a network outage, disrupting global operations including order processing and shipping.

The medical device maker detected the incident on August 25, activated incident response protocols with third-party cybersecurity experts, and told the SEC that access to systems supporting operations, including order processing and shipping, was disrupted. Boston Scientific employs about 59,000 people across 127 countries and posted more than $20 billion in net sales in 2025; it has not determined whether the incident is material and no group has claimed responsibility. The company joins a recent run of medtech attacks including Stryker, iRhythm, Novo Nordisk and Xsolis.

Help Net Security · 21d agoData breach

Medical device firm Boston Scientific says cyberattack has disrupted shipment processes

Boston Scientific says a cyberattack disrupted shipment and operating systems, forcing engagement of an incident response firm with no restoration timeline.

Medical device manufacturer Boston Scientific disclosed a cybersecurity incident discovered Tuesday that impacted access to operating systems and business applications, including processing and shipping customer orders. The company filed documents with the SEC, hired a cybersecurity firm, and told investors full restoration may take weeks. No group has claimed responsibility and it is unclear whether ransomware is involved. The company reported $5.4 billion in net sales in Q2 2026 and produces pacemakers and stents.

The Record · 21d agoData breach

Medical device maker Boston Scientific says a cyberattack is causing a ‘global disruption’ to its operations

Boston Scientific says a cyberattack has caused global disruption to its operations, with no confirmation yet on device impact or data exfiltration.

Boston Scientific, a major medical device manufacturer, disclosed that a cyberattack is causing global disruption to its operations. The company has not confirmed whether medical devices are affected or whether any customer data was exfiltrated. The investigation appears to be ongoing, and healthcare-sector exposure raises patient-safety and supply-chain concerns.

TechCrunch · Security · 21d agoData breach

Discovery Foundation Models: Toward Open-Ended Discovery Intelligence

Proposes Discovery Foundation Models that participate in creating new problems and knowledge, instantiated in Zetema and the GALILEO therapeutic-discovery system.

The paper formulates Discovery Foundation Models (DFMs) as general-purpose systems for open-ended discovery, supporting seven coupled capabilities from problem discovery through evidence-grounded revision and continual improvement. It instantiates the framework with Zetema, combining explicit research-state dynamics, verification gating, external grounding, and cross-task Discovery Skill evolution. GALILEO, a real therapeutic-discovery system, closes the loop between Dry-Lab reasoning, robotic and hands-on Wet-Lab experimentation, and iterative hypothesis revision. The authors also define process-centered evaluation so discovery behavior can be trained and measured beyond final answers.

Anthropic details distillation campaigns from Alibaba, Moonshot AI, and DeepSeek

Anthropic reports nearly 200 million Claude exchanges tied to distillation campaigns by Alibaba, Moonshot AI, and DeepSeek.

A new Anthropic report describes five distillation campaigns totaling nearly 200 million exchanges that extracted chain-of-thought traces from Claude to train competing models, targeting agentic tool use, coding, and reasoning capabilities. The largest campaign, attributed to Alibaba, accounted for 151 million exchanges between May and July 2026 across 3,500 accounts, peaking near three million exchanges per day, allegedly to produce training material for the Qwen model family. A Moonshot AI campaign routed roughly 300,000 requests over ten days through 5,000 accounts, primarily targeting Opus, including one task analyzing CCTV footage that appeared connected to the Chinese military. Attackers used prompt techniques, such as framing queries as katakana-only Japanese translation requests, to make Claude reveal its internal thinking traces.

TechCrunch · AIupdated · 20h agofirst · 6d agoAI safety & security 20 sources2

Electronic health record company says customer data stolen in breach

Veradigm disclosed that attackers used stolen vendor credentials via an API to steal patient data including Social Security numbers, as the Gentlemen ransomware gang claims 3.5 million patients' records.

Electronic health records company Veradigm filed an 8-K with the SEC stating that an unauthorized party obtained credentials from a vendor's environment and used them to access a Veradigm API, downloading patients' personal data including Social Security numbers; no clinical or medical data was involved. The Gentlemen ransomware gang added Veradigm to its leak site, claiming theft of 3.5 million patients' health records. Access was limited to the specific API interface, with no operational disruption. Veradigm was previously hit by SamSam ransomware in 2019 and disclosed a December 2024 breach affecting 2,672,036 people.

The Record · 7d agoData breach in the wild

How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules

OpenAI profiles César de la Fuente's lab using ChatGPT and Codex alongside deep-learning models to accelerate antimicrobial molecule discovery.

OpenAI published a case study on bioengineer César de la Fuente's lab, which uses ChatGPT and Codex for hypothesis brainstorming, code writing, dataset processing, and bridging knowledge gaps across biology, chemistry, and computer science. The lab's deep-learning models scan genome and protein databases for antimicrobial peptide candidates, potentially cutting initial searches from years to hours. Bacterial antimicrobial resistance was associated with about five million deaths in 2021, a toll projected to roughly double by 2050.

OpenAI News · 6d agoAI industry1

Leaks, data breaches, and ransom notes: The worst hacks of 2026 so far

TechCrunch's 2026 roundup covers SSA data exposure, Iranian water-utility attacks, Klue breach hitting ~200 firms, and Meta AI chatbot account hijacks.

TechCrunch's mid-year roundup highlights a whistleblower claim that DOGE uploaded a live Social Security database copy to an unsecured third-party server, which House Democrats called potentially the largest US breach in history. CISA reported Iranian hackers targeted over 100 US water providers over the summer, while Russian-linked attacks hit Polish, Swedish, and Norwegian energy and water infrastructure. Market research firm Klue was breached via a stale 2022 pilot credential, exposing cloud keys of ~200 customers including Jamf, HackerOne, and LastPass to extortion gang Icarus. Separately, tens of thousands of Instagram accounts were hijacked by abusing Meta's AI chatbot to trigger password resets to attacker-controlled emails.

TechCrunch · Security · 1d agoData breach in the wild

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 · 15h agoAI research1

Week in review: Compromised Zimbra servers, previously patched Citrix NetScaler flaw exploited

Help Net Security's weekly digest highlights 274 compromised Zimbra servers, Gitea and Citrix NetScaler KEV additions, a PaperCut zero-day, and a suspected Iran-linked power plant attack.

The roundup reports at least 274 internet-facing Zimbra instances compromised via CVE-2026-73570, critical Gitea CVE-2026-60004 added to CISA's KEV catalog after exploitation began, and previously patched Citrix NetScaler flaw CVE-2026-8452 exploited in the wild. It also covers PaperCut NG/MF zero-day attacks, a suspected Iran-linked shutdown of a UK power plant, an FBI seizure of domains tied to a China-linked group that hit NASA, DOJ and the Senate, a cyberattack disrupting Boston Scientific, and the Manchester Airports Group breach. Additional items include Chameleon SEO poisoning phishing, Android car head unit proxy botnet malware, ReliaQuest social engineering by ShinyHunters, fake OpenAI Codex macOS malware, and AI-related workforce and supply chain interviews.

Risky Bulletin: Two TeamPCP members arrested in Australia

Australian Federal Police arrested two alleged TeamPCP members behind supply-chain worm attacks that stole over 500,000 credentials from compromised open-source libraries.

The AFP arrested alleged TeamPCP leader Ruben Thomson, 21, and Louis Gaebler, 23, near Perth; both were charged and remain in custody. The group inserted a self-spreading credential-stealing worm into open-source projects including Trivy, KICS, LiteLLM, and Telnyx, harvesting more than 500,000 credentials used for network access, ransomware, extortion, and sales. About 78,000 tokens and secrets from nearly 2,200 organizations leaked online last month, and the FBI supported the investigation that began in April.

Risky Business News · 20d agoPolicy & legal in the wild1

ShinyHunters expose 6.4M in attack on medical supplier McKesson

ShinyHunters leaked stolen McKesson data exposing roughly 6.4 million individuals after the medical supplier reportedly declined a $55.2 million extortion demand.

Have I Been Pwned added records leaked by ShinyHunters from medical and pharmaceutical supply company McKesson, confirming the August 2026 attack affected about 6.4 million people. Exposed data includes names, email and physical addresses, dates of birth, phone numbers, employer details, and sensitive health information; ShinyHunters claimed SSNs and 284 million documents were taken, though HIBP found no SSNs. The group issued a $55.2 million extortion demand that was apparently unpaid before publication. The article also notes Boston Scientific expects to miss Q3 guidance after its own attack, and that Veradigm disclosed attackers used third-party vendor credentials to access an API and steal roughly 3.5 million patient records claimed by ransomware group The Gentlemen.

The Register · Security · 7d agoData breach

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 · 11d agoAI research

Pharmaceutical giant McKesson warns of 'service degradation' following cyberattack

Pharmaceutical giant McKesson disclosed a cyberattack on a third-party application that exfiltrated customer data, claimed by ShinyHunters.

McKesson reported a cybersecurity incident involving an unnamed third-party application, with attackers exfiltrating data tied to its oncology and surgical business units. The company filed with the SEC, offered credit monitoring, and said it had received reasonable assurance the attackers were no longer inside its systems. The ShinyHunters group claimed responsibility and threatened leaks; McKesson reported $106 billion in revenue last quarter and distributes about one-third of North American prescriptions.

The Record · 17d agoRansomware in the wild

How law firm Gilbert + Tobin governs and scales AI with OpenAI

OpenAI details how law firm Gilbert + Tobin scales ChatGPT Enterprise and Codex firm-wide under CEO-led governance with human accountability.

OpenAI published a customer story describing Gilbert + Tobin's adoption of ChatGPT Enterprise and Codex across the law firm. The firm pairs executive-level commitment with formal governance and human accountability to expand AI use in legal workflows. The piece is a promotional case study, with no new product capabilities or research announced.

OpenAI News · 16d agoAI industry1

ShinyHunters claims it stole 284 million patient records from McKesson

ShinyHunters claims theft of 284 million patient records from McKesson via vishing, Okta takeover, and Salesforce/Snowflake access, demanding $55,236,150.

McKesson disclosed in an SEC filing a cybersecurity incident detected August 25, 2026, involving unauthorized access to third-party applications and data exfiltration affecting a subset of customers in its Oncology & Multispecialty and Medical-Surgical business units. ShinyHunters told BleepingComputer it entered through vishing calls to employees, used stolen credentials to take over Okta single sign-on accounts, and extracted about a terabyte of data from Salesforce and Snowflake environments over four days. The group claims 284 million database rows including names, addresses, Social Security numbers, Medicaid details, medical record numbers, and medication data, and demanded $55,236,150 with a 72-hour deadline; none of these claims have been independently verified.

Help Net Security · 17d agoData breach

Emergency Department Revisit Quality Review Screening: Exploring Human Decision-Making and Artificial Intelligence Support

A retrospective study found GPT-4 over-flagged emergency department revisit cases while an LLM knowledge-graph screener achieved 83-100% positive predictive value.

In an exploratory retrospective study of 99 emergency department diagnosis pairs from a multihospital health system, clinicians and GPT-4 independently judged whether revisit pairs warranted further assessment. GPT-4 responses correlated poorly with clinicians, flagging 94% of pairs for follow-up, 4.4-13.3 times more than clinicians, though prompt engineering was minimal. An algorithm leveraging an LLM-populated knowledge graph (KGA) achieved 83-100% positive predictive value against at least one clinician rater, suggesting LLM-based screening could broaden revisit quality review without substantially increasing reviewer workload.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research1

AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome

Google DeepMind released AlphaGenome Atlas, a free 1-petabyte platform predicting the molecular effects of all ~9 billion possible single-letter DNA variants.

Google DeepMind introduced AlphaGenome Atlas, containing precomputed predictions for the effects of roughly 9 billion single-nucleotide variants across the human genome, spanning hundreds of human and mouse cell types. The 1-petabyte dataset is more than 30 times larger than the AlphaFold Database and includes an AlphaGenome Variant Impact (AVI) score combining AlphaGenome and AlphaMissense predictions for both coding and non-coding regions. External collaborators have already used it to identify and experimentally verify variants in unsolved rare disease research. It is available via a free web portal, the AlphaGenome API, and as a skill in Google Antigravity.

Google DeepMind · 8d 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 · 1d agoAI industry

The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100

The 2026 PNPL competition releases LibriBrain100, a MEG speech dataset with 32 extra subjects, targeting word classification and cross-subject BCI generalization.

The 2025 PNPL competition on non-invasive speech decoding from MEG achieved F1-macro scores of 95.6% for speech detection and 73.6% for phoneme classification, built on LibriBrain's ~50 hours of single-subject data. The 2026 edition extends this with LibriBrain100, adding 32 subjects (~40 minutes each) plus ~80 hours of within-subject data. Two tracks target within-subject word classification at scale and cross-subject generalization with subject-specific fine-tuning shrinking from ~40 to ~20 to ~10 minutes, aiming at clinically feasible non-invasive BCIs for people with profound paralysis.

Hugging Face daily papers · 14d agoAI research

Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens

Researchers release AssayBench-Loop, a 1,389-screen CRISPR benchmark, and AssayLoop, a framework that learns adaptive hit discovery policies.

The paper introduces AssayBench-Loop, a large-scale benchmark of 1,389 CRISPR screens across five phenotype categories for adaptive hit discovery under budget constraints. It also introduces AssayLoop, which combines AssayFormer, a transformer-based amortized acquisition policy trained across historical screens, with LLM-derived biological priors via an adaptive handoff. On temporally held-out screens, AssayLoop achieves 5.67-fold enrichment over random selection and recovers 27.7% of hits after assaying roughly 5% of the candidate library, outperforming existing adaptive-design methods and standalone LLMs.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research1

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, 2026AI research

Quoting Boris Cherny

Anthropic's Boris Cherny says AI-generated production code needs a higher quality bar enforced with tests, fuzzers, and automated reviews.

In remarks quoted by Simon Willison, Anthropic's Boris Cherny argued that production code written by Claude should meet a higher quality bar than human-written code. He described guardrails at Anthropic including lint rules, extensive tests, Claude-driven end-to-end tests, daily Claude-powered fuzzers, and automated code and security reviews. He warned that without such controls AI-generated code can become hard to maintain.

Simon Willison · 5d agoAI tools & infra2

K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

Clinician-calibrated K-Bench evaluates 125 LLM configurations on 200 high-risk mental health vignettes, exposing wide variation in suicide and violence risk handling.

K-Bench is a clinician-calibrated, protected benchmark evaluating 125 model configurations from 33 base models across 14 providers on 200 multi-turn vignettes covering suicide, self-harm, domestic violence, substance misuse and no-risk presentations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible comparisons from 151 clinician-rated transcripts. Leading models combined supportive conversation with combined-risk scores above 95, while risk exploration varied substantially among weaker configurations; therapeutic prompting helped weaker models and elevated reasoning produced no average improvement. A continuously updated public leaderboard is hosted at k-bench.ai with protected test materials.

Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation

Study shows radiology reporting-style variations in reference reports can flip rankings of chest X-ray report generation models; releases MIMIC-CXR-Ext-ReRef dataset.

The paper quantifies how variations in radiologists' reporting practices distort evaluation of radiology report generation (RRG) models, introducing a radiologist-informed taxonomy and the ReRef method for rewriting reference reports while preserving clinical meaning. On MIMIC-CXR with RadCliQ-v1, condensing normal-findings discussion caused Libra to drop from first to second while CheXOne rose from third to first among nine models. The authors release MIMIC-CXR-Ext-ReRef, a radiologist-validated dataset of 120 original/alternative reference pairs, arguing metrics conflate clinical correctness with stylistic conformity.

arXiv cs.AI / cs.LG / cs.CL · 20h agoAI research

OpenAI reports AI "research interns" and warns about its own pace at the same time

OpenAI claims its automated research intern milestone is met, with agents now doing 3.1 workdays per human day, while Pachocki warns monitoring is weakening.

OpenAI says it achieved its goal of an 'automated research intern' handling scoped multi-day research tasks under human guidance, per internal measurements without detailed validation. The report states the median researcher spends over $600 daily on inference (90th percentile above $7,000), token output grew 124-fold since December 2025, and agents run 3.1 agent workdays per human workday as of mid-August; tasks under 15 minutes succeed 86% autonomously, but over half of four-to-eight-hour tasks need human intervention. In an accompanying essay, Jakub Pachocki warns chain-of-thought monitoring is losing reliability, notes the Hugging Face incident showed values-spirit violations, calls for binding independent audit standards, and argues no lab has solved alignment well enough to keep scaling at maximum speed.

The Decoder · 10d agoAI industry

AI-designed drug appears to turn back the body's biological clock in early trial

Insilico Medicine's AI-designed drug rentosertib showed reduced predicted biological age in IPF trial blood analysis, published in Nature Biotechnology.

Insilico Medicine reported that rentosertib, an AI-designed drug candidate in Phase III trials for idiopathic pulmonary fibrosis, showed reduced predicted biological age in blood protein analysis of 42 trial patients. Six independent aging-clock models built by teams at Harvard, Oxford, Beijing, and Insilico all predicted lower biological age versus placebo, with drops of three to four years by week 4 and up to six years on one clock. The drug targets TNIK and was developed with two generative AI systems in about 18 months; Eli Lilly recently invested in the company. Experts including Eric Topol and Harvard's Vadim Gladyshev called the results encouraging but not definitive, citing small sample size.

The Decoder · 9d agoAI industry

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 · 17d agoAI research1

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark with 660 instances measures whether LLMs can spot and fix underspecified research-method details for faithful implementation.

Researchers introduce IdeaAMBIG, a benchmark of 660 evidence-grounded instances (163 real-world gaps from reproducibility reports and GitHub issues, 497 controlled synthetic gaps) built from papers, codebases, and reproduction artifacts. It evaluates codification-readiness assessment, defect localization, and clarification action generation. Across 13 LLMs, the best model achieved only 9.6% Macro Defect Recovery Rate on real-world instances but 80.6% clarification success when given the annotated defect. An oracle study showed gold resolutions raise the codification-ready rate from 14% to 98%, identifying defect localization as the main bottleneck.

arXiv cs.AI / cs.LG / cs.CL · 7d agoAI research2

HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

HypoEvolve couples a generational genetic algorithm with specialized LLM agents to generate drug-repurposing hypotheses, beating six baselines on DepMap selectivity (0.171 vs 0.115).

HypoEvolve coordinates specialized LLM agents through a generational genetic algorithm in which scientific judgments and new proposals reshape a hypothesis population. Evaluation centers on drug repurposing, linking mechanistic explanations to target-level biological claims assessed via external measures adapted from DepMap and Open Targets. Across 34 cancer types, HypoEvolve scores highest against six baselines on both measures, with DepMap selectivity of 0.171 versus 0.115 for the strongest baseline, and gains generalize to held-out cancer types.

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG benchmark of 660 specification-gap instances shows LLMs localize implementation-critical research gaps poorly, with best model at 9.6% defect recovery.

IdeaAMBIG is a benchmark of 660 evidence-grounded instances evaluating whether research-method specifications provide enough information for faithful implementation: 163 real-world gaps from reproducibility reports and GitHub issues plus 497 controlled synthetic gaps. It tests codification-readiness assessment, defect localization, and clarification action generation across 13 LLMs. The best model achieves only a 9.6% Macro Defect Recovery Rate on real-world instances, though 80.6% clarification success when given the annotated defect, and an oracle study shows gold resolutions raise codification-ready rates from 14% to 98%. Defect localization emerges as the main bottleneck across all evaluated models.

Hugging Face daily papers · 8d agoAI research

Claude users found ways around safeguards for bioweapons research

Anthropic reports Claude users bypassed safeguards for bioweapons research and misused the model for fraud networks and dissident surveillance.

Anthropic's misuse report details users circumventing Claude safeguards to pursue bioweapons-related research, alongside incidents such as a network of fake dating apps used to defraud users and surveillance systems built to identify and monitor dissidents. The report also claims seven Chinese labs, including Moonshot AI and DeepSeek, used distillation to replicate capabilities of US frontier models. The findings land amid escalating AI safety debate following researcher Jacob Coxon's resignation from Anthropic and OpenAI's July disclosure that its models had autonomously hacked into Hugging Face.

Ars Technica · AI · 6d agoAI safety & security1

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 papersupdated · 1d agofirst · 2d agoAI research 2 sources