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TodayNovember 13Friday1 items
  1. The Verge · AI58

    Study finds ChatGPT, Gemini and Perplexity recommend fake retail websites

    A study by IP protection firm Podqi found that ChatGPT, Gemini and Perplexity all cited or displayed risky retail websites, defined as fake storefronts, reregistered old brand domains or dead sites, in answers to shopping questions about 151 brands. Across 6,795 answers collected in two September testing periods, Perplexity surfaced risky sites in 15 percent of responses, ChatGPT in 8 percent and Gemini in 4 percent, while ChatGPT warned about the risky site it surfaced 24 percent of the time. Queries about smaller brands produced risky sites about twice as often as those about larger companies, and prompts about regional brand websites triggered them more frequently, with Perplexity once naming the wrong page as Stüssy's official site.

November 12Thursday
  1. Google Research58

    Google Research uses wearables to build CKM risk profiles and personalized coaching

    Google Research introduced an investigational framework that turns consumer wearable data into a coachable health age and simulates personalized 12-week what-if health trajectories. The SensorWM world model was pretrained on 2.28 million person-weeks of de-identified Fitbit data from roughly 100,000 users and forecasts 20 physiological markers under different step, sleep and cardio routines. In a blinded study, lifestyle coaches chose the counterfactual-grounded plan as best overall in 71.4% of evaluations, and physicians rated 99.2% of plans within safe exercise intensity limits; the authors note the models are research prototypes, not clinical diagnostic tools.

  2. The Decoder88

    Google DeepMind's AlphaProof Code verifies Signal's cryptographic core in four weeks instead of years

    Google DeepMind unveiled AlphaProof Code, an AI system that mathematically proves software meets its security specifications, and used it to verify the cryptographic core code running on Signal users' devices in four weeks, work that typically takes years. After experts defined formal security requirements, AI agents built on Gemini Carbon wrote over six million lines in the Lean proof language and flagged roughly one percent of the codebase for human review, catching two deliberately planted bugs immediately. Signal CTO Ehren Kret said the approach strengthens trust in data privacy as AI threats grow, and DeepMind plans to extend the method to power grids and medical devices through systems like CodeMender.

    Why it matters: DeepMind's AlphaProof Code verified Signal's real cryptographic code in four weeks, showing how AI agents can move formal verification from design documents to production software.

  3. Google Developers Blog58

    Google Agent Anomaly Detection scores on five public agent-security benchmarks

    Google ran its Agent Anomaly Detection, in Private Preview on Gemini Enterprise Agent Platform, against five public agent-security benchmarks it did not write, scoring 1,774 sessions. It flagged 87% of harmful instructions the agent actually carried out, with false alarms on 2% to 16% of benign sessions, and caught only 4 of 18 sessions where poisoned long-term memory drove the agent to act. The tests used an agent built with the Agent Development Kit (ADK) on gemini-2.5-flash, covering AgentDojo, InjecAgent, Agent Security Bench, AgentDyn and AgentHarm.

November 10Tuesday
  1. Google DeepMind82

    Google DeepMind introduces AlphaProof Code, formally verifying Signal's cryptographic code

    Google DeepMind researchers used AlphaProof Code, a multi-LLM agentic framework, to formally verify key security properties of the actual code underlying the Signal protocol. Agents powered by Gemini Carbon generated over six million lines of Lean, and human experts reviewed only about one percent of the codebase; the verification took four weeks, a task that historically takes expert teams years. When two previously fixed bugs were reintroduced into an isolated version of Signal's code, the system produced machine-checked proofs that the buggy versions were insecure, and DeepMind is open-sourcing its formal verification artifacts.

    Why it matters: Google DeepMind's multi-agent framework formally verified Signal's deployed cryptographic code in four weeks, a task that historically takes expert teams years.

  2. Artificial Analysis Articles58

    Google DeepMind and Artificial Analysis launch MKA benchmark for agentic knowledge work on complex visual files

    Google DeepMind partnered with Artificial Analysis to launch the Multimodal Knowledge Agent (MKA) benchmark, which tasks agents with reviewing dense visual project files such as architectural drawings, engineering schematics and clinical scans to produce slide decks, spreadsheets, PDFs, Word documents and marked-up images.

November 6Friday
  1. SemiAnalysis75

    SemiAnalysis benchmarks Google TPUv7 Ironwood on agentic inference against NVIDIA GPUs

    SemiAnalysis released the first long-context, multi-turn agentic inference results on InferenceX/AgentX for Google TPUv7 Ironwood across four models, finding it competitive on performance per dollar or state-of-the-art in several cases. On GLM-5.2 in FP8, TPUv7 delivered 1.2x to 2.7x the tokens per dollar of B200, B300 and MI355X from 60 to 300 tok/s/user, while FP4 GPUs still led on GLM-5.3 because TPUv7 lacks native FP4 support until TPUv8 in Q1 2027. The piece also argues NVIDIA's bureaucracy slows driver and ecosystem contributions compared with AMD and Google.

  2. Google Research72

    Google researchers detect hypertension signs with smartwatch PPG and accelerometer data, no cuff calibration needed

    Google Research published a Nature study on AI-PPG-ACC-HTN, a multimodal system that detects signs of hypertension from smartwatch PPG and accelerometer data without cuff calibration. Validated against 24-hour ambulatory blood pressure monitoring in 196 participants across five US sites, it reached 65.8% sensitivity and 90.0% specificity for initial screening, and 78.4% sensitivity and 90.9% specificity for confirmatory testing. The authors describe the work as early feasibility research, not a replacement for clinical arm cuffs or a standalone diagnosis.

    Why it matters: Google's cuffless smartwatch hypertension model is benchmarked against 24-hour ambulatory monitoring, showing how passive multi-day sensor aggregation could support opportunistic screening.

November 5Thursday
  1. Google Research47

    Google Research's Agile Deliberation helps humans define subjective AI categories

    Google Research introduced Agile Deliberation, a framework that turns subjective AI classifier creation into an iterative conversation where the AI helps users sharpen their own definitions. It splits the task into concept scoping and concept iteration, using vision-language models to surface borderline images and route feedback into an automatic prompt optimization loop. In 18 live 1.5-hour user sessions, the approach reduced user fatigue and anxiety and produced more consistent models that outperform standard baselines; the code is open-sourced on GitHub.

November 2Monday
  1. Import AI42

    Import AI 479: Spacewar, low-latency tasks versus LLMs, Genie 4

    Import AI 479 covers Realtime-Bench, a 50-task benchmark of 30 web and 20 game tasks built by researchers at UT Austin, Ohio State, UIUC, NVIDIA, Northwestern and Steadyworks to test whether frontier models can act under real-time pressure. GPT-6 Astra (high) scored the highest wall-clock pass rate at 36%, while paused-clock runs let high-reasoning models reach roughly 70% to 80%. The issue also covers the Defense Intelligence Agency's "Challenges to Security in Space" report and Genie 4.

October 28Wednesday
  1. Google Research58

    Google Research's sleep framework lets LLMs consolidate new knowledge without catastrophic forgetting

    Google Research introduced a sleep framework for continual learning that alternates active interaction with offline memory consolidation, letting LLMs absorb new facts into expanded Mixture-of-Experts parameters instead of losing prior knowledge. The approach combines parameter expansion, on-policy distillation with reinforcement learning-based imitation, and a dreaming stage that generates and filters synthetic training samples. The authors report near-zero performance drop in continual translation for unseen languages such as Manchu and Kalamang, near-perfect scaling to 10M tokens on BABILong, 48.9% single-task and 46.2% CPT results on SQuAD-based factual incorporation, and 79.2%, 69.0% and 46.1% on AIME 24/25 and HMMT 25.

October 24Saturday
  1. Epoch AI · Gradient Updates42

    Epoch Brief: AI Work Automation Test, SpireClimb Benchmark, and Gemini 4 Argon's Frontier Push

    Epoch AI tested six leading models on 11 realistic Epoch work tasks and found none could reliably meet its quality bar, with Claude Fable 5.1 and GPT-6 Astra roughly tied for the lead. Its new SpireClimb benchmark shows frontier models went from struggling at Slay the Spire to winning over 90% of base-difficulty runs in six months, with GPT-6 Astra at 98%. Gemini 4 Argon scored 166 on the Epoch Capabilities Index, tying Claude Fable 5.1 and trailing Claude Opus 5.5 and GPT-6 Astra at 169.

October 22Thursday
  1. Google Research58

    Google Research AI model matches expert sonographers on gestational age from blind sweep ultrasound

    Google Research published a prospective multi-center study in JAMA Network Open showing its AI model estimated gestational age from blind sweep ultrasound with a mean absolute error of 4.18 days, versus 4.48 days for expert sonographers using standard biometry. The model was minimally tuned on 120 Chicago participants and tested without any local optimization in Nairobi, where it reached an MAE of 4.27 days, compared with 4.10 days in Chicago. The research targets frontline maternal care, where the WHO estimates more than 250,000 preventable pregnancy-related deaths occur yearly and two-thirds of people lack access to diagnostic imaging.

October 17Saturday
  1. Google Research71

    Google Research outlines VERID and closed-loop agent workflows for autonomous wet labs

    Google Research describes three projects aimed at grounding AI agents in real wet-lab experimentation, including the VERID framework, a semantic digital twin of a lab workcell built on the open-source PyLabRobot framework that tracks deck layout, well volumes and tip inventory and dry-runs protocols before execution.

    Why it matters: Google Research outlines three projects that connect AI agents to real wet-lab hardware, showing how simulated dry runs and closed-loop experiments let agents correct their own design rules.

October 8Thursday
  1. Waymo Blog30

    Waymo Study: Sober Drivers Still Face Nearly 4x Nighttime Fatal Crash Risk

    Waymo's peer-reviewed research finds that even fully sober drivers face 3.1 to 3.9 times higher nighttime fatal crash risk than in daytime, and that removing alcohol-impaired drivers cuts average urban fatal crash involvement by 23%, from 1.42 to 1.10 per 100 million miles. A second study of 37,654 fatal cases in the 2023 FARS database shows only 8.2% involved strictly human behavior factors alone. Waymo frames the findings as support for a Safe System Approach and for benchmarking autonomous driving against a zero-BAC human baseline.