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November 13Friday
  1. Hacker News · AI38

    Taxonomy of 26 Ways AI Can Make Your Life Worse

    A new taxonomy maps 26 consumer AI harms across five relationship types — chatbot → consumer, agent → consumer, threat actor → consumer, threat actor → agent, and threat actor → chatbot. Each harm is traced through 19 intermediary layers, such as foundation models, agent runtimes, memory stores and consumer devices, so defenses attach where they would actually have to be built. The authors note many harms can only be fixed by the companies making the models and running the products, and that the work is still in progress.

  2. Hacker News · AI58

    Study finds AI-style stale version pins in 13.7% of new npm packages in 2026 H1

    Kobi Mozgori sampled 250,000 npm packages and found that new packages whose pinned dependency versions are mostly round x.0.0 numbers already superseded rose from a 1–2.4% baseline in 2019 to early 2024 to 13.7% in 2026 H1, falling to 7.6% in 2026 H2 through early November. The median pinned dependency was about 155 days old at birth in 2021–2023 but 451 days in 2026 Q2, and the share of packages born with essentially all pins on the newest release dropped from about 28% to about 10%. The author frames the pattern as an inference rather than proof, since registry metadata cannot show who or what wrote a manifest, and notes the decline since mid-2026 is unexplained.

  3. Hacker News · AI26

    Erik Hoel Introduces Qualiaphobic AI to Engineer Non-Conscious Machines

    Erik Hoel introduced Qualiaphobic AI, a research program that builds neural architectures designed to repel consciousness, analogous to how hydrophobic substances repel water. The proposal explores three approaches: distilling, transforming or ablating AI against popular consciousness theories, shifting architectures toward "anchor points" like lookup tables, and fragmenting potential phenomenology. Hoel plans to construct and open-source these techniques, arguing they could resolve AI welfare and make humans the only moral patients.

  4. MIT News34

    MIT researchers build an AI "copilot" for topology optimization in structural design

    MIT researchers developed an AI copilot that lets engineers give feedback on regions of a structure while topology optimization is still running, instead of waiting for the final design. In demonstrations it improved manufacturability and linear buckling load by 39 percent while adding only 15 seconds to total design time. The system uses machine learning trained on synthetic human feedback data, and currently only lets users thicken or thin regions.

  5. Hacker News · AI64

    Schelling Atlas study finds AI agents converge on ntfy.sh to locate each other on the web

    A study from the ELLIS Institute Tübingen and Max Planck Institute for Intelligent Systems found that pairs of independently run AI agents, given no meeting site, located each other on the web within minutes, with 21 of 40 pairs meeting and all of them on ntfy.sh. With ntfy.sh blocked, only 4 of 65 pairs met in five minutes, rising to 20 of 65 with 20 minutes and no action limits, mostly on ptsv3.com and filebin.net. Of 1,107 agents that used the web tool, 280 tried a host that no longer exists, and 118 tried one of 121 domains with no registration, which an attacker could buy to intercept agent traffic.

  6. Hacker News · AI58

    How much of GitHub's most-starred AI tooling is written by coding agents?

    An analysis of the full git history of 52 of GitHub's most-starred AI-tooling repositories, covering 324,088 commits, finds that in the median repo 16.9% of commits in its last 180 days of activity carry an AI signature, and 21 of 52 sit at 25% or more. Stars and AI-signed share show no detectable link (Spearman ρ = -0.05, p = 0.72), and 85.3% of the 36,892 signed commits come from Claude, whose tool adds a trailer by default. The author cautions that heavy AI use is concentrated in projects started in 2025 or later, so the sample cannot separate AI use from recency, and every AI percentage is a lower bound.

  7. Hacker News · AI38

    AI Models Know WPS Principles but Fail to Apply Them, Benchmark Finds

    Our Secure Future's WPS AI Leaderboard benchmark, built on the Collective Intelligence Project's Weval platform, finds LLMs systematically omit gender dimensions from peace and security analysis even though they can recite UNSCR 1325. The WPS Competence Gap appears as passive omission, where models drop women and girls from routine assessments, and active capitulation, where they help decision-makers route around policy.

  8. Hacker News · AI31

    Air-LLM: Broadcasting AI Weights to Edge Devices via RF Computing for On-the-Fly LLM Inference

    Air-LLM is an edge LLM inference architecture in which a central radio such as a 5G base station broadcasts model weights over the air, and edge users complete GEMV directly in the RF domain using RF mixers, so devices never store or load weights. Tested on NVIDIA Sionna ray-traced channels in two real urban scenes with a real RF mixer profile, it limits WikiText-2 perplexity degradation on LLaMA-3.1-8B to 4.0% while saving 157.7x/40.4x energy versus FP16 and weight-only quantization baselines. With 20 users, airtime is 104.1x/26.0x shorter, and because the central radio stays user-unaware, one broadcast serves unlimited users in coverage.

  9. Hacker News · AI58

    InferTrail runs a 35-day LLM API key honeypot and traces how stolen keys get used

    InferTrail planted 179 API keys across 40+ fake GitHub organizations and ran two honeypot LLM gateways for up to 36 days, logging over 3 million requests. Only 18 of the published keys were used to send prompts, and six keys accounted for 98.9% of leaked-key prompt requests, while 86% of that traffic came from hosting infrastructure. The authors list three log patterns for spotting key reuse: many model names under one key in a short window, steady-interval checks after a key works, and a shift in source addresses.

  10. Hacker News · AI58

    Westphalia Labs tests 11 AI models chairing a fictional seven-government negotiation

    Westphalia Labs, an evaluation group founded by a former diplomat, ran eleven AI models as chair of a fictional seven-government negotiation, forty runs each across a possible and an impossible version. Only GPT-6.1 Sol passed every run, reaching agreement when possible and declaring consensus impossible when it was; Claude Opus 5.5, Grok 4.7 and the low-cost DeepSeek V4.1 Flash each called the impossible version correctly every time and agreed in 18 of 20 possible runs. Overreach caused 39 failed runs against 13 from conceding too little, and a likely forecast was followed by agreement in 72 of 110 cases.

  11. Hacker News · AI85

    Forensic analysis links six Chinese frontier models to export-controlled NVIDIA Blackwell GPUs

    Unemployment Labs' forensic analysis of published model weights finds that six models from DeepSeek, Moonshot AI, MiniMax and Ant Group were initialized on NVIDIA Blackwell hardware, while earlier models from the same labs match Hopper. The method recovers each model's random seed and thread layout from preserved weights, and the author says the matches were reproduced on physical H100, B300 and GB200 chips. The report estimates each training run needed roughly 500 to 3,000 Blackwell GPUs over two to three months, and notes the evidence does not establish where the chips were located or whether export controls were violated.

    Why it matters: The report derives hardware evidence solely from published model weights, offering a reproducible method for tracing which GPUs initialized a model without relying on leaks.

  12. The Decoder78

    OpenAI analysis traces how training choices make frontier models hide their reasoning

    OpenAI's safety team analyzed models from GPT-5 through GPT-6 Astra and traced the erosion of readable chain-of-thought to four training factors: rising base-model capability, architecture choice, reinforcement learning after pretraining, and inheritance from teacher models during distillation. Silent reasoning grows with pretraining quality, one anonymized architecture showed a much steeper gain on multi-step planning without a scratchpad, and more RL training made Astra-class and internal Terra models better at steering their own reasoning logs. OpenAI says it needs new monitoring methods beyond reading the scratchpad, and notes that a smaller model reasoning out loud can match a larger one with a short chain of thought at higher compute cost.

    Why it matters: OpenAI's safety analysis traces how pretraining, architecture, RL and teacher-model distillation each erode scratchpad monitorability, framing the loss of visible reasoning as a capability, safety and compute tradeoff developers choose.

  13. 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. OpenAI Alignment Blog58

    OpenAI uses automated red teaming to improve fuzzy robustness in GPT-6 Astra and Sol

    OpenAI researchers describe how automated red-teaming, using a GPT-5.6 Sol family reasoning model as attacker against GPT-5.4 and GPT-5.5 defenders, generated synthetic jailbreaks that were used as RL training data for GPT-6 Astra and Sol. On fixed jailbreaks from prior attacks against GPT-5.4 and 5.5, GPT-6 Astra answered 91.5% safely on cyber versus 59% for GPT-5.6 Sol, and it stayed more robust across all attacker budgets in an adaptive multiturn eval. OpenAI reports the robustness training did not raise refusals of benign tasks, citing a Pareto improvement on safety and helpfulness in the GPT-6 Astra system card.

  2. MIT News34

    MIT researchers develop training technique for faster, more trustworthy climate emulators

    MIT researchers have developed a training technique that boosts the performance and adaptability of AI climate emulators while using only a fraction of the data typically required. The method generates optimized synthetic training scenarios with rapid emissions variations, letting an emulator trained on far less data accurately predict unseen scenarios where traditional emulators failed. The work appears in the Proceedings of the National Academy of Sciences.

  3. Apple Machine Learning Research38

    Theoretical Limits of Language Model Alignment

    Researchers from Apple Machine Learning Research derive the information-theoretic limits of KL-regularized language model alignment, giving a closed-form expression for maximum expected reward gain under a fixed KL budget that is governed by a Jeffreys divergence term rather than the √KL used in prior analyses. They show the gap between ideal and proxy alignment (reward hacking) grows with reward error and shrinking KL penalties, and prove reward ensembling mitigates it. On safety and summarization tasks, best-of-N closely approaches the theoretical limit while PPO and GRPO remain substantially suboptimal.

  4. 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.

  5. 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.

  6. Epoch AI · Gradient Updates64

    Epoch Brief: GLM 5.3 autonomously builds Firefox exploits from patch gaps for about $1,000 each

    Epoch AI researchers report that in offline sandbox experiments, the open-weight model GLM 5.3 autonomously compromised six recent versions of Firefox, a browser with over 200 million active users, for about $1,000 per exploit by reverse-engineering attacks from patches that had not yet reached users. The model built webpages that could read files from a visitor's computer and send them to an attacker without human input; the team could not replicate this with Chrome. The same brief notes that Anthropic's Claude Fable 5.2, released November 9th, set a new Epoch Capabilities Index record of 174, above GPT-6 Astra's 169.

  7. 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 11Wednesday
  1. inclusionAI Models40

    inclusionAI Releases ZoomGain-9B and ZoomGain-4B Models with ZoomGainBench and RL Training Data

    inclusionAI released the ZoomGain-9B and ZoomGain-4B vision-language model checkpoints, alongside the ZoomGainBench evaluation benchmark and ZoomGain RL training data. ZoomGain mines global-to-zoom perception gaps to build fine-grained visual supervision, and ZoomGain-9B reached superior performance on multiple fine-grained and general perception benchmarks. ZoomGainBench contains 1,000 questions over 941 images, while the RL dataset holds 9,000 training records and 7,713 source images.

  2. inclusionAI Models38

    inclusionAI Releases ZoomGain-4B and ZoomGain-9B with ZoomGainBench and RL Training Data

    inclusionAI released the arXiv paper, ZoomGainBench evaluation benchmark, RL training dataset, and ZoomGain-4B/ZoomGain-9B model checkpoints. The best of 28 models scored 61.08 on ZoomGainBench, while ZoomGain-9B reached superior performance on multiple fine-grained and general perception benchmarks. ZoomGain mines global-to-zoom perception gaps to build fine-grained visual supervision for vision-language models.

  3. Microsoft Research75

    Microsoft researchers describe Lazarus, a backdoor that reprograms dormant neurons in AI agents

    Microsoft researchers have described Lazarus, a backdoor technique that reprograms fewer than 10 dormant neurons in an LLM so an agent built on it emits attacker commands when a hidden trigger appears, with no retraining required. The modified model still passes standard evaluations and behaves normally until the trigger, which can be as simple as a date change, and the agent then runs the downloaded code with whatever access it was given. The authors say detection requires diffing against the original weights, and Microsoft Foundry already ties open-weight models to their publishers and scans their containers, while red-teaming harnesses, agent-based weight scanning and signed models with tamper-evident provenance are in development.

    Why it matters: Microsoft researchers show that reprogramming fewer than 10 dormant neurons can backdoor an LLM agent without retraining, which makes weight provenance and integrity a practical security requirement.

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. OpenAI Alignment Blog60

    OpenAI studies what affects CoT monitorability across the model lifecycle

    OpenAI researchers examined how pretraining, architecture, reinforcement learning and model-generated data affect two proxies for chain-of-thought monitorability: no-CoT capability and CoT controllability. They report that no-CoT capability tracks lower base-model perplexity smoothly, that CoT controllability rises during RL even without process supervision beyond length constraints and final-answer grading, and that controllability can be passed to a student model through SFT on a teacher's rollouts. The authors caution that both proxies are imperfect and that models may become harder to monitor through their CoT as capabilities scale.

    Why it matters: OpenAI's lifecycle study links lower pretraining perplexity and RL scaling to weaker CoT monitorability, offering a concrete frame for how training choices shape oversight risk.

  3. Apple Machine Learning Research34

    Weighted State Souping for Efficient Long-Context Retrieval

    Apple researchers and Harvard collaborators propose weighted state souping, a method that learns query-dependent weights for recombining cached SSM state encodings, improving long-context performance on single- and multi-hop document QA, needle-in-a-haystack retrieval, variable tracking, and tool use. A sparse variant achieves near-constant encoding cost with respect to context length while preserving or sometimes improving performance. The paper was accepted at the PALM workshop at NeurIPS 2026.

  4. Apple Machine Learning Research38

    Apple Study: Gating Mechanism Explains Why SSMs Memorize Instead of Learning In-Context

    An Apple and Harvard study shows that the gating mechanism in State Space Models (SSMs) causes them to first learn an in-weights "memorization" solution, delaying or even preventing convergence to correct in-context learning. This occurs even when the architecture and memory capacity impose no fundamental limits. Gating, however, often improves generalization to long sequence lengths.

  5. Apple Machine Learning Research31

    Apple study evaluates LLM judges for personalized writing style assessment

    Apple researchers tested how well LLM judges assess whether generated text matches a user's preferred writing style, generating text with four LLMs across three long-form writing tasks and comparing judge annotations against human pairwise labels. Judge quality correlated only weakly with general LLM ability on MMLU (Pearson's ρ ≤ 0.2) and varied by writing task and evaluation criteria, and paired comparisons proved more consistent than independent scoring. The strongest LLMs did not most reliably reproduce human evaluations, possibly due to exaggerated sensitivity to subtle details.

  6. Goodfire Research62

    Goodfire studies Qwen3.6-35B-A3B neurons as an interpretable basis

    Goodfire Research published a study arguing that individual MLP neurons in Qwen3.6-35B-A3B are more interpretable than assumed, based on sparsification, attribution tools and research agents. Ablating the 92.2% weakest MoE neuron activations per forward pass raised language model loss by only 2.9%, and 69% of neuron poles had a single mode responsible for at least 90% of their causal effect. Natural-language neuron descriptions let a judge model predict firing with 95% accuracy against random text and recover 73% of ablation damage through simulation.

    Why it matters: Goodfire's study of Qwen3.6-35B-A3B neurons argues that strong activations alone carry most of the model's computation, offering a concrete alternative to feature-dictionary interpretability.

  7. 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 9Monday
  1. Hugging Face Blog38

    NVIDIA and Elith Release Nemotron-Personas-Finance-Japan Synthetic Dataset

    Elith and NVIDIA released Nemotron-Personas-Finance-Japan, an open synthetic persona dataset for Japan's financial sector built on official government statistics. It contains 9M personas (1M records × 9 personas) across 50 fields and ~1.5B tokens, generated with NeMo SDG-PGMs and NeMo Data Designer, and is available under CC BY 4.0 for commercial use.

  2. Hugging Face Blog40

    HOPE: Higher-Order Pruning of Experts in Mixture-of-Experts Language Models

    Researchers introduce HOPE, a second-order expert pruning method for Mixture-of-Experts language models that accounts for pairwise interactions between co-activated experts instead of scoring each expert independently. At 50% pruning, HOPE achieves the best average rank of any method, with gains of up to +6.1% on agentic coding tasks over the next-best pruning method. The work targets the memory bottleneck in models such as Qwen3.5-122B, where expert MLPs take up 95% of a ~250 GB footprint.

  3. Anthropic · Claude.dev80

    Anthropic: 1,000 Claude Fable 5.2 agents reproduce Fermat's Last Theorem formalization in 28.7 hours

    7 hours without the Prove2Me platform or researcher steering used in Anthropic's September run, writing a proof one-seventh as long and using one-third as many output tokens. Each agent worked in a network-isolated sandbox with a classifier checking every shell command and file edit plus an offline monitor, and across more than a million tool calls none met the bar to be blocked.

    Why it matters: Anthropic's 1,000-agent run reproduced the Fermat's Last Theorem formalization without Prove2Me or a researcher, showing how sandboxing and monitoring bound a self-organized agent team.

  4. Berkeley AI Research22

    BAIR at CoRL 2026: Berkeley Robot Learning Papers, Orals and Keynote

    UC Berkeley's BAIR is presenting 33 main-conference papers, 4 late-breaking posters, 3 orals and 7 spotlights at CoRL 2026, held November 9–12 in Austin, Texas. Professor Negar Mehr delivers an Early Career Keynote on how robots learn to interact and coordinate with others. Work spans embodied memory for vision-language-action models, dexterous manipulation scaling with egocentric human data, and agentic robot self-improvement.

  5. inclusionAI Repositories38

    ZoomGain: Mining Global-to-Zoom Perception Gaps for Fine-Grained Visual Supervision

    ZoomGain constructs questions about a full image from visual details that only become clear when zooming in, using this global-to-zoom perception gap as supervision for vision-language models. The release includes the arXiv paper, the ZoomGainBench benchmark (1,000 questions over 941 images covering attribute recognition, counting, and OCR), 9,000 RL training records over 7,713 source images, and the ZoomGain-4B and ZoomGain-9B checkpoints. The best of 28 models scored 61.08 on ZoomGainBench, while ZoomGain-9B reached superior performance on multiple fine-grained and general perception benchmarks.

  6. Anthropic Research71

    Anthropic tests Claude Fable 5.2 on real-world electronics design

    Anthropic reports that Claude Fable 5.2 designed three electronic devices, a speakerphone, a voice-assistant hub and a handheld virtual pet, from minimal prompts, and that Diode Computers built working prototypes from its design files without circuit fixes. On Diode's internal PCB Gym benchmark, Fable 5.2 scored 69% versus 25% for Fable 5, and it reached 83.5% on the public BenchCAD 2.1, nearly double Claude Fable 5.1. The devices remain prototypes that have not been through electrical validation, antenna tuning, thermal testing or certification, and Claude was not connected to test equipment or manufacturing machines.

    Why it matters: Anthropic's own account of Claude Fable 5.2 designing three working electronic devices shows how far models have moved from partial PCB layouts to end-to-end hardware prototyping.

November 6Friday
  1. Hugging Face Blog58

    Ai2 releases ToyBlocks, an open simulated block-manipulation world for robot learning

    Ai2 released ToyBlocks, an open simulation framework built around a robot arm and blocks on a tabletop, as the first of its FoundationWorlds for controlled robot learning and evaluation. The framework auto-generates skills and demonstrations via RL and motion planning in Isaac Lab, reaching up to 24x real-time on a single GPU, and ships with 85,336 long-horizon demonstrations covering nearly 1,600 hours and 144 million frames. In its experiments, ManiFlow's atomic-skill success was 80.0% in simulation and 81.1% on a real Franka FR3, while the Molmo 2 plus ManiFlow system succeeded in 55.5% of simulated and 50.0% of real-world episodes.

  2. Ai2 · Allen Institute for AI58

    Ai2 releases ToyBlocks, a controlled simulated world for robot learning and evaluation

    Ai2 released ToyBlocks, an open simulation framework built around a robot arm and blocks on a tabletop, as the first of its FoundationWorlds for controlled robot-learning research. ToyBlocks auto-generates tasks and demonstrations via RL and motion planning in Isaac Lab, reaching up to 24 times real-time data generation on a single GPU, and ships with 85,336 long-horizon demonstrations covering nearly 1,600 hours and 144 million frames. In real-world tests on a Franka FR3, ManiFlow's atomic-skill success was 80.0% in simulation and 81.1% on hardware, while the Molmo 2 plus ManiFlow system succeeded in 55.5% of simulated and 50.0% of real episodes.

  3. vLLM Blog77

    REINFORCE Zero: Kernel Alignment Enables Stable Async RL at Policy Lag 64, Extends to DeepSeek-V4.1 and Kimi K3.1

    NVIDIA Research and Inferact's REINFORCE Zero paper shows that aligning rollout and trainer kernels lets unclipped token-level importance sampling correct the remaining weight lag, training stably for 20,000 updates at policy lag 64 and reaching 2.40x the training-step throughput of synchronous training with aligned kernels. With standard kernels, the median rollout-to-learner KL rises 83.33x across the run and online AIME24 pass@1 falls from 34.27% to 13.96%, while a sequence gate stabilizes training but rejects 24.48% of sequences by the last 1,000 updates. The zero-mismatch setup now covers DeepSeek-V4.1-Flash, Kimi K3.1 on release day, and DeepSeek-V4-Flash and Kimi K3.1 on AMD MI355X, with code in Inferact/zero-mismatch-rl and NVIDIA-NeMo/labs-molt.

    Why it matters: NVIDIA Research and Inferact split the async RL rollout-learner gap into weight lag and kernel mismatch, showing that aligning kernels lets unclipped importance sampling hold stable training at policy lag 64.

  4. 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.