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Nov 15Sun
  1. Hacker News AI20

    Wojak-Bench 3D: Which AI is best at 3D printing?

    Wojak-Bench 3D is a community preference poll that hides model names until you vote, with one vote per browser per pair. The leaderboard currently shows Claude Fable 5.2 at 64% share (7/0/4, 11 votes) and GPT-6 Astra at 36% (4/0/7, 11 votes), where share = (wins + 0.5 × ties) / votes on same-prompt pairs. The site notes it is community preference rather than a controlled evaluation, that small samples are unreliable, and that votes compare photos of physical prints where available, otherwise renders of the print files.

Nov 14Sat
  1. Hacker News AI34

    AI model for predicting neurophysiological responses to text and pictures

    The report introduces Concept2Brain, a deep neural network architecture that generates synthetic EEG signals from pictures or text, trained on EEG data from 88 observers viewing 360 naturalistic photographs and validated via 10-fold cross-validation. Synthetic signals reproduced empirical ERP components, time-frequency patterns, and participant-level differences, and out-of-sample checks against THINGS MEG/fMRI and ERP-CORE data showed expected correspondences. The model is available as an open cloud platform, and the authors note that text-based outputs should be treated with heightened caution because the architecture was trained on picture-evoked signals.

Nov 13Fri
  1. Microsoft Research22

    Microsoft Research Focus – Edition #98

    This edition of Microsoft Research Focus covers AI-powered universal translation, the resolution of the 31-year-old KLS conjecture in high-dimensional geometry, and a new approach to making AI coding agents faster and more efficient. It also highlights a research intern's journey at Microsoft Research Asia – Tokyo and the Latent Core Tokenizer, which produces more linguistically meaningful tokens across 104 languages.

Nov 11Wed
  1. Artificial Analysis Articles60

    Multimodal Knowledge Agent (MKA) benchmark: testing knowledge work capabilities with complex visual files

    Artificial Analysis partnered with Google DeepMind to launch the Multimodal Knowledge Agent (MKA) benchmark, comprising 200 private real-world visual tasks across 10 industries in which agents work from dense visual files like architectural drawings, engineering schematics, and clinical scans to produce deliverables such as slide decks, spreadsheets, PDFs, Word documents, and marked-up images. Tasks are graded against expert rubrics averaging ~70 weighted criteria split into Regression and Climb checks, with the headline Climb WSR score, run in the open-source Stirrup agent harness with up to 250 turns and graded by GPT-6 Luna (medium) as an agentic judge. Claude Fable 5.2 (max) leads with 57% Climb WSR, followed by Gemini 4 Carbon (high) at 54% and Claude Opus 5.5 (max) at 53%, while Claude Fable 5.2 averages $31.97 per task versus $6.29 for Gemini 4 Carbon and $1.11 for GPT-6.1 Sol.

    Why it matters: The results offer a useful cost-performance and per-vertical breakdown for anyone choosing models for agentic knowledge work over complex visual files.

Nov 10Tue
  1. Hacker News AI62

    Show HN: AI Vision Index, private tests of how well 36 AI models see

    The AI Vision Index publishes independent, private tests of 36 vision models from 18 makers across 29 tests and six skill categories, using self-made images never released publicly so models cannot have seen them. GPT-6 Astra medium leads with 83.7 ±2.0 out of 100 at $38.32 per 1,000 answers, followed by GPT-6.1 Sol medium (81.3) and Claude Fable 5.2 (80.7), while local models such as LFM2.5-VL 1.6B score 12.4. Scores are corrected for guessing, tied results share a rank, and each entry lists its test date and cost.

Nov 9Mon
  1. MIT News AI58

    A single AI model simulates the behavior of many substances, from sliding sand to folding proteins

    Researchers at MIT, UBC and elsewhere have developed WorldParticle, a deep-learning system that represents materials as Lagrangian particles and predicts their motion one step at a time, learning underlying dynamics rather than relying on hand-coded solvers. The model was demonstrated on six physical phenomena, including deformable objects, granular materials and proteins, with higher accuracy than numerical methods and modeled protein structural changes about 100 times faster than numerical simulators. The work will be presented at ACM SIGGRAPH Asia.

Nov 6Fri
  1. Artificial Analysis Articles40

    VIALS-AA: different paths to scientific image reasoning

    VIALS-AA analyzes how 19 models reason over scientific images, classifying 9,177 tool calls from its original test set: 38.3% were image viewing and 47.3% prepared or measured images. Image measurement made up just 5–6% of tool calls for GPT-6 Astra (max) and GPT-6.1 Sol (max), versus 25–27% for Claude Opus 5.5 (max) and Claude Sonnet 5.5 (max). The report also notes that Gemini 3.8 Flash (high) accounts for 86.7% of commentary/no-op calls, that more intelligent models tend to call fewer tools, and that cost per task varies by nearly 100× across models.

  2. MIT News AI34

    Clearing up a robot’s fuzzy memory

    MIT researchers developed UQ-DAAAM, a memory system that quantifies a robot's uncertainty about object descriptions by comparing multiple viewpoints and fusing them into more accurate captions, resolving ambiguities for about 92 percent of objects with only two additional captions each. The system improved object description accuracy by 5 percent over the state of the art and produced more accurate answers about objects, their locations, and when they were seen. The work, led by senior author Luca Carlone with co-lead authors Harry Zhang and Nicolas Gorlo, will be presented at the Conference on Neural Information Processing Systems.

  3. Google Research71

    Detecting signs of hypertension using wearable sensors

    Google researchers describe AI-PPG-ACC-HTN, a multimodal AI system that uses smartwatch PPG and accelerometry data, without cuff calibration, to detect a persistent hypertensive state by aggregating 15-second waveform embeddings over multiple days alongside height. Validated against 24-hour ambulatory blood pressure monitoring in a 196-participant, five-site US study, it reached 65.8% sensitivity and 90.0% specificity for initial screening and 78.4% sensitivity, 90.9% specificity and 96.7% PPV for confirmatory testing among 48 participants with elevated office BP. The authors frame it as early feasibility research for passive opportunistic screening, not a replacement for clinical cuffs or a standalone diagnosis.

    Why it matters: It offers a concrete, clinically benchmarked example of cuffless hypertension screening and its limits, useful for readers tracking AI in consumer health devices.

Nov 5Thu
  1. MarkTechPost39

    Google AI Research Releases Agile Deliberation Framework for Gray-Area Image Classifiers

    Google Research released Agile Deliberation, a human-in-the-loop framework that helps users turn vague concepts like 'healthy food' into structured written definitions that also serve as prompts for a Gemini 2.5 Flash VLM classifier. It finds borderline images by meaning rather than classifier confidence, using borderline queries, CLIP/FAISS retrieval, dictionary-learning clusters and a UCB bandit, and averaged 10.5 F1 points above zero-shot Gemini 2.5 Flash across 2 concepts and 18 live sessions. The code is Apache 2.0 on GitHub with a demo notebook, and the work appears in a CVPR 2026 paper.

Nov 4Wed
  1. Hacker News AI64

    AI tool can predict cancer survival by a single patient selfie

    Researchers at Mass General Brigham developed an AI system, trained on more than 40 million facial photographs, that estimates biological age and predicts survival outcomes for cancer patients from a single selfie, with details published in Lancet Digital Health. The two tools, FAHR-FaceAge and FAHR-FaceSurvival, rely on different facial regions, and among radiotherapy patients in the US those who looked five or more years older than their actual age had a 21% higher risk of dying during the average four-year follow-up, while those who looked five or more years younger had a 17% lower risk. Doctors cautioned that more research is needed before the tool could be widely used.

  2. Hugging Face Blog58

    Palette: teaching a small model to build decks in your design language

    Palette fine-tunes a 20B open-weight model with LoRA to build slide decks in two roles—a designer that writes a structured JSON brief and a coder that writes pptxgenjs code per slide—trained on roughly 1,000 synthetic decks (~10,000 slides) generated by a large open teacher and filtered by a checker. On PresentBench it ranks first on Economics and Education, third on Talk, and second across the three-domain mean (59.7 vs. NotebookLM's 60.8). A check-and-repair harness that keeps a rewrite only when the checker confirms improvement adds 3.5 points on average across 131 decks.

Nov 1Sun
  1. The Decoder58

    Adobe is testing how well AI agents can use editing tools like a Photoshop professional would

    Adobe Research and UC San Diego researchers developed EditAgent, which pairs a general-purpose image editor with specialized tools and uses a standard language model to compare parallel candidates using verification data such as close-ups, change maps, and measured brightness and color statistics. In a 600-edit benchmark blind-rated by five professionals, the general-purpose editor Flux-2-Klein-9B failed to produce a usable result 42.7 percent of the time, and specialized tools solved roughly two-thirds of those failures. With verification records, all seven language models tested beat the best fine-tuned evaluation model, with Qwen3.8-27B making a usable pick 81.3 percent of the time versus 74.8 percent for EditScore-72B, though the agent needs about ten times as much compute as a single editor.

  2. The Decoder42

    Google wants video AI to understand what people need as they work

    Researchers from Google, Stanford, Oxford and several U.S. universities introduced StreamVibe, a video agent that decides for itself when to speak up during tasks like cooking or furniture assembly, handling vague requests, unspoken intentions and mid-task changes. Built on a benchmark of about 4,100 examples from over 183 hours of recordings, StreamVibe-VL reportedly improves response timing and quality by 10.7 percent over other models, while a separate memory system is designed to boost proactive interaction by another 12.3 percent. The paper nonetheless finds that as videos get longer, all tested models become much less likely to time responses correctly, with a key content metric dropping by up to 61.5 percent when models build on their own earlier responses.

Oct 31Sat
  1. The Decoder60

    Google Deepmind creates a global map of forests with satellite data and AI

    Google DeepMind and the World Resources Institute developed ForTy, an AI map that assigns the world's 2020 land area to six classes at 10-meter resolution, including primary forest, planted forest, plantations, and tree crops such as oil palm, rubber, and cocoa. The map is intended mainly to give companies a 2020 reference point for the EU Deforestation-free Products Regulation (EUDR), and experts found it correct in 90.2 percent of 8,190 reviewed sites, though it identified only about half of tree crops and planted forests and 60 percent of plantations. The map, classification layer, and test data are freely available under a CC BY 4.0 license, including in Google Earth Engine.

Oct 28Wed
  1. Hacker News AI50

    Can AI draw San Francisco from memory?

    VOYGR tested six chat models from Anthropic, OpenAI and Google on 41,526 San Francisco coordinates, asking what is at each point from memory with no maps or internet, and checked every answer against OpenStreetMap. At a point every 100 m, Gemini 4 Argon, GPT-6.1 Sol and GPT-6 Astra tied at 86% correct on water, park or everything else, against 61% for answering "not water, not park" everywhere, while on a 500 x 500 m Marina block the best street score was 0.32 versus 0.23 for shuffled answers. The authors conclude models capture the coastline and large parks but not street grids, and recommend a geocoder rather than model memory for reverse geocoding.

Oct 22Thu
  1. Google Research65

    Empowering frontline maternal care with AI-assisted ultrasound

    Google researchers evaluated an AI model that estimates gestational age from standardized "blind sweep" ultrasounds performed by novice operators, reporting a mean absolute error of 4.18 days versus 4.48 days for expert sonographers across cohorts in Chicago and Nairobi. The model, minimally tuned on 120 Chicago participants and tested with no Nairobi data, achieved MAEs of 4.10 and 4.27 days respectively, as published in JAMA Network Open.

    Why it matters: It offers a concrete, peer-reviewed data point on whether AI-assisted ultrasound can generalize across hardware and clinical settings without expert sonographers.

Oct 12Mon
  1. Import AI48

    Import AI 476: Creativity and RSI; worse-than-China open weight models; AI goes to space

    This Import AI issue covers the Humanity's Sixth Sense benchmark from Elorian and Scale AI, where humans score 93.1 pass@1 versus 53.6 for the best model, GPT 6 Astra, with Chinese open weight models scoring far lower. It also discusses P-Zero Research's TasteVal benchmark, which reports that frontier models' experimental research taste has doubled every 3.0 months since December 2025, with Opus 5.5 exceeding the human baseline, and covers Reflection's 501B-parameter Beam and Mistral Large 4, both trailing Chinese open weight models on several benchmarks.