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Only First party#Research
Nov 4Wed
  1. Hugging Face Blog40

    CWE-bench v2: More coverage, quality, and transparency

    Collinear AI released CWE-bench v2, a public set of 100 programmatically scored tasks in which an agent must find and fix a security weakness in a real open-source repository without breaking existing functionality, covering 91 different CWEs, with a held-out set available on request. Of 11 evaluated models, Gemini 4 Argon leads with 67.8% pass@1 and 81.0% pass@4, just ahead of Claude Opus 5.5 (67.3% pass@1, 78.0% pass@4); 95 tasks are solved by at least one model, but even the leader fails 19% of the set on every attempt. The public set, grader and harness are open, and tasks can be run directly from the Hugging Face repo via Harbor.

  2. Hugging Face Blog55

    StateBench: Testing how language models track state changes over long documents

    Researchers introduce StateBench, a benchmark with four difficulty levels of long-context state tracking, from immutable assignment to general finite problems involving assignment by reference. In tests, open pretrained models and frontier models (Gemini 3.8 Flash, Claude Opus 5.5, GPT-6 Sol and Luna) degraded on harder levels as inputs grew, while 275M-parameter Gated DeltaNet and hybrid models trained directly on the tasks generalized far beyond training length, though transformers never learned the hardest level. The work is released with a tech report, data, and code, and notes open questions such as fixed variable counts and the exact hierarchy placement of constrained Gated DeltaNet variants.

  3. Allen Institute for AI Blog55

    StateBench: Testing how language models track state changes over long documents

    Ai2 introduces StateBench, a long-context state-tracking benchmark with four difficulty levels (r-trivial, aperiodic, soluble, and general finite) grounded in Krohn–Rhodes theory. Testing open pretrained models and frontier models including Gemini 3.8 Flash, Claude Opus 5.5, and GPT-6 Sol and Luna, they found accuracy fell sharply on soluble and general finite problems as inputs grew longer, dropping to 13% or lower on the longest general finite problems. In from-scratch training of 275-million-parameter models, Gated DeltaNet and hybrid models generalized almost perfectly on the first three levels out to tens of thousands of assignments, while transformers never learned the hardest level.

  4. 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.

  5. Hugging Face Blog60

    Same Model, Different Harness: CUGA, Hermes, and Deep Agents — When to Pick CUGA?

    IBM ran CUGA, Hermes, and Deep Agents on the same open-weight model (GLM-5.3-flash) across 90 AppWorld tasks, and task completion ranged from 30% to 81% depending only on the harness, with CUGA at 81.1% (73/90) versus Hermes at 46.7% and Deep Agents at 30.0%. CUGA is IBM's Apache 2.0 harness for enterprise API work, adding runtime-enforced policies (intent guards, tool approvals, ToolGuards, output formatting) and deployment inside sovereign or air-gapped environments. The authors note the 90-task sample, non-identical setups, and absence of policy testing as limits, and say CUGA is not the right fit for terminal coding or personal chat assistants.

    Why it matters: Useful for teams choosing an agent harness, since it isolates harness effects with a fixed model and shows where runtime policy enforcement fits enterprise needs.

Nov 3Tue
  1. Apple Machine Learning Research30

    Attention to Mamba: A Recipe for Cross-Architecture Distillation

    The authors propose a two-stage cross-architecture distillation recipe: first distilling a Transformer into a linearized version of Attention using an adaptation of the kernel trick, then distilling that linearized version into an adapted Mamba model with no Attention block. The distilled Mamba model retains most of the original Pythia-1B Transformer's performance, reaching a perplexity of 14.01 versus the teacher's 13.86, with results largely carrying over to downstream tasks though some gaps appear on tasks relying more on in-context recall. They conduct ablations at 1B scale with 10B tokens, scaling analysis on model sizes and distillation tokens, and a sensitivity analysis on token allocation between stages.

  2. MIT News AI38

    Regina Barzilay and Jim Collins win Pritzker Prize for AI and Science Research Excellence for work in AI-enabled antibiotic discovery

    MIT's Regina Barzilay and James Collins received the 2026 Pritzker Prize for AI in Science Research Excellence for their joint work in AI-enabled antibiotic discovery. Their 2020 Cell paper with first author Jonathan Stokes described halicin, the first AI-discovered antibiotic, identified by a deep learning model trained on 2,500 molecules and shown to kill drug-resistant bacteria such as C. difficile, A. baumannii, and M. tuberculosis, with the discovery process completed in weeks rather than years. They later used machine learning to identify abaucin in 2023, a narrow-spectrum antibiotic effective against A. baumannii.

  3. OpenAI News42

    LifeVal

    Introducing LifeVal, a benchmark measuring how well AI helps people complete everyday tasks, from finding childcare to managing finances and government paperwork.

Nov 2Mon
  1. Apple Machine Learning Research48

    Online Dense Reinforcement Learning with Planning-Grounding Agent for UI Navigation on Live iOS Apps

    The authors propose an online reinforcement learning framework that trains UI navigation agents directly on live iOS applications, splitting the agent into a Planning agent and a UI Grounding model and using an LLM-as-a-Judge for dense step-level plus terminal rewards, optimized with PPO and step-level advantage estimation. They introduce GiGPPO, a neighbor-conditioned value estimation method that improves critic stability using reward signals from visually similar states across concurrent trajectories, backed by infrastructure orchestrating hundreds of concurrent virtual iOS devices. On UINavBench, iOSWorld, and AndroidWorld, their 8B RL agents substantially outperform SFT and terminal-only RL baselines, achieve nearly a sevenfold improvement over the SFT baseline in out-of-distribution generalization to Android, and outperform Gemini-3.5-Flash while tying Gemini-3.1-Pro on real iOS applications.

Nov 1Sun
  1. MIT News AI44

    Q&A: Hey AI, who should I vote for this year?

    MIT researchers led by Adam Berinsky, Chara Podimata and Charles Stewart III have built the LLM Election Observatory, a public dashboard that tracks in real time how popular AI chatbots answer a fixed set of questions about the 2026 U.S. midterm elections, re-running them every few days to see how responses vary by user location, party affiliation and identity. Podimata says early 2026 data show differences in how answers are presented, which sources are cited and tone, sometimes when the underlying facts are the same, though stability and effects on real users are not yet established. Berinsky warns that a confident-sounding chatbot is not necessarily accurate and says voters should treat its answers as a starting point rather than a final word.

Oct 30Fri
  1. Wiz Research22

    Key Takeaways: AI Threat Readiness Survey Report 2026

    A survey of 100 senior IT and security decision-makers found that 55% of organizations actively deploy autonomous agentic systems and 31% run more than 25 distinct AI systems in production, yet only 17% maintain a comprehensive AI asset inventory and 52% discovered unauthorized AI applications in their perimeters over the past year. Leaders cited governance and cross-functional ownership complexity (26%) as their top AI security bottleneck, while defensive spending remains fragmented, with 43% stretching legacy cloud tools, 38% funding dedicated AI security budgets, and 37% relying on homegrown scripts. The report outlines a five-priority AI Threat Readiness Framework spanning production-grade controls for AI stacks, graph-based asset context, behavioral least privilege, AI-driven exposure scanning, and code-to-cloud response.

  2. Microsoft Research40

    From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence

    Microsoft describes its Industry Evaluation Data Generation Platform, which builds fully synthetic enterprise environments grounded in public sources such as O*NET occupational data, job postings and SEC filings to evaluate Microsoft Copilot without using customer data. The platform combines synthetic tenants across pharmaceutical, capital markets and IT/software industries with an automated six-stage query generation pipeline and quality frameworks covering structural fidelity, contextual relevance, behavioral realism and content safety. Microsoft reports a 39% improvement on its composite synthetic data quality metric versus its initial approach, some industry environments scoring above 0.9 out of 1.0, and estimated savings of about $100,000 per evaluation dataset and $300,000 in total project savings.

  3. Microsoft Research22

    Microsoft Research Focus – Edition #97

    This edition of Microsoft Research Focus, hosted by Sowbhagya Suresh, highlights recent Microsoft research including ForkPilot, a self-improving agentic framework that reduces AI processing demands by up to 59%, and CABRA, a framework showing that understanding unfamiliar codebases remains a major obstacle for coding agents. It also covers TeleTune for learning agent skills from activity logs, findings that common Copilot uses may mask differences across industries, a Shiksha Copilot study in Indian schools, the Probabilistic Bias Correction framework for extended-range weather forecasting, and the public preview of the BioEmu2 biomolecular model.

  4. Sakana AI Blog40

    Intelligence Report: Analyzing the US Midterm Elections Through SNS AI Analysis

    Sakana AI, which works on defense and intelligence alongside finance, describes an analysis of about 710,000 reactions to 2,042 X posts from both candidates and both parties' state organizations in the Texas Senate race, using its narrative analysis system and its Sakana Marlin product to cross-check X observations against polling, campaign finance, prediction markets and news. It says much of the "support" for Paxton before the Republican runoff was intra-party mobilization and fell from about 50% to under 20% after the runoff, and that shifts in evaluations of the candidates appeared about two months before major raters such as the Cook Political Report changed their judgments. The report argues such findings require human-AI collaboration, with AI deriving hypotheses from data at a scale humans cannot read and humans examining them and making final judgments.

  5. Amazon Science38

    Speeding up language models for travel applications

    Researchers combined knowledge distillation, which trains a compact student model on a large teacher's outputs, with Medusa, a framework that predicts multiple tokens in parallel, and applied the pipeline with Booking.com to three travel features. The final model matched the quality of a 70-billion-parameter teacher while running 10 to 20 times as fast, with reported gains including 2.9% higher engagement versus GPT-3.5 for AI Trip Planner and 15-times-faster Smart Filters responses for 99% of queries. The work received the Industry Track best-paper award at ACL.

Oct 29Thu
  1. Replit Blog60

    ViBench 2.0: A Benchmark for Long-Horizon Software Building

    Replit releases ViBench 2.0, a long-horizon software development benchmark in which an agent builds a product from an empty project and then adds up to eighteen features on top of its own code, spanning seventeen products, 241 feature requests and 544 test plans. In Fireworks' official runs, GPT-6.1 Sol leads with a pass@1 of 65%, followed by GPT-6 Astra at 60% and Opus 5.5 at 49%, and no open-weight model passes a full build. Eight of the products are public on GitHub in the Harbor format, and results can be rerun from release v2.0.6 on the OpenHands-based harness.

    Why it matters: It offers concrete pass rates, costs and failure modes for frontier models on multi-feature product building, with a public, rerunnable harness.

  2. Meta Engineering36

    Meta Andromeda: Powering the Next Generation of Ads Retrieval Through Model-System Co-Design

    Meta describes the next generation of its Andromeda ads retrieval engine, which uses a retrieval mixture-of-experts architecture and a single fused GPU pipeline to score every eligible ad at over one billion ads per second, a 100x throughput lift over the 2024 system. The system delivers near ranking-level model performance at retrieval scale with roughly 300x fewer inference-time FLOPs than the 2024 model, and Meta reports a 2.4% increase in conversions on Facebook since 2025. A joint collaboration with NVIDIA pushed individual components toward hardware limits, with the gating kernel reaching over 10 billion ads per second on a single GH200 and index matching exceeding 100 billion ads per second.

Oct 28Wed
  1. Anthropic Research71

    AI agents shape experts' judgment calls

    In a randomized study, 50 professional analysts using Claude Code on a farming-cooperative analysis task matched their agent's randomly assigned lean 86% of the time across four analytical choices, and 77% later repeated the agent's arguments, often verbatim, while only 15% credited the agent. Analysts who followed the agent's lean were about as confident in the recommendation and as likely to call it their own, and the four leans alone predicted 82% of final recommendations. The authors caution the effect was deliberately strengthened and studied in one task with one model, but note that if experts defer without realizing it, agents' arbitrary or misaligned preferences could shape policy and research decisions.

  2. Browser Use Blog58

    Odysseys2: a long-horizon web agent benchmark

    Carnegie Mellon University and Browser Use released Odysseys2, a benchmark of 100 long-horizon tasks on the live web, each graded on 8 to 12 pass/fail checks. The best agent, GPT-6 Astra, fully completes 47 of 100 tasks with a mean rubric score of 84.6, while no other agent reaches 20 and five complete none. The authors note the LLM judge has not been validated against a human reference, reward-hacking flags are model judgments, and the live web changes.

  3. ElevenLabs Blog22

    What shoppers choose once they’ve tried a retail AI agent

    ElevenLabs ran three studies across the UK, US, Canada, Australia, Europe, South Africa and Latin America in which most shoppers completed a real task with a live voice agent before answering questions. Nearly half chose speaking and hearing replies going forward, and voice was the top choice on all six shopping tasks, though comparing products split shoppers almost evenly; when told an agent had failed once, the share asking for a person rose from 24% for a late delivery to 61% for a wrong charge. Shoppers wanted a written record of resolutions, and 60% said they would abandon an agent entirely if there were no visible way to reach a person.

  4. Google Research58

    The sleep framework: A new way for LLMs to retain knowledge in continual learning

    Researchers introduce a "sleep framework" for continual learning in which LLMs alternate between an active wake phase that stores temporary information in short-term memory and an offline sleep phase that consolidates it into long-term parameters via Mixture-of-Experts parameter expansion, knowledge distillation, and a "dreaming" self-improvement loop. The authors report that this approach avoids catastrophic forgetting in continual translation, scales to 10M tokens on BABILong, reaches 48.9% on single-task and 46.2% on CPT factual-knowledge evaluations, and scores 79.2%, 69.0%, and 46.1% on AIME 24, AIME 25, and HMMT 25.

  5. Allen Institute for AI Blog58

    Can we trust LLMs to judge whether a research idea is novel?

    Ai2 released "Old Ideas, Novel Problems," a study with a benchmark, data, code, and an automated pipeline for testing LLM novelty judges. Across six judges (GPT-5.1, GPT-5.2, GPT-5.4, Claude Sonnet 4.5, Claude Opus 4.5, Claude Opus 4.6), small prompt changes shifted accuracy by more than 50 percentage points on AI-generated ideas, retrieval and extra reasoning did not consistently help, and two purpose-built novelty evaluators scored worse than a simple prompted Claude Opus 4.6 baseline while costing far more.

  6. Hugging Face Blog58

    Can we trust LLMs to judge whether a research idea is novel?

    Ai2 released "Old Ideas, Novel Problems," a study with a benchmark, data, code, and an automated pipeline for testing LLM novelty judges, built from ICLR 2026 peer reviews to form Human-Only and Human+Generated evaluation sets. Across six judges (GPT-5.1, GPT-5.2, GPT-5.4, Claude Sonnet 4.5, Claude Opus 4.5, Claude Opus 4.6), small prompt changes shifted accuracy by more than 50 percentage points on AI-generated ideas, retrieval and extra reasoning did not consistently help, and two purpose-built novelty systems scored below a simple prompted Claude Opus 4.6 baseline.

  7. Meta Engineering36

    Python Typing Survey 2026: AI Tools and the Shifting Checker Landscape

    The 2026 Python Typing Survey, with 1,794 responses, found type hint use stable across three years (85.5% in 2026) while checker selection shifted sharply, with 49.9% of respondents selecting two or more checkers and speed cited by 56.5% as the top reason for choosing one. Missing type information in third-party libraries was the leading difficulty at 65.1%, and AI tools wrote type hints for 42.5% of 1,517 respondents, with 24.4% saying an AI coding tool invoked their checker.

Oct 27Tue
  1. ElevenLabs Blog50

    Why voice wins for complex, stressful conversations

    ElevenLabs compared voice calls and chat across hundreds of production agents and found that in conversations longer than two minutes, voice succeeded 32% more often than text chat and resolved successful long conversations about one third quicker. In a simulated canceled-flight scenario, 85% of 98 participants preferred voice, citing speed of resolution and empathy. A 16% reduction in average end-to-end latency across 315 agent conversations correlated with a 26% lift in success rate, with calls ending within 15 seconds falling from 21.1% to 3.8%.

  2. Redwood Research Blog22

    A basic case for accelerating conceptual reasoning capabilities

    The authors argue that many of the most important alignment and safety challenges have indispensable conceptual components—questions without accessible ground truth that rely on a priori argumentation—while AI R&D and takeover depend much less on such reasoning, so improving AI's conceptual reasoning would be differential. They claim this work is neglected and tractable, citing initial results where basic elicitation and small-scale training made models roughly three to twelve months better on their main conceptual reasoning benchmark, and they point to a follow-up post on how to work on the problem.

Oct 24Sat
  1. Hugging Face Blog31

    LitPivot: Developing research ideas through dynamic contextualization within the literature landscape

    LitPivot is a system developed with University of Pennsylvania collaborators that organizes retrieved literature around the facet of a research proposal being edited, offers critiques supported by excerpts from selected studies, and suggests revisions researchers can accept, edit, or reject. In a study with 17 researchers, domain experts rated ideas developed with LitPivot as better grounded in the literature than those developed with a text editor and an AI chat tool, and participants perceived their revised ideas as more novel and useful. LitPivot will be presented at UIST 2026 in Detroit.

Oct 23Fri
  1. Hugging Face Blog62

    Turning Database Grading into RL Rewards: Post-Training Agents with ThinkingBox

    Microsoft and Hugging Face describe using ThinkingBox's executable terminal-database-state checks as binary rewards to fine-tune Qwen3.8-27B with GRPO for 50 steps on an offline-selected pool of 187 tasks, raising pass@1 from 51.70% to 60.78% and tasks passing all 20 attempts from 38 to 107 on the 507-task ThinkingBox-Bench, while pass@20 dipped slightly from 453 to 446. The post details offline policy-relative difficulty selection via Jeffreys posteriors, the reward loop, cost-per-dependable-task falling from $24.36 to $8.00, and releases the training stack in microsoft/thinkingbox-training.

    Why it matters: It offers a concrete, reproducible recipe for converting stateful agent grading into RL rewards, plus an honest account of the repeatability-versus-coverage tradeoff.

Oct 22Thu
  1. Google DeepMind58

    Beyond tree cover: mapping the world’s forest types

    Google DeepMind and Google Research, with the World Resources Institute and IIASA, released Forest Typology (ForTy) 2020, a global 10-meter-resolution map classifying land into six FAO-based categories: primary forest, naturally regenerating forest, planted forest, plantation forest, tree crops and agroforestry, and other land. An accompanying early time series tracks annual changes from 2017 to 2025, including where natural forests were replaced by plantations or farms. Validated against more than 9,000 expert-reviewed sites, ForTy identified the correct forest type 90% of the time and distinguished forest from non-forest land 95% of the time, and the map and time series are freely available on Google Earth Engine under an open license.

  2. Hugging Face Blog58

    How Specializing the Vocabulary of SLMs Can Cut Inference Cost by 77% in Real Applications

    Dharma-AI describes VocabX, a method that rebuilds a small model's tokenizer around the cost of each token rather than its frequency, combining sub-word merges with multi-word "macro" tokens under a shared cost criterion. In a production Portuguese essay grader built on Qwen3-4B, the approach cut average response length from 350 to 101 tokens and measured inference cost by 77.0% with the same base model and training recipe, while the bench score rose from 0.703 to 0.810. The article previews results from a VocabX paper still under review, noting the numbers may change before publication.

  3. 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.

  4. Microsoft Research78

    Solving a 31-year-old grand challenge in mathematics – the KLS conjecture

    Microsoft researcher Xinzhi Zhang and a collaborator proved the Kannan–Lovász–Simonovits (KLS) conjecture, proposed in 1995, showing that for log-concave distributions the best hyperplane cut is within a universal constant of the best cut of any shape regardless of dimension. The proof emerged from a human-directed agentic process exploring more than 100 candidate approaches, and was followed by independent parallel proofs and a Lean 4 formalization. The authors say unguided attempts with leading AI models using a similar token budget fell short, while the success depended on both AI capabilities and researchers' mathematical judgment.

    Why it matters: It documents a major open problem's resolution and offers a concrete account of how human-guided AI search contributed to a mathematical proof.

Oct 17Sat
  1. Google Research71

    Grounding AI agents in biological reality: Building blocks for autonomous wet labs

    Google researchers describe three projects aimed at connecting AI agents to real wet-lab experimentation: the VERID framework, a semantic digital twin of a lab workcell that tracks 3D state, runs fast virtual dry runs, blocks unsafe commands and logs synchronized video and actions; closed-loop aptamer design over three design-test cycles that raised on-target hit rates to 33.5% for myoglobin and 42.1% for α-thrombin; and agent-guided epitope excision for de novo binder design against FAP, which accelerated each folding cycle more than eightfold and, in a human–agent comparison at equal compute, nearly doubled the in-silico hit rate of a scientist working alone.

    Why it matters: It offers a concrete, example-rich account of how agents are being tied to physical lab hardware and closed experimental loops, including failure modes and safety safeguards.

Oct 16Fri
  1. Microsoft Research58

    Proof-based general-purpose computing with Magnetar

    Microsoft Research introduces Magnetar, a zero-knowledge virtual machine that proves execution of RISC-V programs written in Rust, Go, or C, running 10 to 39 times faster on average than the fastest comparable zkVMs across 15 benchmarks and verifying proofs in about 12 milliseconds. Magnetar's security rests only on standard hash functions such as SHA-256 or SHA-3, requiring no elliptic curves, lattices, special-purpose hash functions, trusted setup, or unproven conjectures. It builds on more than a decade of Microsoft Research work including Spartan, Phalanx, Nova, Brakedown, Lasso, Jolt, and BinarySpartan.

Oct 15Thu
  1. Google Research65

    Putting Earth AI to work: Geospatial agents for our planet's biggest challenges

    Google shares a technical report on its experimental Geospatial Reasoning agent, which connects Gemini to Earth AI foundation models and planet-scale data, and reports evaluations across four benchmarks, including two newly released ones (GeoKTools and GeoExpertQueries). The post describes early partner testing with organizations including WHO AFRO, People in Need, GiveDirectly, the Milken Institute and WPP, and announces expanded early access via Ask Google Earth and an experimental agent-to-agent API.

    Why it matters: It offers a concrete look at how a planning-based geospatial agent is evaluated and applied to real public health, disaster and infrastructure workflows.

Oct 13Tue
  1. Microsoft Research52

    Simulating student learning for better AI tutoring

    Microsoft Research introduces StudentSim, a research system that trains personalized AI student simulators from publicly available, de-identified learner records to evaluate and improve AI tutors before testing with real learners. The work also presents StudentSimEval, a benchmark spanning chess, second-language English writing, and mathematics that measures behavioral fidelity and guidance responsiveness, with StudentSim outperforming baselines such as GPT-5.4 and Maia2. In a chess tutor reinforcement learning study, a tutor trained with StudentSim feedback reached 90.5% accuracy versus 75.7% for the no-RL baseline and 71.6% for the GPT-5.4 reward condition, as rated by blind human chess experts.

Oct 8Thu
  1. Google Research71

    Does better work always mean better workers?

    A three-month field experiment with 133 patent lawyers found that access to a Google Labs AI patent-writing assistant raised drafting scores by 0.34 SD at 10 days and 0.38 SD at 90 days, with gains concentrated in fewer poor scores rather than more excellent ones. On a later redlining task where AI was not allowed, the benefit appeared only for senior lawyers (0.45 SD), while juniors showed no discernible improvement and their scores bifurcated toward more very low and more good results. The authors argue that improving output and building durable professional judgment are different goals that may be in tension for junior professionals.

    Why it matters: Offers a rare blinded, months-long field test that separates AI-assisted output gains from unassisted skill development, with direct implications for how firms train junior professionals.

Oct 6Tue
  1. Google Research58

    Unlocking Earth AI’s planetary geospatial foundation models for global public health

    Google researchers describe using the Population Dynamics Foundation Model (PDFM), part of Google Earth AI, to turn privacy-preserving search trends, mobility, built-environment and environmental data into monthly location embeddings that can be plugged into existing epidemiological models without task-specific fine-tuning. Independent partner evaluations across five public health challenges — MMR vaccination at the US–Canada border, US cardiovascular disease nowcasting, dengue forecasting in Mexico, postpartum depression risk prediction, and cholera emergence in the DRC — reported matched or improved performance over conventional inputs. The authors note current limitations such as static snapshots and say PDFM embeddings are available in Preview as Population Dynamics Insights, with no-cost access for select academic and public health research.