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#Research

All collected and published items, ordered by time. Picks are a selected subset.
First-party

Direct announcements from model vendors, companies and research institutions on their official sites, blogs and accounts.

8/14Fri
  1. Hugging Face Blog62

    Hugging Face releases 2026 summer report on the open-model ecosystem

    Hugging Face has released a report on the open-model ecosystem from January through August 2026, drawing six findings from Hub data. Public model repositories grew from 2.43 million to 2.96 million and datasets from 711,000 to one million, but 85.6% of models have fewer than 200 lifetime downloads, while 1.5% of repositories account for 99.2% of downloads.

    Why it matters: Hugging Face analyzes its Hub data on the open-model ecosystem; findings on downloads versus likes and the scale of Qwen derivatives can inform model selection.

8/13Thu
  1. Microsoft Research36

    MindTopo exposes spatial-reasoning gaps in multimodal LLMs

    Microsoft Research has introduced MindTopo, a benchmark for topological reasoning in multimodal LLMs. It covers connectivity, separation, ordering, containment, and knot relations, across static reasoning and interactive planning. Current models are substantially better at static recognition than interactive planning, but both remain far below human performance; failures occur more often in planning than perception. Image and video generation helps only when topology is preserved within a single frame and remains unreliable across multi-step operations.

8/12Wed
  1. Google Research60

    Google Research proposes knowledge profiling: retrieval, not encoding, is the bottleneck for frontier LLM factuality

    Google Research has proposed a knowledge-profiling framework that distinguishes factual errors caused by encoding failures from those caused by retrieval failures, and released WikiProfile, a benchmark containing 2,150 Wikipedia facts.

    Why it matters: By separating encoding failures from retrieval failures, knowledge profiling offers a new way to diagnose the sources of factual errors in frontier models.

  2. Google Research72

    Google researchers advance AMIE to expert-level clinical interviews with audio and video

    Google Research has released AMIE (Video), which uses Gemini and Project Astra for real-time video clinical interviews. It can perceive nonverbal cues, guide virtual physical examinations, and reason about diagnoses in real time.

    Why it matters: AMIE extends from text to live video consultations, showing how an asynchronous multi-agent architecture can balance conversational latency with clinical reasoning.

  3. Microsoft Research36

    Microsoft Research introduces CARE-X, a clinically oriented radiology VLM combining auxiliary supervision, reward alignment, and tool-assisted measurement

    Microsoft Research has released CARE-X, a research model and unified chest X-ray vision-language model that can generate reports, make structured diagnostic predictions, and localize lesions. It uses reinforcement learning (DAPO) to reward clinical correctness across multiple tasks.

8/11Tue
8/10Mon
  1. Hugging Face Blog48

    Hugging Face proposes efficient knowledge distillation with offline top-K logits and fused, chunked KL loss

    A new Hugging Face paper proposes two systems improvements that sharply reduce the training cost of distilling LLMs: cache the teacher model's top-100 logits offline so teacher and student do not need to reside in GPU memory simultaneously, and use a fused, chunked KL loss to avoid generating a full vocabulary-by-sequence-length matrix.

7/31Fri
  1. Google Research62

    Google Research proposes Science One Framework for verifiable autonomous research with Chain-of-Evidence

    Google Research has proposed Chain-of-Evidence (CoE), a framework for verifiability implemented in the autonomous-research prototype Science One Framework and the automated auditing tool CoE Audit.

    Why it matters: The article reports a verifiability framework and audit metrics, helping readers compare autonomous research systems on citation and code consistency.

  2. Microsoft Research71

    Microsoft Research releases Echoverse, an evolving training environment for computer-use agents

    Microsoft Research has introduced Echoverse, a set of 12 training worlds for computer-use agents: 10 full-domain environments and two capability environments. Each consists of an application, tasks, and a database-based verifier.

    Why it matters: Microsoft Research has released 12 computer-use-agent training environments with verifiers and reports comparisons of transfer performance between deep and shallow environments.

7/29Wed
  1. Berkeley AI Research62

    From CUDA to MLX: how K-Search brings kernel-optimization experience to Apple Silicon

    Berkeley AI Research and IBM Research have extended the evolutionary kernel-search framework K-Search to MLX and added a structured CUDA-to-MLX translation layer that automatically adapts existing CUDA-kernel knowledge to Apple Silicon.

    Why it matters: The article details the CUDA-to-MLX translation layer and reports two kernel benchmarks, showing how kernel knowledge can be transferred across platforms.

7/26Sun
  1. Berkeley AI Research36

    Berkeley AI Research proposes ABBEL, using belief-state supervision to make LLMs efficient at long-horizon interaction

    Berkeley AI Research has proposed ABBEL, a framework that separates an LLM's recursive summaries into natural-language “belief states” and scores their content with supervised signals to reduce performance loss from context compression in long interactions. On the collaborative-programming benchmark CollabBench, reconstruction-based belief scoring cut the performance gap with a full-context model by about 50%, reduced training from 100 steps to 50, and kept peak context length well below the full-context setup.

7/23Thu
  1. Google Research62

    Google introduces SymptomAI, a conversational AI agent for everyday symptom assessment

    Google Research has published SymptomAI, a study in which five Gemini Flash 2.0-based conversational agents interviewed 13,917 participants and evaluated differential diagnoses (DDx).

    Why it matters: Google's randomized study compares five questioning strategies across 13,917 people and, for the first time, compares diagnoses with Fitbit physiological signals, offering evidence on real-world conversational triage.

  2. Google Research62

    Google Research proposes quantum computers that learn from their mistakes

    In a paper published in Nature, Google Research proposes using reinforcement learning to let autonomous agents learn from quantum error-correction detection events, continuously adjusting thousands of control parameters during computation to counter drift.

    Why it matters: Google trains a reinforcement-learning agent on quantum error-correction data to continuously calibrate control parameters without interrupting computation.

7/16Thu
7/15Wed
  1. Hugging Face Blog62

    Hume releases Real World VoiceEQ to measure human quality in voice AI

    Hume has released Real World VoiceEQ, a benchmark for human quality in voice interaction. It covers more than 40 proprietary and open-source voice models, over 15 evaluation dimensions, and more than 60 metrics across ASR, TTS, speech-to-speech, and voice understanding.

    Why it matters: Hume built a voice-AI benchmark from more than a million human ratings, showing why current metrics can overestimate real conversational performance.

7/9Thu
  1. Google Research62

    Google releases SensorFM, a large sensor foundation model for wearable-health data

    Google Research and Google DeepMind have introduced SensorFM, a large sensor foundation model that learns directly from unlabeled wearable data. Its pretraining corpus contains more than one trillion minutes of multimodal sensor signals from five million consenting participants across more than 100 countries, all 50 U.S. states, and over 20 Fitbit and Pixel Watch devices.

    Why it matters: The article reports pretraining-data scale, scaling experiments, and downstream-task comparisons, helping readers assess how generalizable wearable foundation models are.

7/8Wed
  1. Google Research36

    Google Research uses Google Maps routing interventions to ease urban congestion

    In the first large-scale real-world study of its kind, published in Nature Cities, Google Research modified the Google Maps algorithm to direct about 2% of trips to alternative routes across 10 U.S. cities for six months. Median travel speeds on target roads rose by about 2%, median fuel-consumption rates fell by 0.5% to 1.0%, and each city could cut thousands of tonnes of CO2e emissions annually.

7/1Wed
6/30Tue
6/27Sat
  1. Google Research60

    Google speeds up Gemini Nano on Pixel with frozen multi-token prediction

    Google Research has introduced an architecture that attaches a multi-token-prediction (MTP) head to the frozen Gemini Nano v3 model, speeding up on-device generation without changing the backbone weights.

    Why it matters: Google attaches an MTP head to frozen Gemini Nano v3 and reports concrete methods and gains under on-device memory and energy constraints.

6/25Thu
  1. Google Research31

    Google lowers cloud costs with linear elastic caching: Spanner memory use drops 15.5%

    Google Research has proposed linear elastic caching, modeling page eviction as a ski-rental problem and using lightweight machine learning to adjust cache size dynamically. After months of testing in Spanner production, memory use fell 15.5%, cache misses rose just 5.5%, total cost of ownership fell about 5%, and the impact on actual I/O costs was only 0.5%.

  2. Google Research37

    Google Research studies how reasoning unlocks LLMs' parametric knowledge

    A paper from Google Research at COLM 2026 finds that reasoning traces can unlock parametric knowledge in LLMs that they could not otherwise access, even for simple single-hop factual questions. Tests on Gemini-2.5 Flash and Pro and Qwen3-32B identify two mechanisms: extra tokens act as a “computation buffer,” and “fact priming” builds a retrieval bridge by generating related facts.

6/17Wed
  1. Google Research30

    Google Earth AI releases vectorized dataset mapping hedgerows and woodlands in the British countryside

    Google Research has released a vectorized dataset that converts the high-resolution raster maps in Farmscapes 2020 into actionable inventories of hedgerows, stone walls, and scrubland, covering more than 130,000 square kilometers of the UK. The framework fine-tunes an RSF Vision Transformer backbone pretrained on over 300 million global satellite images and uses Polsby–Popper compactness scores to distinguish woodland, woody patches, and linear woody features.

6/16Tue
  1. Google DeepMind60

    Google DeepMind publishes AI Control Roadmap for agent safety through defense in depth

    Google DeepMind has published an AI Control Roadmap that treats internal AI agents as potentially misaligned and adds system-level safety layers beyond model alignment. It proposes grading safeguards by detection capabilities D1–D4 and prevention-and-response capabilities R1–R3.

    Why it matters: DeepMind has published its internal AI control roadmap, with graded detection and response capabilities and analysis of one million coding-agent trajectories.

6/13Sat
  1. Google Research32

    Google studies how AI can help people understand skin concerns

    Google reports on consumer understanding of dermatology AI tools. Among 2,345 participants, more than 62% of those using AI tried to name a skin condition, with 23% accuracy—nearly three times the control group's 8%. The “Wizard of Oz” group reached 36% accuracy. But the AI group showed no statistically significant improvement in deciding what care to seek and was slightly more likely than dermatologists to recommend less urgent care (30% vs. 27%).

6/11Thu
6/8Mon
  1. Google DeepMind62

    Google DeepMind publishes results from AI learning trial in Sierra Leone

    Google DeepMind has published results from a preregistered randomized controlled trial in Sierra Leone. Students using Guided Learning improved their math scores by 0.258 standard deviations over the control group—equivalent to about 1.2 to 1.7 years of typical learning—in eight weeks.

    Why it matters: The study reports quantified results and interaction data from a randomized trial in Sierra Leone, helping readers assess the effects of AI-assisted instruction in real classrooms.

6/5Fri
  1. Google Research67

    Google Research studies passive heart-health monitoring with a phone camera

    In a paper published in Nature, Google Research describes PHRM, a system that uses the front-facing camera to record a few seconds of video after face unlock and estimate heart rate and resting heart rate in the background using deep learning.

    Why it matters: Google passively measures heart rate with a phone's front camera after unlock and has released its largest phone-video dataset to date along with a pretrained model.

5/28Thu
  1. Google Research34

    Google introduces privacy analytics with zero-trust aggregation

    Google has introduced zero-trust aggregation for its privacy analytics service, combining a new cryptographic aggregation method with hardware-protected TEEs. It can receive device data in a single message without multi-round online interaction. Google receives only anonymous, aggregate insights about groups; raw data is neither exposed nor reconstructible, even within the hardware-protected environment. TEE attestation provides participants with proof that the protocol ran as intended.

5/27Wed
  1. Mistral AI38

    After acquiring Emmi AI, Mistral advances physical-AI research with industrial-simulation results

    After acquiring Emmi AI, Mistral is building foundational physical AI for aerospace, automotive, semiconductors, and energy. Its announced results include AB-UPT, which processes raw geometry with 9M surfaces and 140M volume cells on a single GPU without remeshing, and NeuralDEM, an end-to-end deep-learning surrogate for large multiphysics processes.

5/19Tue
  1. Google DeepMind60

    Google DeepMind's Co-Scientist accelerates discovery of genetic clues to reversing cellular aging

    Google DeepMind biologists Omar Abudayyeh and Jonathan Gootenberg used Co-Scientist to support aging research. After scanning tens of thousands of papers, the tool proposed more than 20 new candidate genetic factors. Lab tests validated several hypotheses, successfully making cells younger and improving their overall function. Co-Scientist also analyzed large-scale genetic-screening data, reducing data and literature linkage work that could take up to six months to just days.

    Why it matters: The source reports specific outputs and time comparisons from Co-Scientist's role in aging research, showing how AI agents contribute to scientific hypothesis generation.

5/16Sat
  3. Google DeepMind25

    How Google Co-Scientist connected MIT and Boston Children's Hospital labs to explore ALS treatments

    Google's Co-Scientist helped MIT mechanical engineer Ritu Raman rapidly review ALS literature, turn ideas into testable hypotheses, and rank them by feasibility. Its strongest lead pointed to interactions between molecules on cell surfaces, outside Raman's field, prompting her to work with Boston Children's Hospital chemical biologist Ryan Flynn. Together, using their respective toolkits and iterating with Co-Scientist, they are searching for new RNA mechanisms—and potentially RNA drugs—that could target ALS.

5/12Tue
  1. Google DeepMind71

    Google DeepMind releases Co-Scientist multi-agent research system

    Google DeepMind has published research on Co-Scientist in Nature. Built on Gemini, it is a multi-agent AI system that iteratively generates, debates, and evolves scientific hypotheses.

    Why it matters: The article describes the multi-agent system's three-stage architecture and deployments in several labs, explaining how AI contributes to scientific hypothesis generation.

4/30Thu
  1. Google DeepMind62

    Google DeepMind launches AI co-clinician research program

    Google DeepMind has announced an AI co-clinician research program exploring a “triple-care” model in which AI agents assist with patient care under clinical supervision. In blind evaluation of 98 real primary-care queries, the system had no critical errors in 97 cases, and doctors preferred its responses to those from existing evidence-synthesis tools. It also outperformed other frontier models on open-ended medication questions in OpenFDA's RxQA.

    Why it matters: Google DeepMind reports results from physician blind reviews, RxQA, and simulated remote consultations, offering a view of current medical-AI capabilities.

  2. Google Research62

    Four ways scientists are using Google's Empirical Research Assistance

    Google Research summarizes four areas where scientists use Empirical Research Assistance (ERA): in public health, the team submits weekly influenza forecasts for U.S. states.

    Why it matters: Google uses four real research projects to show ERA deployments in epidemic forecasting, cosmology, carbon monitoring, and neuroscience, illustrating how AI-assisted research is being used.