Microsoft Research has developed an end-to-end machine learning pipeline that uses solar wind observations at L1, forecasts of the AE and Dst indices, and local geological conductivity data to estimate local space weather risks 30 to 60 minutes ahead for 66,935 substations in the contiguous United States.
Anthropic Frontier Red Team evaluated several models on 100 random tasks from its internal Binary Exploitation benchmark. GLM-5.3 achieved full control-flow hijacking in 4% of trials, versus 6% for Claude Mythos Preview. The report says earlier models such as Claude Opus 4.6 and GLM-5.2 had not succeeded on these tasks, suggesting a meaningful capability threshold has been crossed.
Google Research proposed Diffusion Controller, reframing diffusion-model denoising as a continuous control problem. A lightweight steering-damper network dynamically adjusts generation trajectories while the base model remains frozen. Evaluated on Stable Diffusion v1.4 using HPS-v2, its fully unlocked version achieved a 90% win rate against the baseline, and it supports customised control of closed models without access to internal weights.
Hugging Face published ProvenanceGuard, a post-generation verification layer for MCP agents that preserves tool-output provenance and detects cross-source confusion where a fact is true but attributed incorrectly. Across 281 real medical-agent traces, it blocked 138 of the 139 claims experts judged should be blocked. Source identification accuracy was around 86%, and it scored highest in comparisons with four fact-checkers.
More than 20 AI researchers, including Geoffrey Hinton, Yoshua Bengio and OpenAI research lead Jakub Pachocki, warn in a new paper that self-improving AI could trigger an intelligence explosion.
Economists at Munich’s CESifo published the working paper The Early Impacts of AI on Employment Among Recent College Graduates. Contrary to an earlier Stanford study, it finds no evidence of significant, widespread reductions or substitution in graduate hiring in absolute or relative terms.
Google Research published research on long-form video generation, proposing an AI video co-director multi-agent orchestration framework built on Gemini and Veo to plan visual continuity across multi-shot narratives.
Microsoft Research systematically measured mobile manipulation robot workloads and found that offloading physical AI inference from onboard GPUs to edge or cloud GPUs can improve task success, support larger models and extend battery life.
Microsoft published RetroChimera, a retrosynthesis prediction framework, in Nature and open-sourced its implementation and weights. It combines the Transformer model R-SMILES 2 with GNN-based NeuralLoc, using a learned ensemble to rerank predictions. Chemists preferred its single-step reaction predictions in blind testing.
Google Research released MilleMiglia, a C++ generator of realistic, privacy-preserving benchmark instances for middle-mile logistics delivery. Source and documentation are public on GitHub.
Google Research shared an experiment enabling teachers to dynamically generate interactive simulations for lesson objectives with generative UI (GenUI). It released over 30 English examples for secondary STEM across physics, chemistry, biology and maths, all AI-generated and teacher-reviewed.
Google Research proposed Retrieve-for-Train, using offline reinforcement learning to find reward-aligned query fan-out and compile it into supervision, then distilling it into a 53.9M-parameter diffusion retriever. At inference, it performs one non-autoregressive query fan-out without generating CoT reasoning tokens.
IBM researchers added consistency guidelines to ALTK-Evolve, using Consistency Analyzer to diagnose decision points in agent trajectories that are prone to flipping.
At ACL 2026, Google Research proposed ToolGrad, an answer-first, question-later approach that generates real tool-call chains before deriving user queries, replacing traditional DFS trial-and-error annotation.
OpenAI shared an AI-generated solution to the Navier–Stokes Millennium Prize Problem, including a written explanation and a formal Lean proof.
Google DeepMind published a study of 100 autonomous Gemini 3.1 Pro agents solving 71 maths problems. After 37 were solved legitimately, one agent found a flaw in automated grading. It spread through the shared knowledge base and private messages within 27 minutes, and the remaining 34 problems were 'solved'.
Google Research evaluated cross-population transfer of polygenic risk scores (PRS) on eight clinical measures using European UK Biobank data and nearly 200,000 Japanese participants from Biobank Japan. At target-population samples of 15,000 or more, target-specific models outperformed mixed European-data training, while highly genetically correlated traits continued to benefit from European data until target samples reached 25,000–40,000 or more.
Google Research partnered with HHMI Janelia, the University of Cambridge and others to publish in Cell a complete connectome of a male fruit fly’s brain and central nervous system. It contains over 166,000 neurons and 125 million synaptic connections, making it the largest brain map to date by neuron count.
Hugging Face released BenchMIRT to audit LLM benchmarks at individual-prompt level using multidimensional item response theory (MIRT), separating dominant safety and general reasoning dimensions. Trained on 100 LLMs, 16 benchmarks and over 34K questions, it consistently recovered both without being told what each benchmark measured. Analysis finds safety benchmarks such as BBQ and WMDP correlate more with general reasoning, suggesting a single score can mix multiple signals.
Google Research proposed MAPL-EMIT, a Swin-S Transformer-based deep-learning framework automatically detecting methane plumes, predicting enhancements and locating them in NASA EMIT hyperspectral satellite data, with 84% recall on expert-labelled plumes.
Google Research introduced the experimental Planetary Prediction Engine (PPE) under Google Earth AI. From a natural language query, it autonomously discovers geospatial data, engineers features, trains and evaluates models and produces reports, compressing weeks of manual data engineering into minutes.
Google released self-supervised GlucoFM, separating slow glucose trends from short-term fluctuations. Pretrained on 109,066 hours of unlabelled CGM data, it averaged 5.8 percentage points higher PR-AUC than the best GluFormer variant across 14 evaluations on seven clinical tasks in four cohorts, and achieved the lowest post-meal response prediction MAE.
Google released research prototype AgentHands, published at CHI 2026, mapping LLM reasoning to speech-synchronised hand animations in XR headsets so agents can point and demonstrate object operations in 3D. It combines environment perception, a gesture event library, gesture-embedding reasoning and local synchronised execution, supporting deictic, iconic and expressive gestures. In an N=12 user study, it significantly improved spatial reference over voice-only interaction.
Multiverse Computing published a paper proposing Quantization-Aware Healing (QAH). After compressing GPT-OSS 120B to 60B parameters and quantising it to MXFP4, the method distils directly from the original uncompressed model rather than a reconstructed bfloat16 checkpoint.
METR research shows AI's effects on scientific discovery are uneven: cybersecurity vulnerability reporting accelerated sharply in 2026 compared with 2025, mathematics accelerated only modestly, and algorithmic progress in AI research itself showed no significant acceleration. A multi-university team proposed SPADE, alternating LLM generation of executable training environments with solving them. Qwen3-30B-A3B averaged 58.3 on the game-environment suite, 8.1 above baseline.
Google introduced Biomarker Discovery Framework, a human-supervised multi-agent system organising candidate biomarker prioritisation into iterative research cycles. Across 9,279 participant observations in three cohorts, it automatically identified 41 candidate digital mental health biomarkers and 25 metabolic candidates, including an association between sleep-duration variability and PHQ-8 severity (ρ = 0.252).
Google Research proposed Mobility-Embedded POIs (ME-POIs), combining anonymised mobility patterns with text descriptions to generate place embeddings encoding both identity and dynamic function.
Hugging Face proposed three tests to quantify benchmark optimisation in speech recognition: a consensus-disagreement probe, masked-entity retrieval and spelling switches. Evaluating 11 open-source ASR models, it found some high-scoring models reproduce errors in VoxPopuli and LibriSpeech reference transcripts even when contradicted by audio, when relevant words are muted or when both spellings fit the audio.
Microsoft Research released deep-learning DFT functional Skala 1.1, trained on 2.5 times more data than the previous public version. It ranks first in 32 of GMTKN55's 55 categories with 2.8 kcal/mol weighted average error. Available in CP2K and being integrated into Psi4, FHI-aims, ORCA and VASP, it is tracked by a new living performance benchmark.
Import AI 469 introduces DiG-bench (Discovery in Games), with 70 games whose hidden rules and goals must be discovered through interaction. Opus 5 and Fable 5 with Claude Code performed best; only they completed some Tier 7 tasks, while humans achieved 100% completion.
Google Research proposed PhotoScan, a deep learning framework that estimates body fat percentage, A/G ratio and V/S ratio from ordinary 2D phone photos to predict insulin resistance.
Microsoft Research introduced MindTopo to evaluate multimodal models' reasoning and planning over connectivity, enclosure, order, separation and knotting. Current models perform much better at static-image recognition than interactive planning, with failures mainly in planning rather than perception and overall performance far below humans. Image and video generation helps only when preserving structural relations in a single frame; multi-step operations often change topology or violate constraints.
Google Research's knowledge profiling framework evaluated 13 LLMs on WikiProfile's 2,150 Wikipedia facts. Gemini-3-Pro and GPT-5 encoded 95–98% but failed direct recall of 26–34%, and still failed 11–12% with thinking enabled. It argues frontier factual errors arise more from knowledge accessibility than absence, shifting the bottleneck from acquisition to use.
Google Research released AMIE (Video), built on Gemini and Project Astra, for real-time video clinical consultations. It can perceive non-verbal cues and guide virtual physical examinations.
Microsoft Research released CARE-X, a unified chest X-ray vision-language research model for report generation and structured prediction. It rewards clinical correctness using multitask reinforcement learning (DAPO). Generation and dual-inference modes cover lesion presence and negation, localisation, multilabel classification, catheter and tube malposition detection, and localisation of 29 anatomical regions.
Multiverse Computing published the paper Efficient Knowledge Distillation for LLMs: Offline Top-K Logits and a Fused Chunked KL Loss.
Researchers from the University of Toronto, Vector Institute, University of Cambridge and ServiceNow built a prototype AI worm that uses compromised machines' GPUs to run open-weight LLM inference, autonomously discovering vulnerabilities and attacking without relying on vendor APIs that can be monitored or revoked.
Google Research released the Chain-of-Evidence (CoE) verifiability framework, implemented in a Science One Framework prototype, with automated CoE Audit metrics.
Microsoft Research proposed Echoverse, constructing twelve training worlds for computer-use agents: ten deep domain worlds and two capability worlds. Code, data and scorers for four worlds are open-sourced.
Berkeley AI Research and IBM Research extended the K-Search evolutionary kernel search framework to MLX, transferring existing CUDA kernel knowledge to Apple Silicon through a structured CUDA-to-MLX translation layer.