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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.

4/22Wed
  1. Google DeepMind62

    Google DeepMind releases Decoupled DiLoCo distributed-training architecture

    Google DeepMind has released Decoupled DiLoCo, a distributed-training architecture that divides large-scale training into compute islands communicating asynchronously, reducing bandwidth requirements and isolating hardware failures. In tests, training continued and reconnected after an entire learner unit went offline, achieving the same ML performance as traditional methods with Gemma 4 models.

    Why it matters: The source reports distributed pretraining results across bandwidth, fault tolerance, and mixed-generation hardware, helping readers assess engineering tradeoffs.

  2. Google Research62

    Google proposes ReasoningBank, helping agents learn from successes and failures

    In an ICLR paper, Google Research proposes ReasoningBank, an agent-memory framework that extracts high-level, structured reasoning memories from successful and failed experiences. Its code is open-source.

    Why it matters: The paper describes a closed loop for extracting reasoning memories from successes and failures, with success-rate and step-count results on WebArena and SWE-Bench-Verified.

4/20Mon
  1. Berkeley AI Research36

    GRASP: gradient-based long-horizon planning for world models

    A Berkeley AI Research team has proposed GRASP, a gradient-based planner that makes long-horizon planning with learned dynamics (world models) feasible. It lifts trajectories into virtual states for parallel optimization across time, injects stochasticity directly during iterative state refinement to explore, and reshapes gradients so actions receive clearer signals. The method addresses ill-conditioned computation graphs, local minima caused by non-greedy structure, and fragile gradients in high-dimensional visual models.

4/16Thu
  2. Google Research39

    Google Research uses AI-generated neurons to speed brain mapping, reducing reconstruction errors by 4.4%

    Google Research has proposed MoGen (Neuronal Morphology Generation), a model that generates synthetic neuron shapes with point-cloud flow matching. The data supplements training for the PATHFINDER reconstruction model, reducing mouse-axon reconstruction error by 4.4%, mainly by decreasing merge errors. At whole-mouse-brain scale, the work is equivalent to saving 157 person-years of manual proofreading. MoGen is available as an open-source model.

4/14Tue
  1. Google Research50

    Google introduces Vantage, using generative AI to assess future skills

    Google has released Vantage as a research experiment. It uses generative AI to create simulated conversations that assess high-school and university students' future skills, including problem-solving and collaboration. It is available for English registration through Google Labs. An Executive LLM dynamically guides the conversation and an AI Evaluator scores it against a rubric. In a study with New York University involving 188 participants aged 18–25, AI scores aligned with human experts nearly as well as experts aligned with one another.

4/9Thu
  2. Google Research36

    Google introduces PaperVizAgent and ScholarPeer academic agents

    Google has released two AI agents for academic workflows: PaperVizAgent generates figures for papers, while ScholarPeer automates peer review. PaperVizAgent coordinates five agents for retrieval, planning, style, visualization, and review. It scored 60.2 on a 0–100 evaluation, ahead of GPT-Image-1.5, Nano-Banana-Pro, and Paper2Any, and was the only framework to exceed the human baseline of 50.0.

4/3Fri
  1. Google Research38

    Google Research evaluates how closely LLM behavioral tendencies align with humans

    Google Research proposes a framework using situational judgment tests (SJTs), adapted from psychology scales, to assess how closely LLM behavioral tendencies align with human consensus across four traits including empathy and emotional regulation. A large-scale analysis of 25 LLMs finds two kinds of gap: models' tendencies can diverge from annotator consensus, and models may fail to cover the range of human opinions when there is no consensus. Small models under 120B and frontier closed models align nearly perfectly when humans unanimously agree, but alignment levels off just above 80% when consensus is below 90%.

4/1Wed
3/31Tue
  1. Google Research66

    Google white paper details cryptocurrency vulnerabilities to quantum attacks and PQC migration advice

    Google Quantum AI has published a white paper saying future quantum computers may be able to break the elliptic-curve cryptography protecting cryptocurrencies with fewer qubits and gates than previously thought.

    Why it matters: Google Quantum AI presents new estimates of the quantum resources needed to break ECDLP-256 and explains how it disclosed the vulnerability without revealing attack details.

3/25Wed
  1. Google Research62

    Google releases TurboQuant vector-compression algorithm, reducing KV cache to 3 bits without loss of accuracy

    Google has released TurboQuant, a compression algorithm that quantizes KV cache to 3 bits without training or fine-tuning and without loss of model accuracy, reducing KV-memory use by at least 6×.

    Why it matters: Google reports TurboQuant's algorithm details and benchmark results, helping readers assess the usefulness of KV-cache compression for long-context workloads.

  2. Google Research26

    Google Research introduces S2Vec, using self-supervised learning to understand the built environment

    Google Research has released S2Vec, a self-supervised framework that converts the built environment into layered images through S2 Geometry tiling and feature rasterization, then learns general-purpose embeddings with a masked autoencoder (MAE). On zero-shot geographic extrapolation for U.S.-wide socioeconomic predictions such as population density and median income, S2Vec is usually the best single model. Multimodal fusion with image embeddings generally performs better than either modality alone, though tasks such as tree canopy and elevation remain challenging.

3/18Wed
3/17Tue
3/13Fri
  1. Berkeley AI Research38

    Berkeley AI Research proposes SPEX and ProxySPEX for identifying key LLM interactions at scale

    Berkeley AI Research has proposed SPEX and ProxySPEX, algorithms that identify important interactions driving LLM outputs across feature, data, and model-component attribution. SPEX turns interaction search into a sparse-recovery problem by exploiting sparsity and low-order structure; ProxySPEX uses additional hierarchy to match SPEX performance with about ten times fewer ablation experiments.

3/12Thu
  1. Google Research66

    Google and BIDMC study the real-world clinical feasibility of conversational diagnostic AI

    Google Research and Beth Israel Deaconess Medical Center conducted a prospective, single-center feasibility study in which AMIE interviewed patients by text before outpatient primary-care visits, under video supervision by a physician throughout.

    Why it matters: Google and BIDMC have deployed conversational diagnostic AI in a real outpatient workflow for the first time, showing its safety-supervision model and diagnostic performance.

1/10Sat
  1. Berkeley AI Research24

    Berkeley AI Research proposes an information-driven framework for imaging-system design in NeurIPS 2025 paper

    A Berkeley AI Research team proposes a mutual-information-based framework for evaluating and optimizing imaging systems. It estimates information directly from noisy measurements to predict system performance. Validated in color photography, radio astronomy, lensless imaging, and microscopy, optimized designs reach end-to-end-method performance with lower memory and compute needs and no task-specific decoder. The paper appeared at NeurIPS 2025.

11/1Sat
  1. Berkeley AI Research36

    Reinforcement learning without TD: scaling off-policy RL with divide and conquer

    Berkeley AI Research has proposed a divide-and-conquer off-policy reinforcement-learning algorithm that does not rely on temporal-difference (TD) learning. It reduces the number of Bellman-recursion steps from linear to logarithmic, enabling scaling to long-horizon tasks. The method achieves scaling for the first time in goal-conditioned RL, using the triangle inequality for shortest-path distances between states to construct transitive Bellman updates. Selecting the optimal subgoal w remains an open problem.

9/1Mon
  1. Berkeley AI Research34

    What does word2vec actually learn? Berkeley AI Research offers a closed-form theoretical explanation

    Berkeley AI Research proves that under a fourth-order approximate objective, specific hyperparameter constraints, tiny initial embedding weights, and tiny gradient-descent steps, word2vec's learning problem reduces to an unweighted least-squares matrix factorization. Its gradient-flow dynamics have a closed-form solution, and the resulting representations are equivalent to applying PCA to matrix M*.