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Only First party#Research
Oct 6Tue
  1. Google Research34

    Open and Emergent Problems in Agentic Privacy and Security: A Contextual Angle

    Google shares a new workshop report, produced with more than 50 academic and industry leaders at the Contextual Agent Privacy and Security (CAPS) Workshop held in late 2025 in New York City, on foundational privacy and security challenges facing autonomous agents. The report grounds agent privacy and security in the theory of Contextual Integrity, extending it to contextual security, and advocates a contextual policy engine within a supervisor layer alongside system-level sandboxing, model-level reasoning, user-centric controls, multi-agent guardrails, and ecosystem governance. It also calls for standardized multi-agent benchmarks and "Agent Gym" sandboxes, and invites the broader research community to collaborate.

Oct 5Mon
  1. GitHub Blog · AI & ML61

    ReviewBench: An open benchmark for AI code review

    GitHub introduces ReviewBench, an open benchmark for AI code review built from analysis of 103.9 million GitHub pull requests, containing 219 public pull requests across 19 languages with a multi-source golden set labeled for severity and category. It reports grounded and augmented precision, recall, and F1 metrics, uses Claude Sonnet 5 as the LLM grader under a published rubric, and had its ground-truth findings independently re-labeled by senior engineers with 96.6% agreement. GitHub says ReviewBench's offline predictions for a Copilot code review multi-model ensemble matched a later A/B test, which showed an 8.0% rise in addressed rate, 13.6% in recall, 61% in comment volume, and an 8.0% drop in cost per review.

    Why it matters: It offers a concrete, reproducible methodology and public dataset for comparing AI code reviewers, plus evidence on how offline scores tracked a production experiment.

Oct 1Thu
  1. Microsoft Research40

    Forecasting space weather risks on power grids

    A Microsoft Research intern developed a machine learning pipeline that uses forecast-time solar-wind data, AE and Dst index forecasts, local geology and grid data to produce location-specific geomagnetically induced current risk estimates for 66,935 substations in the continental United States, 30 to 60 minutes ahead. During the 2020-2026 evaluation period the pipeline detected nearly 80% of major space-weather events, with detection rates of 76.5% for events ≥10 nT/min, 81.2% for ≥20 nT/min and 64.1% for ≥50 nT/min, and it produced estimates for all substations in about 333 milliseconds. The authors note that further validation with utilities and operational data would be needed before use in grid operations.

Jul 29Wed
  1. Berkeley AI Research60

    From CUDA to MLX: How K-Search Brings Decades of Kernel Expertise to Apple Silicon

    IBM Research extended K-Search, an evolutionary LLM-driven kernel search framework from UC Berkeley Sky Lab, with a native MLX backend and a structured CUDA-to-MLX translation layer that maps CUDA primitives, constraints and optimization patterns onto Apple Silicon equivalents. On Apple Silicon the approach reached 0.97x the speed of the native MLX Attention kernel and up to a 20x prefill speedup over the community mlx-lm implementation on the Mamba SSM kernel, attributed largely to adding a parallel scan. The authors say the bottleneck was not the LLM's ability to write Metal code but the quality of context and constraints supplied to it.

    Why it matters: Describes a concrete method for transferring CUDA kernel expertise to non-NVIDIA hardware, with reported benchmark numbers and an open-source reproduction path.

Jul 26Sun
  1. Berkeley AI Research38

    Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

    The post introduces ABBEL, a framework that replaces full interaction history with periodically updated natural-language "belief states" and supervises their contents through belief grading, including an autoencoding-inspired reconstruction objective. On CollabBench collaborative coding, reconstruction-based belief grading roughly halved the gap to full-context models and trained in 50 steps instead of 100, while using fewer peak tokens; on Combination Lock and multi-objective QA, domain-knowledge grading and a peak belief length penalty further improved efficiency. The authors argue that isolating and supervising summaries addresses the performance cost of recursive summarization in long-horizon tasks.

May 8Fri
  1. Berkeley AI Research38

    Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference Scaling

    This post surveys parallel reasoning methods for LLMs, contrasting fixed approaches like self-consistency, Best-of-N, and tree search with adaptive parallel reasoning (APR), where the model itself decides when to parallelize, how many threads to spawn, and how to coordinate them. It describes fork-join inference designs, including engine-modifying KV cache stitching (Multiverse) versus client-side orchestration (ThreadWeaver), and covers training via SFT demonstrations and RL rewards based on critical-path efficiency gated by correctness. It closes with evaluation differences across Multiverse, ThreadWeaver, NPR, and Parallel-R1, plus open questions on stability and whether parallelization mainly helps as a training-time exploration scaffold.

Apr 20Mon
  1. Berkeley AI Research38

    Gradient-based Planning for World Models at Longer Horizons

    The post introduces GRASP (Gradient Relaxed Stochastic Planner), a gradient-based planner for learned world models that lifts trajectories into virtual states for parallel-in-time optimization, adds Gaussian noise to state iterates for exploration, and stops brittle state-input gradients through the model while retaining action gradients. It argues that long-horizon planning with modern world models is fragile due to ill-conditioned rollouts, non-greedy local minima, and adversarial-robustness-style sensitivity of state Jacobians, and reports Push-T results where GRASP outperforms CEM, GD, and LatCo at horizons from 40 to 80. The work is by Michael Psenka with Mike Rabbat, Aditi Krishnapriyan, Yann LeCun, and Amir Bar (arXiv 2602.00475).

Mar 13Fri
  1. Berkeley AI Research38

    Identifying Interactions at Scale for LLMs

    This blog post introduces SPEX and ProxySPEX, algorithms that use strategically selected ablations and sparse recovery from signal processing and coding theory to identify influential feature, data, and model-component interactions at scales far beyond prior methods, with ProxySPEX exploiting hierarchy to match SPEX using roughly 10x fewer ablations. The authors demonstrate the framework on sentiment analysis, a modified trolley problem with GPT-4o mini, CIFAR-10 data attribution with ResNet, and attention head pruning on MMLU, where a ProxySPEX-informed pruning strategy improved target-task performance. Code for both algorithms is available in the SHAP-IQ repository.

Jan 10Sat
  1. Berkeley AI Research34

    Information-Driven Design of Imaging Systems

    Researchers present a framework that estimates mutual information directly from noisy measurements using a known noise model and a learned model of measurement variation, enabling direct evaluation and optimization of imaging systems by information content. In their NeurIPS 2025 paper, information estimates predicted decoder performance across color photography, radio astronomy, lensless imaging, and microscopy, and their IDEAL method optimized encoder parameters via gradient ascent, matching end-to-end optimization on color filter design without requiring a decoder network. The authors suggest the approach could extend to other sensing domains modeled as deterministic encoding with known noise characteristics.