AI Coding Live News

On November 10, Meta FAIR researcher Swann Bessa announced e-means on X, a clustering algorithm that replaces classic Lloyd k-means and is open-sourced in FAISS 1.16.0. It swaps full-batch iterations for mini-batch updates with exponential moving averages, and fitted scaling laws set the batch size and step size automatically, so k is the only setting. The paper says that on a range of embedding datasets e-means beats the loss Lloyd reaches after 25 epochs in 1.4 to 2.6 epochs, running 10-45x faster than Flash-KMeans on GPU and 8-15x faster than FAISS k-means on CPU (authors' figures). Teams building vector indexes, RAG pipelines, or dedup jobs can speed up this step directly.

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World Action Models (WAMs) represent a foundational robotic paradigm by predicting future visual dynamics and Cartesian actions directly from initial observations. However, existing WAMs struggle with long-horizon execution: synthesizing dense video rollouts incurs prohibitive computational overhead and temporal drift, while predicting single terminal frames deprives the policy of progressive visual guidance. Meta FAIR and Shanghai Jiao Tong University introduce ProWAM (arXiv:2610.02508), an efficient architecture that jointly predicts actions and an ordered sequence of sparse visual sub-goals. Sub-goal prediction scales naturally by learning from large-scale action-free video datasets, decoupling complex visual foresight from low-level policy control. ProWAM executes a single video-backbone forward pass to cache sparse sub-goal features, enabling rapid closed-loop replanning through lightweight action denoising without full video re-generation. Evaluated across extensive robotic benchmarks, ProWAM sets SOTA records on LIBERO-Plus (85.8%, +35.9% relative gain) and randomized RoboTwin (75.7%). In real-world zero-shot physical manipulator trials, ProWAM achieves a 70.0% success rate in unseen environments, outpacing the strongest baseline by +15.0 percentage points (+27.3% relative gain).

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As autonomous software engineering agents operate across multi-hour and multi-day horizons, context management becomes a decisive bottleneck. Prevailing paradigms treat context as an external append-only log, managed through rigid heuristic pruning outside the model's awareness. Researchers from the University of Washington, Meta FAIR, and Ai2 introduce Context Language Models (CLMs), where language models natively manage their own context by treating it as an editable file subject to unrestricted in-situ read/write operations. Zero-shot CLMs decisively outperform state-of-the-art context management: scoring 5% higher with 59% fewer FLOPs on 12-hour EdgeBench, and delivering a 65% improvement at matched compute on a 24-hour multi-repository agent-swarm task. Co-designed with SGLang, a novel Suffix Cache Reuse serving mechanism reduces inference compute by 35%, charting a scalable path for autonomous long-horizon multi-agent systems.

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