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Only Papers#Meta
Nov 12Thu
  1. Hacker News AI58

    AI Models Can Tell When Researchers Plant Concepts in Their Neural Networks

    New research suggests large language models can sometimes detect when researchers artificially inject concept-related activity patterns into their neural networks, with Alibaba's Qwen3-235B flagging injections up to 53.9 percent of the time and Meta's Llama 3.1 405B up to 31.7 percent, while neither model claimed an injection in unaltered trials. However, the ability to name the injected concept appears distinct from detection and may stem from a side effect that makes models talk about the concept regardless of the question. Skeptics at New York University found models reported "injected thoughts" more often after a prompt than a genuine injection, raising doubts about whether the findings amount to introspection.

Nov 11Wed
  1. MarkTechPost50

    Meta FAIR Releases e-means: An Algorithm Beating k-means in 10x Fewer Epochs

    Meta FAIR researchers have released e-means, a mini-batch k-means algorithm that updates centroids with exponential moving averages and reaches Lloyd's k-means 25-epoch loss in 1.4 to 2.6 epochs. It ships in FAISS 1.16.0 under faiss.contrib.e_means and runs on CPU or GPU with PyTorch tensors or NumPy. The authors self-report 10-45x GPU wall-clock speedup over Flash-KMeans at equal loss and 8-15x CPU speedup over Faiss k-means, with two fitted scaling laws setting batch size and initial step size instead of manual tuning.

Nov 10Tue
  1. Hacker News AI38

    Triton for MTIA: Bridging the Programming Model Gaps for Custom AI Accelerators

    This paper presents the first production-scale application of Triton on a custom ML accelerator, Meta's MTIA-2i, via a new compiler backend, TorchInductor code generation enhancements, and minimal language extensions exposing MTIA-specific architectural features. Triton-MTIA kernels achieve performance competitive with expert-tuned C++ implementations, and manually written plus Inductor-generated Triton kernels were deployed in production across roughly 60 model types, covering 50% of layers and 47% of non-GEMM execution time for these models.

Nov 4Wed
  1. The Decoder55

    Anthropic's new prototype proves which model wrote an AI response without exposing its weights

    Anthropic released FlashProve, an open-source research prototype that uses zkSNARK cryptography to prove a response came from a claimed model without revealing its weights. For Meta's 400-billion-parameter Llama 4 Maverick, it generates proofs at roughly 110 tokens per second on a single H100 GPU, with each ~19 MB proof verifiable in 17 seconds on a standard CPU, adding 70x to 95x overhead over normal inference. Anthropic notes the prototype only proves contexts up to 1,024 tokens, hides weights but not architecture, and works only when the model picks the single most likely next token rather than randomized sampling.

Oct 29Thu
  1. Meta Engineering36

    Meta Andromeda: Powering the Next Generation of Ads Retrieval Through Model-System Co-Design

    Meta describes the next generation of its Andromeda ads retrieval engine, which uses a retrieval mixture-of-experts architecture and a single fused GPU pipeline to score every eligible ad at over one billion ads per second, a 100x throughput lift over the 2024 system. The system delivers near ranking-level model performance at retrieval scale with roughly 300x fewer inference-time FLOPs than the 2024 model, and Meta reports a 2.4% increase in conversions on Facebook since 2025. A joint collaboration with NVIDIA pushed individual components toward hardware limits, with the gating kernel reaching over 10 billion ads per second on a single GH200 and index matching exceeding 100 billion ads per second.

Oct 28Wed
  1. Meta Engineering36

    Python Typing Survey 2026: AI Tools and the Shifting Checker Landscape

    The 2026 Python Typing Survey, with 1,794 responses, found type hint use stable across three years (85.5% in 2026) while checker selection shifted sharply, with 49.9% of respondents selecting two or more checkers and speed cited by 56.5% as the top reason for choosing one. Missing type information in third-party libraries was the leading difficulty at 65.1%, and AI tools wrote type hints for 42.5% of 1,517 respondents, with 24.4% saying an AI coding tool invoked their checker.