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Reasoning

Progress in model reasoning: chain of thought, reasoning models, breakthroughs and disputes on math and logic benchmarks.

69 picksLast 30 days 48 itemsCollected 216 items

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Picks archive · page 3

10/1Thu
  1. The Decoder80

    OpenAI says it blocked model-reasoning theft, but the same method still works on Azure

    OpenAI said it blocked a distillation operation intended to steal model reasoning. Activity began at low volume on July 1 and surged on July 24–25 to 16,000 requests from more than 4,000 users across more than 15,000 linked accounts. OpenAI says it shut them all down by July 28, noting that these were attempted extractions and may not have succeeded.

    Why it matters: OpenAI disclosed a model-reasoning distillation theft incident that also raised questions about inconsistent cloud-platform protections, showing a real path for reasoning leakage.

  2. Latent Space82

    Google DeepMind releases Gemini 4 Argon with up to 1M output tokens

    Google DeepMind released Gemini 4 Argon for coding, enterprise knowledge work, and cyber defense. It is initially available to government users and trusted cybersecurity professionals in the Fairwind program, before expanding to developers, businesses, and consumers.

    Why it matters: This summary combines Gemini 4 Argon's benchmark results, pricing, and differences in third-party evaluations, enabling comparison with Astra and Opus 5.5.

  3. The Decoder78

    Google releases Gemini 4 Argon, matching OpenAI and Anthropic but without a clear lead

    Google released frontier model Gemini 4 Argon, matching OpenAI and Anthropic on several benchmarks without a clear lead. It is initially available to 'trusted cyber defenders' in the Fairwind program and Google's internal teams before expanding to developers, businesses, and consumers. Promotional API pricing is $2 per million input tokens and $10 per million output tokens.

    Why it matters: This summary compares third-party and human-preference leaderboards, scores, token use, and pricing for Gemini 4 Argon against OpenAI and Anthropic.

  4. Google DeepMind88

    Google DeepMind releases frontier model Gemini 4 Argon

    Google DeepMind released frontier model Gemini 4 Argon, initially making it available through the Fairwind Program to trusted cybersecurity defenders before gradually expanding access. Pricing starts at $2 per million input tokens and $10 per million output tokens.

    Why it matters: Gemini 4 Argon raises the maximum output to 1M tokens and reports multiple benchmark results and internal deployment data, offering a view of its limits on long-running workflows.

9/26Sat
  1. Kevin Weil · X79

    Anthropic reports Claude solved a nine-loop calculation in planar N=4 super-Yang-Mills

    Anthropic reports that Claude solved a nine-loop scattering-amplitude problem in planar N=4 super-Yang-Mills, running largely unsupervised for days in Claude Science at a total cost of a few thousand dollars. Claude used methods developed by Lance Dixon and colleagues, and Dixon independently verified the result. Kevin Weil, who calculated Feynman diagrams in graduate school, calls the result mind-boggling and expresses excitement about where AI could take high-energy physics over the coming 12 months.

    Why it matters: The reported cost of a few thousand dollars offers a concrete reference point for assessing AI-assisted theoretical physics calculations within an academic compute budget.

9/23Wed
9/22Tue
  1. Latent Space78

    Xiaomi releases MiMo-V2.6-Pro 1T-A42B, an open-source multimodal model trained for $3 million

    Xiaomi has released the MiMo-V2.6 family, including the multimodal MiMo-V2.6-Pro and MiMo-V2.6-Flash, and introduced MiMo-V2.6-Pro-UltraSpeed, which delivers up to 20× faster output.

    Why it matters: Xiaomi's first entry in the frontier open-model lineup, with open RL environments and training recipes, provides a concrete example of post-training cost strategies.

  2. Latent Space76

    TypeSafe AI founder Diogo Almeida on Jev and RLCD: building System 1 models for software, not chat

    In a Latent Space interview, TypeSafe AI founder Diogo Almeida introduces Jev as a System 1 model designed for code consumption and optimized for intelligence per dollar rather than chat experience. Its key innovation is unpublished RLCD (Reinforcement Learning for Calibrated Decisions), which uses calibrated probabilities instead of human-rating feedback.

    Why it matters: Almeida revisits the strategic split between RLHF and RLCD and explains why the model is optimized for reliability rather than benchmark scores.

9/16Wed
  1. NVIDIA Blog62

    NVIDIA Vera Rubin NVL72 makes its MLPerf Inference v6.1 debut with leading performance

    NVIDIA submitted preview results for Vera Rubin NVL72 for the first time in MLPerf Inference v6.1, reporting up to 3.7× the throughput of GB300 NVL72 on Qwen3-VL and up to 2.5× on DeepSeek-R1.

    Why it matters: MLPerf Inference v6.1 reports throughput, scaling efficiency, and software-optimization gains for Vera Rubin NVL72 versus GB300 NVL72, offering a basis for judging inference infrastructure value.

  2. Google DeepMind · X77

    Google DeepMind releases Gemini 3.8 Live and 3.8 Live Extended Thinking

    Google DeepMind has released Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, calling them its best conversational AI yet. The company says the models can converse and think while handling tasks in the background without interrupting the user's workflow.

    Why it matters: The new conversational models give readers an update on Google DeepMind's positioning for real-time dialogue and background task handling.

  3. NVIDIA Blog72

    NVIDIA shares efficiency progress for Vera Rubin and DSX at AI Infra Summit

    At the AI Infra Summit, NVIDIA announced several advances in Vera Rubin and its DSX platform, shifting AI infrastructure measurement from peak performance to useful agentic tokens per megawatt.

    Why it matters: The article reports several measured token-output figures per megawatt for Vera Rubin and DSX, showing how efficiency metrics for AI factories are changing.

9/10Thu
  1. DeepSeek · X68

    DeepSeek releases DeepSeek-V4.1-Flash

    DeepSeek has released DeepSeek-V4.1-Flash, describing it as smarter, faster, and more efficient. It is the smallest model in a new architecture family, with native visual understanding, and is designed for stronger capabilities, faster inference, and higher throughput, with the architecture able to scale to larger models.

    Why it matters: As the smallest model in DeepSeek's new architecture family, its native visual understanding and higher inference throughput offer a view of its lightweight multimodal positioning.

8/25Tue
  1. Hugging Face Blog62

    IBM releases Granite 4.2 reasoning models in 3B, 8B, and 30B sizes

    IBM's Granite team has released Granite 4.2, its first family of dense decoder-only reasoning models, in 3B, 8B, and 30B sizes. They were post-trained from the Granite-4.1 base and are all licensed under Apache 2.0.

    Why it matters: The article discloses Granite 4.2's architecture, SFT data mix, and multi-stage RL curriculum in detail, offering a look at concrete post-training methods for reasoning models.

  2. Hugging Face Blog60

    Multiverse Computing proposes Quantization-Aware Healing: 4-bit compressed model beats its bfloat16 original on seven of nine benchmarks

    Multiverse Computing has published a paper proposing Quantization-Aware Healing (QAH). After compressing GPT-OSS 120B to 60B parameters and quantizing it to MXFP4, the method distills directly from the original model before compression rather than from a recovered bfloat16 checkpoint.

    Why it matters: The paper presents a repair recipe that distills from the original model after compression and quantization, with stability comparisons against QAT.

8/13Thu
  1. DeepSeek · X82

    DeepSeek releases DeepSeek-V4-Pro

    DeepSeek has released DeepSeek-V4-Pro, highlighting upgraded agent capabilities and notable production gains. V4-Pro and V4-Flash offer flexible reasoning levels: low for simple tasks, high for everyday agent workflows, and max for complex tasks. The model natively supports the OpenAI Responses API and is optimized for Codex with one-click configuration. It is now available in the app, on the web in Expert Mode, and through the API; its model name is unchanged.

    Why it matters: The official announcement describes V4-Pro's agent upgrades and three reasoning levels, helping readers understand its position in production workflows.

8/12Wed
  1. Google Research60

    Google Research proposes knowledge profiling: retrieval, not encoding, is the bottleneck for frontier LLM factuality

    Google Research has proposed a knowledge-profiling framework that distinguishes factual errors caused by encoding failures from those caused by retrieval failures, and released WikiProfile, a benchmark containing 2,150 Wikipedia facts.

    Why it matters: By separating encoding failures from retrieval failures, knowledge profiling offers a new way to diagnose the sources of factual errors in frontier models.

7/31Fri
  1. Mira Murati · X73

    Thinking Machines releases Inkling-Small with full weights at a quarter of Inkling’s size

    Mira Murati says Inkling-Small is comparable to Inkling at a quarter the size, with open weights and fine-tuning on Tinker today. She looks forward to seeing what people make with it. In the quoted announcement, @thinkymachines reports 276B total parameters and 12B active, makes the full weights available, and offers text, image, and audio chat on Tinker Playground. https://thinkingmachines.ai/news/inkling-small/

    Why it matters: The comparison with Inkling provides context for the smaller model’s claimed performance, while the parameter counts clarify the scale of the size reduction.

7/29Wed
  1. Berkeley AI Research62

    From CUDA to MLX: how K-Search brings kernel-optimization experience to Apple Silicon

    Berkeley AI Research and IBM Research have extended the evolutionary kernel-search framework K-Search to MLX and added a structured CUDA-to-MLX translation layer that automatically adapts existing CUDA-kernel knowledge to Apple Silicon.

    Why it matters: The article details the CUDA-to-MLX translation layer and reports two kernel benchmarks, showing how kernel knowledge can be transferred across platforms.

7/15Wed
  1. Hugging Face Blog88

    Thinking Machines releases Inkling family of open multimodal models

    Thinking Machines Lab has released the open-source Inkling family on Hugging Face. Inkling is an MoE model with about 1T total and 41B active parameters, natively supporting image, audio, and text inputs with a 1M-token context. The release also includes Inkling-Small, with 276B total and 12B active parameters.

    Why it matters: The article gives Inkling's architecture, deployment setup, and benchmark comparisons, helping readers assess the practical requirements of an approximately 1T-parameter open multimodal model.

7/2Thu
  1. Mistral AI71

    Mistral releases Leanstral 1.5 formal-verification model

    Mistral has released Leanstral 1.5, a formal-verification model licensed under Apache 2.0 with 119B total and 6B active parameters. It scores 100% on miniF2F, solves 587 of 672 PutnamBench problems, and sets state-of-the-art scores of 87% on FATE-H and 34% on FATE-X.

    Why it matters: The source reports Leanstral 1.5's formal-verification benchmark results and open-source availability, helping readers assess its usefulness in proof engineering.