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2026.10.17 · Sat

#推理

#推理

Research

Research papers, technical reports, benchmarks and datasets.

10/16Fri
  1. Microsoft Research79

    Microsoft Research presents Magnetar for proof-based general-purpose computing

    Microsoft Research presents Magnetar, a zero-knowledge virtual machine that proves RISC-V program execution using security based only on standard hash functions. It supports programs written in Rust, Go, or C without requiring developers to rewrite them as cryptographic circuits. Across 15 benchmark programs, Microsoft reports average speeds 10 to 39 times faster than the fastest existing comparable zkVMs on the same machine, with proofs verified in about 12 milliseconds.

    Why it matters: The benchmark comparisons connect proving speed with general-purpose execution and standard hash functions, clarifying the tradeoffs that have limited broader adoption of verifiable computing.

10/15Thu
  1. The Decoder81

    GPT-6-Astra solves 49 of 50 physics research problems at $2.40 apiece

    GPT-6-Astra, running in OpenAI's Codex environment without human hints, solved 49 of 50 TheoPhysEval problems across eleven theoretical physics subfields, costing just under $116 overall. Anthropic's Opus 5.5 scored 92 percent, while both models reached 92 percent without internet access. The researchers emphasize that the benchmark tests calculations using known methods, while choosing promising research questions remains a separate challenge for current agents.

    Why it matters: The benchmark distinguishes proficiency in concrete physics calculations from the ability to choose fruitful research questions, clarifying which parts of scientific work its high scores actually measure.

  2. 雷锋网84

    Shanghai AI Laboratory’s Shusheng-S2 Helps Prove the Three-Dimensional Ghys Conjecture

    Xu Disheng’s team used Shanghai AI Laboratory’s Shusheng-S2 model in a human–AI collaboration to prove the three-dimensional Ghys conjecture, with a preprint posted on arXiv. Researchers guided seven rounds of recursive exploration as the problem graph expanded from 15 to 389 nodes. A Peking University team used its SHEAF framework to formalize all four main theorems, producing approximately 550,000 lines of Lean code in about five days and passing Lean checks.

    Why it matters: The workflow offers a transferable approach to research mathematics, pairing expert guidance with recursive model exploration and organizing formal verification around a dependency graph of checkable proof tasks.

10/13Tue
  1. 量子位80

    GPT-6 Astra helps mathematics enthusiast nor prove five circle-packing cases

    QbitAI reports that mathematics enthusiast nor used GPT-6 Astra to prove optimality for packing 15, 16, 17, 18, and 20 unit disks inside a circle, completing the remaining cases for N≤20. The approach combines geometric constraints, finite computational enumeration, and exact optimality arguments. Nor describes substantial human involvement, including guiding the model, fixing issues, manually verifying validity, and rechecking the work with Astra and Opus 5.5.

    Why it matters: The reported proof strategy shows how geometric constraints can reduce an infinite search space to finite cases, with rigidity arguments connecting computational elimination to exact optimality.

10/12Mon
  1. The Decoder81

    Google DeepMind’s AlphaProof Nexus solves dozens of open math problems in peer-reviewed study

    Google DeepMind’s AlphaProof Nexus solved dozens of open math problems, according to a peer-reviewed study in Science, including nine of 353 open Erdős problems, two unsolved for 56 years. It also proved 44 OEIS conjectures, solved an open algebraic geometry question, and improved a min-max optimization bound. Built around Gemini 3.1 Pro, it drafts and revises proofs in Lean using checker feedback and can call AlphaProof for harder sub-problems, though DeepMind reports it solved the nine Erdős problems without that tool.

    Why it matters: DeepMind’s use of peer review provides a concrete comparison with OpenAI’s publication of AI proofs on GitHub amid a debate over how AI mathematics should be evaluated.

10/11Sun
  1. 量子位88

    OpenAI Posts Two Kakeya Conjecture Manuscripts, Extending Work to Four Dimensions

    On October 7, OpenAI released 722 mathematics manuscripts, including two on the Kakeya conjecture: a 97-page paper claiming a three-dimensional maximal-function result stronger than Wang and Zahl's, and a 175-page paper claiming the four-dimensional Hausdorff dimension case. Both are attributed to OpenAI, are not Lean-formalized or peer-reviewed, and OpenAI's README says some unformalized results may be flawed.

    Why it matters: The piece traces how two OpenAI manuscripts extend Wang and Zahl's three-dimensional Kakeya work to a stronger maximal-function version and to four dimensions, while noting the proofs lack peer review.

10/7Wed
  1. Hugging Face Blog62

    Nemotron fine-tuning reaches gold-medal level at both IOI 2026 and IMO 2026

    The NVIDIA team started with Nemotron 3 and used supervised fine-tuning, reinforcement learning, and a generate-verify-improve reasoning process to build specialized models that reached gold-medal thresholds at both IOI 2026 and IMO 2026.

    Why it matters: The source gives a complete recipe for fine-tuning Nemotron 3 to gold-medal-level results at IOI and IMO, which readers can use to reproduce its data and reasoning workflow.

  2. OpenAI News62

    OpenAI announces progress by an internal frontier model on open math problems

    OpenAI released new results by an internal frontier model on open math problems and published Lean formalizations and research details on GitHub.

    Why it matters: OpenAI made results by an internal frontier model on open math problems public alongside Lean formalizations, showing how model reasoning can be combined with formal verification.

10/3Sat
  1. AI at Meta · X66

    Meta publishes six papers on mathematicians collaborating with Muse Spark

    Meta said that after AI models achieved gold-medal-level performance in five math, physics, and chemistry competitions, it had mathematicians work with Muse Spark 1.1 and 1.2 in Thinking Mode through the meta.ai chat interface to solve open problems with no existing solution path and no custom research scaffolding.

    Why it matters: Meta made six papers from mathematicians' collaboration with Muse Spark public and described the division of work between people and AI and the review process.

8/25Tue
  1. 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.

8/12Wed
  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.

7/29Wed
  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.

6/27Sat
  1. Google Research60

    Google speeds up Gemini Nano on Pixel with frozen multi-token prediction

    Google Research has introduced an architecture that attaches a multi-token-prediction (MTP) head to the frozen Gemini Nano v3 model, speeding up on-device generation without changing the backbone weights.

    Why it matters: Google attaches an MTP head to frozen Gemini Nano v3 and reports concrete methods and gains under on-device memory and energy constraints.

4/22Wed
  1. 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.

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.

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