惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

雷峰网
雷峰网
博客园 - 聂微东
酷 壳 – CoolShell
酷 壳 – CoolShell
宝玉的分享
宝玉的分享
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
罗磊的独立博客
Hugging Face - Blog
Hugging Face - Blog
T
Tailwind CSS Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
IT之家
IT之家
博客园_首页
博客园 - 三生石上(FineUI控件)
博客园 - 叶小钗
Apple Machine Learning Research
Apple Machine Learning Research
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
量子位
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
人人都是产品经理
人人都是产品经理
美团技术团队
小众软件
小众软件
Jina AI
Jina AI
S
SegmentFault 最新的问题
博客园 - Franky
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com

Apple Machine Learning Research

Multilingual Knowledge Transfer under Data Constraints via Lexical Interventions Scaling Laws for Mixture Pretraining Under Data Constraints Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts The P-Completeness of Inverted Index Traversal: On the Complexity of Evaluating Boolean Query DAGs GRPO Beyond English: A Large-Scale Study of GRPO in Non-English and Multilingual Settings MVICAD2: Multi-View Independent Component Analysis with Delays and Dilations A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs Scaling Categorical Flow Maps Beyond Next-Token Prediction: A Performance Characterization of Diffusion versus Autoregressive Language Models Arbitrage: Efficient Reasoning via Advantage-Aware Speculation Locking Pretrained Weights via Deep Low-Rank Residual Distillation DeepAmbigQA: Ambiguous Multi-hop Questions for Benchmarking LLM Answer Completeness Taming Outlier Tokens in Diffusion Transformers Understanding Alignment in Multimodal LLMs: A Comprehensive Study Dimensionality Reduction Meets Network Science: Sensemaking on UMAP’s kNN Graph MoMo: Dial Motion Mode in Robot Manipulation with Spatiotemporal Action Tokenization Memory Efficient Audio Synthesis with Decoupled Temporal Depth Diffusion Transformers GH-ESD: Grounded Hypothesis-Driven Error Slice Discovery for Instance-Level Vision Tasks LEAD: Breaking the No-Recovery Bottleneck in Long-Horizon Reasoning Environment-free Synthetic Data Generation for API-Calling Agents Accelerating Text-to-Video Generation with Calibrated Sparse Attention RayRoPE: Projective Ray Positional Encoding for Multi-View Attention LVSum: A Benchmark for Timestamp-Aware Long Video Summarization Length Value Model: Scalable Value Pretraining for Token-Level Length Modeling When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs Show Me Examples: Inferring Visual Concepts from Image Sets Location-Invariant Properties of Functions Versus Properties of Distributions: United in Testing but Separated in Verification Interactive Proofs for General Distribution Properties Personalizing Incremental Video Search with Hybrid Text and ID Embeddings
Doubly Sub-linear Interactive Proofs of Proximity
2026-07-16 · via Apple Machine Learning Research

AuthorsNoga Amir†, Oded Goldreich†, Guy N. Rothblum

We study doubly sub-linear interactive proofs of proximity (dsIPPs): proofs that are ultra-fast to generate, and can be used to prove approximate assertions about a huge input. Proof generation is ultra-fast in the sense that it only requires reading a small (sub-linear) portion of the input. Approximate verification of the proof is even faster (reading an even smaller portion of the input). Similarly to the property testing literature, approximate verification means the sublinear-time honest prover can make the verifier accept each input in the property, but no prover can fool the verifier into accepting an input that is far from the property. Such proof systems can be used to prove (and verify) claims about the properties of a huge input object, even though the prover cannot read the entire object (and the verifier is even more restricted). We construct such a proof system for any property that can be decided by a constant-width read-once oblivious branching program (ROOBP). We also construct proof systems for approximate verification of an input’s Hamming weight, and for a relaxation of bipartiteness in the bounded-degree graph model.

  • † Weizmann Institute

Related readings and updates.

Suppose Alice has collected a small number of samples from an unknown distribution, and would like to learn about the distribution. Bob, an untrusted data analyst, claims to have run a sophisticated data analysis on the distribution and makes assertions about its properties. When and how is it possible for Alice to efficiently verify Bob’s claims (using fewer resources than would be needed to run the analysis herself)? We construct interactive…

Read more

This paper was accepted at the MATH-AI 2025 (MATH-AI) Workshop at NeurIPS 2025.

Large Language Models (LLMs) demonstrate impressive mathematical reasoning abilities, but their solutions frequently contain errors that cannot be automatically verified. Formal theorem proving systems such as Lean 4 offer automated verification with complete accuracy, motivating recent efforts to build specialized prover LLMs that generate verifiable proofs in…

Read more