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Apple Machine Learning Research

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 Embarrassingly Simple Self-Distillation Improves Code Generation CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning Uncertainty Quantification for LLM Function-Calling One Layer Is Enough: Adapting Pretrained Visual Encoders for Image Generation Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants Multilingual Semantic Retrieval for Apple Music Search Behavioral Privacy Leakage in Agentic Negotiation: Formalizing and Mitigating Inference Attacks via Randomized Policies Incentivizing Temporal-Awareness in Egocentric Video Understanding Models Recursive Language Models Meet Uncertainty: The Surprising Effectiveness of Self-Reflective Program Search for Long Context Unmasking On-Policy Distillation: Where It Helps, Where It Hurts, and Why Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction DynaMiCS: Fine-Tuning LLMs with Performance Constraints Using Dynamic Mixtures LensVLM: Selective Context Expansion for Compressed Visual Representation of Text MT-EditFlow: Reinforcement Learning for Multi-Turn Image Editing with Flow Matching Weblica: Scalable and Reproducible Training Environments for Visual Web Agents FlowEval: Reference-Based Evaluation of Generated User Interfaces A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models Scaling Properties of Continuous Diffusion Spoken Language Models Path-Constrained Mixture-of-Experts Revisiting ASR Error Correction with Specialized Models TopoPrimer: The Missing Topological Context in Forecasting Models Multi-Agent Teams Hold Experts Back VideoFlexTok: Flexible-Length Coarse-to-Fine Video Tokenization Amortizing Maximum Inner Product Search with Learned Support Functions On Robustness and Chain-of-Thought Consistency of RL-Finetuned VLMs MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers Learning Structured Reasoning via Tractable Trajectory Control Learning Unmasking Policies for Diffusion Language Models Residual Context Diffusion Language Models Conformal Thinking: Risk Control for Reasoning on a Compute Budget Anti-Causal Domain Generalization: Leveraging Unlabeled Data Metric-Dependent Annotation Saturation for Learning from Label Distributions Nine Judges, Two Effective Votes: Correlated Errors Undermine LLM Evaluation Panels Introducing the Third Generation of Apple’s Foundation Models IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026 VSAS-Bench: Real-Time Evaluation of Visual Streaming Assistant Models EpiCache: Episodic KV Cache Management for Long-Term Conversation on Resource-Constrained Environments BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning Apple Workshop on Privacy-Preserving Machine Learning & AI 2026 Velox: Learning Representations of 4D Geometry and Appearance RVPO: Risk-Sensitive Alignment via Variance Regularization Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures Text-Conditional JEPA for Learning Semantically Rich Visual Representations What Matters in Practical Learned Image Compression SpecMD: A Comprehensive Study on Speculative Expert Prefetching From Where Things Are to What They’re For: Benchmarking Spatial–Functional Intelligence for Multimodal LLMs STARFlow-V: End-to-End Video Generative Modeling with Normalizing Flows Bootstrapping Sign Language Annotations with Sign Language Models International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2026 Adaptive Thinking: Large Language Models Know When to Think in Latent Space DSO: Direct Steering Optimization for Bias Mitigation StereoFoley: Object-Aware Stereo Audio Generation from Video LaDiR: Latent Diffusion Enhances LLMs for Text Reasoning Local Mechanisms of Compositional Generalization in Conditional Diffusion Learning Long-Term Motion Embeddings for Efficient Kinematics Generation ParaRNN: Large-Scale Nonlinear RNNs, Trainable in Parallel Apple Machine Learning Research at ICLR 2026 Can Large Language Models Understand Context? International Conference on Learning Representations (ICLR) 2026 Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts Efficient Privacy Loss Accounting for Subsampling and Random Allocation ACM Human-Computer Interaction Conference (CHI) 2026 A Theoretical Framework for Acoustic Neighbor Embeddings Governance-Aware Agent Telemetry for Closed-Loop Enforcement in Multi-Agent AI Systems SQUIRE: Interactive UI Authoring via Slot QUery Intermediate REpresentations Personalized Group Relative Policy Optimization for Heterogenous Preference Alignment ProText: A Benchmark Dataset for Measuring (Mis)gendering in Long-Form Texts Beyond Real Data: Synthetic Data through the Lens of Regularization Entropy-Preserving Reinforcement Learning Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting
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

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