2026年9月9日 技术热点总结

项目简介：Lean certificates accompanying Navier-Stokes and Euler results

技术栈：Lean

核心介绍：This repository contains Lean 4 formalizations of the results presented in For every positive viscosity, we prove two results: which no global smooth solution with uniformly bounded kinetic energy exists. initial data and forcing for which no global smooth solution exists. These are alternatives (C) “Breakdown of Navier–Stokes solutions on ℝ³” and (D) “Breakdown of Navier–Stokes Solutions on ℝ³/ℤ³” in the Clay Mathematics Institute’s official problem description

项目数据：⭐ 751 Stars，🍴 58…

技术栈：Python

核心介绍：ffmpeg-skill Give your coding agent a video editor. Local FFmpeg · No cloud · No API keys · Python standard library Claude Code · Cursor · Codex · MCP If ffmpeg and python3 are on your PATH, it works: offline, on footage you would rather not upload. > SPEC (Self-Producing Execution Contract), coined by this project’s author > kajisho5: each tool’s input_schema — the part of its contract > and MCP tool definition that has to track the

项目数据：⭐ 717 Stars，🍴 43 Forks

技术栈：TypeScript

核心介绍：Vista.js is a React 19 framework with App Router conventions, React Server Components, standalone .vista output, and a dual-engine model: Official site: https://vista-js.vercel.app Repository: https://github.com/Mantitup-Org/vista Vista currently ships the following core surface:

关键特性：App Router-style file conventions under app/；React Server Components and streaming SSR；Server Actions and runtime action manifests；Cache APIs: unstable_cache, revalidateTag, revalidatePath, cacheTag, cacheLife；Route groups, parallel routes, interception routes, slot defaults, …

项目简介：See what is really inside macOS System Data and delete it, item by item, from the menu bar.

技术栈：Swift、disk-cleanup、macos、menubar、swiftui、system-data、xcode

核心介绍：System Data A menu bar app that shows what is really inside macOS “System Data” and lets you delete it, item by item. Open System Settings > General > Storage on a developer Mac and “System Data” is often the largest line, with no way to see inside it. Finder puts everything it cannot attribute to an app, photos or documents in that bucket: simulator runtimes, Xcode symbol caches, package-manager stores, virtu…

We present a systems-security case study of a two-node split-LLM training system whose privacy evaluation passed while leaving an observable channel untested. The Trusted Local Node (TLN) sends protected activations to the Untrusted Cloud Node (UCN), the UCN returns its output, and TLN, holding the private loss, returns the output gradient. The frame the UCN receives mixes real rows with decoys, and the loss ignores the decoys. Their gradients are exactly zero, so the pattern of zeros reveals which rows were real. We measure it with a protocol fixed in advance: a leak injected at known stre…

Many NLP tasks, such as summarization and extractive question answering, reduce to retrieving relevant content from documents under two constraints: coverage, retaining enough pertinent information to achieve some goal, and conciseness, removing as much irrelevant information as possible. Conformal prediction methods have been used to guarantee coverage, and must be optimized for conciseness through design of a score function. State-of-the-art scoring functions use hand-engineered LLM prompts asking the model to rate the importance of content, but manual prompt engineering is labor-intensiv…

Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for every new problem: first proposing a causal mechanism, selecting a compatible estimator, and finally training it. Meanwhile, across diverse settings and modalities, much of machine learning has shifted to the paradigm of foundation models: networks pretrained once at scale and applied to new tasks without fine-tuning. Causal foundation models (CFMs) bring this paradigm to causal inference. CFMs are pretrained neural networks that estimate causa…

Safety alignment is usually posed as a topic-level question: is this subject harmful? Deployments ask a narrower one. A civics tutor and a public-sector assistant may share a base model yet need different boundaries inside the same topic, refusing targeted political manipulation while still answering factual questions about the same election. We formulate this as narrow-boundary safety and introduce an offline self-generated framework combining controlled topic generation, coverage repair, in-distribution compensation data, and harmful-benign pairs for training and evaluation. Single-shot g…

We present ENEAS, a unified, text-promptable method for instance tracking and semantic discovery. Text-promptable segmentation models, including the latest foundation models such as SAM 3, still suffer from temporal hallucinations, spatial fragmentation, and semantic misclassification: they fail to report target absence when an object leaves the field of view, segment local textures instead of the complete object during extreme close-ups, and prioritize visual features over ontological reality, so that visually similar artifacts such as statues, paintings, or reflections are segmented as ta…

Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation. To overcome this bottleneck, we introduce diffusion-augmented LLMs, a new class of models that defines an AR model distribution while using diffusion to draw multiple tokens in parallel from that distribution. We decouple the parameters of these models into two sets: AR weights, trained using the standard NTP objective, and lightweight diffusion weights, trained to generate multiple tokens simultaneously. The diffusion wei…