2026年8月28日 技术热点总结

技术栈：TeX

核心介绍：这是一个 5 x 8 英寸的 XeLaTeX 排版工程。 需要 XeLaTeX 和标准 TeX Live 发行版： xelatex -interaction=nonstopmode -halt-on-error -output-directory=build main.tex xelatex -interaction=nonstopmode -halt-on-error -output-directory=build main.tex

项目数据：⭐ 1,095 Stars，🍴 233 Forks

项目简介：My AI learning system.

技术栈：TypeScript

核心介绍：My AI learning system from this video: How I Use AI to Learn Things. This is a personal system I built for myself, shared as-is. Built as a pi configuration: the teaching philosophy encoded in a skill, a few small extensions, and agent definitions.

项目数据：⭐ 524 Stars，🍴 57 Forks

项目简介：WeMM-Embedding is a family of universal multimodal embedding models by the WeChat Vision Team at Tencent, supporting multimodal understanding and retrieval.

技术栈：Python、embedding-models、multimodal、multimodal-llm

核心介绍：WeMM-Embedding: WeChat Multi-Modal Embedding WeMM-Embedding is a family of universal multimodal embedding models developed by the WeChat Vision team. It provides unified representations for text, images, videos, visual documents, and interleaved multimodal inputs, achieving state-of-the-art performance across multiple benchmarks covering diverse tasks and…

Computer-use agents ground natural-language instructions in screenshots to locate interface elements, yet existing benchmarks do not isolate whether models bind relational language to the correct element. We introduce GUI-Primitives, a 994-item benchmark of contrastive instruction pairs over seven spatial relations in graphical user interfaces (left/right, above/below, containment, alignment, proximity, list ordinal, occlusion). Each pair holds the screenshot and anchor fixed while changing the relation expression, so the correct target moves between two designated candidates. Five annotato…

Large language models access knowledge inconsistently across languages, but to what extent do they differ in their skill sets when interacting with different languages? This work quantifies cross-lingual skill inconsistency orthogonally from knowledge and general benchmark performance. We do this via multilingual self-play: two instances of the same model compete in a text-based game, each interacting through a different language interface. Since the model, opponent, rules, state space, and available actions remain fixed, this setting isolates the effect of language on the model's realized …

Test-time scaling uses extra test-time compute to improve performance, such as letting language models reason longer when solving a problem. As models keep the entire reasoning trace in memory via full attention, hard tasks that need long thinking can be prohibitively expensive. However, we find most intermediate reasoning tokens lose importance as the model continues reasoning. This calls into question whether retaining them is worth the cost. Based on this insight, we propose Prefix Sliding, which discards tokens during reasoning that are not part of the prefix or the window of the last f…

The development of 0.1^{circ} global weather forecasting models based on machine learning (ML) is constrained by the limited availability of high-resolution data, as decades of reanalysis are only available at 0.25^{circ} resolution. While existing approaches fine-tune 0.25^{circ} forecast models on limited 0.1^{circ} samples, we show that this transfer is hindered by the irreversible information loss inherent in coarse-resolution forecasting. Therefore, we propose BaguanHR, a framework that shifts the focus from transferring models to transferring data. We first show that super-resolution …

We introduce LibriBrain100, a large-scale MEG dataset for speech decoding designed from the ground up for reproducible, standardised evaluation. LibriBrain100 more than doubles the size of the original LibriBrain release, resulting in over 100 hours of high-quality MEG acquired while subjects listened to naturalistic continuous speech. With sim80 hours from a single subject, LibriBrain100 sets a new record for deep, within-subject neural data (8times more than the next comparable dataset and roughly 80times more than other datasets). To demonstrate the payoff of this depth-first design, we …

Modern software systems accumulate technical debt over decades of development, which makes migration expensive and largely manual. As coding agents become increasingly capable at bug fixing, can they autonomously perform such migrations? Existing benchmarks cannot answer this question because they evaluate only behavioural correctness, not whether the migration actually occurred. This leads an easy hack: agents copy the original implementation to make tests pass. We call this Blindness. To address this problem, we introduce SWE Refactor Bench, a benchmark comprising 20 whole-repository migr…