This repository provides the official implementation of the STATIC (Sparse Transition-Accelerated Trie Index for Constrained decoding) framework, as described in Su et al., 2026. STATIC is a high-performance method for enforcing outputs to stay within a prespecified set during autoregressive decoding from large language models, designed for maximum efficiency on modern hardware accelerators like GPUs and TPUs.
Google is introducing the Web Model Context Protocol (WebMCP) to allow AI agents to interact with websites in a more efficient and reliable way, moving away from screen scraping. This protocol enables direct communication between websites and AI models, defining website capabilities for AI access through HTML attributes or JavaScript APIs. The Early Preview Program (EPP) is being used to refine the protocol and gather data. WebMCP offers lower latency, higher accuracy, and reduced costs compared to traditional methods.
Google is accusing others of cloning its Gemini AI, despite its own history of scraping data without permission to train its models. This raises questions of hypocrisy as companies compete to protect their AI investments and differentiate their offerings, facing challenges like model distillation and the potential for smaller entities to compete.
A review of Google's Auto Browse agent, testing its ability to perform various online tasks, from playing web games to managing playlists and scanning emails. The agent shows promise but requires significant supervision and struggles with certain tasks, particularly those involving prolonged monitoring or complex interfaces.
AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an eventual emergence of a monolithic Artificial General Intelligence (AGI). The alternative AGI emergence hypothesis, where general capability levels are first manifested through coordination in groups of sub-AGI individual agents with complementary skills and affordances, has received far less attention. Here we argue that this patchwork AGI hypothesis needs to be given serious consideration, and should inform the development of corresponding safeguards and mitigations.
This post introduces **GIST (Greedy Independent Set Thresholding)**, a new algorithm for selecting diverse and useful data subsets for machine learning. GIST tackles the NP-hard problem of balancing diversity (minimizing redundancy) and utility (relevance to the task) in large datasets.
**Key points:**
* **Approach:** GIST prioritizes minimum distance between selected data points (diversity) then uses a greedy algorithm to approximate the highest-utility subset within that constraint, testing various distance thresholds.
* **Guarantee:** GIST is guaranteed to find a subset with at least half the value of the optimal solution.
* **Performance:** Experiments demonstrate GIST outperforms existing methods (Random, Margin, k-center, Submod) in image classification and single-shot downsampling.
* **Application:** Already used to improve video recommendation diversity at YouTube.
**GIST provides a mathematically grounded and efficient solution for selecting high-quality data subsets for machine learning, crucial as datasets scale.**
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**Key points:**
* **Approach:** GIST prioritizes minimum distance between selected data points (diversity) then uses a greedy algorithm to approximate the highest-utility subset within that constraint, testing various distance thresholds.
* **Guarantee:** GIST is guaranteed to find a subset with at least half the value of the optimal solution.
* **Performance:** Experiments demonstrate GIST outperforms existing methods (Random, Margin, k-center, Submod) in image classification and single-shot downsampling.
* **Application:** Already used to improve video recommendation diversity at YouTube.
**GIST provides a mathematically grounded and efficient solution for selecting high-quality data subsets for machine learning, crucial as datasets scale.**
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Repeating the input prompt improves performance for popular LLMs (Gemini, GPT, Claude, and Deepseek) without increasing the number of generated tokens or latency, when not using reasoning.
Train your neural network in TensorFlow or PyTorch, and run it inside CircuitPython using a single line of Python code.
The article discusses the increasing usefulness of running AI models locally, highlighting benefits like latency, privacy, cost, and control. It explores practical applications such as data processing, note-taking, voice assistance, and self-sufficiency, while acknowledging the limitations compared to cloud-based models.
LLM Council works together to answer your hardest questions. A local web app that uses OpenRouter to send queries to multiple LLMs, have them review/rank each other's work, and finally a Chairman LLM produces the final response.
