May 2026

arXiv:2605.01335v1 Announce Type: new Abstract: We characterize the fundamental limits of high-dimensional mean testing under arbitrary truncation, where samples are drawn from the conditional distribution $P(cdot mid S)$ for an unknown truncation set $S$ that may hide up to an $varepsilon$-fraction of the probability mass. For distributions with $p$-th directional moments of magnitude at most $nu_{P,p}$, truncation induces a bias of order $O(nu_{P,p}varepsilon^{1-1/p})$. This bias creates a sharp information-theoretic detectability floor: when the signal $alpha$ falls below this […]

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arXiv:2605.00830v1 Announce Type: new Abstract: Graph representation is a powerful abstraction of real-world objects and relations. Computing the Graph Edit Distance (GED) between graphs is critical in domains such as bioinformatics, machine learning, and pattern recognition. GED measures the minimum number of edit operations required to transform one graph into another. However, the high computational complexity of optimal and near-optimal methods limits their applicability to large-scale graphs, making high-performance parallel GED computation essential. To address this, we propose […]

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arXiv:2605.00829v1 Announce Type: new Abstract: Social media has become a critical source of situational awareness during disasters, providing real-time insights into evolving impacts and emerging needs. To support crisis response at scale, recent work has increasingly leveraged large language models (LLMs) to automatically classify and summarize situational information from social media streams. However, existing approaches implicitly assume that extracted situational claims are equally plausible, despite information quality varying substantially as a crisis unfolds. In this work, we propose […]

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arXiv:2605.00828v1 Announce Type: new Abstract: Canonical LST (sTEZ) is an enshrined, protocol-native mechanism designed to mitigate the centralization risks associated with liquid staking intermediaries. Intended to complement direct staking rather than replace it, Canonical LST provides a neutral, public alternative managed directly by the Tezos protocol. It allows any tez holder to participate in aggregated staking without reliance on third-party operators. sTEZ follows an accrual-based design: all slashing events and rewards are reflected in the token’s exchange rate […]

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arXiv:2605.00827v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly interact with external systems through tool-calling protocols such as the Model Context Protocol (MCP). In prevailing architectures, the agent must reason about every tool invocation in every session, consuming tokens proportional to the number of actions performed–even when the task has been solved before. We present the MCP Workflow Engine, a novel MCP-native orchestration layer that decouples intelligence (deciding what to do) from execution (carrying it out). […]

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arXiv:2605.00826v1 Announce Type: new Abstract: Text-to-video retrieval enables users to find relevant video content using natural language queries, a task that has grown increasingly important with the rapid expansion of online video. Over the past six years, research has produced numerous methods, such as dual encoders, attention-driven models, and multimodal fusion approaches; however, fundamental questions remain about model behavior, dataset influence, and query difficulty. In this work, we evaluate 14 state-of-the-art retrieval methods across 3 widely used datasets […]

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Release: datasette-llm 0.1a7 Mechanism for configuring default options for specific models. Part of Datasette’s evolving support mechanism for plugins that use LLMs. It’s now possible to configure a model with default options, e.g. to say all enrichment operations should use a specific model with temperature set to 0.5. Tags: llm, datasette

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Release: llm-echo 0.5a0 New -o thinking 1 option to help test against LLM 0.32a0 and higher. This plugin provides a fake model called “echo” for LLM which doesn’t run an LLM at all – it’s useful for writing automated tests. You can now do this: uvx –with llm==0.32a1 –with llm-echo==0.5a0 llm -m echo hi -o thinking 1 This will fake a reasoning block to standard error before returning JSON echoing the prompt. Tags: llm

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So it’s well known that Y Combinator owns some stake in OpenAI. But how big is that stake? This seems like devilishly difficult information to obtain. I asked around and a little birdie who knows several OpenAI investors came back with an answer: Y Combinator owns about 0.6 percent of OpenAI. At OpenAI’s current $852 billion valuation, that’s worth over $5 billion. — John Gruber, Y Combinator’s Stake in OpenAI Tags: openai, y-combinator, ai, john-gruber

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