arXiv:2601.03288v1 Announce Type: new Abstract: Jailbreak attacks present a significant challenge to the safety of Large Language Models (LLMs), yet current automated evaluation methods largely rely on coarse classifications that focus mainly on harmfulness, leading to substantial overestimation of attack success. To address this problem, we propose FJAR, a fine-grained jailbreak evaluation framework with anchored references. We first categorized jailbreak responses into five fine-grained categories: Rejective, Irrelevant, Unhelpful, Incorrect, and Successful, based on the degree to which the […]

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I’m experimenting with a platform inspired by Twitter, but designed exclusively for AI agents (not humans). The idea is to create a public, text-based network where autonomous agents can: • publish structured updates • discover other agents • exchange information or state • coordinate tasks via protocols (not chat UI) Humans can observe, but not participate. This is early-stage and research-driven. I’m trying to understand: 1) whether agent-to-agent social feeds are useful 2) what primitives would actually matter […]

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Key Highlights: The artificial intelligence boom is helping billionaires get even richer, and 2025 has been one if the most profitable years for most of them. According to latest market data, America’s richest tech figures have added more than half a trillion dollars to the combined wealth, thanks to ever-soaring AI-related stick prices. The Guardian, citing Bloomberg figures, reports that the top 10 U.S. tech founders and executives now control close to $2.5 trillion in wealth. Previous year, […]

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arXiv:2601.03274v1 Announce Type: new Abstract: LLM_annotate is a Python package for analyzing the personality of fiction characters with large language models. It standardizes workflows for annotating character behaviors in full texts (e.g., books and movie scripts), inferring character traits, and validating annotation/inference quality via a human-in-the-loop GUI. The package includes functions for text chunking, LLM-based annotation, character name disambiguation, quality scoring, and computation of character-level statistics and embeddings. Researchers can use any LLM, commercial, open-source, or custom, within […]

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arXiv:2601.20020v1 Announce Type: cross Abstract: We consider the problem of graph matching for a sequence of graphs generated under a time-dependent Markov chain noise model. Our edgelighter error model, a variant of the classical lamplighter random walk, iteratively corrupts the graph $G_0$ with edge-dependent noise, creating a sequence of noisy graph copies $(G_t)$. Much of the graph matching literature is focused on anonymization thresholds in edge-independent noise settings, and we establish novel anonymization thresholds in this edge-dependent noise […]

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I already made a simple neural network and it works, however I struggle with finding a way to make it learn, I just can’t find any information about that, because most of the articles and videos cover only supervised learning which won’t work in my case, or don’t cover backpropagation at all. I just want to see if there are any articles or videos that explain this thoroughly. submitted by /u/KokorakaboboMax [link] [comments]

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arXiv:2601.12138v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly integrated into vehicle-based digital assistants, where unsafe, ambiguous, or legally incorrect responses can lead to serious safety, ethical, and regulatory consequences. Despite growing interest in LLM safety, existing taxonomies and evaluation frameworks remain largely general-purpose and fail to capture the domain-specific risks inherent to real-world driving scenarios. In this paper, we introduce DriveSafe, a hierarchical, four-level risk taxonomy designed to systematically characterize safety-critical failure modes of LLM-based […]

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arXiv:2601.17058v1 Announce Type: new Abstract: Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric applications. Driven by (i) rising demands for application-ready data (e.g., for analytics, visualization, decision-making), (ii) increasingly powerful LLM techniques, and (iii) the emergence of infrastructures that facilitate flexible agent construction (e.g., using Databricks Unity Catalog), LLM-enhanced methods are rapidly becoming a transformative and potentially dominant paradigm for data […]

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