For minimally rigid graphs, the same edge-length data can admit multiple realizations (up to translations and rotations). Finding graphs with exceptionally many realizations is an extremal problem in rigidity theory, but exhaustive search quickly becomes infeasible due to the super-exponential growth of the number of candidate graphs and the high cost of realization-count evaluation. We propose a reinforcement-learning approach that constructs minimally rigid graphs via 0- and 1-extensions, also known as Henneberg moves. We optimize realization-count invariants using […]

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arXiv:2510.04441v2 Announce Type: replace-cross Abstract: Domain generalization (DG) is the problem of generalizing from several distributions (or domains), for which labeled training data are available, to a new test domain for which no labeled data is available. For the prevailing benchmark datasets in DG, there exists a single classifier that performs well across all domains. In this work, we study a fundamentally different regime where the domains satisfy a emph{posterior drift} assumption, in which the optimal classifier might […]

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Large language models are remarkably capable, yet frustratingly opaque. When a model misbehaves — generating responses in the wrong language, repeating itself endlessly, or refusing safe requests — AI devs have very few tools to diagnose why it happened at the level of internal computations. That’s the problem Qwen-Scope is built to solve. Qwen Team just released Qwen-Scope, an open-source suite of sparse autoencoders (SAEs) trained on the Qwen3 and Qwen3.5 model families. The release comprises 14 groups […]

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arXiv:2602.07218v1 Announce Type: cross Abstract: Adaptability has been regarded as a central feature in the foundation models, enabling them to effectively acclimate to unseen downstream tasks. Parameter-efficient fine-tuning methods such as celebrated LoRA facilitate efficient adaptation of large foundation models using labeled, high-quality and generally scarce task data. To mitigate data scarcity in fine-tuning of foundation models, we propose to leverage task similarity across multiple downstream users. Intuitively, users with similar tasks must be able to assist each […]

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Dear RL fellows, My Ph.D. research focus was on Reinforcement Learning (offline, online, offline-to-online, policy regularization). I was definitely not in the top-tier labs, not working on the hottest topics: Robotics and LLM. All I have learned is out of fashion right now. I know people are now using VLA and diffusion policy, etc. I am now working in the LLM industry to do LLM-RL, which is definitely not REAL RL. Sadly, I really want to work in […]

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arXiv:2603.03299v1 Announce Type: new Abstract: Large language models (LLMs) have been noted to fabricate scholarly citations, yet the scope of this behavior across providers, domains, and prompting conditions remains poorly quantified. We present one of the largest citation hallucination audits to date, in which 10 commercially deployed LLMs were prompted across four academic domains, generating 69,557 citation instances verified against three scholarly databases (namely, CrossRef, OpenAlex, and Semantic Scholar). Our results show that the observed hallucination rates span […]

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arXiv:2602.09202v1 Announce Type: new Abstract: The accelerating militarization of artificial intelligence has transformed the ethics, politics, and governance of warfare. This article interrogates how AI-driven targeting systems function as epistemic infrastructures that classify, legitimize, and execute violence, using Israel’s conduct in Gaza as a paradigmatic case. Through the lens of responsibility, the article examines three interrelated dimensions: (a) political responsibility, exploring how states exploit AI to accelerate warfare while evading accountability; (b) professional responsibility, addressing the complicity of […]

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In this paper, we develop a stratification-based semantics for Signal Temporal Logic (STL) in which each atomic predicate is interpreted as a membership test in a stratified space. This perspective reveals a novel correspondence principle between stratification theory and STL, showing that most STL formulas can be viewed as inducing a stratification of space-time. The significance of this interpretation is twofold. First, it offers a fresh theoretical framework for analyzing the structure of the embedding space generated by […]

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Introducing talkie: a 13B vintage language model from 1930 New project from Nick Levine, David Duvenaud, and Alec Radford (of GPT, GPT-2, Whisper fame). talkie-1930-13b-base (53.1 GB) is a “13B language model trained on 260B tokens of historical pre-1931 English text”. talkie-1930-13b-it (26.6 GB) is a checkpoint “finetuned using a novel dataset of instruction-response pairs extracted from pre-1931 reference works”, designed to power a chat interface. You can try that out here. Both models are Apache 2.0 licensed. […]

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arXiv:2601.02241v4 Announce Type: replace Abstract: Graphs arise across diverse domains, from biology and chemistry to social and information networks, as well as in transportation and logistics. Inference on graph-structured data requires methods that are permutation-invariant, scalable across varying sizes and sparsities, and capable of capturing complex long-range dependencies, making posterior estimation on graph parameters particularly challenging. Amortized Bayesian Inference (ABI) is a simulation-based framework that employs generative neural networks to enable fast, likelihood-free posterior inference. We adapt ABI […]

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