arXiv:2603.20656v1 Announce Type: cross Abstract: We propose a dense associative memory for empirical measures (weighted point clouds). Stored patterns and queries are finitely supported probability measures, and retrieval is defined by minimizing a Hopfield-style log-sum-exp energy built from the debiased Sinkhorn divergence. We derive retrieval dynamics as a spherical Hellinger Kantorovich (SHK) gradient flow, which […]
MERIT: Multi-domain Efficient RAW Image Translation
arXiv:2603.20836v1 Announce Type: cross Abstract: RAW images captured by different camera sensors exhibit substantial domain shifts due to varying spectral responses, noise characteristics, and tone behaviors, complicating their direct use in downstream computer vision tasks. Prior methods address this problem by training domain-specific RAW-to-RAW translators for each source-target pair, but such approaches do not scale […]
Causally-Guided Diffusion for Stable Feature Selection
arXiv:2603.20930v1 Announce Type: cross Abstract: Feature selection is fundamental to robust data-centric AI, but most existing methods optimize predictive performance under a single data distribution. This often selects spurious features that fail under distribution shifts. Motivated by principles from causal invariance, we study feature selection from a stability perspective and introduce Causally-Guided Diffusion for Stable […]
Mitigating Selection Bias in Large Language Models via Permutation-Aware GRPO
arXiv:2603.21016v1 Announce Type: cross Abstract: Large language models (LLMs) used for multiple-choice and pairwise evaluation tasks often exhibit selection bias due to non-semantic factors like option positions and label symbols. Existing inference-time debiasing is costly and may harm reasoning, while pointwise training ignores that the same question should yield consistent answers across permutations. To address […]
Bounded Coupled AI Learning Dynamics in Tri-Hierarchical Drone Swarms
arXiv:2603.20333v1 Announce Type: cross Abstract: Modern autonomous multi-agent systems combine heterogeneous learning mechanisms operating at different timescales. An open question remains: can one formally guarantee that coupled dynamics of such mechanisms stay within the admissible operational regime? This paper studies a tri-hierarchical swarm learning system where three mechanisms act simultaneously: (1) local Hebbian online learning […]
Comprehensive Description of Uncertainty in Measurement for Representation and Propagation with Scalable Precision
arXiv:2603.20365v1 Announce Type: cross Abstract: Probability theory has become the predominant framework for quantifying uncertainty across scientific and engineering disciplines, with a particular focus on measurement and control systems. However, the widespread reliance on simple Gaussian assumptions–particularly in control theory, manufacturing, and measurement systems–can result in incomplete representations and multistage lossy approximations of complex phenomena, […]
Compression is all you need: Modeling Mathematics
arXiv:2603.20396v1 Announce Type: new Abstract: Human mathematics (HM), the mathematics humans discover and value, is a vanishingly small subset of formal mathematics (FM), the totality of all valid deductions. We argue that HM is distinguished by its compressibility through hierarchically nested definitions, lemmas, and theorems. We model this with monoids. A mathematical deduction is a […]
Meta-Learning for Repeated Bayesian Persuasion
arXiv:2603.20408v1 Announce Type: cross Abstract: Classical Bayesian persuasion studies how a sender influences receivers through carefully designed signaling policies within a single strategic interaction. In many real-world environments, such interactions are repeated across multiple games, creating opportunities to exploit structural similarity across tasks. In this work, we introduce Meta-Persuasion algorithms, establishing the first line of […]
BuilderBench: The Building Blocks of Intelligent Agents
arXiv:2510.06288v2 Announce Type: replace Abstract: Today’s AI models learn primarily through mimicry and refining, so it is not surprising that they struggle to solve problems beyond the limits set by existing data. To solve novel problems, agents should acquire skills for exploring and learning through experience. Finding a scalable learning mechanism for developing agents that […]
Diffutron: A Masked Diffusion Language Model for Turkish Language
arXiv:2603.20466v1 Announce Type: cross Abstract: Masked Diffusion Language Models (MDLMs) have emerged as a compelling non-autoregressive alternative to standard large language models; however, their application to morphologically rich languages remains limited. In this paper, we introduce $textitDiffutron$, a masked diffusion language model specifically designed for Turkish. Our approach leverages a resource-efficient training pipeline, starting with […]
CERN: Correcting Errors in Raw Nanopore Signals Using Hidden Markov Models
arXiv:2603.20420v1 Announce Type: new Abstract: Nanopore sequencing can read substantially longer sequences of nucleic acid molecules than other sequencing methods, which has led to advances in genomic analysis such as the gapless human genome assembly. By analyzing the raw electrical signal reads that nanopore sequencing generates from molecules, existing works can map these reads without […]
Epistemic Observability in Language Models
arXiv:2603.20531v1 Announce Type: cross Abstract: We find that models report highest confidence precisely when they are fabricating. Across four model families (OLMo-3, Llama-3.1, Qwen3, Mistral), self-reported confidence inversely correlates with accuracy, with AUC ranging from 0.28 to 0.36 where 0.5 is random guessing. We prove, under explicit formal assumptions, that this is not a capability […]