Phase-Associative Memory: Sequence Modeling in Complex Hilbert Space
arXiv:2604.05030v1 Announce Type: new Abstract: We present Phase-Associative Memory (PAM), a recurrent sequence model in which all representations are complex-valued, associations accumulate in a matrix state $S_{t}$ $\in$ $\mathbb{C}^{d \times d}$ via outer products, and retrieval operates through the conjugate...
Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approach
arXiv:2604.05829v1 Announce Type: new Abstract: Approaches to bivariate causal discovery based on the minimum description length (MDL) principle approximate the (uncomputable) Kolmogorov complexity of the models in each causal direction, selecting the one with the lower total complexity. The premise...
Towards Scaling Law Analysis For Spatiotemporal Weather Data
arXiv:2604.05068v1 Announce Type: new Abstract: Compute-optimal scaling laws are relatively well studied for NLP and CV, where objectives are typically single-step and targets are comparatively homogeneous. Weather forecasting is harder to characterize in the same framework: autoregressive rollouts compound errors...
Hierarchical SVG Tokenization: Learning Compact Visual Programs for Scalable Vector Graphics Modeling
arXiv:2604.05072v1 Announce Type: new Abstract: Recent large language models have shifted SVG generation from differentiable rendering optimization to autoregressive program synthesis. However, existing approaches still rely on generic byte-level tokenization inherited from natural language processing, which poorly reflects the geometric...
DQA: Diagnostic Question Answering for IT Support
arXiv:2604.05350v1 Announce Type: new Abstract: Enterprise IT support interactions are fundamentally diagnostic: effective resolution requires iterative evidence gathering from ambiguous user reports to identify an underlying root cause. While retrieval-augmented generation (RAG) provides grounding through historical cases, standard multi-turn RAG...
Multilingual Language Models Encode Script Over Linguistic Structure
arXiv:2604.05090v1 Announce Type: new Abstract: Multilingual language models (LMs) organize representations for typologically and orthographically diverse languages into a shared parameter space, yet the nature of this internal organization remains elusive. In this work, we investigate which linguistic properties -...
MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU
arXiv:2604.05091v1 Announce Type: new Abstract: We present MegaTrain, a memory-centric system that efficiently trains 100B+ parameter large language models at full precision on a single GPU. Unlike traditional GPU-centric systems, MegaTrain stores parameters and optimizer states in host memory (CPU...
Reasoning Through Chess: How Reasoning Evolves from Data Through Fine-Tuning and Reinforcement Learning
arXiv:2604.05134v1 Announce Type: new Abstract: How can you get a language model to reason in a task it natively struggles with? We study how reasoning evolves in a language model -- from supervised fine-tuning (SFT) to reinforcement learning (RL) --...
The Illusion of Latent Generalization: Bi-directionality and the Reversal Curse
arXiv:2604.04943v1 Announce Type: new Abstract: The reversal curse describes a failure of autoregressive language models to retrieve a fact in reverse order (e.g., training on ``$A > B$'' but failing on ``$B < A$''). Recent work shows that objectives with...
A Theoretical Framework for Statistical Evaluability of Generative Models
arXiv:2604.05324v1 Announce Type: new Abstract: Statistical evaluation aims to estimate the generalization performance of a model using held-out i.i.d.\ test data sampled from the ground-truth distribution. In supervised learning settings such as classification, performance metrics such as error rate are...
Graph of Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills
arXiv:2604.05333v1 Announce Type: new Abstract: Skill usage has become a core component of modern agent systems and can substantially improve agents' ability to complete complex tasks. In real-world settings, where agents must monitor and interact with numerous personal applications, web...
Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data
arXiv:2604.05857v1 Announce Type: new Abstract: Clustering mixed-type tabular data is fundamental for exploratory analysis, yet remains challenging due to misaligned numerical-categorical representations, uneven and context-dependent feature relevance, and disconnected and post-hoc explanation from the clustering process. We propose WISE, a...
Exemplar Retrieval Without Overhypothesis Induction: Limits of Distributional Sequence Learning in Early Word Learning
arXiv:2604.05243v1 Announce Type: new Abstract: Background: Children do not simply learn that balls are round and blocks are square. They learn that shape is the kind of feature that tends to define object categories -- a second-order generalisation known as...
SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation
arXiv:2604.05489v1 Announce Type: new Abstract: Text-to-Video (T2V) generation has benefited from recent advances in diffusion models, yet current systems still struggle under complex scenarios, which are generally exacerbated by the ambiguity and underspecification of text prompts. In this work, we...
Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning
arXiv:2604.05396v1 Announce Type: new Abstract: Despite its success, existing in-context learning (ICL) relies on in-domain expert demonstrations, limiting its applicability when expert annotations are scarce. We posit that different domains may share underlying reasoning structures, enabling source-domain demonstrations to improve...
Learning What Matters: Dynamic Dimension Selection and Aggregation for Interpretable Vision-Language Reward Modeling
arXiv:2604.05445v1 Announce Type: new Abstract: Vision-language reward modeling faces a dilemma: generative approaches are interpretable but slow, while discriminative ones are efficient but act as opaque "black boxes." To bridge this gap, we propose VL-MDR (Vision-Language Multi-Dimensional Reward), a framework...
Proximity Measure of Information Object Features for Solving the Problem of Their Identification in Information Systems
arXiv:2604.04939v1 Announce Type: new Abstract: The paper considers a new quantitative-qualitative proximity measure for the features of information objects, where data enters a common information resource from several sources independently. The goal is to determine the possibility of their relation...
OntoTKGE: Ontology-Enhanced Temporal Knowledge Graph Extrapolation
arXiv:2604.05468v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) extrapolation is an important task that aims to predict future facts through historical interaction information within KG snapshots. A key challenge for most existing TKG extrapolation models is handling entities with...
Neural Assistive Impulses: Synthesizing Exaggerated Motions for Physics-based Characters
arXiv:2604.05394v1 Announce Type: new Abstract: Physics-based character animation has become a fundamental approach for synthesizing realistic, physically plausible motions. While current data-driven deep reinforcement learning (DRL) methods can synthesize complex skills, they struggle to reproduce exaggerated, stylized motions, such as...
What Makes a Good Response? An Empirical Analysis of Quality in Qualitative Interviews
arXiv:2604.05163v1 Announce Type: new Abstract: Qualitative interviews provide essential insights into human experiences when they elicit high-quality responses. While qualitative and NLP researchers have proposed various measures of interview quality, these measures lack validation that high-scoring responses actually contribute to...
Adaptive Serverless Resource Management via Slot-Survival Prediction and Event-Driven Lifecycle Control
arXiv:2604.05465v1 Announce Type: new Abstract: Serverless computing eliminates infrastructure management overhead but introduces significant challenges regarding cold start latency and resource utilization. Traditional static resource allocation often leads to inefficiencies under variable workloads, resulting in performance degradation or excessive costs....
IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents
arXiv:2604.05157v1 Announce Type: new Abstract: Computer-Use Agents (CUAs) leverage large language models to execute GUI operations on desktop environments, yet they generate actions without evaluating action quality, leading to irreversible errors that cascade through subsequent steps. We propose IntentScore, a...
Channel-wise Retrieval for Multivariate Time Series Forecasting
arXiv:2604.05543v1 Announce Type: new Abstract: Multivariate time series forecasting often struggles to capture long-range dependencies due to fixed lookback windows. Retrieval-augmented forecasting addresses this by retrieving historical segments from memory, but existing approaches rely on a channel-agnostic strategy that applies...
Non-monotonic causal discovery with Kolmogorov-Arnold Fuzzy Cognitive Maps
arXiv:2604.05136v1 Announce Type: new Abstract: Fuzzy Cognitive Maps constitute a neuro-symbolic paradigm for modeling complex dynamic systems, widely adopted for their inherent interpretability and recurrent inference capabilities. However, the standard FCM formulation, characterized by scalar synaptic weights and monotonic activation...
Operational Noncommutativity in Sequential Metacognitive Judgments
arXiv:2604.04938v1 Announce Type: new Abstract: Metacognition, understood as the monitoring and regulation of one's own cognitive processes, is inherently sequential: an agent evaluates an internal state, updates it, and may then re-evaluate under modified criteria. Order effects in cognition are...
YoNER: A New Yor\`ub\'a Multi-domain Named Entity Recognition Dataset
arXiv:2604.05624v1 Announce Type: new Abstract: Named Entity Recognition (NER) is a foundational NLP task, yet research in Yor\`ub\'a has been constrained by limited and domain-specific resources. Existing resources, such as MasakhaNER (a manually annotated news-domain corpus) and WikiAnn (automatically created...
EvolveRouter: Co-Evolving Routing and Prompt for Multi-Agent Question Answering
arXiv:2604.05149v1 Announce Type: new Abstract: Large language model agents often exhibit complementary strengths, making routing a promising approach for multi-agent question answering. However, existing routing methods remain limited in two important ways: they typically optimize over a fixed pool of...
PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection
arXiv:2604.05424v1 Announce Type: new Abstract: PRISM-MCTS: Learning from Reasoning Trajectories with Metacognitive Reflection Siyuan Cheng, Bozhong Tian, Yanchao Hao, Zheng Wei Published: 06 Apr 2026, Last Modified: 06 Apr 2026 ACL 2026 Findings Conference, Area Chairs, Reviewers, Publication Chairs, Authors...
Simulating the Evolution of Alignment and Values in Machine Intelligence
arXiv:2604.05274v1 Announce Type: new Abstract: Model alignment is currently applied in a vacuum, evaluated primarily through standardised benchmark performance. The purpose of this study is to examine the effects of alignment on populations of models through time. We focus on...
Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing
arXiv:2604.05077v1 Announce Type: new Abstract: Metal additive manufacturing (AM) enables the fabrication of safety-critical components, but reliable quality assurance depends on high-fidelity sensor streams containing proprietary process information, limiting collaborative data sharing. Existing defect-detection models typically treat melt-pool observations as...