Thinking in Different Spaces: Domain-Specific Latent Geometry Survives Cross-Architecture Translation
arXiv:2603.20406v1 Announce Type: new Abstract: We investigate whether independently trained language models converge to geometrically compatible latent representations, and whether this compatibility can be exploited to correct model behavior at inference time without any weight updates. We learn a linear...
From Data to Laws: Neural Discovery of Conservation Laws Without False Positives
arXiv:2603.20474v1 Announce Type: new Abstract: Conservation laws are fundamental to understanding dynamical systems, but discovering them from data remains challenging due to parameter variation, non-polynomial invariants, local minima, and false positives on chaotic systems. We introduce NGCG, a neural-symbolic pipeline...
Distributed Gradient Clustering: Convergence and the Effect of Initialization
arXiv:2603.20507v1 Announce Type: new Abstract: We study the effects of center initialization on the performance of a family of distributed gradient-based clustering algorithms introduced in [1], that work over connected networks of users. In the considered scenario, each user contains...
Optimal low-rank stochastic gradient estimation for LLM training
arXiv:2603.20632v1 Announce Type: new Abstract: Large language model (LLM) training is often bottlenecked by memory constraints and stochastic gradient noise in extremely high-dimensional parameter spaces. Motivated by empirical evidence that many LLM gradient matrices are effectively low-rank during training, we...
Evaluating Uplift Modeling under Structural Biases: Insights into Metric Stability and Model Robustness
arXiv:2603.20775v1 Announce Type: new Abstract: In personalized marketing, uplift models estimate incremental effects by modeling how customer behavior changes under alternative treatments. However, real-world data often exhibit biases - such as selection bias, spillover effects, and unobserved confounding - which...
Announcement of opinions for Wednesday, March 25
On Wednesday, March 25, will be live blogging as the court potentially releases opinions in one or more argued cases from the current term. Click here for a list of […]The postAnnouncement of opinions for Wednesday, March 25appeared first onSCOTUSblog.
DIAL-KG: Schema-Free Incremental Knowledge Graph Construction via Dynamic Schema Induction and Evolution-Intent Assessment
arXiv:2603.20059v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are foundational to applications such as search, question answering, and recommendation. Conventional knowledge graph construction methods are predominantly static, rely ing on a single-step construction from a fixed corpus with a prede...
Learning to Disprove: Formal Counterexample Generation with Large Language Models
arXiv:2603.19514v1 Announce Type: new Abstract: Mathematical reasoning demands two critical, complementary skills: constructing rigorous proofs for true statements and discovering counterexamples that disprove false ones. However, current AI efforts in mathematics focus almost exclusively on proof construction, often neglecting the...
Probing to Refine: Reinforcement Distillation of LLMs via Explanatory Inversion
arXiv:2603.19266v1 Announce Type: cross Abstract: Distilling robust reasoning capabilities from large language models (LLMs) into smaller, computationally efficient student models remains an unresolved challenge. Despite recent advances, distilled models frequently suffer from superficial pattern memorization and subpar generalization. To overcome...
From Flat to Structural: Enhancing Automated Short Answer Grading with GraphRAG
arXiv:2603.19276v1 Announce Type: cross Abstract: Automated short answer grading (ASAG) is critical for scaling educational assessment, yet large language models (LLMs) often struggle with hallucinations and strict rubric adherence due to their reliance on generalized pre-training. While Rretrieval-Augmented Generation (RAG)...
Speculating Experts Accelerates Inference for Mixture-of-Experts
arXiv:2603.19289v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models have gained popularity as a means of scaling the capacity of large language models (LLMs) while maintaining sparse activations and reduced per-token compute. However, in memory-constrained inference settings, expert weights must be...
MOSAIC: Modular Opinion Summarization using Aspect Identification and Clustering
arXiv:2603.19277v1 Announce Type: new Abstract: Reviews are central to how travelers evaluate products on online marketplaces, yet existing summarization research often emphasizes end-to-end quality while overlooking benchmark reliability and the practical utility of granular insights. To address this, we propose...
Vocabulary shapes cross-lingual variation of word-order learnability in language models
arXiv:2603.19427v1 Announce Type: new Abstract: Why do some languages like Czech permit free word order, while others like English do not? We address this question by pretraining transformer language models on a spectrum of synthetic word-order variants of natural languages....
PRIME-CVD: A Parametrically Rendered Informatics Medical Environment for Education in Cardiovascular Risk Modelling
arXiv:2603.19299v1 Announce Type: new Abstract: In recent years, progress in medical informatics and machine learning has been accelerated by the availability of openly accessible benchmark datasets. However, patient-level electronic medical record (EMR) data are rarely available for teaching or methodological...
GT-Space: Enhancing Heterogeneous Collaborative Perception with Ground Truth Feature Space
arXiv:2603.19308v1 Announce Type: new Abstract: In autonomous driving, multi-agent collaborative perception enhances sensing capabilities by enabling agents to share perceptual data. A key challenge lies in handling {\em heterogeneous} features from agents equipped with different sensing modalities or model architectures,...
DPxFin: Adaptive Differential Privacy for Anti-Money Laundering Detection via Reputation-Weighted Federated Learning
arXiv:2603.19314v1 Announce Type: new Abstract: In the modern financial system, combating money laundering is a critical challenge complicated by data privacy concerns and increasingly complex fraud transaction patterns. Although federated learning (FL) is a promising problem-solving approach as it allows...
Ternary Gamma Semirings: From Neural Implementation to Categorical Foundations
arXiv:2603.19317v1 Announce Type: new Abstract: This paper establishes a theoretical framework connecting neural network learning with abstract algebraic structures. We first present a minimal counterexample demonstrating that standard neural networks completely fail on compositional generalization tasks (0% accuracy). By introducing...
A General Deep Learning Framework for Wireless Resource Allocation under Discrete Constraints
arXiv:2603.19322v1 Announce Type: new Abstract: While deep learning (DL)-based methods have achieved remarkable success in continuous wireless resource allocation, efficient solutions for problems involving discrete variables remain challenging. This is primarily due to the zero-gradient issue in backpropagation, the difficulty...
Do Post-Training Algorithms Actually Differ? A Controlled Study Across Model Scales Uncovers Scale-Dependent Ranking Inversions
arXiv:2603.19335v1 Announce Type: new Abstract: Post-training alignment has produced dozens of competing algorithms -- DPO, SimPO, KTO, GRPO, and others -- yet practitioners lack controlled comparisons to guide algorithm selection. We present OXRL, a unified framework implementing 51 post-training algorithms...
Any-Subgroup Equivariant Networks via Symmetry Breaking
arXiv:2603.19486v1 Announce Type: new Abstract: The inclusion of symmetries as an inductive bias, known as equivariance, often improves generalization on geometric data (e.g. grids, sets, and graphs). However, equivariant architectures are usually highly constrained, designed for symmetries chosen a priori,...
ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization
arXiv:2603.19594v1 Announce Type: new Abstract: Indoor localization has become increasingly essential for applications ranging from asset tracking to delivering personalized services. Federated learning (FL) offers a privacy-preserving approach by training a centralized global model (GM) using distributed data from mobile...
Alternating Diffusion for Proximal Sampling with Zeroth Order Queries
arXiv:2603.19633v1 Announce Type: new Abstract: This work introduces a new approximate proximal sampler that operates solely with zeroth-order information of the potential function. Prior theoretical analyses have revealed that proximal sampling corresponds to alternating forward and backward iterations of the...
RiboSphere: Learning Unified and Efficient Representations of RNA Structures
arXiv:2603.19636v1 Announce Type: new Abstract: Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce. We introduce \emph{RiboSphere}, a framework that learns \emph{discrete} geometric representations of...
GoAgent: Group-of-Agents Communication Topology Generation for LLM-based Multi-Agent Systems
arXiv:2603.19677v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) have demonstrated exceptional capabilities in solving complex tasks, yet their effectiveness depends heavily on the underlying communication topology that coordinates agent interactions. Within these systems, successful problem-solving often...
EDM-ARS: A Domain-Specific Multi-Agent System for Automated Educational Data Mining Research
arXiv:2603.18273v1 Announce Type: new Abstract: In this technical report, we present the Educational Data Mining Automated Research System (EDM-ARS), a domain-specific multi-agent pipeline that automates end-to-end educational data mining (EDM) research. We conceptualize EDM-ARS as a general framework for domain-aware...
CORE: Robust Out-of-Distribution Detection via Confidence and Orthogonal Residual Scoring
arXiv:2603.18290v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying deep learning models reliably, yet no single method performs consistently across architectures and datasets -- a scorer that leads on one benchmark often falters on another. We attribute...
MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution
arXiv:2603.18718v1 Announce Type: new Abstract: Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction, yet most existing systems treat construction, retrieval, and utilization as isolated subroutines. This creates two coupled challenges: strategic blindness on the forward path of...
Thinking with Constructions: A Benchmark and Policy Optimization for Visual-Text Interleaved Geometric Reasoning
arXiv:2603.18662v1 Announce Type: new Abstract: Geometric reasoning inherently requires "thinking with constructions" -- the dynamic manipulation of visual aids to bridge the gap between problem conditions and solutions. However, existing Multimodal Large Language Models (MLLMs) are largely confined to passive...
Learned but Not Expressed: Capability-Expression Dissociation in Large Language Models
arXiv:2603.18013v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate the capacity to reconstruct and trace learned content from their training data under specific elicitation conditions, yet this capability does not manifest in standard generation contexts. This empirical observational study...