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...
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...
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...
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...
MedLayBench-V: A Large-Scale Benchmark for Expert-Lay Semantic Alignment in Medical Vision Language Models
arXiv:2604.05738v1 Announce Type: new Abstract: Medical Vision-Language Models (Med-VLMs) have achieved expert-level proficiency in interpreting diagnostic imaging. However, current models are predominantly trained on professional literature, limiting their ability to communicate findings in the lay register required for patient-centered care....
Optimal-Transport-Guided Functional Flow Matching for Turbulent Field Generation in Hilbert Space
arXiv:2604.05700v1 Announce Type: new Abstract: High-fidelity modeling of turbulent flows requires capturing complex spatiotemporal dynamics and multi-scale intermittency, posing a fundamental challenge for traditional knowledge-based systems. While deep generative models, such as diffusion models and Flow Matching, have shown promising...
Document Optimization for Black-Box Retrieval via Reinforcement Learning
arXiv:2604.05087v1 Announce Type: new Abstract: Document expansion is a classical technique for improving retrieval quality, and is attractive since it shifts computation offline, avoiding additional query-time processing. However, when applied to modern retrievers, it has been shown to degrade performance,...
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...
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...
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....
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...
The UNDO Flip-Flop: A Controlled Probe for Reversible Semantic State Management in State Space Model
arXiv:2604.05923v1 Announce Type: new Abstract: State space models (SSMs) have been shown to possess the theoretical capacity to model both star-free sequential tasks and bounded hierarchical structures Sarrof et al. (2024). However, formal expressivity results do not guarantee that gradient-based...
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...
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...
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...
Territory Paint Wars: Diagnosing and Mitigating Failure Modes in Competitive Multi-Agent PPO
arXiv:2604.04983v1 Announce Type: new Abstract: We present Territory Paint Wars, a minimal competitive multi-agent reinforcement learning environment implemented in Unity, and use it to systematically investigate failure modes of Proximal Policy Optimisation (PPO) under self-play. A first agent trained for...
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...
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...
Pressure, What Pressure? Sycophancy Disentanglement in Language Models via Reward Decomposition
arXiv:2604.05279v1 Announce Type: new Abstract: Large language models exhibit sycophancy, the tendency to shift their stated positions toward perceived user preferences or authority cues regardless of evidence. Standard alignment methods fail to correct this because scalar reward models conflate two...
OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward
arXiv:2604.05514v1 Announce Type: new Abstract: The paradigm of programmable diagram generation is evolving rapidly, playing a crucial role in structured visualization. However, most existing studies are confined to a narrow range of task formulations and language support, constraining their applicability...
ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads
arXiv:2604.05426v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is now the dominant method for parameter-efficient fine-tuning of large language models, but achieving a high-quality adapter often requires systematic hyperparameter tuning because LoRA performance is highly sensitive to configuration choices. In...
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...
Vehicle-as-Prompt: A Unified Deep Reinforcement Learning Framework for Heterogeneous Fleet Vehicle Routing Problem
arXiv:2604.05195v1 Announce Type: new Abstract: Unlike traditional homogeneous routing problems, the Heterogeneous Fleet Vehicle Routing Problem (HFVRP) involves heterogeneous fixed costs, variable travel costs, and capacity constraints, rendering solution quality highly sensitive to vehicle selection. Furthermore, real-world logistics applications often...
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...
The 14th Amendment’s citizenship clause is not trapped in amber: a reflection on oral argument
While I have written multiple posts for SCOTUSblog on birthright citizenship, a substantial part of my practice is litigating Second Amendment claims. In light of that experience, I was struck […]The postThe 14th Amendment’s citizenship clause is not trapped in...
SCOTUStoday for Tuesday, April 7
President Donald Trump is not done complaining about the Supreme Court’s tariffs ruling. Keep reading to learn more about his latest message for the justices.The postSCOTUStoday for Tuesday, April 7appeared first onSCOTUSblog.
What the heck is wrong with our AI overlords?
New profile of Sam Altman shines a light on a whole industry.
SCOTUS overturns 5th Circuit ruling that told ISP to kick pirates off Internet
Supreme Court's precedent-setting Cox ruling helps Grande beat music piracy claims.
Firmus, the ‘Southgate’ AI data center builder backed by Nvidia, hits $5.5B valuation
Nvidia-backed Asia AI data center provider Firmus has now raised $1.35 billion in six months.