Search: “governance”

Asymptotic Signal Subspace Recovery in Softmax Attention Models

arXiv:2606.22406v1 Announce Type: new Abstract: Attention mechanisms have demonstrated remarkable empirical success in identifying relevant information from large collections of tokens, yet the theore

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Attention mechanism for scalable mesh-based neural surrogates of free-surface fluids

DGX agent

arXiv:2606.23251v1 Announce Type: cross Abstract: High-fidelity simulations of free-surface flows using Lagrangian methods such as the Particle Finite Element Method (PFEM) are computationally demandi

researcharxiv-cs-lg

23 Jun 2026

Closing the Calibration Gap in Semantic Caching

arXiv:2606.19719v2 Announce Type: replace-cross Abstract: Semantic caching cuts LLM inference costs by serving a cached response to semantically similar queries. Standard practice evaluates these syst

Deep material network for homogenization of piezoelectric composites

arXiv:2606.22566v1 Announce Type: cross Abstract: Piezoelectric composites are widely used in sensors, actuators, transducers, and energy-harvesting devices because their effective electromechanical p

Disentangling Intrinsic Importance from Emergent Structure in Multi-Expert Orchestration

arXiv:2602.04291v2 Announce Type: replace Abstract: Multi-expert systems, where multiple Large Language Models (LLMs) collaborate to solve complex tasks, are increasingly adopted for high-performance

Drowning in Routine: Signal Dilution in Multi-Turn Agent Training

arXiv:2606.22164v1 Announce Type: new Abstract: Multi-turn agents interleave consequential decisions with routine execution: some actions change the downstream return distribution, while others are ne

agentsarxiv-cs-lg

Durability-Aware Multi-Objective Optimization of the Jansen Linkage: Trading Gait Quality Against Joint Wear

arXiv:2606.22129v1 Announce Type: new Abstract: The Jansen linkage is a single-degree-of-freedom planar leg mechanism whose eleven 'holy numbers' were evolved by Theo Jansen to optimize the foot-path

applicationsarxiv-cs-ro

Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis

arXiv:2411.15919v2 Announce Type: replace Abstract: In engineering and applied mathematics, developing accurate mathematical models to predict and understand real-world phenomena is of utmost importan

applicationsarxiv-cs-lg

Expected Free Energy-based Planning as Variational Inference

arXiv:2606.20658v1 Announce Type: cross Abstract: Planning under uncertainty requires agents to balance goal achievement with information gathering. Active inference addresses this through the Expecte

safetyarxiv-cs-lg

Generalized nonparametric regression in reproducing kernel Hilbert spaces: Consistency and rates of convergence

arXiv:2606.22993v1 Announce Type: cross Abstract: We develop a comprehensive theory for regularized M-estimation in reproducing kernel Hilbert spaces. Under mild conditions on the loss we establish ex

Harnessing Agent Skills: Architectural Patterns and a Reference Architecture for Skill-Mediated LLM Agents

arXiv:2606.20631v1 Announce Type: cross Abstract: Agent skills externalise reusable agent-facing behavioural knowledge and guidance as persistent artefacts that can be discovered, activated, and inter

agentsarxiv-cs-lg

Inverse Problem for Partial Differential Equations with Jump Discontinuities in Coefficients by Two-stage Physics-Informed Deep Learning and Statistical Mixture Models

arXiv:2510.14656v2 Announce Type: replace-cross Abstract: This work proposes a two-stage physics-informed deep learning framework that combines neural-network-based sampling with statistical inference

Leaderless Collective Motion in Affine Formation Control over the Complex Plane

arXiv:2604.05648v2 Announce Type: replace Abstract: We propose a method for the collective maneuvering of affine formations in the plane by modifying the original weights of the Laplacian matrix used

researcharxiv-cs-ro

MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?

arXiv:2606.23664v1 Announce Type: new Abstract: Multi-agent systems (MAS) offer a scalable path forward for agentic AI, comprising multiple LLM-based agents, each assigned a system prompt and a positi

agentsarxiv-cs-lg

Tutorials

NewPINNs: Physics-Informing Neural Networks Using Conventional Solvers for Partial Differential Equations

arXiv:2601.17207v2 Announce Type: replace Abstract: We introduce NewPINNs, a physics-informing learning framework that couples neural networks with conventional numerical solvers for solving different

tutorialsarxiv-cs-lg

On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning

arXiv:2606.22352v1 Announce Type: new Abstract: Dictionary learning has long been studied from both optimization and probabilistic perspectives. While formulations with element-wise sparsity regulariz

Path-dependent program induction under resource constraints explains human sequence learning

arXiv:2606.20623v1 Announce Type: cross Abstract: How do people build abstract, reusable knowledge from sequential experience under bounded cognitive resources? To answer this question, we integrate r

Physics-Guided Fully Convolutional Spatiotemporal Learning Toward Digital-Twin-Enabled Microstructure Evolution Prediction

arXiv:2606.20983v1 Announce Type: new Abstract: Understanding and predicting microstructure evolution is central to materials design, yet purely data-driven spatiotemporal learning models often suffer

Spectral Gating via Damped Oscillations for Adaptive Implicit Neural Representations

arXiv:2606.23129v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have been proven successful in encoding continuous signals through coordinate-based networks, yet facing a spectr

safetyarxiv-cs-cv

Stage-dependent integer-binary encoding in factorization-machine black-box optimization

arXiv:2606.23188v1 Announce Type: new Abstract: Black-box optimization (BBO) deals with problems where objective functions lack explicit analytical forms and are expensive to evaluate. Factorization m

Temporal Graph Pattern Machine

arXiv:2601.22454v3 Announce Type: replace Abstract: Temporal graph learning is pivotal for deciphering dynamic systems, where the core challenge lies in explicitly modeling the underlying evolving pat

A theory of learning data statistics in diffusion models, from easy to hard

arXiv:2603.12901v2 Announce Type: replace-cross Abstract: While diffusion models have emerged as a powerful class of generative models, their learning dynamics remain poorly understood. We address thi

safetyarxiv-cs-lg

An Electric Potential-Augmented Benchmark Dataset for Physics-Guided Image Reconstruction of Electrical Capacitance Tomography

model-releasesarxiv-cs-cv

arXiv:2606.12226v1 Announce Type: new Abstract: While deep learning has significantly advanced image reconstruction of Electrical Capacitance Tomography (ECT), most data-driven methods map directly be

Beyond Dark Knowledge: Mixup-Based Distillation for Reliable Predictions

arXiv:2606.12171v1 Announce Type: new Abstract: Knowledge Distillation (KD) and mixup have proven effective at inducing smoothness in class boundaries; KD captures inherent class relationships in prob

researcharxiv-cs-cv

From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning

arXiv:2606.11195v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed how humans access information, but not how we reason with it. Their fluency accelerates consumption whil

From Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference

arXiv:2606.11207v1 Announce Type: new Abstract: We present SemantiClean, a modular framework for extracting structured semantic signals from e-commerce session data and driving pluggable inference tar

Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions

arXiv:2509.10303v2 Announce Type: replace-cross Abstract: Online reinforcement learning (RL) approaches have demonstrated strong performance on Job Shop Scheduling (JSP) and Flexible JSP (FJSP) proble

Geometry of Reason: Spectral Signatures of Valid Mathematical Reasoning

arXiv:2601.00791v2 Announce Type: replace-cross Abstract: Verifying whether a language model is genuinely reasoning or pattern-matching remains an open problem: learned verifiers are expensive, and ou

researcharxiv-cs-ai

Local Ai

HAMNO: A Hierarchical Adaptive Multi-scale Neural Operator with Physics-Informed Learning for Dynamical Systems

arXiv:2606.11963v1 Announce Type: new Abstract: Neural operators provide a powerful framework for learning solution mappings of partial differential equations directly in function space. However, many

local-aiarxiv-cs-lg

Making Models Unmergeable via Scaling-Sensitive Loss Landscape

arXiv:2601.21898v2 Announce Type: replace Abstract: The rise of model hubs has made it easier to access reusable model components, making model merging a practical tool for combining capabilities. Yet

Neural-Parameterized Cellular Automata for Wildfire Spread

arXiv:2606.11676v1 Announce Type: cross Abstract: Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread. To address this we

NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track

arXiv:2606.11199v1 Announce Type: cross Abstract: We present NightFeats, a structured multi-agent retrieval-augmented generation (RAG) system submitted to the MMU-RAGent competition at NeurIPS 2025, w

PermDoRA -- Understanding Adapter Interference in Language Models: Limits of Parameter-Space Geometry

arXiv:2606.11262v1 Announce Type: cross Abstract: Access control in large language models (LLMs) requires modular mechanisms to enable domain-specific behavior without retraining or cross-domain inter

Physically Constrained Ensemble Gaussian Process Modelling for Expensive Quantum Systems with Heteroskedastic Noise

arXiv:2606.11240v1 Announce Type: cross Abstract: Accurate modeling of quantum many-body systems often requires computationally expensive simulations such as Density Matrix Renormalization Group (DMRG

Tutorials

Probabilistic Salary Prediction with Graph Attention Networks and a Mixture Density Network

arXiv:2606.11663v1 Announce Type: cross Abstract: Accurate salary prediction is critical for bridging the information gap between employers and job seekers in modern labor markets. Existing approaches

tutorialsarxiv-cs-lg

Local Ai

PROJECTMEM: A Local-First, Event-Sourced Memory and Judgment Layer for AI Coding Agents

arXiv:2606.12329v1 Announce Type: new Abstract: AI coding assistants now support a growing share of software work, from quick scripts to production applications. Yet these agents remain largely statel

local-aiarxiv-cs-ai

Sustainability assessment using multimodal AI agents

arXiv:2507.17012v2 Announce Type: replace Abstract: Reducing the rapidly growing environmental impact of the computing industry requires assessing the emissions of electronics at scale. However, a tra

agentsarxiv-cs-ai

The Language You Ask In: Language-Conditioned Ideological Divergence in LLM Analysis of Contested Political Documents

model-releasesarxiv-cs-cl

arXiv:2601.12164v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as analytical tools across multilingual contexts, yet their outputs may carry systemati

The Long Tail, Not the Front Page: Cold-Start Prediction of Crowd Highlight Salience

arXiv:2606.11654v1 Announce Type: cross Abstract: A social highlighter's most useful signal -- which passages a crowd of readers marks -- exists only for documents people have already read. Can the ag

researcharxiv-cs-cl

Unifying Learning Dynamics and Generalization in Transformers Scaling Law

arXiv:2512.22088v3 Announce Type: replace-cross Abstract: The scaling law, a cornerstone of Large Language Model (LLM) development, predicts improvements in model performance with increasing computati

applicationsarxiv-cs-ai

uva-irlab-conv at SemEval-2026 Task 8: Multi-Turn RAG with Learned Sparse Retrieval and Listwise Reranking

arXiv:2606.11945v1 Announce Type: new Abstract: This report describes our participation in SemEval-2026 Task 8 on multi-turn retrieval and question answering. The task evaluates conversational systems

applicationsarxiv-cs-cl

Agentic Social Affordance Framework (ASAF): Agent Identity Design as a Collaboration Interface in Multi-Agent Systems

arXiv:2606.09832v1 Announce Type: cross Abstract: As AI systems evolve from single conversational agents to complex multi-agent architectures, a critical design dimension has been overlooked: how the

agentsarxiv-cs-ai

Between Amnesia and Chaos: A Memory Stability Expressivity Trilemma for Trainable Dissipative Oscillator Networks

arXiv:2606.09929v1 Announce Type: cross Abstract: Physical reservoir computing harnesses nonlinear mechanical dynamics but, by convention, freezes the substrate and trains only a linear readout, presu

researcharxiv-cs-ai

Closing the Modality Gap in Zero-Shot HAR: Contrastive Training and Separability-Optimized Prototypes on IMU Data

arXiv:2606.10789v1 Announce Type: new Abstract: Zero-shot learning (ZSL) for inertial measurement unit (IMU)-based human activity recognition (HAR) faces a central challenge: bridging the gap between

safetyarxiv-cs-lg

Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm

arXiv:2606.10504v1 Announce Type: new Abstract: Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.g., images) can guide a (smaller) student m

Do VLMs Reason Like Engineers? A Benchmark and a Stage-wise Evaluation

arXiv:2606.10833v1 Announce Type: new Abstract: Vision-Language Models (VLMs) demonstrate strong performance on general multimodal reasoning benchmarks, yet their ability to perform engineering reason

From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning

arXiv:2606.10595v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning. This paradigm enables privacy with multiple clien

researcharxiv-cs-ai

LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake

arXiv:2606.10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can b