Search: “governance”

Prompts Don't Protect: Architectural Enforcement via MCP Proxy for LLM Tool Access Control

arXiv:2605.18414v1 Announce Type: cross Abstract: Large language models increasingly operate as autonomous agents that select and invoke tools from large registries. We identify a critical gap: when u

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Model Releases

Queue Length Regret Bounds for Contextual Queueing Bandits

DGX agent

arXiv:2601.19300v2 Announce Type: replace Abstract: We introduce contextual queueing bandits, a new context-aware framework for scheduling while simultaneously learning unknown service rates. Individu

model-releasesarxiv-cs-lg

19 May 2026

Structure-Aware Masking for Protein Representation Learning

arXiv:2605.16581v1 Announce Type: new Abstract: Masked language modeling (MLM) is the standard objective for training protein language models, typically implemented by randomly masking individual resi

safetyarxiv-cs-lg

Task-Level AI Readiness Assessment for Business Process Management:The T-IPO Model and LARA Matrix in Financial-Services IT Operations

arXiv:2605.16297v1 Announce Type: cross Abstract: Which tasks inside an enterprise workflow can a large-language-model agent reliably handle, and under what conditions? Most business process modeling

The End of Trust: How Agentic AI Breaks Security Assumptions

arXiv:2605.16436v1 Announce Type: cross Abstract: For decades, the security of digital interaction has rested on an unacknowledged economic constraint. Attackers faced a tradeoff between the fidelity

The Geometry of Projection Heads: Conditioning, Invariance, and Collapse

arXiv:2605.17180v1 Announce Type: new Abstract: We develop a geometric theory of projection heads in self-supervised learning by modeling the head as a trainable Riemannian metric on the backbone repr

The IsalProgram Programming Language

arXiv:2605.17008v1 Announce Type: cross Abstract: We introduce IsalProgram (Instruction Set and Language for Programming), a novel assembly-like programming language with three distinctive theoretical

Theory of Minimal Weight Perturbations in Deep Networks and its Applications for Low-Rank Activated Backdoor Attacks

model-releasesarxiv-cs-lg

arXiv:2601.16880v2 Announce Type: replace Abstract: The minimal norm weight perturbations of DNNs required to achieve a specified change in output are derived and the factors determining its size are

Toy Combinatorial Interpretability Models Reveal Lottery Tickets in Early Feature Space

arXiv:2605.17704v1 Announce Type: new Abstract: The lottery ticket hypothesis posits that dense networks contain sparse subnetworks, ``winning tickets,'' that, when rewound to their initial weights an

Training Infinitely Deep and Wide Transformers

arXiv:2605.17660v1 Announce Type: cross Abstract: Transformers have become the dominant architecture in modern machine learning, yet the theoretical understanding of their training dynamics remains li

Always Learning, Always Mixing: Efficient and Simple Data Mixing All The Time

arXiv:2605.15220v1 Announce Type: cross Abstract: Data mixing decides how to combine different sources or types of data and is a consequential problem throughout language model training. In pretrainin

Can We Trust AI-Inferred User States. A Psychometric Framework for Validating the Reliability of Users States Classification by LLMs in Operational Environments

arXiv:2605.15734v1 Announce Type: new Abstract: The use of large language models to assess user states in conversational and adaptive systems is based on the assumption that the metrics used for such

CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models

arXiv:2605.15549v1 Announce Type: cross Abstract: The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, desi

Fair outputs, Biased Internals: Causal Potency and Asymmetry of Latent Bias in LLMs for High-Stakes Decisions

arXiv:2605.15217v1 Announce Type: new Abstract: Instruction-tuned language models exhibit behavioural fairness in high-stakes decisions while retaining biased associations in their internal representa

Applications

Layer-wise Derivative Controlled Networks

arXiv:2605.15463v1 Announce Type: new Abstract: As machine learning models grow in complexity, they increasingly struggle with three conflicting demands: the need for high accuracy, the requirement fo

applicationsarxiv-cs-lg

Applications

Logical Grammar Induction via Graph Kolmogorov Complexity: A Neuro-Symbolic Framework for Self-Healing Clinical Data Integrity

arXiv:2605.15242v1 Announce Type: new Abstract: The reliability of Healthcare Information Systems (HIS) is frequently compromised by human-induced data entry errors, which existing statistical anomaly

applicationsarxiv-cs-lg

NOVA: Fundamental Limits of Knowledge Discovery Through AI

arXiv:2605.15219v1 Announce Type: new Abstract: Can AI systems discover genuinely new knowledge through iterative self improvement, and if so, at what cost? We introduce the NOVA framework, which mode

local-aiarxiv-cs-ai

Preconditioned Regularized Wasserstein Proximal Sampling

arXiv:2509.01685v2 Announce Type: replace-cross Abstract: We consider sampling from a Gibbs distribution by evolving finitely many particles. We propose a preconditioned version of a recently proposed

safetyarxiv-cs-lg

PrismQuant: Rate-Distortion-Optimal Vector Quantization for Gaussian-Mixture Sources

arXiv:2605.15507v1 Announce Type: cross Abstract: For a Gaussian source under mean-squared error (MSE), classical transform coding is rate--distortion (RD) optimal: the Karhunen--Loeve transform (KLT)

local-aiarxiv-cs-ai

Reversing the Flow: Generation-to-Understanding Synergy in Large Multimodal Models

arXiv:2605.15792v1 Announce Type: new Abstract: The long-standing goal of multimodal AI is to build unified models in which visual understanding and visual generation mutually enhance one another. Des

safetyarxiv-cs-cv

SDOF: Taming the Alignment Tax in Multi-Agent Orchestration with State-Constrained Dispatch

arXiv:2605.15204v1 Announce Type: new Abstract: Multi-agent orchestration frameworks such as LangChain, LangGraph, and CrewAI route tasks through graph-based pipelines but do not enforce the stage con

Time-Varying Deep State Space Models for Sequences with Switching Dynamics

arXiv:2605.15311v1 Announce Type: new Abstract: The identification and modeling of time-varying systems is a fundamental challenge in signal processing and system identification. To address this chall

'Unlimited Realm of Exploration and Experimentation': Methods and Motivations of AI-Generated Sexual Content Creators

arXiv:2601.21028v2 Announce Type: replace-cross Abstract: AI-generated media is radically changing the way content is both consumed and produced on the internet, and in no place is this potentially mo

A Mutual Information Lower Bound for Multimodal Regression Active Learning

arXiv:2605.14917v1 Announce Type: new Abstract: Active learning for continuous regression has lacked an acquisition function that targets epistemic uncertainty when the predictive distribution is mult

Agentic Recommender System with Hierarchical Belief-State Memory

arXiv:2605.14401v1 Announce Type: cross Abstract: Memory-augmented LLM agents have advanced personalized recommendation, yet existing approaches universally adopt flat memory representations that conf

Applications

Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints

arXiv:2605.14067v1 Announce Type: new Abstract: Financial distress prediction remains a significant challenge in enterprise risk analysis due to the highly imbalanced nature of real-world financial da

applicationsarxiv-cs-lg

Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review

arXiv:2504.18544v3 Announce Type: replace-cross Abstract: Generating synthetic tabular health data is challenging, and evaluating their quality is equally, if not more, complex. This systematic review

Cross-Linguistic Transcription and Phonological Representation in the Huitongguanxi Huayiyiyu

arXiv:2605.14480v1 Announce Type: new Abstract: Purpose: This study investigates the transcription principles underlying Huitongguanxi Huayiyiyu (HHY), a series of multilingual glossaries compiled by

researcharxiv-cs-cl

FlowInOne:Unifying Multimodal Generation as Image-in, Image-out Flow Matching

model-releasesarxiv-cs-cv

arXiv:2604.06757v2 Announce Type: replace Abstract: Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason or create within it. We chal

Graph of States: Solving Abductive Tasks with Large Language Models

arXiv:2603.21250v2 Announce Type: replace Abstract: Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former

GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration

arXiv:2605.13848v1 Announce Type: new Abstract: Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer from hallucinated routi

Grokking Finite-Dimensional Algebra

arXiv:2602.19533v2 Announce Type: replace-cross Abstract: This paper investigates the grokking phenomenon, which refers to the sudden transition from a long memorization to generalization observed dur

MD-PNOP: Equation-Recast Neural Operators for Minimal-Data Extrapolation and PDE Solver Acceleration

model-releasesarxiv-cs-lg

arXiv:2509.01416v2 Announce Type: replace Abstract: The computational overhead of traditional numerical solvers for partial differential equations (PDEs) remains a critical bottleneck for large-scale

MechVerse: Evaluating Physical Motion Consistency in Video Generation Models

model-releasesarxiv-cs-cv

arXiv:2605.14843v1 Announce Type: new Abstract: Text- and image-conditioned video generation models have achieved strong visual fidelity and temporal coherence, but they often fail to generate motion

Mitigating Data Scarcity in Psychological Defense Classification with Context-Aware Synthetic Augmentation

arXiv:2605.14380v1 Announce Type: new Abstract: Psychological defense mechanisms (PDMs) are unconscious cognitive processes that modulate how individuals perceive and respond to emotional distress. Au

researcharxiv-cs-cl

nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI

model-releasesarxiv-cs-lg

arXiv:2605.14941v1 Announce Type: cross Abstract: Electroencephalogram (EEG) signals are highly susceptible to artifacts, resulting in a low signal-to-noise ratio which makes extraction of meaningful

Hardware

Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation

arXiv:2603.11045v2 Announce Type: replace-cross Abstract: Inverse problems for stiff parabolic partial differential equations (PDEs), such as the inverse heat conduction problem (IHCP), are severely i

hardwarearxiv-cs-ai

Non-linear Interventions on Large Language Models

arXiv:2605.14749v1 Announce Type: cross Abstract: Intervention is one of the most representative and widely used methods for understanding the internal representations of large language models (LLMs).

ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows

arXiv:2605.14113v1 Announce Type: cross Abstract: While interpretable prototype networks offer compelling case-based reasoning for clinical diagnostics, their raw continuous outputs lack the semantic

ROAD: Adaptive Data Mixing for Offline-to-Online Reinforcement Learning via Bi-Level Optimization

arXiv:2605.14497v1 Announce Type: cross Abstract: Offline-to-online reinforcement learning harnesses the stability of offline pretraining and the flexibility of online fine-tuning. A key challenge lie

S-AI-Recursive: A Bio-Inspired and Temporal Sparse AI Architecture for Iterative, Introspective, and Energy-Frugal Reasoning

arXiv:2605.13872v1 Announce Type: cross Abstract: This article introduces S-AI-Recursive, a bio-inspired Sparse Artificial Intelligence architecture in which reasoning is operationalized as a hormonal

Hardware

Synthetic Sociality: How Generative Models Privatize the Social Fabric

arXiv:2605.14090v1 Announce Type: cross Abstract: We put forth a critical theoretical framework for analyzing generative models both descriptively and normatively. Our thesis is that generative models

hardwarearxiv-cs-lg

Systematic Discovery of Semantic Attacks in Online Map Construction through Conditional Diffusion

arXiv:2605.14396v1 Announce Type: new Abstract: Autonomous vehicles depend on online HD map construction to perceive lane boundaries, dividers, and pedestrian crossings -- safety-critical road element

safetyarxiv-cs-cv

Watch your neighbors: Training statistically accurate chaotic systems with local phase space information

arXiv:2605.14405v1 Announce Type: new Abstract: Chaotic systems pose fundamental challenges for data-driven dynamics discovery, as small modeling errors lead to exponentially growing trajectory discre

local-aiarxiv-cs-lg

What Do AI Agents Talk About? Discourse and Architectural Constraints in the First AI-Only Social Network

model-releasesarxiv-cs-cl

arXiv:2603.07880v5 Announce Type: replace Abstract: Moltbook is the first large-scale social network built for autonomous AI agent-to-agent interaction. Early studies on Moltbook have interpreted its

Your CLIP has 164 dimensions of noise: Exploring the embeddings covariance eigenspectrum of contrastively pretrained vision-language transformers

arXiv:2605.14893v1 Announce Type: cross Abstract: Contrastively pre-trained Vision-Language Models (VLMs) serve as powerful feature extractors. Yet, their shared latent spaces are prone to structural

Agentic Interpretation: Lattice-Structured Evidence for LLM-Based Program Analysis

arXiv:2605.12694v1 Announce Type: cross Abstract: Large language models can consult information that fixed static analyzers cannot, such as documentation, current security advisories, version-specific

local-aiarxiv-cs-ai

14 May 2026

AgenticAITA: A Proof-Of-Concept About Deliberative Multi-Agent Reasoning for Autonomous Trading Systems

arXiv:2605.12532v1 Announce Type: cross Abstract: Conventional algorithmic trading systems are grounded in deterministic heuristics or offline-trained statistical models that cannot adapt to the seman