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Latent Cache Flow: Model-to-Model Communication Without Text

arXiv:2605.22863v1 Announce Type: new Abstract: LLM agents today communicate via text, which incurs considerable latency and information loss due to the need to autoregressively decode the sharer mode

agentsarxiv-cs-lg

Learnability-Informed Fine-Tuning of Diffusion Language Models

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DGX agent

arXiv:2605.22939v1 Announce Type: new Abstract: We aim to improve the reasoning capabilities of diffusion language models (DLMs). While SFT is a popular post-training recipe for autoregressive models,

tutorialsarxiv-cs-cl

25 May 2026

Learned Relay Representations for Forward-Thinking Discrete Diffusion Models

arXiv:2605.22967v1 Announce Type: new Abstract: When Masked Diffusion Models (MDMs) generate sequences through iterative refinement, the rich internal computation over masked positions is discarded, f

Learning a Particle Dynamics Model with Real-world Videos

arXiv:2605.23845v1 Announce Type: new Abstract: Data-driven learning approaches for physics simulation, sometimes referred to as world models, have emerged as promising alternatives to traditional phy

tutorialsarxiv-cs-cv

Learning-Augmented Online Scheduling with Parsimonious Preemption

arXiv:2605.23255v1 Announce Type: new Abstract: Learning-augmented algorithms have emerged as a powerful paradigm to surpass traditional worst-case lower bounds by integrating potentially noisy predic

Learning Decision-Sufficient Representations for Linear Optimization

arXiv:2603.18551v2 Announce Type: replace-cross Abstract: We study how to construct compressed datasets that suffice to recover optimal decisions in linear programs with an unknown cost vector c lying

Learning Dynamic Stability Landscapes in Synchronization Networks

arXiv:2605.23708v1 Announce Type: new Abstract: The robustness of synchronization is typically characterized by scalar, per-node stability indices whose dependence on topology is studied via network s

Applications

Learning Individual Dynamics from Sparse Cross-Sectional Snapshots

arXiv:2605.23470v1 Announce Type: cross Abstract: Predicting how a dynamical unit evolves over time - how an individual ages, an epidemic spreads, or a physical system degrades - typically requires de

applicationsarxiv-cs-ai

Learning Kernel-Based MDPs from Episodic Preferential Feedback

arXiv:2605.23650v1 Announce Type: cross Abstract: Human feedback often arrives as preferences rather than calibrated numeric rewards, motivating reinforcement learning from preferential feedback, also

safetyarxiv-cs-lg

Learning partially observed systems with neural Hamiltonian ordinary differential equations

arXiv:2605.23510v1 Announce Type: new Abstract: When learning dynamical systems from data, embedding physical structure can constrain the solution space and improve generalization, but many physics-in

Learning Safely Without Knowing the World:COMPASS-Hedge

arXiv:2603.22348v3 Announce Type: replace Abstract: Online learning algorithms often face a fundamental trilemma: balancing regret guarantees between adversarial and stochastic settings and providing

Learning Through Noise: Why Subliminal Learning Works and When It Fails

arXiv:2605.23645v1 Announce Type: cross Abstract: In the context of artificial neural networks, subliminal learning refers to the transfer of task-relevant knowledge or unintended biases from teacher

Less Effort, Shorter Proofs: Reinforcement Learning for Security Protocol Analysis in Tamarin

arXiv:2605.23643v1 Announce Type: cross Abstract: Tools like Tamarin and ProVerif have achieved notable success in analyzing and verifying complex real-world protocols such as EMV, 5G, and WPA2, even

Leveraging Foundation Models for Causal Generative Modeling

arXiv:2605.23861v1 Announce Type: cross Abstract: Causal generative modeling is essential for developing reliable and transparent AI systems capable of counterfactual reasoning. While existing approac

LFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document Understanding

arXiv:2605.22829v1 Announce Type: cross Abstract: Multimodal Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for enhancing Large Language Models (LLMs) with external knowledg

Linear Regression with Unknown Truncation Beyond Gaussian Features

arXiv:2602.12534v2 Announce Type: replace-cross Abstract: In truncated linear regression, samples (x,y) are shown only when the outcome y falls inside a certain survival set S^star and the goal is to

Lipschitz Optimization for Formal Verification of Homographies

arXiv:2605.23203v1 Announce Type: cross Abstract: The adoption of vision neural networks in regulated industries requires formal robustness guarantees, especially in safety-critical domains such as he

LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics

arXiv:2601.18685v3 Announce Type: replace-cross Abstract: The capabilities of generative AI in mathematics education are rapidly evolving, posing significant challenges for research to keep pace. Rese

LLM Code Smells: A Taxonomy and Detection Approach

arXiv:2605.22976v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and abilit

LLM-driven design of physics-constrained constitutive models: two agents are better than one

arXiv:2605.23754v1 Announce Type: new Abstract: Developing constitutive models that capture how materials deform under load traditionally requires years of specialized expertise in continuum mechanics

LLM Sparsity Prior for Robust Feature Selection

arXiv:2605.23102v1 Announce Type: cross Abstract: Large language models (LLMs) offer a scalable mechanism to elicit domain-informed prior information for high-dimensional variable selection. However,

LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws

arXiv:2605.23901v1 Announce Type: cross Abstract: Existing scaling laws for Large Language Models (LLMs), predominantly monotonic power laws, fail to explain emerging non-monotonic phenomena such as c

Low-Cost Hard-Label Adversarial Attack with Theoretical Foundations

arXiv:2601.14300v3 Announce Type: replace Abstract: Hard-label black-box attacks, relying solely on top-1 predictions, represent one of the most challenging yet practically threat models. Despite rece

LQ-rPPG: A Label-Quantized Coarse-to-Fine Learning Framework for Remote Physiological Measurement

model-releasesarxiv-cs-cv

arXiv:2605.23174v1 Announce Type: new Abstract: Remote photoplethysmography (rPPG) enables non-contact measurement of physiological signals from facial videos, offering strong potential for remote hea

Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset

arXiv:2605.23777v1 Announce Type: new Abstract: The grading of gemstones is currently a manual procedure performed by gemologists. A popular approach uses reference stones, where those are visually in

MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models

arXiv:2605.23007v1 Announce Type: cross Abstract: We explore the application of LLM-driven algorithm optimization to several common tasks in quantitative finance. MadEvolve, a general-purpose algorith

MaMa: A Game-Theoretic Approach for Designing Safe Agentic Systems

arXiv:2602.04431v2 Announce Type: replace Abstract: LLM-based multi-agent systems have demonstrated impressive capabilities, but they also introduce significant safety risks when individual agents fai

safetyarxiv-cs-lg

MapGCLR: Geospatial Contrastive Learning of Representations for Online Vectorized HD Map Construction

arXiv:2603.10688v2 Announce Type: replace-cross Abstract: Autonomous vehicles rely on map information to understand the world around them. However, the creation and maintenance of offline high-definit

agentsarxiv-cs-cv

MARGIN: Runtime Confidence Calibration for Multi-Agent Foundation Model Coordination

arXiv:2605.22949v1 Announce Type: new Abstract: Foundation model agents increasingly operate in multi-agent deployments where a coordinator must decide which agent's response to trust. The standard ap

agentsarxiv-cs-lg

MARS: Magnitude-Aware Rank Statistics

arXiv:2605.23563v1 Announce Type: new Abstract: Comprehensive evaluation of machine learning models is the key to make sure that they perform as robustly and consistently as desired. In order to summa

MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks

arXiv:2601.14652v5 Announce Type: replace Abstract: While multi-agent systems (MAS) promise elevated intelligence through coordination of agents, current approaches to automatic MAS design under-deliv

MDS-DETR: DETR with Masked Duplicate Suppressor

arXiv:2605.23507v1 Announce Type: new Abstract: The DEtection TRansformer (DETR) is a powerful end-to-end object detector, yet its one-to-one matching strategy suffers from slow convergence and low re

MedExpMem: Adapting Experience Memory for Differential Diagnosis

arXiv:2605.22872v1 Announce Type: cross Abstract: Experienced physicians develop diagnostic expertise through clinical practice, acquiring not only disease knowledge but also the ability to differenti

Mediative Fuzzy Logic: From Type-1 Foundations to Type-2, Type-3 and Quantum Extensions

arXiv:2605.22900v1 Announce Type: new Abstract: Mediative Fuzzy Logic was conceived as a practical scheme for reconciling hesitant or conflicting assessments in fuzzy control and decision-making. Howe

safetyarxiv-cs-ai

Applications

MedSAE: Dissecting MedCLIP Representations with Sparse Autoencoders

arXiv:2510.26411v2 Announce Type: replace Abstract: Artificial intelligence in healthcare requires models that are accurate and interpretable. We advance mechanistic interpretability in medical vision

applicationsarxiv-cs-ai

MELT: A Behavioral Trace Dataset for High-Risk Memecoin Launch Detection

arXiv:2602.13480v2 Announce Type: cross Abstract: Launchpads have become the dominant mechanism for issuing memecoins, exposing investors to a new class of high-risk launches that existing rug-pull de

MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection

arXiv:2605.23723v1 Announce Type: new Abstract: Large language model agents increasingly rely on persistent memory to store past interactions, retrieve relevant demonstrations, and improve long-horizo

agentsarxiv-cs-ai

Memorization Dynamics of Fill-in-the-Middle Pretraining

arXiv:2605.22981v1 Announce Type: cross Abstract: Fill-in-the-middle (FIM) is a pretraining objective widely used to equip causal language models with infilling ability, yet its effect on verbatim mem

Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals

arXiv:2605.23384v1 Announce Type: cross Abstract: Recent RL methods have substantially improved the reasoning abilities of LLMs. Existing reward designs mainly follow two paradigms: (1) Reinforcement

Metadata Predictability Is Not Evidence Dependence: An Intervention-Based Audit for Weak-Label Benchmarks

model-releasesarxiv-cs-cl

arXiv:2605.23701v1 Announce Type: new Abstract: We study a protocol-level test for weak-label benchmarks: whether benchmark outputs change when the provided evidence is intervened on. Metadata-only sh

Millimeter-wave Imaging for Anthropometric Body Measurement

arXiv:2605.23064v1 Announce Type: new Abstract: Body shape and circumferences are clinically informative biomarkers for risk stratification, including measures such as waist to hip ratio, limb and tru

safetyarxiv-cs-cv

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models

arXiv:2406.09250v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly susceptible to sophisticated adversarial attacks, including adaptive strategies specifically de

Mitigating Object Hallucinations via Sentence-Level Early Intervention

arXiv:2507.12455v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have revolutionized cross-modal understanding but continue to struggle with hallucinations - fabricated con

Model Collapse as Cultural Evolution

arXiv:2605.23054v1 Announce Type: cross Abstract: Model collapse, the progressive degradation of LLMs trained on their own outputs, has been characterized statistically but lacks a linguistic explanat

ModeSwitch-LLM: A Lightweight Phase-Aware Controller for Cross-Mode LLM Inference on a Single GPU

model-releasesarxiv-cs-cl

arXiv:2605.23057v1 Announce Type: cross Abstract: ModeSwitch-LLM is a lightweight request-boundary controller for improving single-GPU large language model inference efficiency by routing each request

Moonwalk: Inverse-Forward Differentiation

arXiv:2402.14212v4 Announce Type: replace-cross Abstract: Backpropagation's main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth

Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer

arXiv:2605.23871v1 Announce Type: cross Abstract: We develop a gradient flow on the space of probability measures defined on matrix-valued parameters induced by regularized Muon, an analytically smoot

MuellerPT: Decomposition Driven Pretraining for Dense Learning in Mueller Polarimetry

arXiv:2605.23840v1 Announce Type: new Abstract: Mueller matrix imaging provides rich, physically meaningful contrast for biomedical tissue analysis, but supervised learning is hindered by scarce dense