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MATT-CTR: Unleashing a Model-Agnostic Test-Time Paradigm for CTR Prediction with Confidence-Guided Inference Paths

model-releasesarxiv-cs-lg

arXiv:2510.08932v2 Announce Type: replace Abstract: Recently, a growing body of research has focused on either optimizing CTR model architectures to better model feature interactions or refining train

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Research

Max-Window Scale Estimation for Near-Lossless HiF8 W8A8 Quantization-Aware Training

DGX agent

arXiv:2605.26189v1 Announce Type: cross Abstract: Quantization-aware training (QAT) with low-bit floating-point formats enables efficient LLM deployment, yet introduces subtle failure modes invisible

researcharxiv-cs-ai

27 May 2026

Measuring Prediction Uncertainty in Neural Cellular Automata

arXiv:2605.26726v1 Announce Type: cross Abstract: Neural cellular automata (NCA) provide a lightweight alternative to encoder-decoder segmentation networks. However, it can be difficult to decide when

MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability

arXiv:2605.26343v1 Announce Type: new Abstract: Mechanistic interpretability has identified small sets of attention heads that implement specific behaviours in transformer language models, but recover

safetyarxiv-cs-lg

Med-CoReasoner: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning

arXiv:2601.08267v3 Announce Type: replace Abstract: While reasoning-enhanced large language models perform strongly on English medical tasks, a persistent multilingual gap remains, with substantially

MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis

arXiv:2603.01131v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise in clinical diagnosis but remain limited by unreliable report generation, weak evidence ground

MedGuideX: Internalizing Decision Logic from Executable Guidelines into Large Language Models for Clinical Reasoning

arXiv:2605.26567v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode evidence-based decision logic that clinicians apply by evaluating patient variables, conditional criteria, an

MedVol-R1: Reward-Driven Evidence Grounding for Volumetric Reasoning Segmentation

arXiv:2605.26621v1 Announce Type: cross Abstract: Volumetric Reasoning Segmentation (VRS) aims to segment a target region in a 3D medical scan from a free-form clinical query, where the referent is of

Applications

Membership Inference Risks in Quantized Models: A Theoretical and Empirical Study

arXiv:2502.06567v2 Announce Type: replace-cross Abstract: Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance lev

applicationsarxiv-cs-lg

MemFail: Stress-Testing Failure Modes of LLM Memory Systems

arXiv:2605.26667v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external memory systems to remain consistent across long-horizon interactions, but little empiric

MemMorph: Tool Hijacking in LLM Agents via Memory Poisoning

arXiv:2605.26154v1 Announce Type: cross Abstract: LLM-driven agents are capable of selecting external tools to complete users' tasks. However, attackers could compromise such process, steering agents

Memory Architectures for Multi-Turn Text-to-SQL: A Benchmark and Empirical Study

arXiv:2605.26394v1 Announce Type: new Abstract: Multi-turn Text-to-SQL is central to enterprise analytics yet remains predominantly evaluated in single-turn settings. We introduce EnterpriseMem-Bench,

Memory-Distilled Selection for Noise-Robust Anomaly Detection

arXiv:2605.26676v1 Announce Type: new Abstract: Anomaly detection (AD) under data contamination is critical for deploying unsupervised defect detection in industrial environments, where curating perfe

researcharxiv-cs-cv

MerLean-Prover: A Recursive Looping Harness for End-to-End Lean 4 Theorem Proving

arXiv:2605.26959v1 Announce Type: cross Abstract: MerLean-Prover is an end-to-end Lean4 theorem prover that replaces sorry declarations with kernel-checkable proofs. It is built from three agent types

Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling

arXiv:2505.18728v2 Announce Type: replace-cross Abstract: The recent success of State-Space Models (SSMs) in sequence modeling has motivated their adaptation to graph learning, giving rise to Graph St

Applications

MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022-2025)

arXiv:2509.09544v3 Announce Type: replace Abstract: Financial NLP has evolved rapidly since late 2022, outpacing narrative surveys. We introduce MetaGraph, a methodology for extracting typed knowledge

applicationsarxiv-cs-cl

MetaSICL: Adapting Audiroty LLM via Meta Speech In-Context Learning

arXiv:2601.18904v2 Announce Type: replace-cross Abstract: Auditory Large Language Models (LLMs) have demonstrated strong performance across a wide range of speech and audio understanding tasks. Nevert

METATR: A Multilingual, Evolving Benchmark for Automatic Text Recognition

model-releasesarxiv-cs-cv

arXiv:2605.26712v1 Announce Type: new Abstract: Benchmarks that reflect the diversity and complexity of real-world documents are essential for accurately evaluating Automatic Text Recognition (ATR) sy

Local Ai

MicroSpec: Accelerating Speculative Decoding with Lightweight In-Context Vocabularies

arXiv:2605.26444v1 Announce Type: new Abstract: Large language models typically employ vocabularies of over 100k tokens, which creates a major computational bottleneck at the final linear projection l

local-aiarxiv-cs-cl

Mildly Overparameterized ReLU Networks on Orthogonal Data: Incremental Learning and Implicit Bias

arXiv:2605.27097v1 Announce Type: new Abstract: The successful training of neural networks hinges on the use of first order optimization methods, yet the theoretical characterization of these methods

safetyarxiv-cs-lg

Mind the Tool Failures: Achieving Synergistic Tool Gains for Medical Agents

arXiv:2605.26691v1 Announce Type: new Abstract: Medical AI agents increasingly use external tools for diagnosis, treatment recommendation, and evidence retrieval, yet most existing approaches assume t

Minimal surfaces, Knots, and Neural Networks

arXiv:2605.26234v1 Announce Type: cross Abstract: A recent conjecture by Joel Fine posits a relationship between the coefficients of the HOMFLY polynomial of a knot K in the 3-sphere S^3, and the sign

researcharxiv-cs-lg

MiRD: Reliable Set-Valued Prediction for Open-Ended Question Answering via Miscoverage Risk Decomposition

arXiv:2605.27091v1 Announce Type: cross Abstract: Reliable set-valued prediction provides a principled way to mitigate hallucinations in open-ended question answering (QA), yet existing conformal appr

MobileExplorer: Accelerating On-Device Inference for Mobile GUI Agents via Online Exploration

arXiv:2605.26546v1 Announce Type: new Abstract: Mobile graphical user interface (GUI) agents enable AI models to autonomously operate smartphones on behalf of users. However, most existing systems foc

MobileMoE: Scaling On-Device Mixture of Experts

arXiv:2605.27358v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) has become the de facto architecture for hundred-billion-parameter language models, yet its advantages at sub-billion scales

Model discovery for dynamical systems with complex-valued product units

model-releasesarxiv-cs-cv

arXiv:2605.27158v1 Announce Type: new Abstract: Discovering the governing equations of a dynamical system from observed trajectories provides deeper insight into its structure than mere prediction of

Model Merging on Loss Landscape: A Geometry Perspective

arXiv:2605.26693v1 Announce Type: cross Abstract: Model merging offers a promising avenue for knowledge integration and parallel development without retraining. Yet, existing methods either ignore the

Tutorials

Model Unlearning Objectives Vary for Distinct Language Functions

arXiv:2605.26454v1 Announce Type: new Abstract: Large language models (LLMs) learn undesirable properties during pretraining, including dangerous knowledge and toxic text generation. Just as post-trai

tutorialsarxiv-cs-cl

Modeling Agentic Technical Debt and Stochastic Tax: A Standalone Framework for Measurement, Simulation, and Dashboarding

arXiv:2605.27320v1 Announce Type: new Abstract: Agentic AI systems combine probabilistic reasoning with delegated action through tools, context, memory, orchestration, and external workflow integratio

Modeling Dynamic Mixtures of Time-Delay Systems from Streaming Time Series

arXiv:2605.26191v1 Announce Type: cross Abstract: This research addresses the problem of adaptive modeling in time-series data streams with clear input-output relationships. This problem is challengin

Modernising Reinforcement Learning-Based Navigation for Embodied Semantic Scene Graph Generation

arXiv:2603.25415v2 Announce Type: replace Abstract: Semantic world models enable embodied agents to reason about objects, relations, and spatial context beyond purely geometric representations. In Org

MolPIF: A Parameter Interpolation Flow Model for Molecule Generation

model-releasesarxiv-cs-lg

arXiv:2507.13762v4 Announce Type: replace Abstract: Motivation: Structure-based drug design (SBDD) has advanced with deep generative models, but bridging the gap between continuous atomic coordinates

Local Ai

MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training

arXiv:2605.26842v1 Announce Type: cross Abstract: The Muon optimizer has recently offered a promising alternative to AdamW for large language model training, leveraging matrix orthogonalization to pro

local-aiarxiv-cs-cl

Monte Carlo Permutation Search

arXiv:2510.06381v2 Announce Type: replace-cross Abstract: We propose Monte Carlo Permutation Search (MCPS), a general-purpose Monte Carlo Tree Search (MCTS) algorithm that improves upon the GRAVE algo

More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations

arXiv:2605.26647v1 Announce Type: cross Abstract: Feedforward network (FFN) layers account for a large fraction of parameters and nonlinear expressivity in Transformer-based large language models (LLM

Hardware

Morphling: Fast, Fused, and Flexible GNN Training at Scale

arXiv:2512.01678v5 Announce Type: replace Abstract: Graph Neural Networks (GNNs) present a fundamental hardware challenge by fusing irregular, memory-bound graph traversals with regular, compute-inten

hardwarearxiv-cs-lg

Applications

MotionPRO: Exploring the Role of Pressure in Human MoCap and Beyond

arXiv:2504.05046v2 Announce Type: replace Abstract: Existing human Motion Capture (MoCap) methods mostly focus on the visual similarity while neglecting the physical plausibility. As a result, downstr

applicationsarxiv-cs-cv

MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale

model-releasesarxiv-cs-cv

arXiv:2605.27235v1 Announce Type: new Abstract: Layered image generation and editing is a fundamental capability that enables layer-wise reuse, editing, and composition of generated visual content, an

MSCGC-KAN: Multi-scale Causal Graph Convolution and Kolmogorov-Arnold Feature Mapping for EEG Emotion Recognition

arXiv:2605.26624v1 Announce Type: new Abstract: Electroencephalogram (EEG)-based emotion recognition is an important affective computing task, and recent EEG foundation models provide useful generic r

researcharxiv-cs-cv

MTL-FNO: A Lightweight Multi-Task Fourier Neural Operator for Sparse Field Reconstruction

model-releasesarxiv-cs-lg

arXiv:2605.26718v1 Announce Type: new Abstract: Efficient onboard multi-field sparse reconstruction is essential for the autonomous operation of aerospace vehicles. While existing deep learning models

MuCon: Clipped Muon Updates for LLM Training

arXiv:2605.26459v1 Announce Type: new Abstract: Muon-style optimizers take a matrix-valued momentum or preconditioned update B = U operatorname{diag}(sigma_1,ldots,sigma_r) V^op and replace it with it

researcharxiv-cs-lg

Multi-Agent Causal Discovery Using Large Language Models

arXiv:2407.15073v4 Announce Type: replace Abstract: Causal discovery aims to identify causal relationships between variables and is a fundamental problem across the sciences. Traditional statistical c

Multi-Modal Building Inspection via Perceiver IO Fusion of Satellite and Street-Level Imagery

arXiv:2605.26381v1 Announce Type: new Abstract: We present a multi-modal classification framework that fuses satellite and street-level imagery through a Perceiver IO architecture operating on spatial

researcharxiv-cs-cv

Hardware

Multi-Robot Box Transport over Different Surfaces with Decentralized Role-based Proportional Control

arXiv:2605.26430v1 Announce Type: new Abstract: Collaborative transport of objects via pushing by multiple robots has many applications, ranging from construction and warehouse environments to post di

hardwarearxiv-cs-ro

Multi-Stakeholder LLM Alignment: Decomposing Estimation from Aggregation

arXiv:2605.26878v1 Announce Type: new Abstract: Multi-stakeholder tasks require one output to satisfy users with conflicting preferences. Holistic LLM judges conflate utility estimation and utility ag

MULTISEISMO: A Multimodal Seismic Dataset and Model for Cross-Modal Seismic Understanding

arXiv:2605.26320v1 Announce Type: cross Abstract: The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-s

MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation

arXiv:2605.27366v1 Announce Type: new Abstract: Large language model (LLM) agents rely on reusable skills to solve complex tasks. However, existing skill creation approaches treat skills as isolated a

MVISTA-4D: View-Consistent 4D World Model with Test-Time Action Inference for Robotic Manipulation

arXiv:2602.09878v2 Announce Type: replace Abstract: World-model-based imagine-then-act becomes a promising paradigm for robotic manipulation, yet existing approaches typically support either purely im

safetyarxiv-cs-cv