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arXiv:2608.25810v1 Announce Type: new Abstract: Foundation models are increasingly deployed for medical image analysis. However, under the inter-institutional distribution shift typical of deployment,

applicationsarxiv-cs-cv

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LAC: Linear and Angular Compliance for Humanoid Whole-body Control

DGX agent

arXiv:2608.25405v1 Announce Type: new Abstract: Real-world humanoid tasks involve physical interaction with objects and humans, yet current controllers either reject external forces as disturbances or

safetyarxiv-cs-ro

27 Aug 2026

Ladder Up, Memory Down: Low-Cost Fine-Tuning With Side Nets

arXiv:2512.14237v2 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) is often limited by the memory available on commodity GPUs. Parameter-efficient fine-tuning (PEFT) methods

Large Language Model Few-Shot Prompting with Dilemma Training Outperforms Human Surrogates in Predicting Patient Preferences

arXiv:2608.25771v1 Announce Type: cross Abstract: In serious illness, human surrogates often struggle to accurately predict patient preferences (68% accuracy), causing decision conflict. Personalized

safetyarxiv-cs-lg

Applications

Layer-Order Inversion: Rethinking Latent Multi-Hop Reasoning in Large Language Models

arXiv:2601.03542v2 Announce Type: replace Abstract: Large language models (LLMs) perform well on multi-hop reasoning, yet how they internally compose multiple facts remains unclear. Recent work propos

applicationsarxiv-cs-cl

LDAC-Net: A Learnable Multi-Lag Differencing Attention-Convolution Network for Drift-Robust Recognition with Low-Cost MOX Gas Sensors

arXiv:2608.25646v1 Announce Type: new Abstract: Portable electronic-nose systems based on low-cost metal-oxide (MOX) gas sensors offer a practical solution for gas and odour recognition, but their sig

Learning Continuous Regional Temperature Fields with Lead-Time and Resolution Queries

arXiv:2608.25823v1 Announce Type: new Abstract: Accurate regional near-surface temperature forecasting is fundamental to short-range weather services and downstream risk assessment. Existing deep lear

Learning from waste: Machine Learning for health risk prediction and computer vision-based sorting in Ghana

arXiv:2608.25759v1 Announce Type: cross Abstract: The inappropriate disposal of solid waste remains a significant public health and environmental concern worldwide, including in Ghana. Poor sanitation

Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation

arXiv:2608.25862v1 Announce Type: new Abstract: Demographic imbalance in synthetic face generation can propagate to downstream face recognition systems, making fairness an important consideration when

safetyarxiv-cs-cv

Learning Mixtures of Plackett-Luce Models for Multi-Objective Alignment

arXiv:2608.25200v1 Announce Type: cross Abstract: We consider the problem of learning a mixture of k Plackett-Luce models given multi-way ranking responses from annotators that may represent heterogen

safetyarxiv-cs-cl

Learning New Facts with QLoRA: An Acquisition-Retention Frontier

arXiv:2608.25677v1 Announce Type: new Abstract: Parameter-efficient fine-tuning is often assumed to preserve pretrained capabilities because it updates only a small number of parameters. We show that

Learning spatially varying regularisation parameters of low regularity for image reconstruction

arXiv:2608.25127v1 Announce Type: cross Abstract: In this chapter, we review and discuss the regularity properties of spatially adaptive regularisation weight functions used in variational image recon

local-aiarxiv-cs-cv

Learning to Accelerate Vision-Language-Action Models through Adaptive Visual Token Caching

arXiv:2602.00686v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable generalization capabilities in robotic manipulation tasks, yet their substantial co

safetyarxiv-cs-ro

Learning to Detect Unseen Jailbreak Attacks in Large Vision-Language Models

arXiv:2508.09201v5 Announce Type: replace-cross Abstract: Despite extensive alignment efforts, Large Vision-Language Models (LVLMs) remain vulnerable to jailbreak attacks. To mitigate these risks, exi

safetyarxiv-cs-cv

Learning to Prompt: Improving Student Engagement with Adaptive LLM-based High-School Tutoring

arXiv:2606.20138v2 Announce Type: replace-cross Abstract: LLMs can personalize education, although current static-prompt tutoring systems struggle to adapt to diverse academic disciplines. We develop

Learning to summarize user information for personalized reinforcement learning from human feedback

arXiv:2507.13579v4 Announce Type: replace Abstract: As everyday use cases of large language model (LLM) AI assistants have expanded, it is becoming increasingly important to personalize responses to a

Learning What to Share and What to Personalize: Hierarchical Strategy Co-Evolution for Agent Memory

arXiv:2608.25329v1 Announce Type: cross Abstract: Memory-augmented agents maintain compact user profiles throughout extended conversations, enabling personalized and consistent responses without the n

safetyarxiv-cs-cl

Less can be More: Relieving RAG Bottlenecks via Evidence Frontloading and Pressure-Adaptive Budgeting

arXiv:2608.25115v1 Announce Type: new Abstract: Existing methods for improving Retrieval-Augmented Generation (RAG) efficiency mainly optimize downstream LLM generation, such as context compression or

researcharxiv-cs-cl

Less Contouring, More Accuracy: Lesion-Guided ROI Deep Learning for Ovarian Ultrasound Classification

arXiv:2608.25965v1 Announce Type: new Abstract: Ovarian lesion classification using transvaginal ultrasound remains challenging due to overlapping imaging characteristics and the dependence on expert

Applications

Leveraging Inter-object Affordances for Efficient Planning in Contact-rich Tasks

arXiv:2608.25641v1 Announce Type: new Abstract: Traditional task-and-motion planning (TAMP) approaches primarily focus on defining sequences of actions along with the necessary geometric and kinematic

applicationsarxiv-cs-ro

Leveraging Speech Acts for Low-Data and Cross-Domain Conversation Derailment Forecasting

arXiv:2608.25359v1 Announce Type: new Abstract: Conversational derailment forecasting aims to predict when online discussions will escalate into hostility, enabling proactive moderation. Existing appr

researcharxiv-cs-cl

LibriBrain100: One Hundred Hours of Broad and Deep MEG Data for Neural Speech Decoding at Scale

arXiv:2608.25204v1 Announce Type: cross Abstract: We introduce LibriBrain100, a large-scale MEG dataset for speech decoding designed from the ground up for reproducible, standardised evaluation. Libri

Lightweight Machine Learning-Driven Monocular Sidewalk Path Extraction for Embedded Micromobility Navigation

arXiv:2608.25178v1 Announce Type: new Abstract: Sidewalk-scale path extraction demands perception and planning that run reliably on compact, low-power hardware in cluttered, map-sparse environments. W

safetyarxiv-cs-cv

LLMTrace: A Corpus for Classification and Fine-Grained Localization of AI-Written Text

arXiv:2509.21269v2 Announce Type: replace Abstract: The widespread use of human-like text from Large Language Models (LLMs) necessitates the development of robust detection systems. However, progress

local-aiarxiv-cs-cl

LM-X: Explainable Action Modeling with Progress, Event, and Uncertainty Prediction for Generalist Robot Manipulation

arXiv:2608.25757v1 Announce Type: cross Abstract: Generalist vision--language--action (VLA) policies learn long-horizon behavior mainly through short-horizon action prediction and reveal little beyond

local-aiarxiv-cs-lg

Localize-Then-Decide Guarantees for LLM Judgments

arXiv:2608.25824v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as evaluators to assess output quality and preference alignment, yet providing reliable guarantees of

local-aiarxiv-cs-cl

Tutorials

Long-Term Behavioral Evaluation for Trusted Collaborator Selection via Bidirectional Mamba

arXiv:2608.25232v1 Announce Type: new Abstract: Effective selection of trustworthy collaborators is crucial to ensuring the successful completion of collaborative tasks, which requires accurate assess

tutorialsarxiv-cs-lg

Longitudinal Robot Learning from Demonstration with Care Providers in a Home Environment

arXiv:2608.25196v1 Announce Type: new Abstract: Learning from demonstration (LfD) methods enable non-expert end users to teach robots novel skills without explicit programming. However most evaluation

researcharxiv-cs-ro

LongVU-TTT: Causal Test-Time Training for Visual Resampling in Long Video Understanding

arXiv:2608.25729v1 Announce Type: new Abstract: Long-video MLLMs must model temporal change before a limited visual-token budget removes most frame evidence. We introduce LongVU-TTT, which inserts a c

Loss-Based Active Learning for Neural Abstractive Summarization

arXiv:2608.25881v1 Announce Type: new Abstract: Fine-tuning abstractive summarization models requires high-quality annotated data. However, obtaining such corpora is expensive and time-consuming, as i

Loss Landscape Geometry of Partial Differential Equation Emulators: Or, Symmetry Learning via Gradient Alignment

arXiv:2601.20172v2 Announce Type: replace Abstract: We study how neural emulators of partial differential equation solution operators internalize physical symmetries by introducing an influence-based

Lost but not erased: Finding traces of a forgotten language in neural speech models

arXiv:2608.25976v1 Announce Type: new Abstract: International adoptees retain phonological traces of a birth language they can no longer speak or comprehend, a persistence typically attributed to a bi

researcharxiv-cs-cl

Low-Resolution Perception for Robotic Packing

arXiv:2608.25874v1 Announce Type: new Abstract: This work tackles the problem of scalable perception for robotic packing with low-cost, low-resolution depth sensing. We propose a framework where recon

researcharxiv-cs-ro

Lower-Resource, Higher Scores: Language Bias in LLM Evaluators

arXiv:2607.14480v3 Announce Type: replace Abstract: LLM evaluators (trained reward models and prompted LLM-as-a-Judge) are routinely validated via pairwise accuracy. In a multilingual setting, this op

safetyarxiv-cs-cl

Lowering the Barrier to AI-Driven Inspection: A No-Code Workflow for Automated Structural Defect Detection

arXiv:2608.25176v1 Announce Type: new Abstract: Structural health monitoring (SHM) is essential in modern engineering, providing data for condition-based maintenance, lifecycle assessment, and predict

LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation

model-releasesarxiv-cs-cv

arXiv:2608.25866v1 Announce Type: new Abstract: Quantifying wound tissue composition is essential for monitoring chronic ulcer progression and guiding treatment decisions. However, pixel-level annotat

M-Fibration Theory with Applications to Neural Network Compression

arXiv:2608.25598v1 Announce Type: new Abstract: The purpose of this paper is to provide a general, comprehensive, theoretical framework that allows one to deal with fibrations on graphs labelled on a

researcharxiv-cs-lg

MA-VLA: Multi-Arm Vision-Language-Action Model for Collaboration and Compositional Generalization

model-releasesarxiv-cs-ro

arXiv:2608.25864v1 Announce Type: new Abstract: Multi-arm collaboration is becoming a core capability in embodied manipulation. Recent vision-language-action (VLA) models integrate perception, languag

Agents

MacroAgent: Regularity-Aware Macro Legalization with LLM-Agent-Designed Contour Algorithms

arXiv:2608.24946v1 Announce Type: new Abstract: Macros constitute a large part of the core area in modern very large-scale integration (VLSI) designs. Moreover, macro positions have a significant impa

agentsarxiv-cs-lg

MAMA-FLUX.2: Image-to-Image Synthesis of Post-Contrast Breast DCE-MRI for the MAMA-SYNTH Challenge

model-releasesarxiv-cs-cv

arXiv:2608.25648v1 Announce Type: new Abstract: Dynamic contrast-enhanced breast MRI is central to cancer diagnosis and monitoring, but requires gadolinium-based contrast agents. In this work, we addr

MathAdv: What Theorem Provers Know, Reason, Formalize, and Generalize

arXiv:2608.25449v1 Announce Type: new Abstract: Formal theorem proving enables machine-verifiable evaluation of mathematical reasoning, yet existing benchmarks often emphasize aggregate proof accuracy

Maximum-Volume Nonnegative Matrix Factorization

arXiv:2602.04795v3 Announce Type: replace Abstract: Nonnegative matrix factorization (NMF) is a popular data embedding technique. Given a nonnegative data matrix X, it aims at finding two lower dimens

researcharxiv-cs-lg

MeMark: Membrane-Space Watermarking for Spiking Neural Networks

arXiv:2608.25738v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are increasingly distributed as pretrained checkpoints and reused as backbones for new tasks. However, current SNN wate

Memory-V2V: Memory-Augmented Video-to-Video Diffusion for Consistent Multi-Turn Editing

arXiv:2601.16296v3 Announce Type: replace Abstract: Video-to-video diffusion models achieve impressive single-turn editing performance, but practical editing workflows are inherently iterative. When e

MetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection

arXiv:2608.25903v1 Announce Type: cross Abstract: Relational Deep Learning (RDL) is an effective approach to machine learning over multi-table relational databases. In RDL, a database is modeled as a

MIMONet: Multi-scale Input and Multi-scale Output Network for Salient Object Detection

model-releasesarxiv-cs-cv

arXiv:2608.25733v1 Announce Type: new Abstract: The existing methods for saliency detection task focus on the application of multi-level features, aiming to take advantage of the respective strengths

Mind2Report: Expert-Level Commercial Report Synthesis via Cognitive Deep Research Agent

arXiv:2601.04879v2 Announce Type: replace Abstract: Synthesizing informative commercial reports from massive and noisy web sources is critical for high-stakes business decisions. Although recent deep

Minimax Alternating Regret for the Experts Problem and Online Convex Optimization

arXiv:2608.25182v1 Announce Type: cross Abstract: In this paper, we study alternating regret in online convex optimization (OCO), motivated by the success of alternating learning dynamics in two-playe

researcharxiv-cs-lg