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Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement

arXiv:2409.02428v4 Announce Type: replace-cross Abstract: Achieving the effective design and improvement of reward functions in reinforcement learning (RL) tasks with complex custom environments and m

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Large Language Models and Impossible Language Acquisition: 'False Promise' or an Overturn of our Current Perspective towards AI

DGX agent

arXiv:2602.08437v5 Announce Type: replace Abstract: In Chomsky's provocative critique 'The False Promise of CHATGPT,' Large Language Models (LLMs) are characterized as mere pattern predictors that do

researcharxiv-cs-cl

19 May 2026

Latent Heuristic Search: Continuous Optimization for Automated Algorithm Design

arXiv:2605.17137v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into evolutionary frameworks has established a new paradigm for automated heuristic discovery. Despite t

Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators

arXiv:2601.20888v3 Announce Type: replace-cross Abstract: We study sampling from posterior distributions in Bayesian linear inverse problems where A, the parameters to observables operator, is computa

Lean Meets Theoretical Computer Science: Scalable Synthesis of Theorem Proving Challenges in Formal-Informal Pairs

arXiv:2508.15878v2 Announce Type: replace-cross Abstract: Formal theorem proving (FTP) has emerged as a critical foundation for evaluating the reasoning capabilities of large language models, enabling

Learning Higher-Order Structure from Incomplete Spatiotemporal Data: Multi-Scale Hypergraph Laplacians with Neural Refinement

arXiv:2605.17316v1 Announce Type: cross Abstract: Sensor networks increasingly govern modern infrastructure, yet the data they lose are rarely missing in the uniform-random patterns assumed by standar

Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects

arXiv:2605.17238v1 Announce Type: new Abstract: We study the dynamic joint assortment selection and positioning problem, where the attraction of each product depends on both its intrinsic appeal and i

Learning Lifted Action Models from Traces with Minimal Information About Actions and States

arXiv:2605.18627v1 Announce Type: new Abstract: It has been recently shown that lifted STRIPS models can be learned correctly and efficiently from action traces alone; i.e., applicable action sequence

Learning more physically realistic dynamics in machine-learning based weather forecasting with latent-space constraints

arXiv:2510.04006v2 Announce Type: replace Abstract: Data-driven machine learning (ML) models are reshaping weather forecasting and have shown the potential to accelerate and surpass traditional physic

Learning Normal Representations for Blood Biomarkers

arXiv:2605.18701v1 Announce Type: new Abstract: Blood-based biomarkers underpin clinical diagnosis and management, yet their interpretation relies largely on fixed population reference intervals that

Learning over Positive and Negative Edges with Contrastive Message Passing

arXiv:2605.17854v1 Announce Type: new Abstract: Conventional approaches to learning on graphs involve message passing along existing (i.e., positive) edges to update node features. However, these appr

Learning Quantifiable Visual Explanations Without Ground-Truth

arXiv:2605.18681v1 Announce Type: new Abstract: Explainable AI (XAI) techniques are increasingly important for the validation and responsible use of modern deep learning models, but are difficult to e

Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries

arXiv:2602.21707v2 Announce Type: replace-cross Abstract: State-of-the-art learned reconstruction methods often rely on black-box modules that, despite their strong performance, raise questions about

Learning to Look Benign: Targeted Evasion of Malware Detectors via API Import Injection

arXiv:2605.18624v1 Announce Type: cross Abstract: Machine learning-based malware detectors are widely deployed in antivirus and endpoint detection systems, yet their reliance on static features makes

Learning Unbiased Permutations via Flow Matching

arXiv:2605.16755v1 Announce Type: cross Abstract: Learning permutations is fundamental to sorting, ranking, and matching, but existing differentiable methods based on entropy-regularized Sinkhorn prod

Learning Variable-Length Tokenization for Generative Recommendation

arXiv:2605.17779v1 Announce Type: new Abstract: Generative recommendation reformulates recommendation as next-token prediction over discrete semantic identifiers (IDs). A fundamental yet unexplored de

Lever: Speculative LLM Inference on Smartphones

arXiv:2605.16786v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly needed for interactive mobile applications, but high-quality models exceed the limited DRAM available on s

Leveraging Error Diversity in Group Rollouts for Reinforcement Learning

arXiv:2605.17333v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) typically samples multiple responses per prompt and assigns binary rewards based on individual cor

Leveraging Graph Structure in Seq2Seq Models for Knowledge Graph Link Prediction

arXiv:2605.18211v1 Announce Type: cross Abstract: We introduce Graph-Augmented Sequence-to-Sequence (GA-S2S), a novel framework that integrates a T5-small encoder-decoder with a Relational Graph Atten

Leveraging Multimodal Self-Consistency Reasoning in Coding Motivational Interviewing for Alcohol Use Reduction

arXiv:2605.12987v2 Announce Type: replace Abstract: BACKGROUND: Coding Motivational Interviewing (MI) sessions is essential for understanding client behaviors and predicting outcomes, but it requires

researcharxiv-cs-cl

Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection

arXiv:2409.15980v2 Announce Type: replace-cross Abstract: Traditional machine learning-based visual inspection systems require extensive data collection and repetitive model training to improve accura

Lightweight Physics-Aware Zero-Shot Ultrasound Plane-Wave Denoising

arXiv:2506.21499v2 Announce Type: replace-cross Abstract: Ultrasound Coherent Plane-Wave Compounding (CPWC) enhances image contrast by combining echoes from multiple steered transmissions. While incre

LightZeroNav: Zero-Shot Vision Language Navigation in Continuous Environments Based on Lightweight VLMs

arXiv:2603.16947v2 Announce Type: replace-cross Abstract: Although vision-language navigation (VLN) has progressed rapidly, zero-shot VLN in continuous environments (VLN-CE) remains highly challenging

Lipschitz-Guided Design of Interpolation Schedules in Generative Models

arXiv:2509.01629v3 Announce Type: replace-cross Abstract: We study the design of interpolation schedules in flow and diffusion-based generative models from both statistical and numerical perspectives.

LISA: Language-guided Interference-aware Spatial-Frequency Attention for Driver Gaze Estimation

arXiv:2605.17287v1 Announce Type: new Abstract: Driver gaze estimation serves as a fundamental metric for evaluating driver attentiveness in modern monitoring systems. Beyond being vulnerable to sudde

LiteFrame: Efficient Vision Encoders Unlock Frame Scaling in Video LLMs

arXiv:2605.17260v1 Announce Type: new Abstract: The fundamental challenge in scaling Video Large Language Models (Video LLMs) to long-form video lies in managing the explosion of visual-token context

Long Context Modeling with Ranked Memory-Augmented Retrieval

arXiv:2503.14800v3 Announce Type: replace-cross Abstract: Effective long-term memory management is crucial for language models handling extended contexts. We introduce the Enhanced Ranked Memory Augme

Long-horizon prediction of three-dimensional wall-bounded turbulence with CTA-Swin-UNet and resolvent analysis

arXiv:2605.17888v1 Announce Type: cross Abstract: Long-horizon prediction of three-dimensional (3D) wall-bounded turbulence with machine-learning methods remains a challenging task, due to the rapid a

Longwang: Zero-Shot Global Spatiotemporal Precipitation Downscaling with a Latent Generative Prior

arXiv:2605.17603v1 Announce Type: cross Abstract: High-resolution precipitation information is essential for climate impact assessment, yet global climate models remain too coarse to resolve key small

Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation

arXiv:2605.18329v1 Announce Type: new Abstract: Ensemble disagreement is widely used as a proxy for epistemic uncertainty in medical image segmentation. In practice, many studies form ensembles via K-

Lost or Hidden? A Concept-Level Forgetting in Supervised Continual Learning

arXiv:2605.16374v1 Announce Type: cross Abstract: Continual learning studies how models can adapt to new tasks while retaining previously acquired knowledge. Although a broad spectrum of methods has b

Lotus-2: Advancing Geometric Dense Prediction with Powerful Image Generative Model

arXiv:2512.01030v3 Announce Type: replace Abstract: Recovering pixel-wise geometric properties from a single image is fundamentally ill-posed due to appearance ambiguity and non-injective mappings bet

LURE: Latent Space Unblocking for Multi-Concept Reawakening in Diffusion Models

arXiv:2601.14330v2 Announce Type: replace Abstract: Concept erasure aims to suppress sensitive content in diffusion models, but recent studies show that erased concepts can still be reawakened, reveal

Machine Learnability as a Measure of Order in Aperiodic Sequences

arXiv:2509.18103v3 Announce Type: replace Abstract: Research on the distribution of prime numbers has revealed a dual character: deterministic in definition yet exhibiting statistical behavior reminis

Machine Learning-Based Pre-Test Risk Stratification for PCR-Confirmed Chlamydia Using Patient-Reported Data and Urine Biomarkers

arXiv:2605.16365v1 Announce Type: new Abstract: Early identification of individuals at elevated risk of Chlamydia trachomatis infection may enable optimal use of molecular testing in resource-aware sc

Markerless Motion Capture for Biomechanical Whole-Body Kinematic Estimation in Infants

arXiv:2605.17120v1 Announce Type: new Abstract: arly identification of motor impairment in infancy relies on expert visual assessment of spontaneous movement, motivating the development of automated,

MARQUIS: A Three-Stage Pipeline for Video Retrieval-Augmented Generation

arXiv:2605.17640v1 Announce Type: cross Abstract: Retrieval-augmented generation from videos requires systems to retrieve relevant audiovisual evidence from large corpora and synthesize it into cohere

Matrix-Decoupled Concentration for Autoregressive Sequences: Dimension-Free Guarantees for Sparse Long-Context Rewards

arXiv:2605.06017v2 Announce Type: replace Abstract: Sequence-level evaluations in autoregressive Large Language Models (LLMs) rely on highly dependent token generation. Establishing tight concentratio

Mechanism Learning: Prototype-Anchored Mechanism Inference for Scientific Forecasting

arXiv:2605.17091v1 Announce Type: new Abstract: Scientific forecasting typically relies on direct state prediction, an approach that grows brittle under data scarcity, extended horizons, non-stationar

Mechanistically Interpretable Neural Encoding Reveals Fine-Grained Functional Selectivity in Human Visual Cortex

arXiv:2605.16468v1 Announce Type: cross Abstract: A central goal in understanding human vision is to uncover the visual features that drive neuronal activity. A growing body of work has used artificia

MedMIX: Modality-Internal Expert Fusion for Multimodal Medical Diagnosis

arXiv:2605.16639v1 Announce Type: new Abstract: Multimodal clinical prediction faces three challenges: multiple foundation models (FMs) with complementary strengths per modality, pervasive missing mod

Memory-Augmented Query Intent Understanding for Efficient Chat-based Image Retrieval

arXiv:2605.17365v1 Announce Type: new Abstract: Different from traditional text-to-image retrieval tasks, chat-based image retrieval allows the human-interactive system to iteratively clarify and refi

Memory-Efficient Differentially Private Training with Gradient Random Projection

arXiv:2506.15588v2 Announce Type: replace Abstract: Differential privacy (DP) protects sensitive data during neural network training, but standard methods like DP-Adam suffer from high memory overhead

Memory-Guided Tree Search with Cross-Branch Knowledge Transfer for LLM Solver Synthesis

arXiv:2605.17539v1 Announce Type: new Abstract: Combinatorial optimization (CO) underlies decision-making from logistics to chip design, where infeasible solutions are operationally unusable and small

MetaLab: Few-Shot Game Changer for Image Recognition

arXiv:2507.22057v2 Announce Type: replace Abstract: Difficult few-shot image recognition has significant application prospects, yet remaining the substantial technical gaps with the conventional large

Metric-Guided Feature Fusion of Visual Foundation Models for Segmentation Tasks

arXiv:2605.16864v1 Announce Type: cross Abstract: Although large-scale visual foundation models (VFMs) achieve remarkable performance in semantic understanding, they still underperform in instance-awa

MHMamba: Multi-Head Mamba for 3D Brain Tumor Segmentation

arXiv:2605.16464v1 Announce Type: cross Abstract: Brain tumors exhibit high heterogeneity in morphology and multimodal contrast, making manual slice-by-slice de lineation time-consuming and experience

Mirror Descent-Type Algorithms for the Variational Inequality Problem with Functional Constraints

arXiv:2605.16262v1 Announce Type: new Abstract: Variational inequalities play a key role in machine learning research, such as generative adversarial networks, reinforcement learning, adversarial trai