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Learning in Structured Stackelberg Games

arXiv:2504.09006v4 Announce Type: replace-cross Abstract: We initiate the study of structured Stackelberg games, a novel form of strategic interaction between a leader and a follower where contextual

safetyarxiv-cs-lg

Learning Normalized Energy Models for Linear Inverse Problems

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Human DGX agent

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arXiv:2605.15487v1 Announce Type: cross Abstract: Generative diffusion models can provide powerful prior probability models for inverse problems in imaging, but existing implementations suffer from tw

researcharxiv-cs-cv

18 May 2026

Learning Selective Merge Policies for Deadline-Constrained Coded Caching via Deep Reinforcement Learning

arXiv:2605.15236v1 Announce Type: cross Abstract: With the coded caching, the server can use the information the users have cached to serve multiple users at a time by sending a single coded multi-cas

Learning Sim-Grounded Policies for Bimanual Rope Manipulation from Human Teleoperation Data

arXiv:2605.16043v1 Announce Type: cross Abstract: Deformable Linear Objects (DLOs) such as ropes and cables are widely encountered in both household and industrial applications, yet remain challenging

safetyarxiv-cs-ai

Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision

model-releasesarxiv-cs-ro

arXiv:2604.02812v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) have recently demonstrated strong capabilities in mapping multimodal observations to robot behaviors. However, most cu

Learning Where It Matters: Geometric Anchoring for Robust Preference Alignment

arXiv:2602.04909v3 Announce Type: replace Abstract: Direct Preference Optimization (DPO) and related methods align large language models from pairwise preferences by regularizing updates against a fix

local-aiarxiv-cs-lg

Learning with Conflicts of Interest

arXiv:2605.15504v1 Announce Type: cross Abstract: Financial, social, and political factors often prevent the interests of the owners of ML systems and services and their users from being perfectly ali

Linked Multi-Model Data on Russian Domestic and Foreign Policy Speeches

arXiv:2605.15886v1 Announce Type: new Abstract: This paper introduces a dataset of interlinked multimodal political communications from the Russian government, addressing persistent deficiencies in th

safetyarxiv-cs-cl

LLM-EDT: Large Language Model Enhanced Cross-domain Sequential Recommendation with Dual-phase Training

arXiv:2511.19931v2 Announce Type: replace-cross Abstract: Cross-domain Sequential Recommendation (CDSR) has been proposed to enrich user-item interactions by incorporating information from various dom

LoCO: Low-rank Compositional Rotation Fine-tuning

arXiv:2605.15916v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as an critical technique for adapting large-scale foundation models across natural language process

Logic of Hypotheses: from Zero to Full Knowledge in Neurosymbolic Integration

arXiv:2509.21663v2 Announce Type: replace-cross Abstract: Neurosymbolic integration (NeSy) blends neural-network learning with symbolic reasoning. The field can be split between methods injecting hand

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

Long Range Frequency Tuning for QML

arXiv:2602.23409v2 Announce Type: replace-cross Abstract: Angle-encoded variational quantum circuits admit a truncated Fourier series representation of their output, but approximating functions with m

Agents

Look Before You Leap: Autonomous Exploration for LLM Agents

arXiv:2605.16143v1 Announce Type: new Abstract: Large language model based agents often fail in unfamiliar environments due to premature exploitation: a tendency to act on prior knowledge before acqui

agentsarxiv-cs-ai

Looped SSMs: Depth-Recurrence and Input Reshaping for Time Series Classification

arXiv:2605.16048v1 Announce Type: cross Abstract: State Space Models (SSMs) are inherently recurrent along the sequence dimension, yet depth-recurrence - reusing the same block repeatedly across layer

LPDS: Evaluating LLM Robustness Through Logic-Preserving Difficulty Scaling

arXiv:2605.15393v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed to perform tasks with minimal human oversight, it is crucial that these models operate robustl

researcharxiv-cs-lg

LRCP: Low-Rank Compressibility Guided Visual Token Pruning for Efficient LVLMs

arXiv:2605.15621v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong multimodal understanding, but their inference cost grows rapidly with the number of visual tokens, e

LUIVITON: Learned Universal Interoperable VIrtual Try-ON

arXiv:2509.05030v2 Announce Type: replace Abstract: To enable large-scale reuse of real-world 3D assets, where garments and characters rarely share skeletons, templates, or dense correspondences, we p

MAgSeg: Segmentation of Agricultural Landscapes in High-Resolution Satellite Imagery using Multimodal Large Language Models

arXiv:2605.16179v1 Announce Type: new Abstract: Agricultural landscape segmentation in the Global South is challenging as it is characterized by fragmented plots, high intra-class variance, and a scar

Margin-Adaptive Confidence Ranking for Reliable LLM Judgement

arXiv:2605.15416v1 Announce Type: cross Abstract: Jung et al. (2025) introduce a hypothesis testing framework for guaranteeing agreement between large language models (LLMs) and human judgments, relyi

Martingale Neural Operators: Learning Stochastic Marginals via Doob-Meyer Factorization

arXiv:2605.15806v1 Announce Type: new Abstract: Neural operators excel as deterministic surrogates, but inevitably collapse to the conditional mean when applied to stochastic PDEs, discarding the vari

researcharxiv-cs-lg

Mask-Morph Graph U-Net: A Generalisable Mesh-Based Surrogate for Crashworthiness Field Prediction under Large Geometric Variation

arXiv:2605.15231v1 Announce Type: cross Abstract: Nonlinear finite element crash simulations are accurate but computationally expensive, limiting their use in iterative design optimisation. Machine-le

MaTe: Images Are All You Need for Material Transfer via Diffusion Transformer

arXiv:2605.15660v1 Announce Type: new Abstract: Recent diffusion-based methods for material transfer rely on image fine-tuning or complex architectures with assistive networks, but face challenges inc

MaxSketch: Robust Distinct Counting in Streams via Random Projections

arXiv:2605.15571v1 Announce Type: cross Abstract: Estimating the number of distinct elements in a data stream is well understood when repeated elements are identical. In modern settings, however, obse

researcharxiv-cs-lg

Measuring Maximum Activations in Open Large Language Models

model-releasesarxiv-cs-cl

arXiv:2605.15572v1 Announce Type: new Abstract: The dynamic range of activations is a first-order constraint for low-bit quantization, activation scaling, and stable LLM inference. Prior work characte

Agents

Mecha-nudges for Machines

arXiv:2603.23433v2 Announce Type: replace Abstract: AI agents are becoming active decision-makers on the Internet. As they make decisions in the same environments as humans, the environments themselve

agentsarxiv-cs-ai

Applications

Metropolis-Scale Road Network Datasets for Fine-Grained Urban Traffic Modeling

arXiv:2510.02278v2 Announce Type: replace Abstract: Modeling traffic dynamics is a critical challenge for urban computing, with applications from real-time traffic management to infrastructure plannin

applicationsarxiv-cs-lg

MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2605.15589v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in the mental health domain, yet it remains unclear how well they capture related biomedical knowledg

MI-CXR: A Benchmark for Longitudinal Reasoning over Multi-Interval Chest X-rays

arXiv:2605.15574v1 Announce Type: new Abstract: Longitudinal chest X-ray (CXR) interpretation requires reasoning over disease evolution across multiple patient visits, yet most existing medical VQA be

MIND: Decoupling Model-Induced Label Noise via Latent Manifold Disentanglement

arXiv:2605.16081v1 Announce Type: cross Abstract: The paradigm of learning from automatic annotations driven by pre-trained experts and Foundation Models dominates data-hungry applications. However, i

local-aiarxiv-cs-cv

Mind Dreamer: Untethering Imagination via Active Latent Intervention on Latent Manifolds

arXiv:2605.16030v1 Announce Type: new Abstract: Model-Based Reinforcement Learning (MBRL) leverages latent imagination for sample efficiency, yet remains constrained by Historical Tethering: imaginati

safetyarxiv-cs-lg

Mind the Motions: Benchmarking Theory-of-Mind in Everyday Body Language

arXiv:2511.15887v2 Announce Type: replace Abstract: Our ability to interpret others' mental states through nonverbal cues (NVCs) is fundamental to our survival and social cohesion. While existing Theo

researcharxiv-cs-cl

Minerva-Ego: Spatiotemporal Hints for Egocentric Video Understanding

arXiv:2605.15342v1 Announce Type: new Abstract: Video reasoning models are a core component of egocentric and embodied agents. However, standard benchmarks for assessing models provide only evaluation

Missing-Data-Induced Phase Transitions in Spectral PLS for Multimodal Learning

arXiv:2601.21294v2 Announce Type: replace Abstract: Partial Least Squares (PLS) learns shared structure from paired data via the top singular vectors of the empirical cross-covariance (PLS-SVD), but m

safetyarxiv-cs-lg

Misspecified Explore-then-Exploit Leads to Supra-Competitive Prices

arXiv:2605.16064v1 Announce Type: cross Abstract: We study whether simple algorithmic pricing systems can systematically produce collusive-like prices in multi-firm markets. We consider firms using an

Modeling Music as a Time-Frequency Image: A 2D Tokenizer for Music Generation

arXiv:2605.15831v1 Announce Type: cross Abstract: Autoregressive music generation depends strongly on the audio tokenizer. Existing high-fidelity codecs often use residual multi-codebook quantization,

Monotone and Separable Set Functions: Characterizations and Neural Models

arXiv:2510.23634v3 Announce Type: replace-cross Abstract: Motivated by applications for set containment problems, we consider the following fundamental problem: can we design set-to-vector functions s

safetyarxiv-cs-ai

MorphoHELM: A Comprehensive Benchmark for Evaluating Representations for Microscopy-Based Morphology Assays

arXiv:2605.15383v1 Announce Type: new Abstract: Microscopy images contain rich information about how cells respond to perturbations, making them essential to applications like drug screening. To quant

MR2-ByteTrack: CNN and Transformer-based Video Object Detection for AI-augmented Embedded Vision Sensor Nodes

arXiv:2605.15423v1 Announce Type: cross Abstract: Modern smart vision sensors need on-device intelligence to process video streams, as cloud computing is often impractical due to bandwidth, latency, a

local-aiarxiv-cs-ai

mRadNet: A Compact Radar Object Detector with MetaFormer

arXiv:2509.16223v3 Announce Type: replace-cross Abstract: Frequency-modulated continuous wave radars have gained increasing popularity in the automotive industry. Their robustness against adverse weat

researcharxiv-cs-cv

Multi-Fidelity Flow Matching: Cascaded Refinement of PDE Solutions

model-releasesarxiv-cs-lg

arXiv:2605.16118v1 Announce Type: new Abstract: The source distribution in conditional flow matching is a design parameter that can be calibrated to data, not a default isotropic prior. We exploit thi

Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection

arXiv:2605.16107v1 Announce Type: new Abstract: Machine-generated texts (MGTs) pose risks such as disinformation and phishing, underscoring the need for reliable detection. Metric-based methods, which

local-aiarxiv-cs-cl

Multi-level Self-supervised Pretraining on Compositional Hierarchical Graph for Molecular Property Prediction

arXiv:2605.16088v1 Announce Type: cross Abstract: Self-supervised pretraining on molecular graphs has emerged as a promising approach for molecular property prediction, yet most existing methods opera

Multi-Probe Zero Collision Hash (MPZCH): Mitigating Embedding Collisions and Enhancing Model Freshness in Large-Scale Recommenders

model-releasesarxiv-cs-lg

arXiv:2602.17050v3 Announce Type: replace Abstract: Embedding tables are critical components of large-scale recommendation systems, facilitating the efficient mapping of high-cardinality categorical f

Multimodal Object Detection Under Sparse Forest-Canopy Occlusion

arXiv:2605.15326v1 Announce Type: new Abstract: Reliable detection of humans beneath forest canopy remains a difficult remote-sensing challenge due to sparse, structured, and viewpoint-dependent occlu

researcharxiv-cs-cv

MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion

model-releasesarxiv-cs-lg

arXiv:2605.15235v1 Announce Type: new Abstract: Multimodal physiological data powers clinical AI systems from intensive care units to wearable devices, but sensors routinely fail in practice. Two fail

MyoChallenge 2025: A New Benchmark for Human Athletic Intelligence

model-releasesarxiv-cs-ro

arXiv:2605.15650v1 Announce Type: new Abstract: Athletic performance represents the pinnacle of human motor intelligence, demanding rapid choices, precise control, agility, and coordinated physical ex

Navigating Potholes with Geometry-Aware Sharpness Minimization

arXiv:2605.16134v1 Announce Type: cross Abstract: Sharpness-aware minimization (SAM) encourages flat minima by perturbing parameters along directions of high loss curvature, but treats all parameter d