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LangMAP: A Language-Adaptive Approach to Tokenization

model-releasesarxiv-cs-cl

arXiv:2606.23566v2 Announce Type: replace Abstract: Language-specific tokenizers improve tokenization quality and the downstream performance of models on those languages. However, using such a tokeniz

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Large-Language-Model Discovery of Quantum LDPC Codes through Structured Concept Evolution

DGX agent

arXiv:2606.24808v1 Announce Type: cross Abstract: Quantum computers could outperform classical machines on important problems, but only if the errors that pervade quantum hardware can be corrected at

model-releasesarxiv-cs-ai

24 Jun 2026

Latent Visual States for Efficient Multimodal Reasoning

arXiv:2606.24233v1 Announce Type: new Abstract: The integration of visual evidence has significantly enhanced the capabilities of large multimodal models. However, this integration predominantly relie

safetyarxiv-cs-cv

Layer-wise Probing of wav2vec 2.0 and Whisper for Consonant Cluster Reduction in African American English

arXiv:2606.23948v1 Announce Type: new Abstract: Self-supervised and supervised speech models are increasingly used to investigate which linguistic information their internal representations encode, an

Learning the Koopman Operator using Attention Free Transformers

arXiv:2606.23957v1 Announce Type: new Abstract: Learning Koopman operators with autoencoders enables linear prediction in a latent space, but long-horizon rollouts often drift off the learned manifold

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

arXiv:2606.23993v1 Announce Type: cross Abstract: High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (extit{triggering}) under tight constraint

LecturaAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching

arXiv:2606.16428v2 Announce Type: replace-cross Abstract: Effective personalized AI-assisted learning demands systems that can not only generate accurate learner-specific educational materials, but al

safetyarxiv-cs-ai

Legal Reasoning Is Not Lawyering: Rethinking Legal Benchmarks for Pro Se Access to Justice

arXiv:2606.23716v1 Announce Type: cross Abstract: Legal AI benchmark research frequently invokes the assumption that large language models can improve access to justice, including for people who canno

Applications

Legible and Intuitive Multi-modal Robot State and Intent Communication Validated in Online and Real-world Studies

arXiv:2606.24445v1 Announce Type: new Abstract: Effective robot-to-human communication can increase transparency and trust, reduce uncertainty, and contribute to safer collaboration in shared workspac

applicationsarxiv-cs-ro

LemonHarness Technical Report

arXiv:2606.24311v1 Announce Type: new Abstract: As large language model (LLM) agents are applied to longer tasks, they increasingly modify workspace state across multiple rounds of iteration. However,

Less is More: Quality-Aware Training Data Selection for Scientific Summarization

arXiv:2606.24828v1 Announce Type: new Abstract: Scientific long-document summarization datasets commonly treat author-written abstracts as gold reference summaries, although their quality and alignmen

safetyarxiv-cs-cl

Light-weight Pronunciation Assessment via Discrete Speech Token Surprisal

arXiv:2606.19910v2 Announce Type: replace Abstract: Training automated pronunciation assessment often relies on labeled learner errors or non-native corpora that are costly to collect. We propose a li

safetyarxiv-cs-cl

Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition

arXiv:2601.04181v2 Announce Type: replace Abstract: Reliable long-term decoding of gestures from surface electromyography (EMG) is hindered by signal drift caused by electrode displacement, muscle fat

safetyarxiv-cs-lg

Lightweight Transformer Models for On-Device Fault Detection: A Benchmark Study on Resource-Constrained Deployment

arXiv:2606.24173v1 Announce Type: cross Abstract: On-device fault detection enables real-time diagnostics without cloud dependency, but deploying machine learning models on resource-constrained hardwa

Listening makes Vision Clear for VLMs

arXiv:2606.23763v1 Announce Type: cross Abstract: Recent work typically assesses vision--language consistency using attention distributions of answer-side tokens. However, we observe that highest atte

safetyarxiv-cs-ai

Applications

Lite Any Stereo V2: Faster and Stronger Efficient Zero-Shot Stereo Matching

arXiv:2606.24457v1 Announce Type: new Abstract: Recent advances in stereo matching have achieved remarkable accuracy, but often rely on large models, heavy computation, or additional foundation-model

applicationsarxiv-cs-cv

LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes

arXiv:2603.13673v2 Announce Type: replace Abstract: Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage

LLMs are Bayesian, In Expectation, Not in Realization

arXiv:2507.11768v3 Announce Type: replace-cross Abstract: Bayesian accounts of in-context learning face a direct objection: exact posterior predictives for exchangeable data are invariant to task-pres

researcharxiv-cs-lg

Local Ai

LLMs Prompted for Legal Context Object More: Overrefusal from Small On-Premises LLMs in Criminal Legal Context

arXiv:2606.24585v1 Announce Type: new Abstract: While the validity of LLMs' use in the legal context remains subject to ethical and legal debate, legal professionals are already experimenting with per

local-aiarxiv-cs-ai

LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings

arXiv:2602.18934v2 Announce Type: replace Abstract: Membership inference attacks (MIAs) threaten the privacy of machine learning models by revealing whether a specific data point was used during train

Local Ai

Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs

arXiv:2606.17110v2 Announce Type: replace-cross Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private healthcare and financial records to user convers

local-aiarxiv-cs-lg

LoT-Pass: Long-term-robust Image Watermarking for Image to Video Generation

arXiv:2509.17773v2 Announce Type: replace Abstract: The rapid progress of image-guided video generation (I2V) has raised concerns about its potential misuse in misinformation and fraud, underscoring t

Low-power analogue neural networks with trainable nonlinear connections for continuous control

arXiv:2606.23742v1 Announce Type: cross Abstract: Physical neural networks promise low-power machine learning by computing directly with analogue device physics, but most architectures force nonlinear

Low-rank Updates in Slowly Time-varying Graphs for Spatial-Temporal Signal Interpolation

arXiv:2606.24011v1 Announce Type: cross Abstract: A crucial assumption in graph signal processing (GSP) is the existence of an underlying graph that captures the pairwise similarities between nodes, a

M^2C-EvDet: Multi-Domain Multi-Order Cross-Modal Knowledge Distillation for Event-based Object Detection

arXiv:2606.24248v1 Announce Type: new Abstract: Event-based object Detection (EvDet), as a biologically inspired visual perception paradigm, demonstrates superior performance in scenarios demanding hi

researcharxiv-cs-cv

M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for optical-SAR Object Detection

arXiv:2505.10931v4 Announce Type: replace Abstract: Single-source remote sensing object detection using optical or SAR images struggles in complex environments. Optical images offer rich textural deta

Machine Learning and Deep Learning for Exoplanet Detection and Atmospheric Characterization with JWST and the Upcoming Ariel Mission

arXiv:2606.23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the up

Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?

arXiv:2108.02283v3 Announce Type: replace-cross Abstract: Classification outperforms regression across matched machine learning models in portfolio construction. A stacking ensemble of gradient booste

researcharxiv-cs-lg

Machine Learning Modeling for Real-Time Melt Pool Monitoring in Laser Powder Bed Fusion Additive Manufacturing: A Hybrid Approach

arXiv:2606.23851v1 Announce Type: cross Abstract: This work investigates the implementation of artificial intelligence and machine learning (AI/ML) for real-time monitoring in laser powder bed fusion

Macro Graph of Experts for Billion-Scale Multi-Task Recommendation

arXiv:2506.10520v5 Announce Type: replace-cross Abstract: Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale gra

Maestro Order: A Model-Agnostic Orchestration Harness

arXiv:2606.23983v1 Announce Type: cross Abstract: A single forward pass of a capable model is a fast, fluent, and unreliable problem-solver: it is right often enough to be useful and wrong often enoug

Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning

arXiv:2407.06136v4 Announce Type: replace Abstract: Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes from limited examples while preserving knowledge of previously

MambaRaw: Selective State Space Modeling for Efficient 4K Raw Image Reconstruction

arXiv:2606.24479v1 Announce Type: new Abstract: In-camera JPEG previews are ubiquitous in raw image formats and provide an sRGB reference at negligible storage cost. Although existing metadata-based r

Agents

Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation

arXiv:2606.24340v1 Announce Type: new Abstract: In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operat

agentsarxiv-cs-lg

Applications

MATCH: Flow Matching for Multi-View Anomaly Detection

arXiv:2606.24375v1 Announce Type: new Abstract: Detecting anomalies in industrial objects is an important topic for increasing production efficiency. More complex objects often require the analysis of

applicationsarxiv-cs-cv

Matching Tasks to Objectives: Fine-Tuning and Prompt-Tuning Strategies for Encoder-Decoder Pre-trained Language Models

arXiv:2606.24841v1 Announce Type: new Abstract: Prompt-based learning has emerged as a dominant paradigm in natural language processing. This study explores the impact of diverse pre-training objectiv

Measuring User's Mental Models of Speech Translation in Human-AI Collaboration

arXiv:2606.24644v1 Announce Type: new Abstract: Millions of people use machine translation (MT) tools daily, yet little is known about their perception of what systems can and cannot do. This paper st

MedBench v5: A Dynamic, Process-Oriented, and Hallucination-Aware Benchmark for Clinical Multimodal Models

model-releasesarxiv-cs-cl

arXiv:2606.24155v1 Announce Type: new Abstract: Existing medical AI benchmarks lack process visibility, atomic skill evaluation, and integrated hallucination detection. We introduce MedBench v5, a red

MedPCFM: Improving Medical Point Cloud Completion by Integrating Point Transformers and Flow Matching

arXiv:2606.24433v1 Announce Type: cross Abstract: Medical point cloud completion is important for anatomical reconstruction and downstream clinical workflows, yet generative modeling in this setting r

Meet UD_Czech-PDTC: A Large and Genre-Rich Treebank in Universal Dependencies

arXiv:2606.24337v1 Announce Type: new Abstract: Czech has been part of Universal Dependencies since its first release in 2015. It has also been one of the best represented languages, with the Prague D

MEMPROBE: Probing Long-Term Agent Memory via Hidden User-State Recovery

model-releasesarxiv-cs-cl

arXiv:2606.24595v1 Announce Type: new Abstract: Long-term memory promises LLM agents that grow more capable across sessions, maintaining an accurate, evolving understanding of the user that interactio

MERGE: Minimal Expression-Replacement GEneralization Test for Natural Language Inference

arXiv:2510.24295v2 Announce Type: replace Abstract: As many benchmarks have become saturated, it has become increasingly important to create new datasets that evaluate the generalization capacity of c

Metis: Bridging Text and Code Memory for Self-Evolving Agents

arXiv:2606.24151v1 Announce Type: cross Abstract: Self-evolving agents improve over time by distilling experience from past executions and reusing it in future tasks. Existing systems represent such e

MGI: Member vs Generated Inference

arXiv:2606.23872v1 Announce Type: cross Abstract: As generative models increasingly produce samples that are indistinguishable from human-created content, it becomes difficult to determine whether a g

Mind the Heads: Topological Representation Alignment for Multimodal LLMs

arXiv:2606.23885v1 Announce Type: cross Abstract: Representation alignment has emerged as an effective approach to improve Multimodal Large Language Models (MLLMs) by regularizing their internal repre

Minimisation of Quasar-Convex Functions Using Random Zeroth-Order Oracles

arXiv:2505.02281v3 Announce Type: replace-cross Abstract: This paper explores the performance of a random Gaussian smoothing zeroth-order (ZO) scheme for minimising quasar-convex (QC) and strongly qua

MinInter: Minimizing Trajectory Interpolation During Data Augmentation for Imitation Learning

model-releasesarxiv-cs-ro

arXiv:2606.24078v1 Announce Type: new Abstract: Imitation learning enables robots to acquire complex manipulation skills from demonstrations, but its effectiveness is limited by the cost of collecting

Mixtures Closest to a Given Measure: A Semidefinite Programming Approach

arXiv:2509.22879v2 Announce Type: replace-cross Abstract: Mixture models, such as Gaussian mixture models, are widely used in machine learning to represent complex data distributions. A key challenge,