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{alpha}Depth: Learning Single-Pass Soft Boundary Decomposition for Stereo Conversion

arXiv:2606.00386v1 Announce Type: new Abstract: Accurately modeling soft boundaries, e.g., hair and defocus blur, is a fundamental challenge in stereo conversion due to the ambiguous blending of foreg

local-aiarxiv-cs-cv

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AlphaToken: Decoupling Adaptation and Stability for Path-Aware Response Token Valuation in LLM Post-Training

DGX agent

arXiv:2606.01635v1 Announce Type: cross Abstract: Token selection is pivotal for effective LLM post-training. However, existing methods mostly rely on local heuristics and rarely formulate token selec

local-aiarxiv-cs-ai

2 Jun 2026

Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification

arXiv:2602.16224v2 Announce Type: replace Abstract: Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from the normal da

local-aiarxiv-cs-lg

AMP: A Vendor-Neutral Wire Format for Agent Memory Operations

arXiv:2606.01138v1 Announce Type: cross Abstract: Agent-memory frameworks - mem0, Letta/MemGPT, Cognee, Zep/Graphiti, MemoryOS, MemTensor - each ship their own SDK, storage layout, and operational voc

agentsarxiv-cs-ai

An Abstract Worlds Semantic Framework for Belief Change Operators

arXiv:2606.02163v1 Announce Type: new Abstract: This article proposes a set-theoretic framework for belief change, called Abstract Worlds Semantics, in which no logical syntax is assumed. Inspired by

Applications

An Algebraic View of the Expressivity of Recurrent Language Models

arXiv:2606.01765v1 Announce Type: cross Abstract: What formal languages can a recurrent neural language model recognize? Formal results in the literature conflict: some authors report Turing-completen

applicationsarxiv-cs-cl

An Asynchronous Two-Speed Kalman Filter for Real-Time UUV Cooperative Navigation Under Acoustic Delays

arXiv:2604.02878v2 Announce Type: replace Abstract: In Global Navigation Satellite System (GNSS)-denied underwater environments, individual unmanned underwater vehicles (UUVs) suffer from unbounded de

agentsarxiv-cs-ro

An Attribute-Based Measure of Video Complexity

arXiv:2606.00640v1 Announce Type: new Abstract: A new framework for the estimation of the complexity posed by video-question pairs to video-LLMs, Video Attribute-Based Complexity (VideoABC), is propos

An Effective Solution for the CVPR 2026 8th UG2+ Challenge Track 3: Dynamic Object Segmentation in Turbulence

arXiv:2606.00522v1 Announce Type: new Abstract: In this work, we present our solution for the 8th UG2+ Challenge (CVPR 2026) Track 3: Dynamic Object Segmentation in Turbulence (DOST). Our method is bu

An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models

arXiv:2606.01462v1 Announce Type: new Abstract: Studies of human reasoning have shown that people are typically stronger at evaluating reasoning than producing it from scratch. In contrast, large reas

Tutorials

An explainable hierarchical self attention-based approach for tremor detection in the time domain

arXiv:2606.00461v1 Announce Type: new Abstract: Tremor is a common movement disorder associated with conditions like Parkinson's disease and Essential tremor, traditionally diagnosed through expert cl

tutorialsarxiv-cs-cv

Tutorials

An Exploratory Study into using Machine-Learning for Fast Step-by-step Emulation of Numerical Mechanical Thrombectomy Simulations for Ischemic Stroke

arXiv:2606.00892v1 Announce Type: new Abstract: The treatment of ischemic stroke using mechanical thrombectomy involves difficult decisions under intense time constraints. Numerical physics simulation

tutorialsarxiv-cs-lg

An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction

arXiv:2508.11931v3 Announce Type: replace Abstract: We present an oracle-efficient, near-optimal algorithm for linear contextual bandits with adversarial losses and stochastic action sets, only requir

safetyarxiv-cs-lg

An NLP-Driven Framework for Curriculum-Labor Market Alignment: Schema-Constrained LLM Extraction, ESCO-Anchored Semantic Matching, and Multi-Dimensional Gap Quantification

arXiv:2606.01982v1 Announce Type: new Abstract: Schema-constrained information extraction from diverse educational and labor-market corpora remains an open challenge in natural language processing bec

An Open-Source Benchmark and Baseline for Multi-temporal Referring Segmentation

arXiv:2606.00987v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown strong visual understanding and language-guided grounding abilities, yet their capacity for multi-temp

Analysis of Ethnic Disparities in Autism Spectrum Disorder among Toddlers

arXiv:2606.01217v1 Announce Type: new Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by challenges in communication and behavior. This study examines the relat

ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment

arXiv:2606.01279v1 Announce Type: new Abstract: AI agents are increasingly being tasked with automating AI research itself, particularly the critical post-training phase that transforms base LLMs into

Annotation-Informed Block-Sparse Bayesian Modeling for cis-Expression Prediction

arXiv:2606.00483v1 Announce Type: cross Abstract: Genotype-based cis-expression prediction depends on accurately modeling local regulatory architecture. We present block-sparse Bayesian sparse linear

local-aiarxiv-cs-lg

AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection

arXiv:2602.08868v2 Announce Type: replace-cross Abstract: Time-series anomaly detection (TSAD) with multimodal large language models (MLLMs) is an emerging area, yet a persistent challenge remains: ML

AnyEdit++: Adaptive Long-Form Knowledge Editing via Bayesian Surprise

arXiv:2606.01053v1 Announce Type: new Abstract: Editing complex, long-form knowledge in Large Language Models remains a significant challenge due to the difficulty of maintaining generation coherence.

Hardware

APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention

arXiv:2601.21444v2 Announce Type: replace-cross Abstract: The efficiency of long-video inference remains a critical bottleneck, mainly due to the dense computation in the prefill stage of Large Multim

hardwarearxiv-cs-ai

APE: Agentic Prompt Enhancer for Image Generation and Editing

model-releasesarxiv-cs-cv

arXiv:2606.00204v1 Announce Type: new Abstract: Natural language has become a powerful interface for image generation and editing, yet text-guided visual systems remain highly sensitive to prompt form

APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL

arXiv:2602.16720v2 Announce Type: replace-cross Abstract: Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments. The

Application of Algorithms in Energy-Efficient Design Platforms for Green Building

arXiv:2606.01229v1 Announce Type: new Abstract: During green building design, computer-aided energy assessment is widely used to improve efficiency and achieve overall optimization. This paper present

agentsarxiv-cs-ai

Approximate Imitation Learning for Event-based Quadrotor Flight in Cluttered Environments

arXiv:2603.07578v2 Announce Type: replace Abstract: Event cameras offer high temporal resolution and low latency, making them ideal sensors for high-speed robotic applications where conventional camer

safetyarxiv-cs-ro

Approximating f-Divergences with Rank Statistics

model-releasesarxiv-cs-lg

arXiv:2601.22784v2 Announce Type: replace-cross Abstract: We introduce a rank-statistic approximation of f-divergences that avoids explicit density-ratio estimation by working directly with the distri

ARCA: Adapter-Residual Credit Assignment When Token Signals Degenerate

arXiv:2606.00257v1 Announce Type: cross Abstract: Token-level credit assignment for language-model reinforcement learning is usually formulated as if the policy were fully trainable, while practical L

Applications

Are Large Reasoning Models Interruptible?

arXiv:2510.11713v4 Announce Type: replace Abstract: Real-world applications of Large Reasoning Models (LRMs) often require reasoning about changing prompts or environments. In this work, we challenge

applicationsarxiv-cs-cl

Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework

arXiv:2602.18008v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promise in constructing mechanistic models from data. However, existing evaluations largely focus on s

Argument Collapse: LLMs Flatten Long-Form Public Debate

arXiv:2606.01736v1 Announce Type: cross Abstract: As LLMs are increasingly used to draft public-facing arguments, they may flatten public debate by repeatedly introducing the same polished, plausible

ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems

arXiv:2606.02256v1 Announce Type: new Abstract: Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference

local-aiarxiv-cs-lg

ART: Attention Run-time Termination for Efficient Large Language Model Decoding

arXiv:2606.00024v1 Announce Type: new Abstract: Long-context decoding in Large Language Models (LLMs) is severely constrained by the memory bandwidth required to fetch the extensive Key-Value (KV) cac

researcharxiv-cs-cl

ASAP: Advancing Medical Volumetric Representation Learning with Anatomy-aware Semantically-adaptive Pre-training

model-releasesarxiv-cs-cv

arXiv:2606.00602v1 Announce Type: new Abstract: Learning transferable and interpretable representations from medical volumetric scans remains challenging due to complex anatomical structures and weak,

ASE-26: a curriculum for agentic software engineering as a discipline

arXiv:2606.01152v1 Announce Type: cross Abstract: The work of a professional software engineer has begun to consist, increasingly, of directing agents rather than writing code, and the empirical evide

Tutorials

Ask4VG: Risk-Aware Question Selection for Reducing Prior-Driven Answers in Medical VQA

arXiv:2606.01044v1 Announce Type: new Abstract: Medical visual question answering requires models to ground their responses in image evidence, because visually unsupported answers can mislead downstre

tutorialsarxiv-cs-cv

ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition

arXiv:2601.19919v2 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is one of the most effective paradigms for compressing large-scale foundation models into deployable architectures

Assessment of Generative Named Entity Recognition in the Era of Large Language Models

model-releasesarxiv-cs-cl

arXiv:2601.17898v2 Announce Type: replace Abstract: Named entity recognition (NER) is evolving from a sequence labeling task into a generative paradigm with the rise of large language models (LLMs). W

ATLAS: Agentic Test-time Learning-to-Allocate Scaling

model-releasesarxiv-cs-lg

arXiv:2606.01667v1 Announce Type: new Abstract: Test-time scaling has become a major way to improve large language model reasoning, but its orchestration has remained designer-engineered: a fixed samp

Atmospheric Predictability Beyond 30 Days with Machine Learning

arXiv:2504.20238v2 Announce Type: replace-cross Abstract: Atmospheric predictability research has long held that rapid error growth at small spatial scales imposes an intrinsic limit of roughly two we

researcharxiv-cs-lg

Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows

arXiv:2602.14849v2 Announce Type: replace-cross Abstract: LLM agents execute multi-step workflows that mutate external state through tools. Common orchestrators treat tool return as the settlement tri

agentsarxiv-cs-ai

Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning

arXiv:2606.01558v1 Announce Type: new Abstract: The effectiveness of Chain-of-Thought (CoT) prompting in Multimodal Large Language Models (MLLMs) remains uncertain: across several visual reasoning ben

Attention mechanisms and transfer learning for robust peach leaf damage classification under domain shift

arXiv:2606.02045v1 Announce Type: cross Abstract: Artificial intelligence provides a practical framework for crop damage assessment from imagery data, supporting early decision-making in agricultural

au_0-WM: A Unified Video-Action World Model for Robotic Manipulation

arXiv:2606.01027v1 Announce Type: new Abstract: Robotic manipulation requires models that generate executable actions while anticipating and evaluating their future consequences before physical execut

safetyarxiv-cs-ro

Auditing Asset-Specific Preferences in Financial Large Language Models: Evidence from Bitcoin Representations and Portfolio Allocation

model-releasesarxiv-cs-lg

arXiv:2606.02528v1 Announce Type: cross Abstract: Large language models now power robo-advisors and trading agents, yet whether they carry built-in biases toward specific assets is largely untested. W

Auditing Near-Optimal Policies Can Be Exponentially Hard: Conditional Query Lower Bounds via Occupancy Rashomon Capacity

arXiv:2606.00414v1 Announce Type: new Abstract: When many reinforcement-learning policies achieve near-optimal return, a post-hoc auditor may have to distinguish among many behaviorally distinct but r

researcharxiv-cs-lg

Applications

Auteur: Language-Driven Cinematographic Framing for Human-Centric Video Generation

arXiv:2606.01900v1 Announce Type: new Abstract: Generative video models have achieved remarkable visual fidelity and temporal coherence, yet intentional camera control remains elusive. Existing framew

applicationsarxiv-cs-cv

Authenticity Debt and the Synthetic Content Threat Landscape: A Layered Framework for Trust, Provenance, and IP Governance in the Generative AI Era

arXiv:2606.00621v1 Announce Type: cross Abstract: Generative artificial intelligence has fundamentally changed how content is now produced. It has enabled how high-fidelity text, images, audio, and vi

AutoEval Done Right: Using Synthetic Data for Model Evaluation

arXiv:2403.07008v3 Announce Type: replace-cross Abstract: The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming. AI-labeled synthetic data c