All entries

Hierarchical Semantic-Constrained Heterogeneous Graph for Audio-Visual Event Localization

model-releasesarxiv-cs-ai

arXiv:2606.07033v1 Announce Type: new Abstract: Open-vocabulary audio-visual event localization (OV-AVEL) jointly models audio-visual cues to recognize and temporally localize events, including catego

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

HKJudge: A Legal Discourse-Annotated Corpus for Interpreting What Courts Find, How They Reason, and What They Rule

DGX agent

arXiv:2606.06679v1 Announce Type: cross Abstract: Court judgments are central to legal practice and jurisprudence, yet discourse analysis of Hong Kong judgments has received limited attention, owing l

model-releasesarxiv-cs-ai

8 Jun 2026

HKVM-RAG: Key-Value-Separated Hypergraph Evidence Organization for Multi-Hop RAG

arXiv:2606.07218v1 Announce Type: cross Abstract: Multi-hop RAG poses a data-engineering problem beyond passage matching: under fixed retrieval budgets, a system must organize retrieved text into evid

researcharxiv-cs-cl

HORUS: A Mixed Reality Interface for Managing Teams of Mobile Robots

arXiv:2506.02622v2 Announce Type: replace Abstract: Mixed Reality (MR) interfaces have been extensively explored for controlling mobile robots, but there is limited research on their application to ma

researcharxiv-cs-ro

How AI Agents Reshape Knowledge Work: Autonomy, Efficiency, and Scope

arXiv:2606.07489v1 Announce Type: new Abstract: Frontier AI systems are bridging the gap between intelligence and utility by shifting from conversational assistants to autonomous agents that execute t

agentsarxiv-cs-ai

Local Ai

How Far Can Chord-Symbol Time-Series Adaptation Carry Genre Identity? Capabilities and Boundaries in Multi-Genre Chord-Symbol Modeling

arXiv:2606.07334v1 Announce Type: cross Abstract: Harmony is a compact symbolic layer where mathematical pitch relations, acoustic consonance, and musical convention meet. This report treats chord-sym

local-aiarxiv-cs-lg

How Language Models Fail: Token-Level Signatures of Committed and Persistent Reasoning Failures

arXiv:2606.06635v1 Announce Type: cross Abstract: Failures in language model reasoning emerge through distinct processes that leave identifiable signatures in the reasoning trace. We characterize thes

How reliable are LLMs when it comes to playing dice?

arXiv:2606.07515v1 Announce Type: cross Abstract: We investigate the probabilistic reasoning capabilities of large language models through a controlled benchmarking study on discrete probability probl

HybridCodec: Fast Dual-Stream, Semantically Enhanced Neural Audio Codec

arXiv:2606.06743v1 Announce Type: cross Abstract: The popularity of neural audio codecs as speech tokenizers has surged with the advent of Multimodal Large Language Models. New codec architectures wit

IDDMBSE: Integrating Data-Driven and Model-Based Systems Engineering for Trusted Autonomous Cyber-Physical Systems

arXiv:2606.06727v1 Announce Type: new Abstract: Autonomous cyber-physical systems (CPS) sit at the intersection of Model-Based Systems Engineering (MBSE) and data-driven Machine Learning and Artificia

agentsarxiv-cs-ro

Image class translation: visual inspection of class-specific hypotheticals and classification based on translation distance

arXiv:2408.08973v3 Announce Type: replace Abstract: Purpose: A major barrier to the implementation of artificial intelligence for medical applications is automated CNNs' lack of explainability and hig

researcharxiv-cs-cv

Impact of Synthetic Lesional MR Images in Automated Focal Cortical Dysplasia Detection in Low-Data Scenarios

arXiv:2606.07381v1 Announce Type: cross Abstract: Background and Purpose: Automated detection of focal cortical dysplasia (FCD) requires large volumes of voxelwise lesion-delineated MRI data, which ar

Implicit Data Synthesis for Contrastive Unsupervised Data Augmentation

arXiv:2606.07498v1 Announce Type: new Abstract: Scientific observations generate large quantities of unlabeled data which is laborious to hand-label, making unsupervised learning techniques valuable f

researcharxiv-cs-cv

Improving Cross-Lingual Factual Recall via Consistency-Driven Reinforcement Learning

arXiv:2606.06586v1 Announce Type: new Abstract: Large language models (LLMs) trained predominantly on English data encode substantial world knowledge, yet often fail to express it reliably in other la

Inheritance Between Feedforward and Convolutional Networks via Model Projection

model-releasesarxiv-cs-lg

arXiv:2602.06245v2 Announce Type: replace-cross Abstract: Neural-network techniques are often transferred across architecture families by analogy, but such transfer is valid only when the assumptions

Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers

arXiv:2606.06664v1 Announce Type: cross Abstract: Despite high accuracy, Vision Transformer (ViT) predictions can be driven by spurious cues, raising the need to understand their inner workings before

Interpretable factorization of clinical questionnaires to identify latent factors of psychopathology

arXiv:2312.07762v3 Announce Type: replace Abstract: Psychiatry research seeks to understand the manifestations of psychopathology in behavior, as measured in questionnaire data, by identifying a small

researcharxiv-cs-lg

Interpreting Brain Responses to Language with Sparse Features from Language Models

arXiv:2606.06857v1 Announce Type: new Abstract: A central goal of cognitive neuroscience is to characterize the features that are represented by human language cortex. Artificial language models (LMs)

safetyarxiv-cs-cl

Interpreting Learning Under Competing Models: Joint and Stepwise Approaches for Dynamic Cognitive Diagnosis

arXiv:2606.06804v1 Announce Type: new Abstract: Digital learning environments record learners' responses to individual items, making it possible to study the development of specific skills rather than

safetyarxiv-cs-lg

IRAF: Interference-Resilient Adaptive Fusion for Noise-Robust End-to-End Full-Duplex Spoken Dialogue Systems

arXiv:2606.06559v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models allow voice agents to listen and speak concurrently, enabling natural interaction with real-time overlap. However,

agentsarxiv-cs-ai

It's a TRAP! Task-Redirecting Agent Persuasion Benchmark for Web Agents

model-releasesarxiv-cs-ai

arXiv:2512.23128v2 Announce Type: replace-cross Abstract: Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking. Their r

JA-SIREN: Deterministic Initialization for Sinusoidal Networks via Spectral Matching

arXiv:2606.06671v1 Announce Type: new Abstract: Existing implicit neural representation (INR) approaches suffer from stochastic initialization that does not guarantee consistent or high-quality perfor

researcharxiv-cs-cv

Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates

arXiv:2601.18510v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights a

KIT's Submission to Cross-Lingual Voice Cloning in IWSLT 2026

arXiv:2606.07240v1 Announce Type: new Abstract: Cross-lingual voice cloning aims to generate speech in a target language while preserving speaker identity from a source-language reference. This task i

researcharxiv-cs-cl

Korean Culture into LLM Alignment: Toward Cultural Coherence

arXiv:2606.06797v1 Announce Type: new Abstract: Cultural-aspect work on large language models is dominated by a negative target: which outputs to suppress. We argue that a constructive counterpart is

safetyarxiv-cs-cl

LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization

arXiv:2603.02970v2 Announce Type: replace Abstract: We introduce LAGO, a LocAl-Global Optimization framework coupling Bayesian Optimization (BO) and gradient-based trust region local refinement throug

researcharxiv-cs-lg

Lane Change Trajectory Planning for Personalized Driving Comfort and Mobility Efficiency

arXiv:2606.06805v1 Announce Type: cross Abstract: Lane changing entails simultaneous longitudinal and lateral motions that affect driving comfort and mobility efficiency. Because these motions are tig

LARA: Latent Action Representation Alignment for Vision-Language-Action Models

arXiv:2606.07100v1 Announce Type: new Abstract: Visual-language action (VLA) models enable robots to predict actions directly from observations and language instructions, but their performance depends

safetyarxiv-cs-cv

Latent-space Attacks for Refusal Evasion in Language Models

arXiv:2605.21706v2 Announce Type: replace Abstract: Safety-aligned language models are trained to refuse harmful requests, yet refusal behavior can be suppressed by steering their internal representat

Lean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory

model-releasesarxiv-cs-ai

arXiv:2606.06523v1 Announce Type: new Abstract: Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge in artificial intelligence. Despite recen

Learn to Match: Two-Sided Matching with Temporally Extended Feedback

model-releasesarxiv-cs-lg

arXiv:2606.06744v1 Announce Type: new Abstract: Two-sided matching markets often involve information that unfolds over time through interviews, repeated interaction, learning, and separation. Existing

Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspension

arXiv:2606.06790v1 Announce Type: cross Abstract: This paper presents ERNEST, a four-wheeled planetary rover concept equipped with a two-degree-of-freedom Active Gimbal Suspension that combines yaw an

safetyarxiv-cs-lg

Local Ai

Learning Explicit Behavioral Models with Adaptive Questions and World-Model Probes

arXiv:2606.07127v1 Announce Type: new Abstract: Interactive agents trained only against task return can achieve high scores while failing to represent the mechanisms that make their actions succeed. T

local-aiarxiv-cs-lg

Learning Fair Demand Models

arXiv:2606.06830v1 Announce Type: cross Abstract: Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail. By learning demand models from customer fea

safetyarxiv-cs-lg

Learning Perspectivist Social Meaning via Demographic-Conditioned Fusion Embeddings

arXiv:2606.07123v1 Announce Type: new Abstract: Social meaning in language is inherently perspectival, varying across annotator backgrounds, demographics, and ideological positions. However, most NLP

Local Ai

Learning to Execute Graph Algorithms Exactly with Graph Neural Networks

arXiv:2601.23207v2 Announce Type: replace-cross Abstract: Understanding what graph neural networks can learn, especially their ability to learn to execute algorithms, remains a central theoretical cha

local-aiarxiv-cs-ai

Tutorials

Lighting-Aware Representation Learning under Controllable Lighting Variation

arXiv:2606.06899v1 Announce Type: new Abstract: Variations in illumination remain a major challenge for visual representation learning, as they induce substantial appearance changes both across and wi

tutorialsarxiv-cs-cv

Limitations of Normalization in Attention Mechanism

arXiv:2508.17821v3 Announce Type: replace-cross Abstract: This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the i

LIMMT: Less is More for Motion Tracking

arXiv:2606.06953v1 Announce Type: new Abstract: We argue that high-quality motion data can steer tracking policies toward better optimization trajectories early in training. In this work, we introduce

researcharxiv-cs-ro

LiQSS: Post-Transformer Linear Quantum-Inspired State-Space Tensor Networks for Real-Time 6G

model-releasesarxiv-cs-lg

arXiv:2601.12375v3 Announce Type: replace-cross Abstract: Proactive and agentic control in Sixth-Generation (6G) Open Radio Access Networks (O-RAN) requires control-grade prediction under stringent Ne

LLM Agent-Assisted Reverse Engineering with Quantitative Readability Metrics

arXiv:2606.06838v1 Announce Type: cross Abstract: Automatic decompilers produce functionally correct but often unreadable C code. This paper addresses one stage of the reverse engineering workflow: im

agentsarxiv-cs-ai

LLM-Augmented Digital Twin for Policy Evaluation in Short-Video Platforms

arXiv:2603.11333v2 Announce Type: replace Abstract: Short-video platforms are closed-loop, human-in-the-loop ecosystems where platform policy, creator incentives, and user behavior co-evolve. This fee

LLM-Guided Evolution for Medical Decision Pipelines

arXiv:2606.07342v1 Announce Type: new Abstract: Adapting large language models (LLMs) to clinical workflows often requires costly fine-tuning or manual prompt and pipeline engineering. We study LLM-gu

LLM-Guided Search for Deletion-Correcting Codes

arXiv:2504.00613v2 Announce Type: replace Abstract: Finding deletion-correcting codes of maximum size has been an open problem for over 70 years, even for a single deletion. We adapt FunSearch, a larg

Applications

LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification

arXiv:2606.06864v1 Announce Type: new Abstract: Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level pre

applicationsarxiv-cs-cv

Applications

LUCID: Learning Unified Control for Image Deflaring and Exposure Mastery in Nighttime Photography

arXiv:2606.06901v1 Announce Type: new Abstract: Photography is the art of painting with light, yet nighttime scenes are shaped by competing degradations: intense flares obscure scene structure, while

applicationsarxiv-cs-cv

Hardware

LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling

arXiv:2603.19100v2 Announce Type: replace Abstract: Electroencephalography (EEG) enables non-invasive monitoring of brain activity across clinical and neurotechnology applications, yet building founda

hardwarearxiv-cs-ai

M^3Exam: Benchmarking Multimodal Memory for Realistic User-Agent Interactions

arXiv:2606.07402v1 Announce Type: new Abstract: Language agents are increasingly deployed over accumulating multimodal information, yet existing benchmarks assume a human-human form with sparse visual