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Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defense

arXiv:2606.08067v1 Announce Type: new Abstract: Graph neural networks (GNNs) are widely deployed on relational data, yet they can leak sensitive or proprietary information about the training graph adj

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Beyond Humans: Multispecies Animal Face Recognition Using Transfer Learning

DGX agent

arXiv:2606.09353v1 Announce Type: cross Abstract: Individual animal recognition can be useful in the search for lost or stolen pets, the tracking of individuals of endangered species, and the recognit

researcharxiv-cs-ai

9 Jun 2026

Beyond Item IDs: Scaling Short-Form-Video Recommendation via Semantic-Native Long Sequence Modeling

arXiv:2606.07546v1 Announce Type: cross Abstract: Capturing user interests across extensive watch histories is critical for short-form video recommendation, yet scaling sequence length is limited by t

applicationsarxiv-cs-ai

Beyond Linear Activation Steering: Invertible Latent Transformations for Controlling LLM Behavior

arXiv:2606.08454v1 Announce Type: new Abstract: Activation steering provides a lightweight inference-time mechanism for controlling large language models (LLMs) by modifying their internal activation

Beyond Neural Collapse: Task-Intrinsic Geometry Governs Neural Representations in Modular Arithmetic

arXiv:2606.08985v1 Announce Type: new Abstract: While neural collapse (NC) predicts that a K-class-balanced classifier should organize terminal representations as a (K-1)-dimensional simplex equiangul

Beyond Pass Rate: A Multilingual, Execution-Grounded Evaluation of Open Code LLMs

arXiv:2606.08840v1 Announce Type: new Abstract: Code generation models are typically compared using compact execution benchmarks and aggregate pass rates, but such summaries obscure how performance va

Beyond Pass/Fail: Using Process Mining to Understand How LLMs Resist (and Fail) Red Team Attacks

arXiv:2606.07833v1 Announce Type: cross Abstract: Standard AI red teaming evaluations reduce adversarial campaigns to a single binary outcome, attack success rate (ASR), not taking into account the se

Local Ai

Beyond Point Estimates: Benchmarking Uncertainty Quantification Methods on the AION-1 Astronomical Foundation Model

arXiv:2606.07771v1 Announce Type: cross Abstract: Foundation models for astronomical surveys offer powerful learned representations that can be transferred to downstream regression tasks such as galax

local-aiarxiv-cs-ai

Beyond Probabilistic Similarity: Structural, Temporal, and Causal Limitations of Retrieval-Augmented Generation in the Legal Domain

arXiv:2606.09724v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard architectural response to unreliability in legal AI, yet high-profile failures, including fab

applicationsarxiv-cs-ai

Beyond Raw Signals: Undecoded Generative Latents as Privileged Synthetic Data

arXiv:2606.08336v1 Announce Type: new Abstract: While multimodal integration significantly improves computer vision models, deploying them incurs prohibitive inference costs and requires scarce, perfe

Beyond Scalar Rewards by Internalizing Reasoning into Score Distributions

arXiv:2606.09076v1 Announce Type: new Abstract: Reward models are central to text-to-image post-training, but visual preference is subjective and better represented as a distribution over rubric score

safetyarxiv-cs-cv

Beyond Spherical Harmonics: Rethinking Appearance Models for Radiance Reconstruction

arXiv:2606.09794v1 Announce Type: new Abstract: View-dependent appearance modeling remains a challenging problem in novel-view synthesis and reconstruction. Accurately representing complex angular eff

Beyond the Thin-Layer Limit: Differentiable Volumetric Training for Visible-Range Diffractive Neural Networks

arXiv:2606.07896v1 Announce Type: cross Abstract: Diffractive deep neural networks (D2NNs) promise miniaturized, power-efficient, light-speed optical front-ends for machine vision, yet the most mature

Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents

arXiv:2606.07538v1 Announce Type: cross Abstract: Large language model (LLM)-based agents provide a novel paradigm for the automated processing of remote sensing(RS) data. Their success in complex RS

Bidirectional Small-Granularity Search between Code and Text

arXiv:2606.07519v1 Announce Type: cross Abstract: We introduce the novel task of bidirectional small-granularity search between code and text, where the queries are small snippets of text or code and

Biological Reasoning-Informed Regression for Interpretable Regulatory DNA Activity Prediction

arXiv:2606.08147v1 Announce Type: cross Abstract: DNA cis-regulatory elements (CREs) such as enhancers control gene expression levels. Accurately predicting regulatory activity from DNA sequences is v

BioVid: Autoregressive Video Generation with Biological Behavior Semantic Comprehension

arXiv:2606.08674v1 Announce Type: cross Abstract: Existing video generation frameworks treat sequence duration as an externally prescribed parameter -- fixed frame counts or text prompts -- producing

BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batching

arXiv:2411.16102v2 Announce Type: replace Abstract: Offline batch inference, which leverages the flexibility of request batching to achieve higher throughput and lower costs, is becoming more popular

BLM-SGAN: Bidirectional Language Modeling for Semantic-Spatial Text-to-Image Generation

arXiv:2606.08847v1 Announce Type: cross Abstract: Despite the success of image generation from text descriptions, it still faces challenges that are difficult to overcome in domains such as natural la

researcharxiv-cs-ai

Tutorials

Blockchain Infrastructure for Intelligent Cyber--Physical--Social Systems:Post-Quantum Security, Interoperability, and Trustworthy Data Economies in the Era of Embodied AI

arXiv:2606.06895v1 Announce Type: cross Abstract: The deployment of embodied artificial intelligence via world-model-based robotics presents a transformative opportunity for blockchain infrastructure,

tutorialsarxiv-cs-ai

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving

arXiv:2606.08684v1 Announce Type: new Abstract: We present BLUE, a minimal method for better language use in vision-language-action (VLA) models for autonomous driving (AD). Through extensive analysis

Bokeh Diffusion: Defocus Blur Control in Text-to-Image Diffusion Models

arXiv:2503.08434v5 Announce Type: replace-cross Abstract: Recent advances in large-scale text-to-image models have revolutionized creative fields by generating visually captivating outputs from textua

Boundary Variance Inflation Causes Acquisition Bias in Gaussian Processes

arXiv:2606.07561v1 Announce Type: new Abstract: Gaussian processes with stationary kernels on bounded domains exhibit inflated posterior variance near the boundary. Despite being a long-recognized art

Hardware

BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks

arXiv:2602.14033v1 Announce Type: cross Abstract: Future sixth-generation (6G) mobile networks will demand artificial intelligence (AI) agents that are not only autonomous and efficient, but also capa

hardwarearxiv-cs-ai

Brain-Prompt Injection: A Route-Safety Audit for BCI-LLM Agents

arXiv:2606.09315v1 Announce Type: cross Abstract: BCI-to-agent pipelines turn decoded neural activity into an authorization channel for tool-use agents, exposing a new attack surface we call brain-pro

Brain2Text Decoding Model Reveals the Neural Mechanisms of Visual Semantic Processing

arXiv:2503.22697v3 Announce Type: replace-cross Abstract: Decoding sensory experiences from neural activity to reconstruct human-perceived visual stimuli and semantic content remains a challenge in ne

researcharxiv-cs-ai

BrainSurgery: Reproducible and Reliable Declarative Weight Manipulations for Model Editing and Upcycling

arXiv:2606.09707v1 Announce Type: new Abstract: As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alte

Breaking the Bubble: Asynchronous Pipeline Parallel Training with Bounded Weight Inconsistency

model-releasesarxiv-cs-lg

arXiv:2606.07881v1 Announce Type: new Abstract: Pipeline parallelism is essential for training large neural networks, but existing schedules trade off throughput, memory, and optimization consistency.

Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families

arXiv:2606.09456v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has become a core technique in the post-training of Large Language Models (LLMs) for transferring knowledge from domain exp

Bridged SBI: Correcting Biased Low-Fidelity Posteriors for Cost-Efficient High-Fidelity Inference

model-releasesarxiv-cs-ro

arXiv:2606.09155v1 Announce Type: new Abstract: Accurate calibration of particle-based simulators is crucial for robotic earthwork simulation, but analytical calibration is challenging due to this tas

Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution

arXiv:2606.08800v1 Announce Type: new Abstract: In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practiti

Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis

arXiv:2606.07533v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) effectively integrate text and audio to interpret context in complex interactive dialogues. However, the inte

BSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation

arXiv:2606.09257v1 Announce Type: cross Abstract: High-Dimensional Low-Sample Size (HDLSS) tabular domains (e.g., omics) are characterized by n ll m, where n = number of samples, and m = number of fea

BUDDY: BUdget-Driven DYnamic Depth Routing for Adaptive Large Language Model Inference

model-releasesarxiv-cs-lg

arXiv:2606.09514v1 Announce Type: new Abstract: Large language models (LLMs) incur high inference cost due to their depth and parameter scale. Depth pruning can reduce latency by skipping redundant Tr

Bulk-boundary decomposition of neural networks

arXiv:2511.02003v2 Announce Type: replace Abstract: We present the bulk--boundary decomposition as a new framework for understanding the training dynamics of deep neural networks. Starting from the st

Agents

Byzantine Cheap Talk: Adversarial Resilience and Topology Effects in LLM Coordination Games

arXiv:2606.07790v1 Announce Type: new Abstract: Multi-agent LLM systems increasingly rely on communication protocols for coordination, yet their robustness under adversarial and structural constraints

agentsarxiv-cs-lg

Tutorials

C^3ache: Accelerating World Action Models with Cross Inference Chunk Cache

arXiv:2606.08962v1 Announce Type: cross Abstract: World Action Models (WAMs) generalize better than standard Vision-Language-Action (VLA) policies to novel motions and environments, because a video-mo

tutorialsarxiv-cs-cv

C3VD-DEFCOL: A Deformable Colonoscopy Dataset with Time-Resolved 3D Ground Truth and Realistic Appearance

arXiv:2606.07891v1 Announce Type: new Abstract: 3D reconstruction could improve colonoscopy by estimating mucosal coverage and alerting clinicians to missed regions during screening. However, algorith

CAAL: Contextual Bandits based Online Hand-Craft Active Learning Strategy Selection

arXiv:2606.07910v1 Announce Type: new Abstract: The challenge with active learning algorithms is the uncertainty of the statistical distribution of unlabeled data, making it difficult to choose the be

CAD-Prompted SAM3: Geometry-Conditioned Instance Segmentation for Industrial Objects

arXiv:2602.20551v3 Announce Type: replace Abstract: Verbal-prompted segmentation is inherently limited by the expressiveness of natural language and struggles with uncommon, instance-specific, or diff

applicationsarxiv-cs-cv

Calibration of Structured Ignorance Certificates for Diagnosing Unknown Unknowns in Reasoning Models

arXiv:2606.08571v1 Announce Type: cross Abstract: Large language models frequently fail in a characteristic way: rather than acknowledging ignorance, they produce fluent but incorrect answers to quest

CAMF-Det: Closure-Aware Multimodal Fusion for LiDAR-Camera 3D Object Detection on UAV Platforms

arXiv:2606.09143v1 Announce Type: new Abstract: Multimodal 3D object detection based on LiDAR and cameras has demonstrated excellent performance in ground-vehicle scenarios, but has not been explored

CamoSAM2: SAM2-oriented Prompt Auto-Refinement for Video Camouflaged Object Detection

arXiv:2504.00375v2 Announce Type: replace Abstract: The Segment Anything Model 2 (SAM2), a prompt-guided video foundation model, has remarkably performed in video object segmentation, drawing signific

Can Data Work be Reparative?

arXiv:2606.09408v1 Announce Type: cross Abstract: We present an ethnographic study of an alternative approach to data work, developed by a civic-tech initiative that builds datasets for training and b

Can Global XAI Methods Reveal Injected Behaviours in LLMs? SHAP vs Rule Extraction vs RuleSHAP

arXiv:2505.11189v3 Announce Type: replace Abstract: Large language models (LLMs) can amplify misinformation, undermining societal goals such as the UN SDGs. We study three documented drivers of misinf

Can LLMs extract scientific consensus? A case study in high-temperature superconductivity

arXiv:2606.07570v1 Announce Type: cross Abstract: Scientific knowledge is increasingly dispersed across vast and heterogeneous scientific literature, where important claims are often implicit, evolvin

applicationsarxiv-cs-lg

Can the Environment Speak for Itself? T^{2}-GRPO: A Turn-Trajectory Group Relative Policy Optimization for Caregiver Agents

arXiv:2606.08875v1 Announce Type: new Abstract: Optimizing large language models (LLMs) for long-horizon caregiver agents requires balancing delayed task objectives with immediate environment dynamics

Can we stabilize an inverted pendulum with feedback from a time-of-flight camera?

model-releasesarxiv-cs-ro

arXiv:2606.09237v1 Announce Type: new Abstract: Time-of-flight cameras are popular in robotics for providing direct depth information while being compact, inexpensive, and robust to lighting condition