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Benchmarking Multi-Modal Graph-based Social Media Popularity Prediction

arXiv:2606.27539v1 Announce Type: cross Abstract: Social media popularity prediction aims to forecast the future reach or influence of online content from early-stage observations. Accurate prediction

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Benchmarking on Tasks That Matter: Dataset Selection for Preserving Model Rankings

DGX agent

arXiv:2606.27997v1 Announce Type: new Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive. For efficiency, it is preferable to perform evaluations

model-releasesarxiv-cs-lg

29 Jun 2026

Beyond MoCap: Scaling Motion Tokenizers with Synthetic Human Motion for Generative Modeling

arXiv:2606.27547v1 Announce Type: new Abstract: Human motion generation models are fundamentally constrained by the limited diversity of motion capture datasets, which predominantly contain common, re

Beyond Points: Spherical Distributional Part Prototypes for Interpretable Classification

arXiv:2606.27582v1 Announce Type: new Abstract: Prototype-based neural networks aim to provide intrinsic interpretability by grounding predictions in a small set of part prototypes. However, modern vi

Local Ai

Beyond Sequential Distance: Inter-Modal Distance Invariant Position Encoding

arXiv:2603.10863v2 Announce Type: replace Abstract: Despite the remarkable capabilities of Multimodal Large Language Models (MLLMs), they still suffer from visual fading in long-context scenarios. Spe

local-aiarxiv-cs-cv

Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection

arXiv:2606.28134v1 Announce Type: cross Abstract: Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to substantial financia

applicationsarxiv-cs-ai

BiDeMem: Bidirectional Degradation Memory for Explainable Image Restoration

arXiv:2606.28112v1 Announce Type: cross Abstract: Degradation-aware prompts, conditions, and latent priors are increasingly used in image restoration, yet they are usually judged by a single endpoint:

Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel Generation

arXiv:2606.27732v1 Announce Type: cross Abstract: Discrete diffusion language models (dLLMs) recover masked tokens in parallel, offering significant speedups over autoregressive (AR) generation. Howev

Booster Lab: A Data-Centric Pipeline for Learning Deployable Humanoid Locomotion Policies

arXiv:2606.27813v1 Announce Type: new Abstract: Humanoid robot motion learning requires not only task-oriented control policies but also physically feasible and natural behaviors that can be transferr

researcharxiv-cs-ro

Boundary condition fidelity for bottom-hole pressure and CO2 plume prediction in geological carbon storage

arXiv:2606.27515v1 Announce Type: new Abstract: Accurate prediction of bottom-hole pressure (BHP) and CO2 plume migration is essential for safe geological carbon storage, yet practical simulations oft

researcharxiv-cs-lg

Bridging Ab Initio Symmetries and Global Nuclear Masses with Interpretable Neural Networks

model-releasesarxiv-cs-lg

arXiv:2606.28287v1 Announce Type: cross Abstract: Ab initio modeling has established Wigner's SU(4) and Elliott's SU(3) as dominant symmetries of the nuclear force in light and intermediate-mass nucle

Building a Scalable, Reproducible, Evaluatable, and Closed-Loop Simulation Environment Foundation for Embodied Intelligence Cloud-Native Simulation Infrastructure for Embodied Intelligence Training, Evaluation, and Data Collection

model-releasesarxiv-cs-ro

arXiv:2606.27962v1 Announce Type: new Abstract: This paper presents a cloud-native simulation infrastructure framework for embodied intelligence that supports large-scale training, standardized evalua

CacheMPC: Certified Cached Model Predictive Control for Quadruped Locomotion

arXiv:2606.28300v1 Announce Type: new Abstract: Model Predictive Control (MPC) is the standard predictive layer in hierarchical quadruped controllers, but the per-cycle QP solve limits the update rate

safetyarxiv-cs-ro

CalBrief: A Pilot Diagnostic Benchmark for Evidence-Calibrated Scientific Briefing with Large Language Models

arXiv:2606.27383v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as research assistants, yet it remains unclear whether they can calibrate research takeaways to the

Calibrating Biophysical Models for Grape Phenology Prediction via Multi-Task Learning

arXiv:2508.03898v2 Announce Type: replace-cross Abstract: Accurate prediction of grape phenology is essential for timely vineyard management decisions, such as scheduling irrigation and fertilization,

applicationsarxiv-cs-ai

Can Generative Artificial Intelligence Survive Data Contamination? Theoretical Guarantees under Contaminated Recursive Training

arXiv:2602.16065v2 Announce Type: replace-cross Abstract: As artificial intelligence (AI)-generated content proliferates, models are increasingly trained on their own outputs, risking progressive degr

Can LLMs Judge Better Than They Generate? Evaluating Task Asymmetry, Mechanistic Interpretability and Transferability for In-Context QA

arXiv:2606.28050v1 Announce Type: cross Abstract: LLM-as-a-Judge and self-evaluation pipelines implicitly assume that evaluation is easier than generation. We test this in a controlled in-context QA s

CANNs: A Toolkit for Research on Continuous Attractor Neural Networks

arXiv:2606.27783v1 Announce Type: cross Abstract: Continuous attractor neural networks (CANNs) are the canonical computational framework for how the brain encodes continuous variables such as spatial

researcharxiv-cs-lg

Agents

CascadeOcc: Rethinking 3D Occupancy World Models with Cascaded VQ Representations

arXiv:2606.27644v1 Announce Type: new Abstract: This letter proposes CascadeOcc, a novel occupancy world model that prioritizes intrinsic structural hierarchy over extrinsic auxiliary modalities for a

agentsarxiv-cs-cv

Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026

model-releasesarxiv-cs-cl

arXiv:2606.27446v1 Announce Type: new Abstract: This paper describes team HSA_CORAL's submission to the FinCausal 2026 shared task on extracting cause-effect relations from financial narratives via ex

CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching

arXiv:2602.20094v2 Announce Type: replace Abstract: As large language models (LLMs) witness increasing deployment in complex, high-stakes decision-making scenarios, it becomes imperative to ground the

Tutorials

CBD: API-Only LLM Black-Box Unlearning through Controlled Behavioral Divergence

arXiv:2606.27683v1 Announce Type: cross Abstract: Edge devices increasingly invoke large language models (LLMs) through API services for context aware edge intelligence, while edge generated data may

tutorialsarxiv-cs-ai

Agents

Characterizing Driver Interactions with Autonomous Vehicles via Response Maps

arXiv:2606.27656v1 Announce Type: cross Abstract: Understanding human responses to autonomous vehicle (AV) behaviors is essential for socially aware interaction, which is crucial for socially compatib

agentsarxiv-cs-ro

Check Yourself Before You Wreck Yourself: Selectively Quitting Improves LLM Agent Safety

arXiv:2510.16492v4 Announce Type: replace Abstract: As Large Language Model (LLM) agents increasingly operate in complex environments with real-world consequences, their safety becomes critical. While

safetyarxiv-cs-cl

Chronic Kidney Disease Prognosis Prediction Using Transformer

arXiv:2511.02340v3 Announce Type: replace Abstract: Chronic Kidney Disease (CKD) affects nearly 10% of the global population and often progresses to end-stage renal failure. Accurate prognosis predict

Class-frequency Guided Noise Schedule for Diffusion Models

arXiv:2606.27696v1 Announce Type: cross Abstract: In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For sc

Cluster, Route, Escalate: Cascaded Framework for Cost-Aware LLM Serving

arXiv:2606.27457v1 Announce Type: cross Abstract: Efficient deployment of large language models (LLMs) in production forces a trade-off between accuracy and cost. Operators often default to a single m

applicationsarxiv-cs-cl

Local Ai

CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection

arXiv:2504.01882v2 Announce Type: replace Abstract: The use of DNS over HTTPS (DoH) tunneling by an attacker to hide malicious activity within encrypted DNS traffic poses a serious threat to network s

local-aiarxiv-cs-lg

COCOLogic-V2: Identifying Logical Inconsistencies via Truly Hard-Negatives

arXiv:2606.28194v1 Announce Type: new Abstract: While interpretable models such as concept bottleneck models (CBMs) and program synthesis methods enable verification of model decisions, their evaluati

applicationsarxiv-cs-lg

Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction

arXiv:2606.28186v1 Announce Type: cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction. Exis

CoIn: Comprehensive 2D-3D Inpainting with Gaussian Splatting Guidance

arXiv:2606.27584v1 Announce Type: cross Abstract: 3D scene inpainting is essential for reconstructing areas corrupted by occlusions or limited viewpoints. While recent methods leverage Gaussian Splatt

Complex-Valued 2D Gaussian Representation for Computer-Generated Holography

model-releasesarxiv-cs-cv

arXiv:2511.15022v2 Announce Type: replace Abstract: Complex-valued Gaussian primitives have recently been explored for representing holographic radiance fields in 3D novel view synthesis. In this work

Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder

arXiv:2606.27411v1 Announce Type: cross Abstract: We study a quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI data. The approach leverages angle encoding to map image pa

Conservative Equilibrium Discovery in Offline Game-Theoretic Multiagent Reinforcement Learning

arXiv:2603.00374v2 Announce Type: replace Abstract: Offline learning of strategies takes data efficiency to its extreme by restricting algorithms to a fixed dataset of state-action trajectories. We co

Contagion Networks: Evaluator Preference Propagation in Multi-Agent LLM Systems

arXiv:2606.20493v2 Announce Type: replace-cross Abstract: When large language models serve as evaluators in multi-agent systems, their strategy preferences -- whether induced by explicit prompts or by

Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuits

model-releasesarxiv-cs-lg

arXiv:2603.26629v2 Announce Type: replace Abstract: Multimodal fusion requires integrating information from multiple sources that may conflict depending on context. Existing fusion approaches typicall

Local Ai

Continual Learning for Sequential Personalization of Small Language Models: A Stability Monitoring Analysis

arXiv:2606.27634v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly being considered for deployment on edge devices such as laptops, enabling private, low-latency, and locall

local-aiarxiv-cs-lg

Continual Memorization of Factoids in Language Models

arXiv:2411.07175v3 Announce Type: replace Abstract: As new knowledge rapidly accumulates, language models (LMs) with pretrained knowledge quickly become obsolete. A common approach to updating LMs is

researcharxiv-cs-cl

Contrastive Language-Colored Pointmap Pretraining for Unified 3D Scene Understanding

arXiv:2604.02546v2 Announce Type: replace Abstract: Pretraining 3D encoders by aligning with Contrastive Language Image Pretraining (CLIP) has emerged as a promising direction to learn generalizable r

safetyarxiv-cs-cv

Controllable Histopathology Image Synthesis with Training-free Structural Initialization and Textural Modulation

arXiv:2606.27935v1 Announce Type: new Abstract: Deep learning has demonstrated remarkable success in high-throughput histopathology image analysis. However, the performance of learning-based models cr

Agents

COOPA: A Modular LLM Agent Architecture for Operations Research Problems

arXiv:2606.27611v1 Announce Type: new Abstract: Operations Research (OR) provides a rigorous framework for high-stakes decision-making, but effective OR modeling requires substantial domain knowledge,

agentsarxiv-cs-lg

CPAgents: Agentic Composite Phenotype Generation for Cardiac Disease Association

arXiv:2606.28179v1 Announce Type: cross Abstract: Identifying robust associations between cardiac imaging phenotypes and clinical diseases is fundamental to population-scale cardiovascular research an

Cross-Platform Chinese Offensive Comment Detection via Dual-Threshold Hard Example Mining

arXiv:2606.27629v1 Announce Type: cross Abstract: Cross-platform deployment of offensive comment detection for Chinese social media suffers performance degradation. The paper proposes a dual-threshold

Cross-view Multimodal Vision-Based Assessment Framework for Traditional Chinese Medicine Rehabilitation Training

arXiv:2606.28104v1 Announce Type: new Abstract: Vision-based assessment can provide convenient and cost-effective evaluation in Traditional Chinese Medicine (TCM) rehabilitation training, where action

CSD: Content-aware Speculative Decoding for Efficient Image Generation

arXiv:2606.27829v1 Announce Type: new Abstract: Speculative decoding (SD) has emerged as a key solution to accelerate the inference of autoregressive models. However, in the field of image generation,

safetyarxiv-cs-cv

Curriculum-guided Change Detection Training: Toward Accurate Serac Fall Monitoring

model-releasesarxiv-cs-cv

arXiv:2606.28012v1 Announce Type: new Abstract: Change Detection (CD) aims to identify semantic or structural changes from nearly registered multi-temporal images. While recent advances in training me

CWI: Composite Humanoid Whole-Body Imitation System for Loco-manipulation

arXiv:2606.27676v1 Announce Type: new Abstract: Achieving everyday tasks with humanoid robots requires coordinating stable locomotion with versatile manipulation. However, existing whole-body controll

safetyarxiv-cs-ro

Dangerous Liaisons of Convex Learning and Non-Affine Aggregation

arXiv:2606.28123v1 Announce Type: new Abstract: Last-iterate convergence and generalization guarantees in first-order convex learning hinge on the monotonicity of the update operator. While linear ave

safetyarxiv-cs-lg