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Robust CurveMoE: Multi-Norm Adversarial Defense for Mixture-of-Experts Models via Mode Connectivity

arXiv:2608.26043v1 Announce Type: new Abstract: Multi-norm adversarial defense aims to protect neural networks against perturbations defined by different norm constraints, but existing methods typical

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Research

Rollout-Decoded Reconstruction for Long-Horizon Prediction in Latent World Models

DGX agent

arXiv:2608.25017v1 Announce Type: new Abstract: A latent world model trains its decoder on latents anchored to observations, then deploys it on the model's own free-running rollout, hundreds of steps

researcharxiv-cs-lg

27 Aug 2026

ROMNet: a hybrid reduced order modeling and machine learning approach to waveform inversion

arXiv:2608.25160v1 Announce Type: cross Abstract: Waveform inversion seeks to estimate the wave speed of a heterogeneous, inaccessible medium, from time-resolved measurements of the waves at user cont

ROS2 Connect: A new ROS2 over WAN Solution

arXiv:2608.25102v1 Announce Type: new Abstract: The Robot Operating System 2 (ROS2) has become a widely adopted framework for the development of distributed robotic systems. However, its communication

researcharxiv-cs-ro

Routed Graph Handoff: Adaptive Format Selection for Multi-Agent LLM Delegation

arXiv:2608.25277v1 Announce Type: new Abstract: Multi-agent LLM systems coordinate through natural-language messages that consume 40--60% of their token budget. Replacing these with structured graphs

agentsarxiv-cs-cl

RSFusionDet: Underwater RGB-Sonar Multimodal Object Detection

arXiv:2608.25367v1 Announce Type: new Abstract: Underwater unimodal object detection faces many challenges in sensor imaging, such as optical images limited by underwater noise and visible distance, a

Saliency-Depth Conditioning for Zero-Shot Segmentation of Communication-Tower Components in Cluttered UAV Imagery

arXiv:2608.25435v1 Announce Type: new Abstract: Fine-grained segmentation of communication-tower components in UAV imagery is essential for automated inspection, yet task-specific models are hard to d

Same-Player Verification for Account Consistency in Counter-Strike 2

arXiv:2608.24893v1 Announce Type: cross Abstract: In competitive first-person shooter (FPS) games such as Counter-Strike 2 (CS2), account-integrity review often asks whether an account's recent behavi

SAMpLE: A SystemC-AMS Machine LEarning-based Framework for Virtual Prototyping

arXiv:2608.25910v1 Announce Type: new Abstract: Machine Learning (ML) is increasingly used in virtual prototypes of embedded systems to model behaviors that are difficult to capture analytically. Howe

researcharxiv-cs-cl

Sample Margin-Aware Recalibration of Temperature Scaling

arXiv:2506.23492v2 Announce Type: replace-cross Abstract: Recent advances in deep learning have significantly improved predictive accuracy. However, modern neural networks remain systematically overco

SAUSS: Stochastic Approximation with Unbiased Simulated Scores for Limited Dependent Variable Models

arXiv:2608.25304v1 Announce Type: cross Abstract: Multinomial choice models allow flexible substitution patterns but become computationally demanding with many alternatives or observations. With a fix

local-aiarxiv-cs-lg

Hardware

Scalable Multi-GPU Simulation of 3D Multicellular Growth with RNN-Based Workload Balancing

arXiv:2608.25890v1 Announce Type: cross Abstract: Detailed multicellular growth simulations based on subcellular element models (SEMs) can capture complex tissue development, but their element-level i

hardwarearxiv-cs-lg

Scalable Self-Supervised Learning for Multiphase AC-OPF in Distribution Systems with Topology Reconfiguration

arXiv:2608.25095v1 Announce Type: cross Abstract: The proliferation of distributed energy resources (DERs) in distribution grids enables the active coordination of these assets to reduce costs and ena

Scalable Supervision for Software Agents via Patch Reasoning

arXiv:2510.22775v2 Announce Type: replace Abstract: While language model agents have advanced software engineering, existing test-based supervision is limiting its scalability on real-world issues. Th

agentsarxiv-cs-cl

Scene2Demo: Self-Evolving Embodied Data Generation via Object-Action Graph

model-releasesarxiv-cs-ro

arXiv:2602.12065v2 Announce Type: replace Abstract: We present Scene2Demo, a self-evolving framework for offline embodied data generation. Given a single real-world RGB image and a user query, Scene2D

SciMIF: Understanding Multimodal Instruction Following in Scientific Domains

arXiv:2608.25973v1 Announce Type: cross Abstract: Understanding instruction-following capabilities in scientific domains is essential for effectively leveraging Multimodal Large Language Models (MLLMs

Scorpio: Serving Right Requests at the Right Time for Heterogeneous SLOs in LLM Inference

arXiv:2505.23022v2 Announce Type: replace Abstract: Large Language Model (LLM) serving increasingly underpins online Web services such as conversational agents, Web search, and programming assistants,

See More, Detect Less? Taming Information Leakage in Multi-View Anomaly Detection

arXiv:2608.25168v1 Announce Type: new Abstract: In multi-view anomaly detection, more cross-view information can actually hurt. When multiple inspection views are naively fused in a reconstruction-bas

SEG-SAM: Semantic-Guided SAM for Unified Medical Image Segmentation

arXiv:2412.12660v2 Announce Type: replace Abstract: Recently, developing unified medical image segmentation models gains increasing attention, especially with the advent of the Segment Anything Model

safetyarxiv-cs-cv

Segmentation-Based Attention Entropy: Detecting and Mitigating Object Hallucinations in Large Vision-Language Models

arXiv:2603.16558v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on many multimodal tasks, but object hallucinations severely undermine their reliabi

researcharxiv-cs-cv

SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation

arXiv:2608.25123v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves large language models by incorporating external knowledge without retraining, but existing methods often u

researcharxiv-cs-cl

Semantic Variability of Replies Across LLMs: Implications for Designing Conversation-Based Assessment

arXiv:2608.24920v1 Announce Type: new Abstract: This study examines whether LLM-generated replies remain semantically consistent when the underlying LLM changes. Using messages from real collaborative

safetyarxiv-cs-cl

Semi-Supervised Adaptation of Vision-Language Models for Image Classification

arXiv:2608.25485v1 Announce Type: new Abstract: Vision-language models like CLIP have shown sig- nificant potential in handling natural images, yet their perfor- mance is often limited by the distinct

Applications

Sequential Object Placement Optimization with Convex Decomposition

arXiv:2608.25162v1 Announce Type: new Abstract: Robotic object packing has been a core challenge for robotic deployment in logistics, industry, etc., due to the curse of dimensionality in combinatoria

applicationsarxiv-cs-ro

SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering

arXiv:2608.25630v1 Announce Type: new Abstract: Volumetric medical VQA requires reasoning over long and redundant 3D visual token sequences, especially in multi-sequence MRI where complementary modali

SHIFT-LLM: Distribution Shift Correction in Depth-Pruned LLMs

arXiv:2608.25068v1 Announce Type: new Abstract: Depth pruning removes entire Transformer blocks to reduce the inference cost of large language models, but disrupts the hidden-state distributions expec

Short Horizons and Sparse Concepts: a Mathematical View of the Readout in the J-lens

arXiv:2608.25347v1 Announce Type: new Abstract: The Jacobian lens (J-lens) has been proposed as a way to read verbalizable representations from language models. However, its principle and meaning lack

safetyarxiv-cs-cl

Applications

SHSP: Structure-Aware Hierarchical Solution Prediction for Mixed-Integer Linear Programming

arXiv:2608.25282v1 Announce Type: new Abstract: Mixed-Integer Linear Programming (MILP) is a fundamental optimization paradigm in combinatorial optimization and has been widely applied across real-wor

applicationsarxiv-cs-lg

ShuttleArena: Interpretable Self-Play in Physics-Based Badminton

arXiv:2608.25246v1 Announce Type: new Abstract: Badminton is a compact but challenging domain for game AI: a player must choose a physically feasible shuttle trajectory, anticipate the opponent's inte

safetyarxiv-cs-lg

SimLens for Early Exit in Large Language Models: Eliciting Accurate Latent Predictions with One More Token

model-releasesarxiv-cs-cl

arXiv:2507.17618v3 Announce Type: replace Abstract: Intermediate-layer predictions in large language models (LLMs) are informative but hard to decode accurately, especially at early layers. Existing l

Simulating Cognitive Smart Freight Corridors with Agent-Based Models and Reinforcement Learning

arXiv:2608.25193v1 Announce Type: cross Abstract: Smart freight corridors offer a practical pathway for connected and automated vehicle (CAV) deployment in freight transportation, but physical experim

agentsarxiv-cs-lg

Simultaneous inference of environmental and interaction forces in collective dynamics

arXiv:2608.25181v1 Announce Type: new Abstract: Collective dynamics arise in a wide range of physical, biological, and engineering applications. Examples include cell migration, swarm robotics, social

Skeleton-based Zero-Shot Spatio-Temporal Action Localization via Weakly-Supervised Pretraining

arXiv:2608.25701v1 Announce Type: new Abstract: We propose a novel pretraining strategy for skeleton-based zero-shot spatio-temporal action localization to estimate unseen actions for person instances

Skill Issue: Are Skills Language-Invariant in LLMs?

model-releasesarxiv-cs-cl

arXiv:2608.25832v1 Announce Type: new Abstract: Large language models access knowledge inconsistently across languages, but to what extent do they differ in their skill sets when interacting with diff

SkyDrive: Learning to Drive in a New City from Aerial Traffic Monitoring

model-releasesarxiv-cs-ro

arXiv:2608.25142v1 Announce Type: new Abstract: Autonomous driving has made remarkable progress through imitation learning with massive human demonstration data. However, a trained planner often degra

SMART: MLLM-guided Temporal Alignment for Unifying Sign Language Recognition and Spotting

arXiv:2608.25493v1 Announce Type: new Abstract: Continuous sign language recognition (CSLR) aims to recognize gloss sequences from unsegmented sign videos under weak sequence-level supervision. Howeve

safetyarxiv-cs-cv

SNAP-KG: Streaming Node Assignment via Projection for Knowledge Graph Entity Integration

arXiv:2608.25149v1 Announce Type: new Abstract: Knowledge graph (KG) construction pipelines must continuously integrate newly arriving entities into a growing graph. Unlike inserting triples between e

Socialized Detector Learning: Trajectory-Guided and Reciprocal Distillation for Heterogeneous Object Detectors

arXiv:2608.25836v1 Announce Type: new Abstract: Object detection knowledge is fragmented across independently trained, heterogeneous detectors with complementary category supports. In socialized learn

safetyarxiv-cs-cv

SonicNudge: Controlled Displacement of Hovering UAVs via Estimator-Controller Coupling

arXiv:2608.25319v1 Announce Type: cross Abstract: UAV displacement attacks have traditionally relied on spoofing sensors that directly report position or translational motion, such as GNSS and optical

safetyarxiv-cs-ro

Spatio-temporal dual-stage hypergraph MARL for human-centric multimodal corridor traffic signal control

arXiv:2602.17068v2 Announce Type: replace Abstract: Human-centric traffic signal control in corridor networks must increasingly account for multimodal travelers, particularly high-occupancy public tra

agentsarxiv-cs-lg

Tutorials

Spectral Allocation: Why Muon Outperforms Adam, and How to Improve Muon

arXiv:2608.25990v1 Announce Type: new Abstract: Orthogonal optimisers such as Muon can substantially accelerate large language model pretraining relative to Adam, yet the mechanism remains incompletel

tutorialsarxiv-cs-lg

StablePDENet: Enhancing Neural Operator Stability through Physics-Informed Residual-Sensitivity Regularization

arXiv:2601.06472v2 Announce Type: replace Abstract: Learning solution operators for differential equations with neural networks has shown great potential in scientific computing, but ensuring their st

Steer the Sampling, Not the Kernel Grid: Geometry-Guided Sampling Operator for Volumetric Segmentation

arXiv:2608.25819v1 Announce Type: new Abstract: Accurate 3D segmentation is central to quantitative lesion assessment and anatomy mapping for clinical planning and follow-up. Thin, elongated, and fine

researcharxiv-cs-cv

StreamPI: Streaming Multimodal Temporal Modeling for Vision-Language-Action Models

arXiv:2608.26067v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have demonstrated effectiveness in robot manipulation, yet state-of-the-art models such as pi0.5 operate under a sin

SUPER ODOMETRY 2.0: Resilient Odometry via Hierarchical Adaptation

arXiv:2608.25427v1 Announce Type: new Abstract: Resilient and robust odometry is crucial for autonomous systems operating in complex and dynamic environments. Existing odometry systems often struggle

safetyarxiv-cs-ro

Super Star: Towards Streaming Real-time Interactive Agents for Digital Humans

arXiv:2608.24909v1 Announce Type: cross Abstract: Existing co-speech gesture generation methods are predominantly studied in offline settings, where gestures are synthesized from complete speech segme

agentsarxiv-cs-cv

SwarmWorld: Stigmergic technological evolution in societies of language-model agents

arXiv:2608.26081v1 Announce Type: cross Abstract: Collective intelligence can emerge when individuals coordinate through a shared environment, allowing local actions to accumulate into durable social

local-aiarxiv-cs-cl

Synergising Local Geo-Environmental Characteristics with Spatial Context for Enhancing Landslide Susceptibility Mapping

arXiv:2608.24956v1 Announce Type: new Abstract: Data-driven methods are widely used in landslide susceptibility mapping (LSM) because they can effectively model the complex relationships between lands