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Detecting LLM-Assisted Vietnamese Writing via Keystrokes under Behavioral Manipulation

arXiv:2610.07700v1 Announce Type: new Abstract: We study the robustness of keystroke dynamics for detecting large language model (LLM)-assisted writing. We introduce a Vietnamese keystroke dataset cap

researcharxiv-cs-cl

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Human DGX agent

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Safety

Dexterous Control of an 11-DOF Redundant Robot for CT-Guided Needle Insertion With Task-Oriented Weighted Policies

DGX agent

arXiv:2503.14753v3 Announce Type: replace Abstract: Computed tomography (CT)-guided needle biopsies are critical for diagnosing a range of conditions, including lung cancer, but present challenges suc

safetyarxiv-cs-ro

7 Oct 2026

DHCG: Dynamic Construction of Hierarchical Collaboration Graphs for LLM-Based Multi-Agent Reasoning

arXiv:2610.07835v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) have demonstrated strong capabilities in solving complex problems across diverse domains. Recently, the dynamic orch

Diffusion Model-Based Video Editing: A Survey

arXiv:2407.07111v2 Announce Type: replace-cross Abstract: The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making 'what you want is what you see

Digital Twin-Driven Real2Sim2Real: Simulator-Conditioned Generation via Paired Driving-Scene Reconstruction

arXiv:2610.08339v1 Announce Type: new Abstract: Camera-based 3D perception for autonomous driving relies heavily on large annotated datasets, and deploying such a system to a new target region typical

agentsarxiv-cs-cv

DIPrune: Task-Aware Token Pruning with Dual Importance for Efficient Multimodal Language Models

model-releasesarxiv-cs-cv

arXiv:2610.08341v1 Announce Type: new Abstract: Recent training-free pruning approaches for Multimodal Large Language Models (MLLMs) effectively cut computational overhead by exploiting visual redunda

Direct Regret Optimization in Bayesian Optimization

arXiv:2507.06529v2 Announce Type: replace Abstract: Bayesian optimization (BO) is a powerful paradigm for optimizing expensive black-box functions. Traditional BO methods typically rely on separate ha

safetyarxiv-cs-lg

DirectSpeech2LLM: A Simple End-to-End Framework to Mitigate Prompt Overfitting in Speech-LLMs

arXiv:2610.08085v1 Announce Type: new Abstract: Speech-LLMs often exhibit prompt overfitting, where models solely trained on automatic speech recognition (ASR) instruction fail to generalize to new in

safetyarxiv-cs-cl

Disentangling Dual Image References in Frequency Aware Diffusion Models for Personalized Generation

arXiv:2610.07684v1 Announce Type: cross Abstract: Personalized image generation aims to synthesize text-driven images conditioned on reference images, while mainly casting the generation as image cust

Disentangling Models from Personas in Heterogeneous LLM Simulations

arXiv:2610.07535v1 Announce Type: cross Abstract: Multi-agent simulations with large language models (LLMs) often operate networks of agents with a single base model. This overlooks the inter-model ef

Disentangling Paradigm, Identifier, and Decoding in Generative Retrieval

arXiv:2610.08716v1 Announce Type: cross Abstract: Generative retrieval trains a language model to generate the identifier of a relevant document. Recent work replaces the autoregressive decoder with d

researcharxiv-cs-cl

Distribution-Transfer Safe-Horizon MPC under Mode Uncertainty

arXiv:2610.07277v1 Announce Type: new Abstract: Scenario-based MPC is an attractive strategy for chance-constrained motion planning that approximates uncertainty via a finite set of sampled scenarios.

researcharxiv-cs-ro

Distributionally Robust Mixture-of-Experts Training

arXiv:2610.07207v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) transformers scale capacity by activating only a few experts per token, but this sparsity creates a hidden reliability proble

DistScene: Object-to-Scene Distillation for 3D Scene Generation

arXiv:2610.06960v1 Announce Type: new Abstract: We present DistScene, a framework for single-image compositional 3D scene generation by jointly modeling the environment and individual objects. Unlike

researcharxiv-cs-cv

Diverse Motion Customization via Control-based Dynamic Optimization

arXiv:2610.07911v1 Announce Type: cross Abstract: Despite recent advances in video generation, motion customization remains challenging due to content leakage, where appearance attributes from the ref

DLoop: Looped Speculative Decoding

arXiv:2610.07659v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens t

researcharxiv-cs-cl

Do Higher-Order Models Win for Higher-Order Reasons? Rethinking Performance Gains in Hypergraph Learning

arXiv:2610.07981v1 Announce Type: new Abstract: Higher-order models (e.g., hypergraph neural networks) often outperform lower-order baselines on hypergraph learning benchmarks, and their advantages ar

researcharxiv-cs-lg

Local Ai

Do I Need the Cloud? Uncertainty-Aware Step-Level Handoff for Small Language Model Agents

arXiv:2610.07816v1 Announce Type: new Abstract: Small language models (SLMs) are attractive as local agent controllers because they reduce remote inference, latency, and deployment footprint, yet stru

local-aiarxiv-cs-ai

Applications

Do LLMs Act on What They Know? From Partner Representations to Cooperative Actions

arXiv:2610.08129v1 Announce Type: new Abstract: Cooperation with unfamiliar partners requires adapting to communication conventions that are not known in advance. We study this problem in a controlled

applicationsarxiv-cs-lg

Tutorials

Do Neural PDE Solvers Learn the Right Dynamics?

arXiv:2610.06952v1 Announce Type: new Abstract: Neural PDE solvers can achieve low prediction errors, but do they reproduce the dynamics of the systems they model? Prediction scores alone offer an inc

tutorialsarxiv-cs-lg

Does a Learned Corrector Beat a Simple Retreat? Evidence from a Frozen VLA

model-releasesarxiv-cs-ro

arXiv:2610.06921v1 Announce Type: new Abstract: Before deploying runtime recovery for a frozen vision-language-action (VLA) policy, one must establish that an intervention improves success beyond ordi

Does an Agent's History Tell You When Compaction Will Hurt? A Modest, Bounded Effect on the TRACE Paired-Replay Corpus

arXiv:2610.08722v1 Announce Type: new Abstract: Many long-horizon agents compact their context on a global rule, usually a token budget, blind to what the agent was doing. We ask whether the agent's r

Does Muon Need Fine-Grained Spectral Shaping?

arXiv:2610.07497v1 Announce Type: new Abstract: Muon combines current and past gradients into matrix momentum. For M=USigma V^op, the idealized polar update Q=UV^op gives every singular direction the

Does On-Policy Distillation for Safety Pose Backdoor Risks?

arXiv:2610.07654v1 Announce Type: cross Abstract: On-policy distillation (OPD) has attracted growing attention as an effective way to transfer capabilities from teacher models to student models. Recen

safetyarxiv-cs-ai

Does Steering Break Your Model? A Multi-Dimensional Evaluation Suite for LLM Steering Methods

model-releasesarxiv-cs-cl

arXiv:2610.07722v1 Announce Type: new Abstract: Activation steering provides a lightweight and flexible way to control large language model (LLM) behavior. However, effective steering requires more th

Does the Model Use the Feature? Separating Steering from Mechanism in LLMs

arXiv:2610.07270v1 Announce Type: new Abstract: Internal features in LLMs are often interpreted as mechanisms when they track a concept and their manipulation changes a related behavior. Yet steering

DRtool: An Interactive Tool for Analyzing High-Dimensional Clusterings

arXiv:2509.04603v4 Announce Type: replace-cross Abstract: When faced with new data, we often conduct a cluster analysis to obtain a better understanding of the data's structure and the archetypical sa

researcharxiv-cs-lg

DrugMCTS: a drug repurposing framework combining multi-agent, RAG and Monte Carlo Tree Search

arXiv:2507.07426v4 Announce Type: replace Abstract: Recent advances in large language models have demonstrated considerable potential in scientific domains such as drug repositioning. However, their e

DSV-Mem: Evaluating Multimodal Memory in Professional Workflows for MLLM Agents

arXiv:2610.08102v1 Announce Type: new Abstract: Conversational MLLM agents are increasingly expected to assist in professional workflows, from AI research and engineering design to product management

DTFormer: Text-Guided Semantic Alignment for RGB-D Segmentation

model-releasesarxiv-cs-cv

arXiv:2610.07014v1 Announce Type: new Abstract: RGB-D semantic segmentation has made notable progress by fusing RGB and Depth, yet mainstream models still learn features almost exclusively from pixel-

Dual-Modality Multi-Stage Adversarial Safety Training: Robustifying Multimodal Web Agents Against Cross-Modal Attacks

arXiv:2603.04364v2 Announce Type: replace-cross Abstract: Multimodal web agents that process both screenshots and accessibility trees are increasingly deployed to interact with web interfaces, yet the

safetyarxiv-cs-ai

Dynamic Alignment and Calibration for Multimodal Learning

arXiv:2610.07928v1 Announce Type: cross Abstract: Dynamic multimodal learning aims to learn robust representations by adaptively modeling information discrepancies across modalities. However, existing

Dynamic Budget Allocation for LLM Evaluation under Hard Resource Constraints

model-releasesarxiv-cs-lg

arXiv:2610.07362v1 Announce Type: new Abstract: We evaluate large language models (LLMs) in multi-turn interactions through their time-to-event: the number of interaction steps required to produce an

Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models

arXiv:2610.07848v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks. However, most existing P

Dynamical low-rank equilibrium computation for stochastic games between advanced persistent threats and moving target defense

arXiv:2610.06885v1 Announce Type: cross Abstract: Moving target defense (MTD) against advanced persistent threats (APTs) in industrial control systems (ICS) has well-established game-theoretic formula

Dynamics Modeling of a Multi-UAV Slung Load System Using a Discrete-Link Cable Approach

model-releasesarxiv-cs-ro

arXiv:2610.07456v1 Announce Type: new Abstract: A common assumption to simplify the problem of controlling a multi-UAV slung load system (MUSLS) is that the flexible cables can be modeled as massless

Local Ai

DySCo: Dynamic Sharding for Collaborative Edge-Cloud LLM Inference with Depth-Synchronized Batching

arXiv:2610.08268v1 Announce Type: cross Abstract: Pervasive intelligent applications are increasingly deployed on mobile and Internet of Things (IoT) edge devices. Consequently, Large Language Models

local-aiarxiv-cs-ai

Early Memory Selection for Balanced Adam

arXiv:2610.08624v1 Announce Type: cross Abstract: We propose a method for choosing the shared memory parameter eta_1=eta_2=eta in Adam from a short pilot training. The selected eta remains fixed durin

EC-RAG: Event Chain Retrieval-Augmented Generation for Long Video Understanding

arXiv:2610.08674v1 Announce Type: new Abstract: Current large video-language models (LVLMs) still face challenges when dealing with long videos, mainly because frames are often processed independently

researcharxiv-cs-cv

Tutorials

Educating future engineers about LLMs: A scalable workshop

arXiv:2610.07027v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly integrated into engineering workflows, students require hands-on experience to learn how to collabora

tutorialsarxiv-cs-ro

Efficient Gaussian Splatting Sequence Compression with Standard Video Codecs

arXiv:2610.07795v1 Announce Type: new Abstract: This paper presents a novel effective Gaussian Splatting (GS) sequence Compression method that utilizes the Video codec (GSCV). Existing video-based GS

researcharxiv-cs-cv

Hardware

Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion

arXiv:2610.07466v1 Announce Type: new Abstract: Multimodal systems often encode every available input, even when a subset suffices for prediction. Adaptive acquisition can reduce this cost by using pr

hardwarearxiv-cs-lg

Applications

EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning

arXiv:2610.08726v1 Announce Type: cross Abstract: Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a

applicationsarxiv-cs-ai

EigenDEXplore: Structured Exploration for Dexterous Manipulation with Human Priors

arXiv:2610.07681v1 Announce Type: cross Abstract: Dexterous manipulation poses a challenging high-dimensional optimization problem, as useful behaviors require coordinated motion across many hand join

EIO-Agents: The Missing Semantic Layer for AI Agent Evaluation

arXiv:2610.07675v1 Announce Type: new Abstract: AI agents are entering production in increasingly consequential environments without a shared semantic standard for what their evaluations actually mean

Tutorials

ElasticFit: Fit-Aware 3D Object Insertion via VLM Reasoning and Generative Adaptation

arXiv:2610.07460v1 Announce Type: cross Abstract: Inserting objects into existing 3D scenes requires more than selecting a plausible location: the inserted object must also fit local geometry while pr

tutorialsarxiv-cs-ai

Embedded Bare-Metal Radar-Inertial Odometry

arXiv:2610.07278v1 Announce Type: new Abstract: Compact extraplanetary rovers and micro aerial vehicles require robust state estimation frameworks designed to operate under strict computational constr

safetyarxiv-cs-ro

EmbodiedSmith: Scaling Embodied Data through Recursive Self-Improvement Flywheel in Simulation

arXiv:2610.07969v1 Announce Type: new Abstract: Scaling robotic foundation models requires diverse training data and reliable evaluation environments. Simulation offers a scalable solution, yet existi

safetyarxiv-cs-cv