Search: “models”

VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling

arXiv:2610.12104v1 Announce Type: cross Abstract: Building a capable video editor remains significantly harder than a video generator: editing requires (source, instruction, edited) triplets that are

researcharxiv-cs-ai

9 Oct 2026

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What 30,000 Hours of Ego-centric Video Does Not Teach

DGX agent

arXiv:2610.12464v1 Announce Type: new Abstract: World models offer a promising alternative to physics-based simulators, yet remain far from practical deployment. We ask how far scaling ego-centric hum

model-releasesarxiv-cs-cv

9 Oct 2026

WorldBench: Evaluating LLMs on Three.js Voxel World Generation

arXiv:2610.10622v1 Announce Type: cross Abstract: Large language models can now write complete, interactive 3D worlds as code, but grading those worlds automatically is unreliable. Existing judges tak

9 Oct 2026

Arctic Questions, Missing Answers: A Dataset and Benchmark for LLM Abstention in Arctic Science

arXiv:2610.09446v1 Announce Type: new Abstract: Large language models (LLMs) should abstain from scientific multiple-choice questions when no option is valid, but frequent abstention alone does not de

Beyond Anonymous Captions: Grounding Character Identity in Video Captioning and Question Answering

arXiv:2610.10163v1 Announce Type: new Abstract: Linking people's appearance and actions to character identities is essential for understanding video narratives. We present a framework for identity-awa

Cache the Encoder Within:Compact, Reusable Memory across LLM Queries

arXiv:2610.10058v1 Announce Type: new Abstract: Repeated queries over shared documents incur redundant encoding, while caching model states introduces persistent storage costs. Building on CoMem's int

Despite Instructions: Frontier Agents Improvise Covert Channels at Test Time

arXiv:2609.32701v2 Announce Type: replace-cross Abstract: In security-sensitive applications, language-model agents are often required to coordinate without disclosing confidential information. Yet re

Input-Blind Controls Produce Substantial Oracle Headroom for Layer Programs in Multiple-Choice Evaluation

arXiv:2610.10368v1 Announce Type: cross Abstract: Adaptive computation aims to improve language-model inference by tailoring execution to each input. For layer programs, oracle evaluations use known a

PalmSpace: Towards a Versatile On-Palm Interaction Space through Unified Touch Modeling

arXiv:2610.10370v1 Announce Type: cross Abstract: As smart glasses and lightweight MR devices become increasingly practical, input remains a key challenge. The bare palm is an always-available, tactil

researcharxiv-cs-cv

Pre-training, Reasoning, Benchmarking: X-ray Report Generation on CheXpert Plus Dataset

arXiv:2610.08813v1 Announce Type: new Abstract: X-ray image-based Radiology Report Generation (RRG) constitutes a critical research direction within medical artificial intelligence, with great potenti

Stability and Diversity of Networked Self-Consuming Generative Ecosystems

arXiv:2610.09409v1 Announce Type: new Abstract: The widespread deployment of generative AI has made it increasingly difficult to distinguish synthetic content from real data. Consequently, synthetic d

researcharxiv-cs-lg

Tucker Bottleneck Attention for Multi-Dimensional Sequence Modeling

arXiv:2610.09090v1 Announce Type: new Abstract: The quadratic cost of self-attention limits scalability to long sequences from multidimensional data. We introduce Tucker bottleneck attention (TuBA), w

researcharxiv-cs-lg

WAPR: A Foundation Model for Wide-Angle Refinement in Unseen Object Pose Estimation

arXiv:2610.09535v1 Announce Type: new Abstract: Real-world applications require 6D pose estimation to be accurate, fast, and scalable to unseen objects. This paper introduces WAPR, a zero-shot wide-an

local-aiarxiv-cs-cv

A Stevens's Power Law Check-up of GPT-5.5's Image-Based Visualization Reading

arXiv:2610.08365v1 Announce Type: new Abstract: We adapt Stevens's power law to measure the innate ability of AI models to read visualizations, which can reveal the built-in perceptual mechanisms of a

Cascadia: Resident 975B MoE Inference on Eleven AI PCs

arXiv:2610.07219v1 Announce Type: new Abstract: Mixture-of-experts models make nearly trillion-parameter capacity accessible with sparse per-token computation, provided that the serving system can dis

CausalBind: Causal Modeling and Learning for Protein-Molecule Virtual Screening

arXiv:2610.07340v1 Announce Type: cross Abstract: Protein-molecule virtual screening is increasingly cast as a problem of representation learning in a shared embedding space. Existing methods rely on

local-aiarxiv-cs-ai

DecepEval: A Benchmark for Evaluating Deception in LLM Agents

arXiv:2610.07967v1 Announce Type: new Abstract: As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their re

Defense-in-Depth for LLMs: Evaluating Memory Gates Against Activation-Induced and Memory-Induced Sycophancy

arXiv:2610.07403v1 Announce Type: new Abstract: Long-term memory allows Large Language Models (LLMs) to maintain personalized context across interactions, but retrieved user history can induce memory-

Dynamic Budget Allocation for LLM Evaluation under Hard Resource Constraints

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

Evaluating Behavioral Context for Interpretable IAM Policy Risk Scoring in Cloud Environments

arXiv:2610.07345v1 Announce Type: cross Abstract: IAM policy analysis typically emphasizes the authorization capabilities encoded in a policy, but security analyst review priority may also depend on t

Federated Mixture-of-Experts Alignment on Mobile Edge Networks under Data Heterogeneity

arXiv:2603.21276v2 Announce Type: replace-cross Abstract: The growing demand for on-device large language model (LLM) services on mobile edge devices has driven the adoption of Mixture-of-Experts (MoE

Agents

FRAGMENTA: Efficient End-to-end Fragmentation-based Generative Model with Agentic Tuning for Drug Lead Optimization in Small Data Regime

arXiv:2511.20510v3 Announce Type: replace Abstract: Molecule generation from extremely limited training data is a key challenge in drug discovery. Existing fragment-based methods are more suitable tha

agentsarxiv-cs-ai

Identifying Introspection From the Inside

arXiv:2610.07186v1 Announce Type: new Abstract: Large language models make claims about themselves that are both consequential and increasingly difficult to verify from behavior alone. How can we dist

researcharxiv-cs-cl

Label-Efficient Deep Learning for ECG Delineation: A Multi-Dataset Benchmark against Widely Used Delineation Tools

arXiv:2610.07885v1 Announce Type: cross Abstract: Electrocardiogram (ECG) delineation, the identification of waveform boundaries, is a foundational step that translates raw ECG signals into clinically

Agents

Large Language Model Orchestration under Heterogeneous Preferences via Explicit Persona Inference

arXiv:2610.07587v1 Announce Type: new Abstract: LLM orchestration investigates how an orchestrator coordinates a group of autonomous agents to achieve common goals or maximize collective welfare. The

agentsarxiv-cs-cl

Learning to Retrieve via Reinforcement Learning in Embedding Space

arXiv:2610.07731v1 Announce Type: cross Abstract: Dense retrieval models are typically trained with contrastive objectives that learn effective representations but do not directly optimize retrieval m

LOGIC: An LLM Benchmark for Intent-Grounded Change Impact in Aerospace Electrical Systems

arXiv:2610.07580v1 Announce Type: new Abstract: Aerospace electrical-design revisions can contain multiple genuine changes, although an engineering request may authorize only a subset. Propagating eve

MedHorizon: Towards Long-context Medical Video Understanding in the Wild

arXiv:2605.06537v2 Announce Type: replace Abstract: Medical multimodal large language models (MLLMs) have advanced image understanding and short-video analysis, but real clinical review often requires

Natural Language Questions as an Interface for Knowledge Graphs: QRAKEN Graph Distillation and Semantic Self-Healing

arXiv:2610.08095v1 Announce Type: new Abstract: Natural-language access to RDF knowledge graphs is a core Semantic Web ambition. Large language models (LLMs) have advanced Text-to-SPARQL, yet on unfam

Reactive Exploration of Unknown Environments for Redundant Robots using Virtual Model Control

arXiv:2610.08110v1 Announce Type: new Abstract: The exploration of confined, occluded, and partially known spaces poses significant challenges in robotic manipulation. The overall pose of the robotic

researcharxiv-cs-ro

Reward-Driven Learning under Prompt-Level Differential Privacy

arXiv:2610.07212v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains a language model on problems that may themselves be confidential, and the trained model can

ServeLearnBench: How Well Can Agents Self-Improve from Serving Experience?

arXiv:2610.07792v1 Announce Type: cross Abstract: Large language model agents are increasingly deployed to perform complex tasks in real-world environments. However, the knowledge required for correct

TIDE 2.0: an open, model-agnostic engine for keyed de-identification of clinical notes

arXiv:2610.07224v1 Announce Type: cross Abstract: Clinical notes capture most of what is documented about a patient's care, but they cannot be used for research until protected health information (PHI

researcharxiv-cs-ai

WorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?

arXiv:2610.08720v1 Announce Type: new Abstract: LLM-based agents are increasingly advancing scientific and engineering problem solving, with physics simulation emerging as a challenging yet practical

Agents

AgentDiscover: Autonomous Discovery with Minimal Search Scaffolding

arXiv:2610.05334v1 Announce Type: cross Abstract: Frameworks that use large language models for scientific discovery typically rely on a fixed, human-designed algorithm that decides what the model see

agentsarxiv-cs-lg

ColdDDI: Evaluating Knowledge Utilization in Cold-Start Drug-Drug Interaction Prediction

arXiv:2610.05590v1 Announce Type: new Abstract: Cold-start drug-drug interaction (DDI) prediction tests whether models can identify clinically significant interactions for drugs without training-time

Do More Modalities Always Help? A Geometric Perspective on Missing-Modality Robustness

arXiv:2610.04792v1 Announce Type: cross Abstract: Missing modality remains a longstanding challenge in multimodal learning. Existing methods typically address this issue through modality recovery or a

Does Explainability Survive Data Drift?

arXiv:2610.05379v1 Announce Type: new Abstract: Model performance monitoring is a standard practice in machine learning deployments. Detection performance is tracked continuously, and model decay is e

local-aiarxiv-cs-lg

FLAT: Smoothing the Rugged Landscape for Learnable, Sample-Efficient Traffic Calibration

arXiv:2610.04337v1 Announce Type: cross Abstract: Calibrating microscopic traffic models for digital twins is an expensive black-box optimization problem: tuning car-following and lane-changing parame

MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning

arXiv:2507.07297v2 Announce Type: replace Abstract: Recent advances in large vision-language models have led to impressive performance in visual question answering and multimodal reasoning. However, i

Render to Reason: Novel-View Semantic Prediction Improves Spatial Understanding in VLMs

arXiv:2610.05417v1 Announce Type: new Abstract: Recent works augment Vision-Language Models with geometry features from pretrained 3D models, expecting that the geometric signal will boost spatial rea

researcharxiv-cs-cv

Safety

Scenario-Based Compositional Statistical Model Checking for Safety Specifications

arXiv:2610.05571v1 Announce Type: cross Abstract: In safety-critical domains such as autonomous driving, systems must be evaluated across a large number of environment conditions, often represented as

safetyarxiv-cs-ro

StageVLN: Spatial and Trajectory Auxiliary Guidance for Efficient Vision-Language Navigation

arXiv:2610.05664v1 Announce Type: new Abstract: Vision-and-Language Navigation (VLN) policies increasingly benefit from strong semantic priors provided by large vision-language models (VLMs). However,

Steering by Influence: Curvature Aware Data Weighting for Activation Steering

arXiv:2610.06383v1 Announce Type: new Abstract: Inference-time steering offers cheap, fine-grained control over a language model's outputs by estimating a concept's representation in activation space

researcharxiv-cs-lg

Verifier-Guided Synthetic Augmentation for 3D Human Shape Generation

arXiv:2610.04006v1 Announce Type: new Abstract: Limited training data diversity constrains generative modeling of 3D human bodies: conservative models remain close to observed examples, whereas explor

local-aiarxiv-cs-cv

Voltic: Distinguishing Volatility from Stochasticity in Recurrent Memory

arXiv:2610.05700v1 Announce Type: new Abstract: Recurrent sequence models must decide how strongly to overwrite their memory at each token. Read as Bayesian filtering, this write is the gain of a Kalm

Budgeted-GS: Real-Time Large-Scale Gaussian Splatting via Factoring LOD

arXiv:2610.03162v1 Announce Type: cross Abstract: 3D Gaussian Splatting achieves excellent visual quality with real-time rendering, but at the scale of entire cities it does not fit: a trained model c

5 Oct 2026

D2K-Bench: Can LLM Agents Turn Expert Designs into Efficient GPU Kernels?

arXiv:2610.03226v1 Announce Type: cross Abstract: GPU kernels generated by large language model (LLM) agents can remain less efficient than expert implementations, but runtime alone does not reveal ho