Search: “evaluation”

Diagnose-to-Generate: Eliminating Structural Distortions in Flow Models via Patch-Level Grounded Alignment

arXiv:2610.17882v1 Announce Type: new Abstract: Flow-based image generators achieve strong semantic synthesis but still produce localized structural distortions, such as malformed hands and limbs. Exi

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Model Releases

Diagnosing Generalization Boundaries in AI-Based Vulnerability Detection via Calibrated Embedding Distances

DGX agent

arXiv:2610.14296v1 Announce Type: cross Abstract: High benchmark scores in learning-based vulnerability detection mask severe degradation under distribution shifts. This paper demonstrates that calibr

model-releasesarxiv-cs-lg

14 Oct 2026

Diffusion-Based Auctions for Multi-Winner Banner Ads

arXiv:2610.14473v1 Announce Type: cross Abstract: We propose a new framework for banner ad auctions, leveraging diffusion models to allow for multiple bidders to co-win a single banner ad opportunity,

Hardware

DIGS: Distribution-Informed Gaussian Splatting for Training-Free Open-Vocabulary 3D Segmentation

arXiv:2610.16177v1 Announce Type: new Abstract: Open-vocabulary 3D Gaussian segmentation typically binds semantic features to the per-Gaussian state via per-scene gradient training (1-4 h) or the more

hardwarearxiv-cs-cv

Directional Pullback Regularization for Long-Horizon Rollout of Latent World Models in Autonomous Driving

arXiv:2610.16139v1 Announce Type: new Abstract: Learned world models for autonomous driving operate in unconstrained latent spaces, yet the physical systems they represent are governed by strict non-h

agentsarxiv-cs-ro

Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving

arXiv:2610.14571v1 Announce Type: cross Abstract: Large Language Model (LLM) inference is driving a growing share of datacenter carbon emissions. Although carbon-aware scheduling can reduce these emis

Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems

arXiv:2610.15996v1 Announce Type: new Abstract: In this paper, we propose the Dissipative Lagrangian Neural Network (dLNN), a continuous-time system identification technique that separately learns the

applicationsarxiv-cs-lg

Diversity You Can Actually Measure: A Fast, Model-Free Diversity Metric for Robotics Datasets

arXiv:2603.11634v2 Announce Type: replace Abstract: Robotics datasets for imitation learning typically consist of long-horizon trajectories of different lengths over states, actions, and high-dimensio

safetyarxiv-cs-ro

Do My Samples Help? Compute Budget Allocation for Real-World Sampling-Based MPC in Contact-Rich Manipulation

arXiv:2610.14064v1 Announce Type: new Abstract: Sampling-based model predictive control (SMPC) is increasingly practical for contact-rich manipulation because GPU-based physics simulators enable real-

safetyarxiv-cs-ro

Tutorials

Does Pretraining Teach Chemical Language Models Chemistry?

arXiv:2610.18651v1 Announce Type: new Abstract: Chemical language models (CLMs) are pretrained on large corpora of molecules with the aim of learning representations that transfer to diverse downstrea

tutorialsarxiv-cs-lg

Does self-distillation improve reasoning?

arXiv:2610.18613v1 Announce Type: new Abstract: Self-distillation is an emerging approach that trains a model to imitate its own predictions when conditioned on privileged information, such as correct

Dog-Breed Text-to-image Flow Matching from Pretraining to Fine-Tuning on 4 Consumer GPUs

arXiv:2610.18870v1 Announce Type: new Abstract: We generally perceive that training text-to-image diffusion models requires heavy compute: it is true if we aim at a comprehensive frontier model. This

DoSA: Dots Sparse Attention

model-releasesarxiv-cs-lg

arXiv:2610.13990v1 Announce Type: new Abstract: Sparse attention visits few keys, yet finding them can still require scoring every key with every indexer head. We introduce DoSA (Dots Sparse Attention

DPAL: A Test-Time Adaptation Loss for Financial Time Series Forecasting

arXiv:2610.14376v1 Announce Type: new Abstract: Test-time adaptation (TTA) enables time series forecasters to update after deployment as new observations become available. However, adaptation objectiv

Dream2Discern: Learning from Hard Latent Variants for Runtime Failure Prediction in Multi-Agent Systems

arXiv:2610.16071v1 Announce Type: new Abstract: Reliable deployment of LLM-based multi-agent systems calls for runtime auditors that predict terminal outcomes from observed execution prefixes. These a

agentsarxiv-cs-lg

DreamWorld: Unified World Modeling in Video Generation

arXiv:2603.00466v2 Announce Type: replace Abstract: Despite impressive progress in video generation, existing models remain limited to surface-level plausibility, lacking a coherent and unified unders

safetyarxiv-cs-cv

Drift, Then Spread: Learning One-Step Generative Policies for Online Reinforcement Learning

arXiv:2610.15662v1 Announce Type: new Abstract: Generative policies offer expressive action distributions for online reinforcement learning (RL), but many diffusion- and flow-based policies require co

DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving

arXiv:2610.17903v1 Announce Type: new Abstract: Vision-Language Models (VLMs) and world models have demonstrated strong capabilities in semantic understanding and future scene evolution for autonomous

agentsarxiv-cs-cv

DRMOT: A Dataset and Framework for RGBD Referring Multi-Object Tracking

arXiv:2602.04692v3 Announce Type: replace Abstract: Referring Multi-Object Tracking (RMOT) aims to track specific targets based on language descriptions, playing a vital role in interactive AI systems

agentsarxiv-cs-cv

DRNOISE: Benchmarking Deep Research Agents in Misleading Evidence Environments

model-releasesarxiv-cs-lg

arXiv:2607.17291v2 Announce Type: replace Abstract: Deep research agents increasingly operate over the open web, where relevant records coexist with redundant summaries, outdated reports, and misleadi

DVTR-KD: Distilling Vision-Language Models via Intermediate Visual Token Reconstruction

arXiv:2610.17853v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved strong performance on diverse vision-language tasks, but their computational demands limit deploy

researcharxiv-cs-cv

E8 Lattice Key Quantization for Low-Bit KV-Cache Compression

arXiv:2610.15384v1 Announce Type: new Abstract: Compressing the key-value (KV) cache is essential for serving long-context large language models (LLMs) under memory constraints. While current scalar q

EarthAlign: Learning Language-Aligned Representations of Earth System Data

arXiv:2610.14624v1 Announce Type: new Abstract: Interpreting Earth system conditions is central to understanding environmental risks and informing decisions across weather- and climate-sensitive secto

safetyarxiv-cs-cv

Efficient Frechet Distributional Decoder Alignment for Latent Generative Models

arXiv:2610.17466v1 Announce Type: new Abstract: Frechet Distributional (FD) objectives provide an effective distribution-level signal for post-training generative models. For latent generative models,

safetyarxiv-cs-cv

Efficient Hybrid Hard--Soft Signal Temporal Logic MPC for Driving under Multimodal Uncertainty

arXiv:2610.15934v1 Announce Type: new Abstract: Motion planning for automated vehicles in interactive traffic has to simultaneously ensure safety, respect traffic rules, and maintain comfortable progr

safetyarxiv-cs-ro

Efficient Scene-Text Refinement via Pixel-Space Grouped Image Generation

arXiv:2610.18284v1 Announce Type: new Abstract: Generated images often have coherent layouts and visual content, yet local errors in small text and complex characters still limit their readability and

Efficient Test-Time Adaptation through Latent Subspace Coefficients Search

arXiv:2510.11068v4 Announce Type: replace Abstract: Real-world deployment often exposes vision models to distribution shifts, making test-time adaptation (TTA) important for reliable prediction. Exist

applicationsarxiv-cs-lg

EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ

arXiv:2610.18872v1 Announce Type: cross Abstract: Learning humanoid loco-manipulation from human demonstrations rests on observing how locomotion, posture, and object interaction evolve together as ta

researcharxiv-cs-cv

ELECT: Settling Identity Before Rendering for Scalable Story Visualization

arXiv:2610.15555v1 Announce Type: new Abstract: Training-free story visualization without reference images must preserve recurring entities across frames while scaling to long narratives. Existing met

Embedding Graph Kernel Entropy (EGKE): Fine-grained Hallucination Detection for LLMs from Embeddings

arXiv:2610.14214v1 Announce Type: new Abstract: Hallucination detection is critical for ensuring the reliability of Large Language Models (LLMs) in real-world deployments. While recent works, such as

applicationsarxiv-cs-lg

Embodied Zero-Shot Task Planning via Hardware-Aware LLM-VLM Integration for Grounded Robotic Manipulation

arXiv:2610.14106v1 Announce Type: new Abstract: Large language models can translate natural-language instructions into plausible manipulation plans, but robotic execution requires physically grounded

local-aiarxiv-cs-ro

Embodiment-Aware Inference-Time Search for Human Reaction Selection in Humanoid Retargeting

model-releasesarxiv-cs-ro

arXiv:2610.15274v1 Announce Type: new Abstract: Training humanoid robots to learn human behaviors requires expensive data collection, and human-robot interaction data can be even more difficult and co

EnactOpt: Diagnosis-Guided Harness Evolution for Embodied Agents

arXiv:2610.14977v1 Announce Type: new Abstract: Embodied agents built on foundation models rely on an execution harness to organize observations, guide actions, and incorporate execution feedback into

agentsarxiv-cs-ro

Engram Neuron Inspired Adaptive Memorization for Lifelong Learning

arXiv:2610.14940v1 Announce Type: new Abstract: Lifelong learning enables artificial systems to adapt to changing environments. To achieve this, an effective artificial learner should dynamically reor

Ensemble-MIX: Enhancing Sample-Efficiency in Multi-Agent RL via Selective Ensemble-Based Exploration

arXiv:2506.02841v5 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms

Equilibrium Learning in Unbounded Stochastic Games with Gaussian Transitions

arXiv:2610.16149v1 Announce Type: cross Abstract: Learning equilibria from data is difficult when several decision makers interact indefinitely and the system state has no prescribed bounds. We examin

EvoSpatial: Experience-Driven Skill and Tool Evolution for Spatial Reasoning Agents

arXiv:2610.16679v1 Announce Type: new Abstract: Tool-augmented vision-language agents compose perception and measurement to reason about spatial relationships, but effective execution also requires tr

Exploration strategies for Gaussian process bandit optimization with inference regret guarantees

arXiv:2610.16187v1 Announce Type: cross Abstract: Existing cumulative regret guarantees for Bayesian optimization (BO) do not necessarily characterize the quality of the algorithm's final output. This

Exploring the Latent Structure of AMD Severity Using Retinal Foundation Models

arXiv:2610.14138v1 Announce Type: cross Abstract: Retinal foundation models have demonstrated strong performance across a wide range of ophthalmic tasks, yet relatively little is known about how disea

applicationsarxiv-cs-cv

Failure Recovery via Belief Updates and World Modeling for Long-Horizon Agents

arXiv:2610.18082v1 Announce Type: new Abstract: Long-horizon agents often struggle to recover from failures whose causes lie several steps earlier, beyond what local reflection or backtracking can res

local-aiarxiv-cs-lg

FALLNEST: Dual-Domain Dataset Condensation of Imbalanced IMU Data for Wearable Fall Detection

arXiv:2610.18260v1 Announce Type: new Abstract: Wearable fall detection is hampered by rare fall events, scarce labeled IMU data, and the cost of training deep models on large, imbalanced corpora. Exi

Hardware

FastEEP: Fast Intra-Instance Elastic Scaling for Expert-Parallel MoE Serving

arXiv:2610.18451v1 Announce Type: cross Abstract: Large-scale expert parallelism (EP) has become the standard deployment configuration for Mixture-of-Experts (MoE) models in production. Meanwhile, the

hardwarearxiv-cs-lg

Tutorials

Feature Normalization under Finite Training Budgets

arXiv:2610.16845v1 Announce Type: cross Abstract: Standardizing a rare feature can help stochastic gradient descent learn its effect within a finite budget, but it also changes the noise cost of learn

tutorialsarxiv-cs-lg

FEM-Validated Real-Time Modelling of Soft Tactile Deformation

arXiv:2610.16667v1 Announce Type: new Abstract: Accurate modelling of soft tactile sensors remains difficult because finite element methods (FEM) are computa- tionally expensive, while real-time simul

local-aiarxiv-cs-ro

FireNav: Decision-Oriented Reconstruction Evidence Routing for Vision-Language Navigation in Indoor Fire Environments

arXiv:2610.15595v1 Announce Type: new Abstract: In real-world indoor fire environments, visual degradation severely impairs instruction grounding, waypoint prediction, and long-horizon exploration in

Fitted Q-Iteration with Continuous Actions and Adaptive Data: Smoothed Coverage and Minimax Rates

arXiv:2605.05791v2 Announce Type: replace Abstract: Fitted Q-iteration (FQI) is well understood when the data come from a fixed distribution and the action set is finite, whereas value-based continuou

FlowCred: Hierarchical Credit Assignment and Functional Coordination for Joint Text-to-Video Post-Training

arXiv:2610.18142v1 Announce Type: new Abstract: Prompt optimization and generator post-training are two complementary ways to improve text-to-video (T2V) generation, and recent systems increasingly tr

local-aiarxiv-cs-cv

Tutorials

FlowVLA: Enabling World Dynamics Understanding through Pluggable 3D Flow Learning

arXiv:2610.16160v1 Announce Type: new Abstract: Recent advances in 3D reconstruction enable the extraction of temporally consistent 3D motion from RGB observations; however, current Vision-Language-Ac

tutorialsarxiv-cs-ro