All entries

arXiv:2512.22819v2 Announce Type: replace Abstract: Panoramic depth estimation captures the complete 360^irc scene geometry, being essential for robotics and AR/VR applications. While perspective dept

applicationsarxiv-cs-cv

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Derandomizing Dense Binary Hypervector Codebooks for Quantized Scalars

DGX agent

arXiv:2609.38471v1 Announce Type: cross Abstract: Hyperdimensional computing and vector symbolic architectures often represent quantized scalar levels by dense binary codebooks whose level-to-level si

safetyarxiv-cs-ai

1 Oct 2026

Detail in Context: A Dual-Scale Machine Learning Framework for Mycosis Fungoides Detection

arXiv:2609.38560v1 Announce Type: new Abstract: Mycosis fungoides (MF) is a rare form of cutaneous T-cell lymphoma that is often misdiagnosed in early stages due to its visual similarity to benign inf

researcharxiv-cs-cv

Determining Vertical Displacement of Agricultural Areas Using UAV-Photogrammetry and a Heteroscedastic Deep Learning Model

arXiv:2609.39756v1 Announce Type: new Abstract: This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UAV

researcharxiv-cs-cv

Diagnosing On-Policy Self-Distillation for Reasoning Language Models

arXiv:2609.39118v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has attracted growing interest as a promising approach to improve the reasoning ability of language models. Without e

safetyarxiv-cs-cl

DiDA: Video Object Segmentation with Distillation Learning of Deformable Attention

model-releasesarxiv-cs-cv

arXiv:2401.13937v3 Announce Type: replace Abstract: Video object segmentation is a fundamental research problem in computer vision. Recent techniques have often applied attention mechanism to object r

DiFF: Doppler-informed Flow Matching for Human Motion Flow

model-releasesarxiv-cs-cv

arXiv:2609.39098v1 Announce Type: cross Abstract: Perceiving human motion via privacy-preserving 4D millimeter-wave (mmWave) radar is critical for next-generation human-robot interaction (HRI), where

Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transport

arXiv:2509.02109v3 Announce Type: replace Abstract: The Expectation-Maximisation (EM) algorithm is a central tool in statistics and machine learning, widely used for latent-variable models such as Gau

Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders

arXiv:2609.38618v1 Announce Type: cross Abstract: We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unkno

Diffusable Latents from Structure-Agnostic Distillation

arXiv:2609.39657v1 Announce Type: new Abstract: Distilling pretrained foundation models into an autoencoder bottleneck improves latent diffusability, enabling diffusion models to converge faster and r

researcharxiv-cs-cv

Diffusion-2BC: Hybrid Diffusion and Regression Training for Offline Behavior Cloning in Autonomous Driving

arXiv:2609.38472v1 Announce Type: cross Abstract: Behavior cloning provides an offline route to autonomous-driving policy learning, but mean-squared-error regression is poorly matched to demonstration

DiffWAM: A Fast and Efficient Navigation World Action Model

arXiv:2609.39763v1 Announce Type: cross Abstract: Pretrained video foundation models encode rich semantic and spatiotemporal priors for embodied navigation, yet converting these priors into UAV motion

Dimension-Free Rank Lifting from Random Hyperplane Arrangements

arXiv:2609.39855v1 Announce Type: new Abstract: We study the width required for a randomly initialized hidden layer of a neural network to achieve rank lifting. Namely, given a dataset X in R^{m imes

Disagreement-Regularized Imitation Learning for Image-Based Continuous Control with Gaussian and Beta Policies

arXiv:2609.38407v1 Announce Type: new Abstract: Purpose: Behavior cloning can accumulate errors when a learned controller visits states outside the demonstrated distribution. This study evaluates whet

safetyarxiv-cs-lg

Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts

model-releasesarxiv-cs-ro

arXiv:2609.39526v1 Announce Type: new Abstract: Efficient action generation in vision-language-action (VLA) models requires capturing both coarse action structure and fine-grained details. Discrete ac

Discrete Score Matching Enables Causal Discovery from Count Data

arXiv:2609.39326v1 Announce Type: cross Abstract: Count data pose a challenge for score-matching-based causal discovery: derivatives are unavailable, and simply replacing them with finite differences

Disentangling Computation in Multi-Task Neural Networks with the Green's Operator

arXiv:2609.40292v1 Announce Type: new Abstract: How is computation organized and reused across tasks and time in a trained recurrent network? Most analyses emphasize the geometry of neural activity, d

Disentangling Self-Distillation: Measuring and Modeling Acquisition and Retention

arXiv:2609.39494v1 Announce Type: new Abstract: Self-distillation with privileged context adapts a language model from demonstrations by letting the model, once conditioned on a reference response, te

Distill the Visual Evidence, Not Just the Answer: Cross-World On-Policy Distillation for Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2609.38777v1 Announce Type: new Abstract: A central goal of vision-language model (VLM) distillation is to transfer both the teacher's language capabilities and its visual understanding. However

Local Ai

Distilling Diffusion Score Discrepancy for Efficient Training Data Attribution

arXiv:2609.38776v1 Announce Type: cross Abstract: Training data attribution for diffusion models aims to identify the training samples that influence a generated instance, but existing methods either

local-aiarxiv-cs-ai

Distribution Matching Distillation for Continuous Diffusion Language Models

arXiv:2609.40235v1 Announce Type: cross Abstract: Continuous diffusion language models generate all tokens in parallel, yet high-quality generation can still require hundreds of network evaluations (N

researcharxiv-cs-cl

Distributionally robust linear regression through the lens of adversarial training

arXiv:2609.39449v1 Announce Type: cross Abstract: Distributionally robust optimization (DRO) studies parameter estimation under uncertainty in the underlying probability distribution and has emerged a

Diversity Combining for Multi-Path LLM Reasoning

arXiv:2609.38829v1 Announce Type: new Abstract: Multi-path reasoning methods such as self-consistency (SC) sample K reasoning paths and choose the most frequent answer. However, their gains quickly pl

Agents

Divide and Collapse: MAPF-Collapse via Exact Decomposition into Independent Sub-Instances

arXiv:2609.39559v1 Announce Type: new Abstract: In this work we study the problem of MAPFC, a post-optimization step for Multi-Agent Path Finding (MAPF) plans where we are given a feasible plan produc

agentsarxiv-cs-ai

Do Better Goal Representations Improve Goal-Conditioned Reinforcement Learning?

arXiv:2609.39901v1 Announce Type: cross Abstract: Goal-conditioned reinforcement learning (GCRL) relies heavily on how target goals are represented to the policy. While recent methods encode goals via

Agents

Do Self-Evolving Skills Generalize to Held-Out Tasks?

arXiv:2609.39148v1 Announce Type: new Abstract: AI agents can externalize what they learn from past tasks into reusable skills, such as procedures, checklists, code, or other executable artifacts, tha

agentsarxiv-cs-ai

Doc2LoRA Provides Decodable Representations of Scientific Ideas

arXiv:2609.38374v1 Announce Type: new Abstract: Representing scientific papers as points in a space lets us search for similar papers and inquire about how fields relate to one another and drive innov

researcharxiv-cs-cl

DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles

arXiv:2609.38873v1 Announce Type: new Abstract: Robots operating in human-centered environments must safely navigate among multiple dynamic obstacles to avoid collisions with people and surrounding in

safetyarxiv-cs-ro

Does a Shared Temperature Imply a Shared Angular Scale in Probabilistic Contrastive Learning?

arXiv:2609.38784v1 Announce Type: new Abstract: In probabilistic contrastive learning, a shared temperature is commonly interpreted as a shared similarity scale, but this interpretation does not hold

Does Gradient Conflict Predict the Understanding--Generation Trade-off? A Controlled Audit of Conflict-Metric Validity in Unified Multimodal Models

arXiv:2609.38465v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) are increasingly designed around gradient conflict between understanding and generation objectives. The premise that

Does Learning Protein Folding Generalize to Broader Reasoning?

arXiv:2609.38879v1 Announce Type: cross Abstract: Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protei

Does Text Steer Neural PDE Surrogates? A Controlled Diagnostic with OperatorCLIP

arXiv:2609.38517v1 Announce Type: new Abstract: Lower error from a text-conditioned neural surrogate does not, by itself, show that the model uses the meaning of the text. We examine this attribution

safetyarxiv-cs-lg

Agents

Does This Action Still Explain the Task? Reverse Scoring for Diffusion Language Model Agents

arXiv:2609.38536v1 Announce Type: cross Abstract: Diffusion-based large language models (dLLMs) promise to break the sequential latency bottleneck of autoregressive agents through parallel decoding, b

agentsarxiv-cs-ai

DoGBench: Can Agents Meet Expert Standards for User-Facing Documentation?

arXiv:2609.39909v1 Announce Type: cross Abstract: We introduce DoGBENCH (Documentation Generation Benchmark), to our knowledge, the first benchmark for generating and maintaining real user-facing soft

Don't Inoculate Everything: Stratified Inoculation Prompting Narrows Backdoor Triggers and Preserves Desired Traits

arXiv:2609.35356v2 Announce Type: replace Abstract: Supervised fine-tuning can teach language models undesired behaviours alongside desired ones. Inoculation prompting (IP) aims to limit unwanted gene

Don't Repeat Yourself: Self-Supervised Fine-Tuning for Coverage

arXiv:2609.31688v2 Announce Type: replace Abstract: In verifiable domains such as math and coding, finding one correct solution among many attempts can matter more than the pass rate of each attempt.

researcharxiv-cs-cl

Applications

Draft: A Parametric Tool for Robot Design Exploration

arXiv:2609.38405v1 Announce Type: new Abstract: Robot performance is often limited by the cost of iterating on morphology and control together, since every computer-aided design (CAD) change has to be

applicationsarxiv-cs-ro

Drape-Compatible Tool-Tip Localization for Hand-Held Laparoscopic Instruments via UWB Carrier-Phase Ranging and Trocar-Constrained Geometry

arXiv:2609.39129v1 Announce Type: new Abstract: A surgical robot policy needs to know where each instrument's working end sits relative to the camera and to the other instrument, yet a laparoscopic op

safetyarxiv-cs-ro

Dream4ACT: A Shared Visual Action Interface for Multi-Embodiment Video-Action Modeling

arXiv:2609.40153v1 Announce Type: new Abstract: Video generation models (VGMs) offer strong spatiotemporal priors for embodied observation--action modeling. However, joint-space action vectors lack ex

researcharxiv-cs-ro

Drift Inspector: Exploring and Measuring Scientific Drift with Atomic Contribution Claims

model-releasesarxiv-cs-cl

arXiv:2609.39710v1 Announce Type: new Abstract: Scientific abstracts mix contributions with background, motivation, and meta-language, so tools that read them as-is cannot separate what a field produc

DrivingBench: Can Vision-Language Models Drive a Toyota Corolla?

arXiv:2609.38948v1 Announce Type: cross Abstract: Frontier models excel at many digital benchmarks, yet their ability to drive a real car, an everyday human skill, remains largely untested. We present

Drone Soccer: Learning to Manipulate with Multicopter Downwash

arXiv:2609.38588v1 Announce Type: new Abstract: Although multicopter drones are traditionally designed for 'perception-only' tasks, like mapping and exploration, recent work has sought to develop Unma

safetyarxiv-cs-ro

DSDyn-VLA: A Dual-Stream Dynamic Manipulation Framework with Motion Perception, Future Awareness, and Realtime Correction

model-releasesarxiv-cs-ro

arXiv:2609.39198v1 Announce Type: new Abstract: While Vision-Language-Action (VLA) models excel in static tasks, they struggle in dynamic environments where objects are in motion (e.g., conveyor belt

DualCast: A Dual-Path Language Model for Bimodal Financial Time-Series Forecasting

arXiv:2609.38197v1 Announce Type: cross Abstract: Financial time-series forecasting must capture price dynamics across heterogeneous assets while incorporating news available at prediction time. We in

safetyarxiv-cs-ai

DuplexAct-Bench: Broadening Full-Duplex Speech Evaluation toward Proactive Interaction across Diverse Behavioral Requirements

model-releasesarxiv-cs-cl

arXiv:2609.39446v1 Announce Type: new Abstract: Existing full-duplex speech benchmarks cover only subsets of real-time interaction behaviors, often under limited contextual conditions. We introduce Du

Hardware

DynaFlow: Transparent and Flexible Intra-Device Parallelism via Programmable Operator Scheduling

arXiv:2605.21603v1 Announce Type: cross Abstract: Intra-device parallelism addresses resource under-utilization in ML inference and training by overlapping the execution of operators with different re

hardwarearxiv-cs-lg

DynaHarness: A Dynamic Physical Harness for Self-Evolving Robot Agents

arXiv:2609.40306v1 Announce Type: cross Abstract: Pretrained robot policies provide useful action priors, but long-horizon manipulation still requires coordination between semantic reasoning and physi

safetyarxiv-cs-ai

Dynamic LoRA-Experts and Prototype-Ensemble Matching for Class-Incremental Learning

arXiv:2609.39839v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previously acquired knowledge. Parameter-efficient fine-tunin