Search: “inference”

Contrastive and Adaptive Multi-modal Masked Autoencoder for Spatial Transcriptomics

arXiv:2606.21156v1 Announce Type: new Abstract: The high cost of spatial transcriptomics (ST) has driven extensive studies into predicting gene expression directly from H&E histology images. However,

tutorialsarxiv-cs-cv

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Model Releases

CoRDE: Concept-Prior Routed Diffusion Experts for Structural Generalization in Robot Manipulation

DGX agent

arXiv:2606.21935v1 Announce Type: new Abstract: Diffusion models excel at capturing multi-modal action distributions in robot imitation learning. However, in multi-task and long-horizon scenarios, mon

model-releasesarxiv-cs-ro

23 Jun 2026

CQD-SHAP: Explainable Complex Query Answering via Shapley Values

arXiv:2510.15623v2 Announce Type: replace Abstract: Complex query answering (CQA) goes beyond the widely studied link prediction task by addressing more sophisticated queries that require multi-hop re

Cross-Modal Corroboration for Annotation-Free Wildlife Monitoring

arXiv:2606.21613v1 Announce Type: new Abstract: Scaling wildlife monitoring for real-world conservation deployments requires automated analysis of smart sensors that operate under severe annotation sc

safetyarxiv-cs-cv

Customizing Video Portraits via Identity-ActionDecoupling

arXiv:2606.22347v1 Announce Type: new Abstract: Identity-Preserving Text-to-Video Generation (IPT2V) seeks to synthesize a temporally coherent video from a reference image and a textual description, w

safetyarxiv-cs-cv

D2HDMap: Non-visible Driveline Map Prior for Online Vectorized HD Map Prediction

arXiv:2606.20725v1 Announce Type: new Abstract: Accurate, up-to-date representations of road structures are critical for the safe operation of autonomous vehicles. Existing systems rely either on cost

safetyarxiv-cs-cv

DCD-PFN: A Decoupling-Aware Foundation Model for Causal Discovery

arXiv:2606.21212v1 Announce Type: new Abstract: Causal discovery is critical for understanding complex data-generating mechanisms, yet traditional algorithms often struggle with highly non-linear and

local-aiarxiv-cs-lg

Deep Learning-Based Sign Language Recognition from Videos and Cross-Lingual Translation to Indian Vernaculars

arXiv:2606.22494v1 Announce Type: cross Abstract: Sign language is a primary mode of communication for the global deaf and hard-of-hearing community, yet automated tools that recognize sign gestures f

Deep Learning for Individual Heterogeneity

arXiv:2010.14694v4 Announce Type: replace-cross Abstract: This paper integrates deep neural networks (DNNs) into structural models to increase flexibility and capture rich heterogeneity while preservi

Diffusion-Driven State Space Models

arXiv:2606.21036v1 Announce Type: cross Abstract: In many domains, practitioners seek models that produce accurate forecasts while faithfully capturing latent system dynamics. Existing approaches typi

tutorialsarxiv-cs-lg

Diffusion Integrated Gradients: Controllable Path Generation for Flexible Feature Attribution

arXiv:2606.22314v1 Announce Type: cross Abstract: Path-based attribution methods such as Integrated Gradients (IG) are widely adopted for their strong axiomatic properties and effectiveness in attribu

tutorialsarxiv-cs-cv

Distilling Collaborative Dynamics into Latent Space for Implicit Coordination in Decentralized Multi-Agent Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.22982v1 Announce Type: new Abstract: Multi-arm manipulation demands precise spatiotemporal coordination, yet many centralized approaches scale poorly as team size increases. To address this

DiT-Reward: Generative Representations for Text-to-Image Reward Modeling

arXiv:2606.23626v1 Announce Type: new Abstract: Can representations learned for image generation also support the evaluation of generated images? We study text-to-image reward prediction as a downstre

Domain-incremental audio classification using domain-specific experts and prototype classifier

arXiv:2606.22952v1 Announce Type: cross Abstract: This technical report presents submission systems for Task 7(domain-incremental audio classification) of the DCASE 2026 Challenge. The main obstacle i

Eigenspace-Based Clustering for Personalized System Identification

arXiv:2606.20811v1 Announce Type: cross Abstract: We study the problem of system identification in heterogeneous settings, where different systems may follow distinct underlying dynamics. Existing clu

local-aiarxiv-cs-lg

ELDiff: When Evidential Learning Meets Text-to-Image Diffusion

model-releasesarxiv-cs-cv

arXiv:2606.20924v1 Announce Type: new Abstract: In multi-object text-to-image (T2I) diffusion, ensuring semantic consistency between textual prompts and generated visual content is crucial for image s

Error Highways: Scaling Predictive Coding to Very Deep Networks

arXiv:2606.22744v1 Announce Type: new Abstract: Predictive coding networks (PCNs) offer a biologically-plausible, local-learning alternative to back-propagation of errors (backprop). Nevertheless, the

local-aiarxiv-cs-lg

Essential Subspace Merging for Multi-Task Learning

arXiv:2606.19164v2 Announce Type: replace Abstract: Model merging aims to enable multi-task learning by integrating the capabilities of multiple models fine-tuned from the same pre-trained checkpoint

Applications

Estimating Learners' Skill Acquisition Without Temporal Information

arXiv:2606.20611v1 Announce Type: cross Abstract: Recent research in educational data mining, especially knowledge tracing, has focused on predicting learners' future knowledge states to support adapt

applicationsarxiv-cs-lg

Explore-Execute Chain: Towards an Efficient Structured Reasoning Paradigm

arXiv:2509.23946v3 Announce Type: replace Abstract: Many LLMs plan before they act, yet planning and execution are often still entangled in one long generation trace, enforced only through prompts, or

Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems

arXiv:2606.22346v1 Announce Type: cross Abstract: Principled regression for stochastic processes is a long-standing challenge with deep connections to scientific inverse problems. We introduce Flow An

Flow6D: Discrete-to-Continuous Flow Matching for Efficient and Accurate Category-Level 6D Pose Estimation

arXiv:2606.23293v1 Announce Type: new Abstract: 6D pose estimation is a key task in computer vision and embodied AI, widely used in robotic manipulation, augmented reality, etc. Existing methods direc

local-aiarxiv-cs-cv

ForeHOI: Feed-forward 3D Object Reconstruction from Daily Hand-Object Interaction Videos

arXiv:2602.06226v2 Announce Type: replace Abstract: The ubiquity of monocular videos capturing daily hand-object interactions presents a valuable resource for embodied intelligence. While 3D hand reco

From Markov to Laplace: How Mamba In-Context Learns Markov Chains

arXiv:2502.10178v2 Announce Type: replace Abstract: While transformer-based language models have driven the AI revolution thus far, their computational complexity has spurred growing interest in viabl

Applications

From Pixels to Concepts: Growing Rich 3D Semantic Scene Graph Forests utilizing Foundation Models

arXiv:2606.23312v1 Announce Type: new Abstract: Operating in complex real-world environments requires robots to understand their surroundings on a functional semantic level. This demands a detailed mu

applicationsarxiv-cs-ro

From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis

arXiv:2512.13491v3 Announce Type: replace-cross Abstract: We inspect the deductive connection between the neural scaling law and Zipf's law -- two statements discussed in machine learning and quantita

Geometric Reconstruction of Extrinsic Contact Trajectories using Tactile Sensing and Proprioception for Tool Manipulation

arXiv:2606.22251v1 Announce Type: new Abstract: Tactile sensing enables robots to perceive rich contact information at the grasp, supporting tasks such as object recognition, in-hand pose estimation,

researcharxiv-cs-ro

Agents

GRADE: Graph Representation of LLM Agent Dependency and Execution

arXiv:2606.22741v1 Announce Type: new Abstract: Can one graph represent every kind of LLM agent's run? A trace records what each step did, never what it relied on, the state it read, and the results i

agentsarxiv-cs-lg

HaineiFRDM: Structure-Preserving Diffusion for Film Restoration under Fast Motion and Diverse Defects

model-releasesarxiv-cs-cv

arXiv:2512.24946v2 Announce Type: replace Abstract: Existing film-restoration methods frequently fail under fast motion, producing limb disappearance and structural distortion due to inaccurate motion

Hard or Just Unreached? Diagnosing the Sampling Blind Spot in Math-Reasoning Difficulty Estimation

arXiv:2606.19636v2 Announce Type: replace Abstract: Math and science reasoning benchmarks rely on pass@k, the fraction of sampled chains that reach gold, as the canonical per-example difficulty signal

Hardware

HERALD: High-Throughput Block Diffusion LLM Serving via CPU-GPU Cooperative KV Cache Retrieval

arXiv:2606.21633v1 Announce Type: new Abstract: Diffusion LLMs (dLLMs) improve GPU utilization over autoregressive decoding by generating multiple tokens per forward pass, but their KV cache still gro

hardwarearxiv-cs-lg

Homographic Navigation: Geometry-Driven Camera Guidance for Deterministic Planar Capture

arXiv:2606.22834v1 Announce Type: new Abstract: We present homographic navigation, a geometry-centric framework for guiding camera acquisition toward precise capture of planar regions. Rather than tre

local-aiarxiv-cs-cv

Hybrid Compression: Integrating Pruning and Quantization for Optimized Neural Networks

model-releasesarxiv-cs-cv

arXiv:2606.22935v1 Announce Type: new Abstract: Deep neural networks have witnessed remarkable advancements in recent years and have become integral to various applications. However, alongside these d

In LLM Reasoning, there is Irrationality on top of Value Misalignment

arXiv:2606.20624v1 Announce Type: cross Abstract: Significant progress has been made in aligning LLMs with target value functions. We argue that, even when an LLM has been well aligned in (post-)train

Intent-Handover: Grounding Language in Human-Usage Regions for Trustworthy Robot-to-Human Handovers

arXiv:2503.03579v2 Announce Type: replace-cross Abstract: Spoken instructions in robot-to-human handovers may specify either an object ('the cup') or an intended use ('pour water'); in both cases, suc

Interpretable Uncertainty Routing Separating Emotion Ambiguity from Distribution Shift in Facial Expression Recognition

arXiv:2606.22725v1 Announce Type: new Abstract: Facial expression recognition (FER) is inherently ambiguous: human annotators frequently disagree, and models deployed in real environments face distrib

IRumAI: Reinforcement Learning for Indian Rummy

arXiv:2606.21975v1 Announce Type: cross Abstract: Despite its massive player base and complex hidden-information dynamics, Indian Rummy has received no reinforcement learning attention. Existing agent

JPPD: Joint Prediction_Planning Diffusion with Differentiable Safety Guidance for Dynamic Obstacle Avoidance in Intelligent Transportation Systems

arXiv:2606.20686v1 Announce Type: new Abstract: Shared-space transportation operation requires low-speed autonomous platforms to navigate safely and efficiently among pedestrians, service robots, micr

safetyarxiv-cs-ro

Applications

KEMO: Event-Driven Keyframe Memory for Long-Horizon Robot Manipulation with VLA Policies

arXiv:2606.23589v1 Announce Type: new Abstract: Long-horizon robot manipulation remains challenging because similar observations may occur at different execution stages, while the appropriate action d

applicationsarxiv-cs-ro

Kiwano: A Cutting-Edge Open-Source Toolkit for Speaker Verification

arXiv:2606.22369v1 Announce Type: cross Abstract: In this paper, we present Kiwano, an open-source toolkit designed to advance research and evaluation for speaker verification. Kiwano provides a light

Learning-Based Modeling of Soft Robots via Cosserat Rod Theory

arXiv:2606.20958v1 Announce Type: new Abstract: Modeling soft robot dynamics is challenging due to their continuum structure and typically nonlinear dynamics. Creating models based on first-order prin

researcharxiv-cs-ro

Learning Cross-View Semantic Priors for Single-Reference Unseen Object Pose Estimation

arXiv:2606.22076v1 Announce Type: new Abstract: Single-reference unseen object 6D pose estimation reduces object onboarding by estimating poses of arbitrary novel objects from only one reference view.

Learning to See While Learning to Act: Diffusion Models for Active Perception in Robot Imitation

arXiv:2606.23625v1 Announce Type: new Abstract: Most imitation learning methods assume full observability in table-top settings. In practice, objects are often occluded, requiring robots to both searc

safetyarxiv-cs-ro

Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks

arXiv:2606.23208v1 Announce Type: new Abstract: Deep Learning (DL) has advanced various fields by extracting complex patterns from large datasets. However, the computational demands of DL models pose

tutorialsarxiv-cs-lg

Lightweight 3D Feature Pretraining by Bayesian Inversion of 2D Foundation Models

arXiv:2606.21292v1 Announce Type: new Abstract: We present Casper3D, a lightweight probabilistic framework for converting noisy multi-view 2D foundation-model embeddings into a latent 3D semantic repr

Lightweight Neural Framework for Robust 3D Volume and Surface Estimation from Multi-View Images

arXiv:2606.23653v1 Announce Type: new Abstract: Accurate volume and surface area estimation is critical for diverse applications, from marine ecology to medical diagnostics. However, existing methods

Look Before You Zoom: Adaptive Routing for the Resolution-Context Trade-off in Visual RAG

arXiv:2606.21968v1 Announce Type: new Abstract: Vision-Language Models (VLMs) struggle as query-relevant objects become smaller. To address this, recent training-free approaches dynamically retrieve a

local-aiarxiv-cs-cv

Measuring Intent Comprehension in LLMs

arXiv:2506.16584v3 Announce Type: replace-cross Abstract: People judge interactions with large language models (LLMs) as successful when outputs match what they want, not what they type. Yet LLMs are