Search: “research”

Not All Tokens Need 40 Steps: Heterogeneous Step Allocation in Diffusion Transformers for Efficient Video Generation

arXiv:2605.06892v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have achieved state-of-the-art video generation quality, but they incur immense computational cost because standard infere

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NSPOD: acceleratingthe convergence ofKrylov-based iterative linearsolvers via approximated PODs

DGX agent

arXiv:2605.07828v1 Announce Type: cross Abstract: The convergence of Krylov-based linear iterative solvers applied to parametric partial differential equations (PDEs) is often highly sensitive to the

researcharxiv-cs-lg

11 May 2026

Nurnberg NLP at PsyDefDetect: Multi-Axis Voter Ensembles for Psychological Defence Mechanism Classification

arXiv:2605.07606v1 Announce Type: cross Abstract: Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous. In the PsyDefDetect shared task at BioNLP 20

On Privacy Leakage in Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics

arXiv:2605.06835v1 Announce Type: cross Abstract: Tabular data plays an important role in many fields and industries, including those with elevated privacy considerations and risks. As such, there is

On the Complexity of the Matching Problem of Regular Expressions with Backreferences

arXiv:2605.07289v1 Announce Type: cross Abstract: ReDoS is a well-known type of algorithmic complexity attack, where an adversary supplies maliciously crafted strings to a regular expression matching

On the Divergence of Differential Temporal Difference Learning without Local Clocks

arXiv:2605.06874v1 Announce Type: new Abstract: Learning rate is a critical component of reinforcement learning (RL). This work uses global and local clocks to distinguish two types of learning rates.

On the Robustness of Distribution Support under Diffusion Guidance

arXiv:2605.07220v1 Announce Type: new Abstract: Diffusion guidance is a powerful technique that enables controllable and high-fidelity sample generation with diffusion models. At a high level, it modi

On the Role of Strain and Vorticity in Numerical Integration Error for Flow Matching

arXiv:2605.06680v1 Announce Type: cross Abstract: Flow matching generates data by integrating a learned velocity field, where the number of integration steps (NFE) directly determines inference cost.

Online Goal Recognition using Path Signature and Dynamic Time Warping

arXiv:2605.07736v1 Announce Type: new Abstract: Online goal recognition in continuous domains poses two central challenges: efficiently encoding large trajectories and effectively comparing them. Rece

Online Localized Conformal Prediction

arXiv:2605.05497v2 Announce Type: replace Abstract: Conformal prediction is a framework that provides valid uncertainty quantification for general models with exchangeable data. However, in the online

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Operating Within the Operational Design Domain: Zero-Shot Perception with Vision-Language Models

arXiv:2605.07649v1 Announce Type: cross Abstract: Over the last few years, research on autonomous systems has matured to such a degree that the field is increasingly well-positioned to translate resea

safetyarxiv-cs-ai

OphEdit: Training-Free Text-Guided Editing of Ophthalmic Surgical Videos

arXiv:2605.07695v1 Announce Type: new Abstract: High-fidelity surgical video generation can greatly improve medical training and the development of AI, adapting these generative models for precise vid

Optimising MFCC parameters for the automatic detection of respiratory diseases

arXiv:2408.07522v2 Announce Type: replace-cross Abstract: Voice signals originating from the respiratory tract are utilized as valuable acoustic biomarkers for the diagnosis and assessment of respirat

Overview of the TREC 2025 RAGTIME Track

arXiv:2602.10024v2 Announce Type: replace-cross Abstract: The principal goal of the RAG TREC Instrument for Multilingual Evaluation (RAGTIME) track at TREC is to study report generation from multiling

Parallel Lifted Planning via Semi-Naive Datalog Evaluation

arXiv:2605.07584v1 Announce Type: new Abstract: Lifted classical planners operate directly on first-order planning tasks to avoid the computationally demanding grounding step. However, lifted planning

Penalty-Based First-Order Methods for Bilevel Optimization with Minimax and Constrained Lower-Level Problems

arXiv:2605.08006v1 Announce Type: cross Abstract: We study a class of bilevel optimization problems in which both the upper- and lower-level problems have minimax structures. This setting captures a b

PersonaGest: Personalized Co-Speech Gesture Generation with Semantic-Guided Hierarchical Motion Representation

arXiv:2605.07252v1 Announce Type: cross Abstract: Co-speech gesture generation aims to synthesize realistic body movements that are semantically coherent with speech and faithful to a user-specified g

PET-Adapter: Test-Time Domain Adaptation for Full and Limited-Angle PET Image Reconstruction

arXiv:2605.08030v1 Announce Type: new Abstract: Positron Emission Tomography (PET) image reconstruction is inherently challenged by Poisson noise and physical degradation factors, which are further ex

Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning

arXiv:2605.06756v1 Announce Type: new Abstract: Real-time supervisory control of thermal energy distribution systems requires digital twins that are accurate, interpretable, and uncertainty-aware, yet

Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics

arXiv:2605.07738v1 Announce Type: cross Abstract: Physics-informed operator learning is an attractive candidate for surrogate modeling of microstructures, especially in multiscale finite-element simul

PicoEyes: Unified Gaze Estimation Framework for Mixed Reality with a Large-Scale Multi-View Dataset

arXiv:2605.07188v1 Announce Type: new Abstract: We present PicoEyes, a unified gaze estimation framework that directly predicts all key attributes of gaze, including 3D eye parameters, eye-region segm

PISTO: Proximal Inference for Stochastic Trajectory Optimization

arXiv:2605.07215v1 Announce Type: new Abstract: Stochastic trajectory optimization methods like STOMP enable planning with non-differentiable costs, offering substantial flexibility over gradient-base

researcharxiv-cs-ro

PolarAdamW: Disentangling Spectral Control and Schur Gauge-Equivariance in Matrix Optimisation

arXiv:2605.07067v1 Announce Type: new Abstract: Muon's matrix-level update couples two distinct effects: spectral control via a polar map, and equivariance under orthogonal changes of multiplicity-spa

PolySQL: Scaling Text-to-SQL Evaluation Across SQL Dialects via Automated Backend Isomorphism

arXiv:2605.07796v1 Announce Type: new Abstract: SQL dialects vary in syntax, types, and functions across database engines. Text-to-SQL benchmarks, however, predominantly support only SQLite. This crea

PPI-Net connects molecular protein interactions to functional processes in disease

arXiv:2605.07838v1 Announce Type: cross Abstract: Understanding how molecular alterations propagate across biological systems to drive disease remains a central challenge. Although high-throughput pro

Pre-trained Tabular Foundation Models as Versatile Summary Networks for Neural Posterior Estimation

arXiv:2605.07765v1 Announce Type: new Abstract: In this work, we study TabPFN as a training-free, modular summary network for simulation-based Bayesian inference (SBI). Tabular foundation models such

Pre-training Enables Extraordinary All-optical Image Denoising

arXiv:2605.07810v1 Announce Type: cross Abstract: Optical neural networks are emerging as powerful machine learning and information processing tools because of their potential advantages in speed and

Pretty Good Measurement for Radiomics: A Quantum-Inspired Multi-Class Classifier for Lung Cancer Subtyping and Prostate Cancer Risk Stratification

arXiv:2603.00223v2 Announce Type: replace Abstract: We investigate a quantum-inspired approach to supervised multi-class classification based on the Pretty Good Measurement (PGM), viewed as an operato

Privately Estimating Black-Box Statistics

arXiv:2510.00322v3 Announce Type: replace-cross Abstract: Standard techniques for differentially private estimation, such as Laplace or Gaussian noise addition, require guaranteed bounds on the sensit

ProteinJEPA: Latent prediction complements protein language models

arXiv:2605.07554v1 Announce Type: cross Abstract: Protein language models are trained primarily with masked language modeling (MLM), which predicts amino-acid identities at masked positions. We ask wh

ProtSent: Protein Sentence Transformers

arXiv:2605.06830v1 Announce Type: cross Abstract: Protein language models (pLMs) produce per-residue representations that capture evolutionary and structural information, yet their mean-pooled sequenc

QuadNorm: Resolution-Robust Normalization for Neural Operators

arXiv:2605.07375v1 Announce Type: new Abstract: Normalization layers in neural operators usually compute statistics by uniformly averaging discrete grid values, making the normalization itself discret

R^3L: Reasoning 3D Layouts from Relative Spatial Relations

arXiv:2605.06758v1 Announce Type: cross Abstract: Relative spatial relations provide a compact representation of spatial structure and are fundamental to relative spatial reasoning in 3D layout genera

Rebalancing gradient to improve self-supervised co-training of depth, odometry and optical flow predictions

arXiv:2605.07945v1 Announce Type: new Abstract: We present CoopNet, an approach that improves the cooperation of co-trained networks by dynamically adapting the apportionment of gradient, to ensure eq

Regret-Oracle Complexity Tradeoffs in Agnostic Online Learning

arXiv:2605.07155v1 Announce Type: new Abstract: Agnostic online learning is classically solved via a reduction to the realizable setting, utilizing Littlestone's Standard Optimal Algorithm (SOA) as a

Regulating Branch Parallelism in LLM Serving

arXiv:2605.06914v1 Announce Type: cross Abstract: Recent methods expose intra-request parallelism in LLM outputs, allowing independent branches to decode concurrently. Existing serving systems execute

Relay Buffer Independent Communication over Pooled HBM for Efficient MoE Inference on Ascend

arXiv:2605.06055v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) inference requires large-scale token exchange across devices, making dispatch and combine major bottlenecks in both p

Reliable Chain-of-Thought via Prefix Consistency

arXiv:2605.07654v1 Announce Type: cross Abstract: Large Language Models often improve accuracy on reasoning tasks by sampling multiple Chain-of-Thought (CoT) traces and aggregating them with majority

Rethinking Dense Sequential Chains: Reasoning Language Models Can Extract Answers from Sparse, Order-Shuffling Chain-of-Thoughts

arXiv:2605.07307v1 Announce Type: new Abstract: Modern reasoning language models generate dense, sequential chain-of-thought traces implicitly assuming that every token contributes and that steps must

Rethinking Experience Utilization in Self-Evolving Language Model Agents

arXiv:2605.07164v1 Announce Type: new Abstract: Self-evolving agents improve by accumulating and reusing experience from past interactions. Existing work has largely focused on how experience is const

Retrieval from Within: An Intrinsic Capability of Attention-Based Models

arXiv:2605.05806v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) typically treats retrieval and generation as separate systems. We ask whether an attention-based encoder-decode

Retrieval Heads are Dynamic

arXiv:2602.11162v2 Announce Type: replace Abstract: Recent studies have identified 'retrieval heads' in Large Language Models (LLMs) responsible for extracting information from input contexts. However

Retrieve, Integrate, and Synthesize: Spatial-Semantic Grounded Latent Visual Reasoning

arXiv:2605.07106v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have made remarkable progress on vision-language reasoning, yet most methods still compress visual evidence int

Revisiting Adam for Streaming Reinforcement Learning

arXiv:2605.06764v1 Announce Type: cross Abstract: Learning from a sequence of interactions, as soon as observations are perceived and acted upon, without explicitly storing them, holds the promise of

RL-RIG: A Generative Spatial Reasoner via Intrinsic Reflection

arXiv:2602.19974v2 Announce Type: replace Abstract: Recent advancements in image generation have achieved impressive results in producing high-quality images. However, existing image generation models

Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling

arXiv:2605.07634v1 Announce Type: cross Abstract: We consider a first order stochastic optimization framework where, at each iteration, K independent identically distributed (i.i.d.) data point sample

Saliency-Aware Regularized Quantization Calibration for Large Language Models

arXiv:2605.05693v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is an effective approach for deploying large language models (LLMs) under memory and latency constraints. Most exis

SAM 3D Animal: Promptable Animal 3D Reconstruction from Images in the Wild

arXiv:2605.07604v1 Announce Type: cross Abstract: 3D animal reconstruction in the wild remains challenging due to large species variation, frequent occlusions, and the prevalence of multi-animal scene