Search: “research”

HiRes: Inspectable Precedent Memory for Reaction Condition Recommendation

arXiv:2605.21420v1 Announce Type: new Abstract: Reaction condition recommendation sits immediately after retrosynthetic disconnection selection, and in practice, chemists require both accurate predict

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Research

How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective

DGX agent

arXiv:2502.17773v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate survey responses, but synthetic data can be misaligned with the human populatio

researcharxiv-cs-lg

21 May 2026

How You Move Tells What You'll Do: Trajectory-Conditioned Egocentric Prediction

arXiv:2605.20388v1 Announce Type: new Abstract: Predicting how a person's first-person view will evolve (what action will follow, what plan completes a task, whether an in-progress shot will score) is

Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data

arXiv:2605.20997v1 Announce Type: new Abstract: Integrating machine learning (ML) with physical models (PM) has emerged as a promising way of retrieving geophysical parameters from remote sensing data

Improved Guarantees for Constrained Online Convex Optimization via Self-Contraction

arXiv:2605.21107v1 Announce Type: new Abstract: We consider Constrained Online Convex Optimization (COCO) with adversarially chosen constraints. At each round, the learner chooses an action before obs

Improving 3D Gaussian Splatting Compression by Scene-Adaptive Lattice Vector Quantization

arXiv:2509.13482v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) is rapidly gaining popularity for its photorealistic rendering quality and real-time performance, but it generates mass

Instance Discrimination for Link Prediction

arXiv:2605.20257v1 Announce Type: new Abstract: Recently, instance discrimination models have emerged as a major solution for self-supervised learning. Having already demonstrated its effectiveness in

Interpretable Discriminative Text Representations via Agreement and Label Disentanglement

arXiv:2605.20693v1 Announce Type: new Abstract: Interpretable text representations should expose coordinates that are not only predictive, but also meaningful enough for independent auditors to apply.

iReasoner: Trajectory-Aware Intrinsic Reasoning Supervision for Self-Evolving Large Multimodal Models

arXiv:2601.05877v3 Announce Type: replace Abstract: Recent work shows that large multimodal models (LMMs) can self-improve from unlabeled data via self-play and intrinsic feedback. Yet existing self-e

Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization

arXiv:2603.00086v2 Announce Type: replace Abstract: Automatic speech recognition for French medical conversations remains challenging, with word error rates often exceeding 30% in spontaneous clinical

Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction

arXiv:2603.23531v2 Announce Type: replace Abstract: Political polarization emerges from a complex interplay of beliefs about policies, figures, and issues. However, most computational analyses reduce

Latent Dynamics for Full Body Avatar Animation

arXiv:2605.21478v1 Announce Type: new Abstract: Pose-driven full-body avatars built on neural rendering produce high-quality novel views of a captured subject. Yet loose clothing and other dynamic ele

Learning First Integrals via Backward-Generated Data and Guided Reinforcement Learning

arXiv:2605.21160v1 Announce Type: new Abstract: The discovery of first integrals is of fundamental scientific importance for understanding conservation laws in dynamical systems. However, existing sym

Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation

arXiv:2605.21258v1 Announce Type: new Abstract: Current 3D-aware pretraining methods for embodied perception and manipulation are largely built on differentiable rendering frameworks, producing either

researcharxiv-cs-ro

Less Data, Faster Training: repeating smaller datasets speeds up learning via sampling biases

arXiv:2605.20314v1 Announce Type: new Abstract: This work investigates the ``small-vs-large gap'', where repeating on fewer samples can lead to compute saving during training compared to using a large

Local-sensitive connectivity filter (ls-cf): A post-processing unsupervised improvement of the frangi, hessian and vesselness filters for multimodal vessel segmentation

arXiv:2605.21251v1 Announce Type: cross Abstract: A retinal vessel analysis is a procedure that can be used as an assessment of risks to the eye. This work proposes an unsupervised multimodal approach

Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning

arXiv:2605.20201v1 Announce Type: new Abstract: Recent large language models support inputs of up to 10 million tokens, yet they perform poorly on long-context tasks that require complex reasoning. Su

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy

arXiv:2507.06929v2 Announce Type: replace-cross Abstract: We investigate Machine-Learned Force Fields (MLFFs) trained on approximate Density Functional Theory (DFT) and Coupled Cluster (CC) level pote

Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models

arXiv:2605.20523v1 Announce Type: new Abstract: Advanced fibrosis is a major determinant of liver-related morbidity in metabolic dysfunction-associated steatotic liver disease (MASLD). FIB-4 is widely

Map-Mono-Ego: Map-Grounded Global Human Pose Estimation from Monocular Egocentric Video

arXiv:2605.20889v1 Announce Type: new Abstract: Monocular egocentric human pose estimation is essential for ubiquitous activity monitoring. However, understanding the user's absolute location within t

Matryoshka Concept Bottleneck Models

arXiv:2605.20612v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) have emerged as a prominent paradigm for interpretable deep learning, learning by grounding predictions in human-unders

Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference

arXiv:2511.21223v2 Announce Type: replace-cross Abstract: Variational inference (VI) is a cornerstone of modern Bayesian learning, enabling approximate inference in complex models. However, its formul

Mercer Large-Scale Kernel Machines from Ridge Function Perspective

arXiv:2307.11925v3 Announce Type: replace Abstract: To present Mercer large-scale kernel machines from a ridge function perspective, we recall the results by Lin and Pinkus from {it Fundamentality of

MeshTailor: Cutting Seams via Generative Mesh Traversal

arXiv:2603.27309v2 Announce Type: replace-cross Abstract: We present MeshTailor, the first mesh-native generative framework for synthesizing edge-aligned seams on 3D surfaces. Unlike prior optimizatio

Metaphors in Literary Post-Editing: Opening Pandora's Box?

arXiv:2605.21178v1 Announce Type: new Abstract: This paper investigates how post-editors of literary texts react and respond to the way metaphors have been translated by Neu ral Machine Translation (N

Mind Your Margin and Boundary: Are Your Distilled Datasets Truly Robust?

arXiv:2605.20606v1 Announce Type: new Abstract: Dataset distillation (DD) compresses a large training set into a small synthetic set for efficient training, but most DD methods optimize only clean acc

Modeling and Control of a Pneumatic Morphing Soft Quadrotor based on the SOFA Framework for Dynamic Soft Robotic Simulation

arXiv:2605.21031v1 Announce Type: new Abstract: This article presents a novel SOFA based finite element method for the soft body modeling and the corresponding dynamic simulation and control of a pneu

researcharxiv-cs-ro

Modeling Temporal scRNA-seq Data with Latent Gaussian Process and Optimal Transport

arXiv:2605.20989v1 Announce Type: new Abstract: Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot

Modular Multimodal Classification Without Fine-Tuning: A Simple Compositional Approach

arXiv:2605.20674v1 Announce Type: new Abstract: We introduce CoMET, extit{extbf{C}omposing extbf{M}odality extbf{E}ncoders with extbf{T}abular foundation models}, a simple yet highly competitive metho

Most Transformer Modifications Still Do Not Transfer at 1-3B: A 2020-2026 Update to Narang et al. (2021) with Downstream Evaluation and a Noise Floor

arXiv:2605.20798v1 Announce Type: cross Abstract: Narang et al. (2021) evaluated 40+ Transformer modifications at T5-base scale and concluded that most did not transfer. Five years later, the typical

Motion-Robust Deep Reconstruction for Free-Breathing Cardiac Cine MRI

arXiv:2605.20687v1 Announce Type: cross Abstract: Conventional cardiac cine MRI relies on breath-hold Cartesian acquisitions, which are vulnerable to motion artifacts and can be uncomfortable or infea

Multi-Channel Replay Speech Detection using Acoustic Maps

arXiv:2602.16399v2 Announce Type: replace-cross Abstract: Replay attacks remain a critical vulnerability for automatic speaker verification systems, particularly in real-time voice assistant applicati

Musical Attention Transformer: Music Generation Using a Music-Specific Attention Model

arXiv:2605.21081v1 Announce Type: cross Abstract: This study aims to enhance the quality of music generation using Transformers by incorporating meta-information. While Transformer-based approaches ar

NeighborDiv: Training-free Zero-shot Generalist Graph Anomaly Detection via Neighbor Diversity

arXiv:2605.20879v1 Announce Type: new Abstract: Graph Anomaly Detection (GAD) is increasingly shifting to Generalist GAD (GGAD) for cross-domain 'one-for-all' detection, but existing GGAD methods pred

Neural Estimation of Pairwise Mutual Information in Masked Discrete Sequence Models

arXiv:2605.20187v1 Announce Type: new Abstract: Understanding dependencies between variables is critical for interpretability and efficient generation in masked diffusion models (MDMs), yet these mode

New VIDEO: From LLM Wikis to LLM Artifacts Shared all my thoughts on why LLM wikis and HTML artifacts are a big deal. Plus, new tools to hel…

New VIDEO: From LLM Wikis to LLM Artifacts Shared all my thoughts on why LLM wikis and HTML artifacts are a big deal. Plus, new tools to help you build wikis and artifacts with agents. Just getting st

researchdair-ai--x

Nonlocal operator learning for fMRI encoding and decoding tasks

arXiv:2605.20389v1 Announce Type: new Abstract: Functional MRI data exhibit high-dimensional spatiotemporal structure, making both prediction and decoding challenging. In this work, we investigate neu

OCTOPUS: Optimized KV Cache for Transformers via Octahedral Parametrization Under optimal Squared error quantization

arXiv:2605.21226v1 Announce Type: new Abstract: The key-value (KV) cache dominates memory bandwidth and footprint in long-context autoregressive inference. Recent rotation-preconditioned codecs (Turbo

On the Cost and Benefit of Chain of Thought: A Learning-Theoretic Perspective

arXiv:2605.21260v1 Announce Type: new Abstract: We develop a learning-theoretic framework for understanding Chain of Thought (CoT). We model CoT as the interaction between an answer map and a chain ru

One Operator to Rule Them All? On Boundary-Indexed Operator Families in Neural PDE Solvers

arXiv:2603.01406v2 Announce Type: replace Abstract: Neural PDE solvers are often described as learning solution operators that map problem data to PDE solutions. In this work, we argue that this inter

OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI

arXiv:2605.20539v1 Announce Type: new Abstract: The advent of machine learning (ML) and computer vision has significantly accelerated seismic inversion workflows by reducing the computational cost of

Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guarantees

arXiv:2410.15761v4 Announce Type: replace Abstract: Large Language Models excel in generative tasks but exhibit inefficiencies in structured text selection, particularly in extractive question answeri

Oracle Supervision Transfers for Hyperparameter Prediction in Model-Based Image Denoising

arXiv:2605.20479v1 Announce Type: new Abstract: Hyperparameter prediction is a critical practical bottleneck for model-based image denoisers, ranging from classical TV/TGV variational solvers to moder

Ordering Matters: Rank-Aware Selective Fusion for Blended Emotion Recognition

arXiv:2605.21417v1 Announce Type: new Abstract: Blended emotion recognition is challenging because emotions are often expressed as mixtures of subtle and overlapping multimodal cues rather than a sing

PerpetualWonder: Long-Horizon Action-Conditioned 4D Scene Generation

arXiv:2602.04876v2 Announce Type: replace Abstract: We introduce PerpetualWonder, a hybrid generative simulator that enables long-horizon, action-conditioned 4D scene generation from a single image. C

Physics-informed convolutional neural networks for fluid flow through porous media

arXiv:2605.20250v1 Announce Type: new Abstract: Accurate simulation of fluid flow in porous media is challenging due to complex pore-space geometries and the computational cost of solving the Navier-S

Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry

arXiv:2605.20496v1 Announce Type: cross Abstract: The Strong Platonic Representation Hypothesis suggests that representational convergence in artificial neural networks can be harnessed constructively

Playing Devil's Advocate: Off-the-Shelf Persona Vectors Rival Targeted Steering for Sycophancy

arXiv:2605.21006v1 Announce Type: cross Abstract: We study the effect of different persona on extbf{sycophancy}: model's agreement with users even when the user is incorrect. The standard mitigation,