research

HARMONI: Aligning Human and Scene Priors for Multi-View 4D Reconstruction

arXiv:2603.12789v3 Announce Type: replace Abstract: Recent advances in 3D foundation models have enabled joint reconstruction of humans and their surrounding environments. However, combining independe

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Hidden Reasoning Must Leak, but Need Not Be Readable: Fundamental Opportunities and Limits for Chain-of-Thought Monitoring

DGX agent

arXiv:2609.37312v1 Announce Type: cross Abstract: Can reasoning models trick chain of thought (CoT) monitors and perform hidden computation without revealing it in their thinking traces? We show that

researcharxiv-cs-ai

30 Sept 2026

Hierarchical Utility Calibration for Structured Multiclass Decisions

arXiv:2609.36532v1 Announce Type: cross Abstract: In multiclass probabilistic prediction, Utility Calibration (UC), which focuses auditing on specified utilities, has recently received attention as a

High-Resolution Dynamic Functional Connectivity Generation with Graph-Variate Flow Matching

arXiv:2609.37037v1 Announce Type: new Abstract: High-resolution dynamic functional connectivity (DFC) can reveal rapidly evolving brain-network interactions, but short temporal windows yield noisy, of

Honeycomb: Constant-Size Scene Memory Representation for Video World Models

arXiv:2609.37690v1 Announce Type: new Abstract: Video world models require persistent scene memory to maintain consistency during long-horizon video generation. Existing spatial memory systems accumul

How Medical VLMs Underutilize Their Vision Encoders: A Dermatology Perspective

arXiv:2609.36557v1 Announce Type: cross Abstract: Medical Vision-Language Models (VLMs) show significant promise for clinical image understanding, offering accurate diagnosis with interpretable reason

Human-AI Collaboration: From Paradoxes to Patterns

arXiv:2609.36481v1 Announce Type: new Abstract: Evidence shows that humans and AI systems perform better together, by collaborating, than alone. This paper examines two key design dimensions of human-

Human-inspired, Task-Dimension-Guided Exploration for Efficient Learning in High Dimensions

arXiv:2609.36672v2 Announce Type: new Abstract: Efficient exploration in high-dimensional decision spaces remains a central challenge for decision-making systems. Humans, in contrast, can navigate lar

Hybrid Ensemble Learning for EEG-Based Epileptic Seizure Forecasting

arXiv:2609.35876v1 Announce Type: new Abstract: Epileptic seizure forecasting aims to provide actionable warnings before seizure onset, yet patient-independent generalization and false-alarm control r

Hybrid Feature Learning for Handwriting Verification

arXiv:1812.02621v2 Announce Type: replace Abstract: We propose an effective Hybrid Deep Learning (HDL) architecture for the task of determining the probability that a questioned handwritten word has b

Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios

arXiv:2609.36044v1 Announce Type: cross Abstract: We develop two hybrid techniques that approach the performance of neural simulation-based inference (NSBI) analyses while substantially reducing the c

HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classification

arXiv:2609.37740v1 Announce Type: new Abstract: While attaining remarkable results for many applications, Deep Learning models are notoriously difficult to explain. This work introduces HyDI, a hybrid

Hyper Yoshimura: How a slight tweak on a classical folding pattern unleashes meta-stability for deployable robots

arXiv:2505.09919v3 Announce Type: replace Abstract: Deployable structures inspired by origami have provided lightweight, compact, and reconfigurable solutions for various robotic and architectural app

researcharxiv-cs-ro

HyperSAM: A Promptable Foundation Model for Hyperspectral Remote Sensing

arXiv:2609.37340v1 Announce Type: new Abstract: Hyperspectral remote sensing provides dense spectral measurements that are indispensable for material-level Earth observation, yet the construction of a

Hyperspherical Semantic Trajectory Analysis: Mapping Technological Diffusion across Academic Preprints, Patent Signals, and Compute Scaling

arXiv:2609.35845v1 Announce Type: new Abstract: Macroeconomic productivity metrics, such as Total Factor Productivity, register technological breakthroughs with multi-year reporting lags due to admini

HyperZip: Efficient Data Compression through Personalized Diffusion LLMs with Hypernetworks

arXiv:2609.36357v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for lossless data compression, but existing approaches are constrained by the high computationa

Identifying ODEs from Unstructured Data with Causal Representation Learning

arXiv:2609.37083v1 Announce Type: cross Abstract: We study the problem of recovering the governing ODE of a dynamical system from unstructured, high-dimensional observations such as images. Existing m

Imagine3D-LLM: Teaching MLLMs to Imagine 3D Scenes Before Answering

arXiv:2609.38177v1 Announce Type: cross Abstract: Reasoning about the 3D world from multi-view images remains a fundamental challenge for Multimodal Large Language Models (MLLMs). While modern MLLMs h

Implementing Cumulative Functions with Generalized Cumulative Constraints

arXiv:2508.01751v3 Announce Type: replace Abstract: Modeling scheduling problems with conditional time intervals and cumulative functions has become a common approach when using modern commercial cons

Improved Distributional Diffusion Models

arXiv:2609.37147v1 Announce Type: new Abstract: Distributional Diffusion Models (DDMs) replace the standard mean-prediction denoiser with a distributional denoiser trained via a scoring rule objective

Inferring Soil Friction Angle from Robot Foot-Ground Force Histories: A Bayesian Inverse Approach to Proprioceptive Soil Sensing

arXiv:2609.36582v1 Announce Type: new Abstract: Foot-ground interaction signals recorded by quadruped robots may enable spatially distributed, in situ characterization of soil strength. As a first ste

researcharxiv-cs-ro

Information-theoretic receding-horizon active learning of nonlinear dynamical systems

arXiv:2609.36712v1 Announce Type: cross Abstract: Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this challe

InsightMap: Structured Spatial Modeling for Embodied Multimodal Reasoning

arXiv:2609.37187v1 Announce Type: new Abstract: Language-guided navigation requires connecting partial observations to a persistent spatial reference and learning how actions change that representatio

Inter-patient ECG Arrhythmia Classification with LGNs and LUTNs

arXiv:2601.11433v2 Announce Type: replace Abstract: Deep Differentiable Logic Gate Networks (LGNs) and Lookup Table Networks (LUTNs) are demonstrated to be suitable for the automatic classification of

Interpretable intrinsic dimension estimation through componentwise calibration of distance and angle

arXiv:2609.37114v1 Announce Type: new Abstract: DANCo (Dimensionality from Angle and Norm Concentration) jointly calibrates nearest-neighbor distance and angular statistics and consistently reaches st

Into the danger zone: stable extrapolation in high-dimensional function and operator learning

arXiv:2609.36709v1 Announce Type: cross Abstract: Out-of-distribution (OOD) generalization is a central challenge in scientific machine learning. We study regression problems in which the test distrib

Intrinsic Associative Memory on Riemannian Manifolds: Curvature, Capacity, and Emergent Modes

arXiv:2609.35948v1 Announce Type: cross Abstract: Geometry does more than constrain an associative memory: curvature determines what it remembers and which states it creates. We develop intrinsic dens

It's All Training: A Fully Synthetic Single-Stage Recipe for LLMs

arXiv:2609.37891v1 Announce Type: new Abstract: Current pre-training datasets are derived from web crawls, with all their issues, and were not designed to support mid- and post-training pipelines--for

It's Not What the Image Shows: Irrelevant Context Destabilises VLM Judges Without Informing Them

arXiv:2609.37863v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in place of human annotators, making it important that substitutability tests reflect the model ra

JEV-as-a-Judge: Accept When Confident, Escalate When Unsure

arXiv:2609.26550v3 Announce Type: replace Abstract: LLM-as-a-judge scales evaluation, but reasoning judges are slow and costly. We study JEV-as-a-Judge: evaluation with JEV, a decision-only judge that

Just Initialize: A Training-Free Initialization Component for Large-Scale Routing Optimization

arXiv:2609.35443v2 Announce Type: replace Abstract: Large-scale routing problems are difficult to solve efficiently as their search spaces grow rapidly with problem size. Existing approaches primarily

Kinematic Nonlinear Spatio-Temporal Trajectory Warping for Contact-Rich Dexterous Manipulation Demonstrations

arXiv:2609.36676v1 Announce Type: new Abstract: We present a straightforward but effective method for repurposing existing contact-rich dexterous manipulation demonstrations. Starting from inputs of h

researcharxiv-cs-ro

Latent Inference-Time Guidance of Time Series Foundation Models

arXiv:2609.38058v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on i

Layer-Informed Fine-Tuning via Three-Stage Functional Segmentation of LLMs

arXiv:2609.38027v1 Announce Type: new Abstract: In recent years, the performance of large language models (LLMs) on reasoning tasks has been remarkable, even surpassing human capabilities on various b

LDM-is-AE: Latent Diffusion Model is an Auto-Encoder for End-to-End Image Generation

arXiv:2609.37080v1 Announce Type: new Abstract: Latent Diffusion Models (LDMs) typically adopt a two-stage pipeline: an auto-encoder (AE) is first pre-trained to define a latent space, then a diffusio

Learned Queries and Keys Are All You Need: Replacing the Value Projection with Structured Transforms

arXiv:2609.36698v1 Announce Type: new Abstract: To reduce the number of parameters and cache memory requirements of transformers we introduce dual-headed transformers instead of three heads. We studie

Learning as Deepfakes Evolve: RF-Prompt for Continual Audio Deepfake Detection

arXiv:2609.37586v1 Announce Type: cross Abstract: Continual audio deepfake detection requires learning newly emerging deepfake methods while retaining discrimination of previously encountered speech.

Learning Causal Normalizing Flows from Incomplete Data via Observed-Data Likelihood

arXiv:2609.37664v1 Announce Type: new Abstract: Causal Normalizing Flows (CNFs) enable causal inference from observational data given the causal structure, but they assume fully observed training data

Learning Distributionally Robust First-Order Methods for Convex Optimization

arXiv:2605.06585v2 Announce Type: replace Abstract: We propose a distributionally robust approach to learning hyperparameters (e.g., step sizes and momentum coefficients) for first-order methods in co

Learning Nonlinear Finite Element Solution Operators using Multilayer Perceptrons and Energy Minimization

arXiv:2412.04596v3 Announce Type: replace Abstract: We develop and evaluate a method for learning solution operators to nonlinear problems governed by partial differential equations (PDEs). The approa

Learning to Prove, Not Just to Answer: Reinforcement Learning from Formal Verification for Natural-Language Logical Reasoning

arXiv:2609.37203v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed for natural-language logical reasoning, where the final answer is easy to check but the proof beh

Learning to Retrieve Missing Evidence for Long-Term Memory QA

arXiv:2609.37443v1 Announce Type: cross Abstract: Long-term memory enables language models to use past interactions in future conversations. However, evidence needed to answer a question may be scatte

Learning via Self-Consistency for Diffusion-based Video Reasoning

arXiv:2609.36826v1 Announce Type: new Abstract: Video generation models have demonstrated emerging zero-shot capabilities for visual reasoning, perception, and other vision tasks. However, diffusion-b

Learning When to Update: A Near-Optimal Timing Bandit Approach

arXiv:2609.37932v1 Announce Type: new Abstract: Systems operating in dynamic environments require timely updates to sustain performance. For resource-intensive systems such as machine learning models

Less Uniform Discrete Diffusion is More Powerful and Scalable

arXiv:2609.35817v1 Announce Type: cross Abstract: Although uniform diffusion language models (UDLMs) represent a promising diffusion paradigm, scaling them remains challenging. We identify the core ob

LLM Judge Validation Under Sparse Overlap: From Inference to Design

arXiv:2609.31857v2 Announce Type: replace Abstract: Validating an LLM-as-a-judge requires estimating its agreement with humans, yet annotation budgets rarely allow every item to be multiply labeled. W

LLMs are not stochastic parrots: Evidence for meaning-mediated abstraction from conlang-like tasks

arXiv:2609.34187v2 Announce Type: replace-cross Abstract: The strong version of the stochastic parrot argument claims that, although large language models (LLMs) may exceed rote regurgitation, they ca

Local Search with Correlated Randomness

arXiv:2607.17469v2 Announce Type: replace-cross Abstract: How much does an algorithm's running-time distribution under independent randomness reveal about its behavior when independence is no longer g