Search: “research”

A Full Adam Theorem for Spectral Heavy-Tail Onset

arXiv:2609.12996v1 Announce Type: new Abstract: We prove a full Adam theorem for spectral heavy-tail onset in a closed Gaussian Stein-Hermite teacher-student state-evolution model. The theorem begins

A Hierarchical Coverage Path Planning Algorithm for Unknown Environments

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DGX agent

arXiv:2609.12595v1 Announce Type: new Abstract: This paper presents an online coverage path planning algorithm for unknown environments. During navigation, the initially unknown search area is progres

researcharxiv-cs-ro

14 Sept 2026

A Multimodal Explainable Deep Learning Framework for Alzheimer's Disease Diagnosis using 3D Magnetic Resonance Imaging and Clinical Data

arXiv:2609.12410v1 Announce Type: new Abstract: Dementia is a major and growing global health burden, with Alzheimer's disease (AD) accounting for most cases. Timely and accurate diagnosis is central

A Ranking Approach for Measuring Calibration

arXiv:2609.13100v1 Announce Type: cross Abstract: When providing forecasted probabilities with a predictive model, the ideal model offers perfect calibration: the true probability of the outcome (i.e.

A Robot Among People:From Social Imitation to the Social Becoming of Human Groups

arXiv:2609.12937v1 Announce Type: new Abstract: Robots designed to mediate human groups often fall into the solutionist trap: they are framed as sociable agents that fix problems such as conflict, dis

A Splitting Method for SDE Terminal-Law Estimation

arXiv:2609.12513v1 Announce Type: cross Abstract: In many settings involving stochastic differential equations, including in diffusion based generative AI, our aim is to accurately generate samples fr

Adaptive AI: Energy Efficient Multi-exit TinyML on Intelligent Vision Systems at the Edge

arXiv:2609.11939v1 Announce Type: cross Abstract: Traditional TinyML systems for edge devices achieve high accuracy by relying on fixed-depth models that require a constant number of multiply-accumula

Aligned Radiometric RGB-Thermal Fusion for UAV Facade Anomaly Screening

arXiv:2609.12521v1 Announce Type: new Abstract: Unmanned aerial vehicle facade inspection can combine red, green, and blue (RGB) imagery with thermal measurements to screen surface and subsurface anom

All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing

arXiv:2410.17355v4 Announce Type: replace Abstract: Due to their capacity to acquire world knowledge from large corpora, pre-trained language models (PLMs) are extensively used in ultra-fine entity ty

Amortized Low-Rank Adaptation for Model-Based Reinforcement Learning

arXiv:2609.12278v1 Announce Type: new Abstract: World models let agents plan by predicting the consequences of their actions, but changes in the environment can make them inaccurate. We study the prob

An Automated Thickness Evaluation Procedure Using an Integrated Structured Light 3D Camera in a Robotic Bioprinting Framework

arXiv:2609.12206v1 Announce Type: new Abstract: Bioprinting is emerging as a tissue engineering technique to replace common treatment methods for large scale injuries. While thickness of the BioPrinte

An End-to-End Automated Pipeline for Controllable Crack Data Synthesis

arXiv:2609.12431v1 Announce Type: new Abstract: Automated crack inspection increasingly relies on deep learning, yet its reliability is limited by scarce and weakly controllable defect data. Existing

An Ultra-Widefield Swept-Source OCTA Dataset and a Polar-Gated Mamba Network for Retinal Vessel Segmentation

arXiv:2609.12574v1 Announce Type: new Abstract: Ultra-widefield (UWF) swept-source optical coherence tomography angiography (SS-OCTA) enables large-area retinal vascular imaging, yet vessel segmentati

Banger paper from Meta. This work shows that byte-level models start out behind token models and then pass them as compute grows. They show …

Banger paper from Meta. This work shows that byte-level models start out behind token models and then pass them as compute grows. They show this for distilled 1B models trained on up to 1 trillion byt

researchdair-ai--x

Benford's Law as a Distributional Prior for Post-Training Quantization of Large Language Models

arXiv:2602.00165v2 Announce Type: replace Abstract: Post-training quantization (PTQ) is a practical way to reduce the memory footprint of large language models, but low-bit quantization is sensitive t

Beyond Accuracy: Uncertainty-Guided Boundary Refinement for Reliable Biomedical Image Segmentation

arXiv:2609.12892v1 Announce Type: new Abstract: Accurate biomedical image segmentation requires not only high global overlap but also reliable delineation of clinically meaningful boundaries. In blood

Beyond Argmax: A Mechanistic Study of Semantic Retention in Frozen Foundation-Model Composition for Generalized Few-Shot 3D Segmentation

arXiv:2609.12099v1 Announce Type: new Abstract: Classical classifier-combination work distinguishes score-level fusion from hard decision-level voting. We revisit this distinction where independently

Beyond ID Embeddings: Process-Grounded Language Modeling for Cognitive Diagnosis

arXiv:2609.12403v1 Announce Type: cross Abstract: Cognitive Diagnosis Models (CDMs) play a pivotal role in personalized online learning. Traditional CDMs rely on discrete, ID-based embeddings to repre

Beyond the Query: Do Retrieval Signals Improve Adaptive Multimodal RAG Routing?

arXiv:2609.12437v1 Announce Type: new Abstract: Adaptive RAG often uses retrieval-time signals to decide whether another retrieval, reranking, or multimodal step should run. We ask whether these signa

Block-Norm Geometries for Online Mirror Descent with Sparse Losses

arXiv:2602.13177v2 Announce Type: replace-cross Abstract: The performance of online mirror descent depends critically on the geometry induced by its mirror map, yet standard algorithms largely rely on

Calibrated Ambiguity in Multimodal Language Models: Humans reach for cultural references, while models describe the picture

arXiv:2609.12575v1 Announce Type: new Abstract: Ambiguity is often treated as a bug for AI systems to resolve---but in human communication and culture, ambiguity can also be a generative resource. Fro

Chopthin-Consensus Power Sampling: A Diversity-Preserving Approach to LLM Decoding

arXiv:2609.12243v1 Announce Type: new Abstract: Inference-time power sampling via Sequential Monte Carlo (SMC) can substantially improve large language model (LLM) reasoning without requiring post-tra

Clustering-Based Balanced Sampling and Allocation with Data Parallelism for High-Performance Fine-Tuning

arXiv:2609.12584v1 Announce Type: new Abstract: Instruction-tuning datasets for large language models (LLMs) are often large, redundant, and imbalanced, limiting efficient adaptation. Naive large-batc

Communication-Constrained Multi-Robot Exploration With Adaptive Communication Windows

arXiv:2609.12502v1 Announce Type: new Abstract: Exploring unknown environments with multi-robot teams can improve efficiency by allowing robots to explore in parallel. However, realizing these gains r

Confidence-Gated Transductive Test Generation for Code Reranking

arXiv:2609.12489v1 Announce Type: cross Abstract: Test case synthesis is crucial for evaluating and ranking programs generated by large language models (LLMs). However, constructing high-quality test

Context-Aware Causal Gaze Forecasting for Human-Vehicle Interaction During In-Cabin Tracking Dropouts

arXiv:2609.12374v1 Announce Type: new Abstract: Dashboard-mounted gaze trackers often lose sight of the driver's eyes during large head rotations, including shoulder checks, mirror glances, and inters

Control Architecture for Safe Grasping of Fragile Objects Using a Coarse Position-Controlled Gripper

arXiv:2609.12737v1 Announce Type: new Abstract: Robots are increasingly used in unstructured environments. The need for them to safely grasp unknown objects without damaging them becomes crucial. Huma

Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise and Holder Smoothness

arXiv:2609.12785v1 Announce Type: new Abstract: Classical convergence guarantees for stochastic gradient methods typically assume Lipschitz-smooth objectives and finite-variance gradient noise, both f

Creating an Atomic User Model for Personality-Aware Large Language Model Interaction

arXiv:2609.12086v1 Announce Type: cross Abstract: Assistants built on large language models are expected to write as their user would, and the dominant approach is single-channel: preferences summaris

CRFCAN: A Complex-Valued Cross-Domain Residual Network for Joint Channel and Phase Noise Estimation in Sub-THz OFDM Systems

arXiv:2609.12244v1 Announce Type: new Abstract: In sub-terahertz (sub-THz) communications, the coupling of ultra-wide bandwidth and severe phase noise (PN) impairments renders conventional joint chann

CueMem: Cue-Guided Context Reconstruction for Long-Term Conversational Memory

arXiv:2609.12354v1 Announce Type: new Abstract: Long-term conversational agents must answer user queries by recalling information from extended dialogue histories, yet directly using the full history

DATAFARM: Distribution-Aligned Task and Motion Planning for Fine-Tuning Vision-Language-Action Models

arXiv:2609.12316v1 Announce Type: new Abstract: Collecting high-quality robot data remains a fundamental challenge for training robot foundation models. Task and motion planning (TAMP) offers a scalab

Decentralized Evolution of Hexapod Gaits with Independent Leg Controllers

arXiv:2609.12400v1 Announce Type: cross Abstract: This paper presents a novel approach to hexapod locomotion by evolving each leg's gait independently through a decentralized evolutionary algorithm. U

DERA: Detached Edge-Residual Adaptation for Prohibited item Detection

arXiv:2609.12411v1 Announce Type: new Abstract: Prohibited-item detection in X-ray imagery remains challenging due to object superposition, weak texture, and material clutter which obscure both semant

Dimension-Corrected Hitting Times for Heavy-Tailed Spectral Emergence in Neural Optimizer Dynamics

arXiv:2609.12994v1 Announce Type: new Abstract: Heavy-tailed empirical spectral densities of neural-network weight matrices are widely used as diagnostics of implicit self-regularization, but the step

Direct Topology Tracking in Continuous Implicit Models

arXiv:2609.12157v1 Announce Type: cross Abstract: We present a framework for tracking topological features directly within continuous implicit models. Such models, including implicit neural representa

Does Video Memory Use What It Retrieves? A Causal Audit of Memory Specificity

arXiv:2609.12090v1 Announce Type: new Abstract: Video models increasingly use memory to preserve information over long sequences, with the assumption that gains come from retrieving and using the corr

DRDN: Decoupled Representation Dynamic Network for From-Scratch ViT Class-Incremental Learning

arXiv:2607.01630v2 Announce Type: replace Abstract: Dynamic expansion methods for class-incremental learning (CIL) protect task-specific knowledge by growing dedicated tokens or subnetworks, yet our a

Duplicating baseline benchmarks [D]

researchr-machinelearning

Suppose I create two machine learning models suppose tree and neural network for a task let's suppose regression problem, now suppose I am sending both of this paper to two different journals, now the

Exact ReLU realization of binary affine refinement iterates via reflection folding and cone switching

arXiv:2609.11962v1 Announce Type: cross Abstract: We study vector-valued binary affine refinement operators with finitely supported matrix masks and compactly supported continuous piecewise linear inp

Explainable Prediction from Mobile Sensing Data through LLM-guided Concept Integration

arXiv:2609.11995v1 Announce Type: new Abstract: Mobile sensing enables longitudinal monitoring of behavioral and physiological patterns in everyday settings. However, accurate prediction remains chall

Explaining Time Series Forecasting with Horizon-Resolved Attribution

arXiv:2609.12639v1 Announce Type: new Abstract: Recent advances in explaining time series (TS) models have produced methods that identify which past values a prediction depends on. However, most exist

Explanations-Driven Active Feature Acquisition for Algorithmic Recourse

arXiv:2609.12179v1 Announce Type: new Abstract: Algorithmic recourse methods typically assume that a predictive model has access to all features of an individual. In practice, decisions are often made

Fast and Faithful: Principled Conditional Flow Matching for Inverse Problems

arXiv:2609.12953v1 Announce Type: new Abstract: Flow matching approaches to imaging inverse problems commonly incorporate measurements in two ways. Conditioning-based approaches supply measurement-der

Fast BIB simulation at a future Muon Collider with generative machine learning

arXiv:2609.12054v1 Announce Type: cross Abstract: Beam-induced background (BIB) from muon decay products will be an overwhelming and unavoidable background at a future Muon Collider. In order to devel

Fewer Words, Not Fewer Tokens: Measuring the Sanskrit Tokenization Penalty per Proposition

arXiv:2609.12960v1 Announce Type: new Abstract: Sanskrit fuses case, number, person and tense into word endings and chains clauses into compounds, so it is information-dense per word. Whether that den

Model Releases

FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences

model-releasesarxiv-cs-lg

arXiv:2609.11993v1 Announce Type: new Abstract: Machine learning research in financial services is limited by the scarcity of representative open-source datasets. Existing resources are often narrowly

GLaMoR: Consistency Checking of OWL Ontologies using Graph Language Models

arXiv:2504.19023v2 Announce Type: replace-cross Abstract: Semantic reasoning aims to infer new knowledge from existing knowledge, with OWL ontologies serving as a standardized framework for organizing