Search: “research”

Analyzing Shapley Additive Explanations to Understand Anomaly Detection Algorithm Behaviors and Their Complementarity

arXiv:2602.00208v2 Announce Type: replace-cross Abstract: Unsupervised anomaly detection is a challenging problem due to the diversity of data distributions and the lack of labels. Ensemble methods ar

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AnatomicalNets: A Multi-Structure Segmentation and Contour-Based Distance Estimation Pipeline for Clinically Grounded Lung Cancer T-Staging

DGX agent

arXiv:2511.19367v2 Announce Type: replace-cross Abstract: Accurate tumor staging in lung cancer is crucial for prognosis and treatment planning and is governed by explicit anatomical criteria under fi

researcharxiv-cs-ai

23 Apr 2026

AROMA: Augmented Reasoning Over a Multimodal Architecture for Virtual Cell Genetic Perturbation Modeling

arXiv:2604.20263v1 Announce Type: cross Abstract: Virtual cell modeling predicts molecular state changes under genetic perturbations in silico, which is essential for biological mechanism studies. How

Auto-Unrolled Proximal Gradient Descent: An AutoML Approach to Interpretable Waveform Optimization

arXiv:2603.17478v2 Announce Type: replace-cross Abstract: This study explores the combination of automated machine learning (AutoML) with model-based deep unfolding (DU) for optimizing wireless beamfo

Automated Description Generation of Cytologic Findings for Lung Cytological Images Using a Pretrained Vision Model and Dual Text Decoders: Preliminary Study

arXiv:2403.18151v2 Announce Type: replace-cross Abstract: Objective: Cytology plays a crucial role in lung cancer diagnosis. Pulmonary cytology involves cell morphological characterization in the spec

Believing without Seeing: Quality Scores for Contextualizing Vision-Language Model Explanations

arXiv:2509.25844v3 Announce Type: replace Abstract: When people query Vision-Language Models (VLMs) but cannot see the accompanying visual context (e.g. for blind and low-vision users), augmenting VLM

Beyond ZOH: Advanced Discretization Strategies for Vision Mamba

arXiv:2604.20606v1 Announce Type: cross Abstract: Vision Mamba, as a state space model (SSM), employs a zero-order hold (ZOH) discretization, which assumes that input signals remain constant between s

BIG PERSONAL UPDATE. I've joined a16z as a partner investing in infra and AI. I'm also stepping down as CEO of Rosebud AI. I reflect in this…

BIG PERSONAL UPDATE. I've joined a16z as a partner investing in infra and AI. I'm also stepping down as CEO of Rosebud AI. I reflect in this article on my 8 years of building in generative AI. At @a16

researchdavid-ha--x

Bio-inspired Color Constancy: From Gray Anchoring Theory to Gray Pixel Methods

arXiv:2604.20243v1 Announce Type: new Abstract: Color constancy is a fundamental ability of many biological visual systems and a crucial step in computer imaging systems. Bio-inspired modeling offers

Build your own harness, folks. You won't regret it. These days, you just have to fix things yourself. It's doable, and it will set you up to…

Build your own harness, folks. You won't regret it. These days, you just have to fix things yourself. It's doable, and it will set you up to easily deal with some of the madness that's happening in th

researchdair-ai--x

Causal-Transformer with Adaptive Mutation-Locking for Early Prediction of Acute Kidney Injury

arXiv:2604.20259v1 Announce Type: new Abstract: Accurate early prediction of Acute Kidney Injury (AKI) is critical for timely clinical intervention. However, existing deep learning models struggle wit

Centering Ecological Goals in Automated Identification of Individual Animals

arXiv:2604.20626v1 Announce Type: cross Abstract: Recognizing individual animals over time is central to many ecological and conservation questions, including estimating abundance, survival, movement,

Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation

arXiv:2604.19826v1 Announce Type: cross Abstract: AI coding assistants increasingly generate code alongside tests. How developers structure test code, whether inline with the implementation or in sepa

CoAuthorAI: A Human in the Loop System For Scientific Book Writing

arXiv:2604.19772v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in scientific writing but struggle with book-length tasks, often producing inconsistent structure a

Cold-Start Forecasting of New Product Life-Cycles via Conditional Diffusion Models

arXiv:2604.20370v1 Announce Type: new Abstract: Forecasting the life-cycle trajectory of a newly launched product is important for launch planning, resource allocation, and early risk assessment. This

Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding

arXiv:2604.19921v1 Announce Type: new Abstract: Negation is a common and important semantic feature in natural language, yet Large Language Models (LLMs) struggle when negation is involved in natural

Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models

arXiv:2602.12036v2 Announce Type: replace Abstract: Large-scale verifiable prompts underpin the success of Reinforcement Learning with Verifiable Rewards (RLVR), but they contain many uninformative ex

Computing the Reachability Value of Posterior-Deterministic POMDPs

arXiv:2602.07473v2 Announce Type: replace Abstract: Partially observable Markov decision processes (POMDPs) are a fundamental model for sequential decision-making under uncertainty. However, many veri

Concept Graph Convolutions: Message Passing in the Concept Space

arXiv:2604.20082v1 Announce Type: new Abstract: The trust in the predictions of Graph Neural Networks is limited by their opaque reasoning process. Prior methods have tried to explain graph networks v

Confidence-Based Mesh Extraction from 3D Gaussians

arXiv:2603.24725v2 Announce Type: replace Abstract: Recently, 3D Gaussian Splatting (3DGS) greatly accelerated mesh extraction from posed images due to its explicit representation and fast software ra

Context Attribution with Multi-Armed Bandit Optimization

arXiv:2506.19977v2 Announce Type: replace Abstract: Understanding which parts of the retrieved context contribute to a large language model's generated answer is essential for building interpretable a

Control Consistency Losses for Diffusion Bridges

arXiv:2512.05070v2 Announce Type: replace-cross Abstract: Simulating the conditioned dynamics of diffusion processes, given their initial and terminal states, is an important but challenging problem i

Cover meets Robbins while Betting on Bounded Data: ln n Regret and Almost Sure lnln n Regret

arXiv:2604.20172v1 Announce Type: new Abstract: Consider betting against a sequence of data in [0,1], where one is allowed to make any bet that is fair if the data have a conditional mean m_0 in (0,1)

CrackForward: Context-Aware Severity Stage Crack Synthesis for Data Augmentation

arXiv:2604.19941v1 Announce Type: new Abstract: Reliable crack detection and segmentation are vital for structural health monitoring, yet the scarcity of well-annotated data constitutes a major challe

CXR-LanIC: Language-Grounded Interpretable Classifier for Chest X-Ray Diagnosis

arXiv:2510.21464v2 Announce Type: replace Abstract: Deep learning models have achieved remarkable accuracy in chest X-ray diagnosis, yet their widespread clinical adoption remains limited by the black

Deconstructing Superintelligence: Identity, Self-Modification and Differance

arXiv:2604.19845v1 Announce Type: new Abstract: Self-modification is often taken as constitutive of artificial superintelligence (SI), yet modification is a relative action requiring a supplement outs

Degrees, Levels, and Profiles of Contextuality

arXiv:2603.26692v3 Announce Type: replace-cross Abstract: We introduce a new notion, that of a contextuality profile of a system of random variables. Rather than characterizing a system's contextualit

Depression Risk Assessment in Social Media via Large Language Models

arXiv:2604.19887v1 Announce Type: cross Abstract: Depression is one of the most prevalent and debilitating mental health conditions worldwide, frequently underdiagnosed and undertreated. The prolifera

Diagnosing Urban Street Vitality via a Visual-Semantic and Spatiotemporal Framework for Street-Level Economics

arXiv:2604.19798v1 Announce Type: cross Abstract: Micro-scale street-level economic assessment is fundamental for precision spatial resource allocation. While Street View Imagery (SVI) advances urban

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation

arXiv:2604.20596v1 Announce Type: new Abstract: Federated learning (FL) enables training of a global model while keeping raw data on end-devices. Despite this, FL has shown to leak private user inform

Distributional Value Estimation Without Target Networks for Robust Quality-Diversity

arXiv:2604.20381v1 Announce Type: new Abstract: Quality-Diversity (QD) algorithms excel at discovering diverse repertoires of skills, but are hindered by poor sample efficiency and often require tens

Do you want to know why LLMs feel sharp on surface semantics but hollow on the fine-grained stuff? “From Tokens to Thoughts: How LLMs and Hu…

Do you want to know why LLMs feel sharp on surface semantics but hollow on the fine-grained stuff? “From Tokens to Thoughts: How LLMs and Humans Trade Compression for Meaning” Come by the poster tomor

researchyann-lecun--x

Effects of Cross-lingual Evidence in Multilingual Medical Question Answering

arXiv:2604.20531v1 Announce Type: new Abstract: This paper investigates Multilingual Medical Question Answering across high-resource (English, Spanish, French, Italian) and low-resource (Basque, Kazak

Efficient Multi-Cohort Inference for Long-Term Effects and Lifetime Value in A/B Testing with User Learning

arXiv:2604.20777v1 Announce Type: new Abstract: In streaming platforms churn is extremely costly, yet A/B tests are typically evaluated using outcomes observed within a limited experimental horizon. E

Efficient Test-Time Inference via Deterministic Exploration of Truncated Decoding Trees

arXiv:2604.20500v1 Announce Type: new Abstract: Self-consistency boosts inference-time performance by sampling multiple reasoning traces in parallel and voting. However, in constrained domains like ma

Emergence Transformer: Dynamical Temporal Attention Matters

arXiv:2604.19816v1 Announce Type: new Abstract: The Transformer, a breakthrough architecture in artificial intelligence, owes its success to the attention mechanism, which utilizes long-range interact

Enhancing Speaker Verification with Whispered Speech via Post-Processing

arXiv:2604.20229v1 Announce Type: cross Abstract: Speaker verification is a task of confirming an individual's identity through the analysis of their voice. Whispered speech differs from phonated spee

ESGLens: An LLM-Based RAG Framework for Interactive ESG Report Analysis and Score Prediction

arXiv:2604.19779v1 Announce Type: new Abstract: Environmental, Social, and Governance (ESG) reports are central to investment decision-making, yet their length, heterogeneous content, and lack of stan

Eventually LIL Regret: Almost Sure lnln T Regret for a sub-Gaussian Mixture on Unbounded Data

arXiv:2512.12325v3 Announce Type: replace Abstract: We prove that a classic sub-Gaussian mixture proposed by Robbins in a stochastic setting actually satisfies a path-wise (deterministic) regret bound

Explicit Dropout: Deterministic Regularization for Transformer Architectures

arXiv:2604.20505v1 Announce Type: new Abstract: Dropout is a widely used regularization technique in deep learning, but its effects are typically realized through stochastic masking rather than explic

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields

arXiv:2604.19979v1 Announce Type: cross Abstract: Neural fields, also known as implicit neural representations (INRs), offer a powerful framework for modeling continuous geometry, but their effectiven

FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels

arXiv:2604.20825v1 Announce Type: cross Abstract: Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed client

First time fine-tuning, need a sanity check — 3B or 7B for multi-task reasoning? [D]

researchr-machinelearning

This Reddit discussion post addresses a beginner's question about choosing between 3B and 7B parameter models for fine-tuning on multi-task reasoning problems. The post likely contains advice from exp

Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder

arXiv:2604.20483v1 Announce Type: cross Abstract: In this paper, we propose a proof-of-concept Graph Neural Network model that can successfully predict network flow-level traffic (NetFlow) by accurate

Formal Verification of Minimax Algorithms

arXiv:2509.20138v2 Announce Type: replace Abstract: Minimax-based search algorithms with alpha-beta pruning and transposition tables are a central component of classical game-playing engines and remai

Formalising the Logit Shift Induced by LoRA: A Technical Note

arXiv:2604.20313v1 Announce Type: cross Abstract: This technical note provides a first-order formalisation of the logit shift and fact-margin change induced by Low-Rank Adaptation (LoRA). Using a firs

Fourier Series Coder: A Novel Perspective on Angle Boundary Discontinuity Problem for Oriented Object Detection

arXiv:2604.20281v1 Announce Type: new Abstract: With the rapid advancement of intelligent driving and remote sensing, oriented object detection has gained widespread attention. However, achieving high

Fourier Weak SINDy: Spectral Test Function Selection for Robust Model Identification

arXiv:2604.20141v1 Announce Type: new Abstract: We introduce Fourier Weak SINDy, a minimal noise-robust and interpretable derivative-free equation learning method that combines weak-form sparse equati