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Toward Human-AI Complementarity Across Diverse Tasks

arXiv:2605.04070v1 Announce Type: cross Abstract: Human-AI complementarity, the idea that combining human and AI judgments can outperform either alone, offers a promising pathway toward robust oversig

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Towards Distillation-Resistant Large Language Models: An Information-Theoretic Perspective

DGX agent

arXiv:2602.03396v3 Announce Type: replace Abstract: Proprietary large language models (LLMs) embody substantial economic value and are generally exposed only as black-box APIs, yet adversaries can sti

researcharxiv-cs-cl

7 May 2026

Towards Self-Referential Analytic Assessment: A Profile-Based Approach to L2 Writing Evaluation with LLMs

arXiv:2605.04298v1 Announce Type: new Abstract: Automated essay scoring (AES) research often relies on rank-based correlation metrics to validate analytic assessment. However, such metrics obscure bot

Transformed Latent Variable Multi-Output Gaussian Processes

arXiv:2605.05133v1 Announce Type: new Abstract: Multi-Output Gaussian Processes (MOGPs) provide a principled probabilistic framework for modelling correlated outputs but face scalability bottlenecks w

Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity

arXiv:2605.04827v1 Announce Type: new Abstract: Label Distribution Learning (LDL) models supervision as an instance-wise probability distribution, enabling fine-grained learning under inherent ambigui

Understanding LoRA as Knowledge Memory: An Empirical Analysis

arXiv:2603.01097v2 Announce Type: replace Abstract: Continuous knowledge updating for pre-trained large language models (LLMs) is increasingly necessary yet remains challenging. Although inference-tim

Understanding Transformers through the Lens of Pavlovian Conditioning

arXiv:2508.08289v2 Announce Type: replace Abstract: Transformer architectures have revolutionized artificial intelligence (AI) through their attention mechanisms, yet the computational principles unde

Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions

arXiv:2605.04209v1 Announce Type: cross Abstract: We present Sparse Backdoor, a supply-chain attack that plants a provably undetectable backdoor in pre-trained image classifiers, including convolution

Unintended Negative Impacts of Promotional Language in Patent Evaluation

arXiv:2605.04926v1 Announce Type: new Abstract: Promotional language has been increasingly used to aid the communication of innovative ideas in science. Yet, less is known about its role in the contex

UniPCB: A Generation-Assisted Detection Framework for PCB Defect Inspection

arXiv:2605.04635v1 Announce Type: new Abstract: Printed Circuit Board (PCB) defect inspection faces two compounding challenges: scarce and imbalanced defect samples that limit model training, and insu

VC-FeS: Viewpoint-Conditioned Feature Selection for Vehicle Re-identification in Thermal Vision

arXiv:2605.04750v1 Announce Type: new Abstract: Identification of less-articulated objects using single-channel images, such as thermal images, is important in many applications, such as surveillance.

Visual Disentangled Diffusion Autoencoders: Scalable Counterfactual Generation for Foundation Models

arXiv:2601.21851v2 Announce Type: replace Abstract: Foundation models, despite their robust zero-shot capabilities, remain vulnerable to spurious correlations and 'Clever Hans' strategies. Existing mi

Vol-Mark: A Watermark for 3D Medical Volume Data Via Cubic Difference Expansion and Contrastive Learning

arXiv:2605.04705v1 Announce Type: cross Abstract: Today, advances in medical technology extensively utilize 3D volume data for accurate and efficient diagnostics. However, sharing these data across ne

What Can Be Recovered Under Sparse Adversarial Corruption? Assumption-Free Theory for Linear Measurements

arXiv:2510.24215v4 Announce Type: replace-cross Abstract: Recovery from linear measurements under sparse adversarial corruption is typically formulated as an exact-recovery problem: one seeks structur

What Matters in Practical Learned Image Compression

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One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human visual system. Despit

What’s next for IVF

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. Forty-eight years ago this July

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When Does Gene Regulatory Network Inference Break? A Controlled Diagnostic Study of Causal and Correlational Methods on Single-Cell Data

arXiv:2605.04930v1 Announce Type: new Abstract: Despite theoretical advantages, causal methods for Gene Regulatory Network (GRN) inference from single-cell RNA-seq data consistently fail to match or o

When Engineering Outruns Intelligence: Rethinking Instruction-Guided Navigation

arXiv:2507.20021v3 Announce Type: replace-cross Abstract: Recent ObjectNav systems credit large language models (LLMs) for sizable zero-shot gains, yet it remains unclear how much comes from language

When Relations Break: Analyzing Relation Hallucination in Vision-Language Model Under Rotation and Noise

arXiv:2605.05045v1 Announce Type: cross Abstract: Vision-language models (VLMs) achieve strong multimodal performance but remain prone to relation hallucination, which requires accurate reasoning over

Why Geometric Continuity Emerges in Deep Neural Networks: Residual Connections and Rotational Symmetry Breaking

arXiv:2605.04971v1 Announce Type: cross Abstract: Weight matrices in deep networks exhibit geometric continuity -- principal singular vectors of adjacent layers point in similar directions. While this

3D Human Face Reconstruction with 3DMM face model from RGB image

arXiv:2605.03996v1 Announce Type: new Abstract: Nowadays as convolution neural networks demonstrate its powerful problem-solving ability in the area of image processing, efforts have been made to reco

4RC: 4D Reconstruction via Conditional Querying Anytime and Anywhere

arXiv:2602.10094v2 Announce Type: replace Abstract: We present 4RC, a unified feed-forward framework for 4D reconstruction from monocular videos. Unlike existing approaches that typically decouple mot

A Certifably Correct Algorithm for Generalized Robot-World and Hand-Eye Calibration

arXiv:2507.23045v2 Announce Type: replace Abstract: Automatic extrinsic sensor calibration is a fundamental problem for multi-sensor platforms. Reliable and general-purpose solutions should be computa

researcharxiv-cs-ro

A Closed-Form Adaptive-Landmark Kernel for Certified Point-Cloud and Graph Classification

arXiv:2605.04046v1 Announce Type: new Abstract: We introduce PALACE (Persistence Adaptive-Landmark Analytic Classification Engine), the data-adaptive companion to PLACE, paying a small cross-validatio

A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion

arXiv:2605.03360v1 Announce Type: cross Abstract: We present A-CODE, a fully atomic unified one-stage protein co-design model that simultaneously refines discrete atom types and continuous atom coordi

A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis

arXiv:2605.03440v1 Announce Type: new Abstract: The rapid growth of electronic communication has necessitated more robust systems for email classification and sentiment detection. This study presents

A Comprehensive Analysis of Tokenization and Self-Supervised Learning in End-to-End Automatic Speech Recognition applied on French Language

arXiv:2605.03696v1 Announce Type: new Abstract: The performance of end-to-end automatic speech recognition (ASR) systems enables their increasing integration into numerous applications. While there ar

A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning

arXiv:2605.03499v1 Announce Type: new Abstract: We study expected generalization bounds for the Hierarchical Federated Learning (HFL) setup using Wasserstein distance. We introduce a generalized frame

A note on the unique properties of the Kullback--Leibler divergence for sampling via gradient flows

arXiv:2507.04330v2 Announce Type: replace-cross Abstract: We consider the problem of sampling from a probability distribution pi which admits a density w.r.t. a dominating measure. It is well known th

A Paradigm for Interpreting Metrics and Identifying Critical Errors in Automatic Speech Recognition

arXiv:2605.03671v1 Announce Type: new Abstract: The most commonly used metrics for evaluating automatic speech transcriptions, namely Word Error Rate (WER) and Character Error Rate (CER), have been he

A Partition-Based Generating Function for Row-Convex Polyominoes

arXiv:2605.03203v1 Announce Type: cross Abstract: An alternative generating function is proposed to enumerate row-convex polyominoes without internal holes on a discrete grid. The approach is based on

A Perfectly Truthful Calibration Measure

arXiv:2508.13100v3 Announce Type: replace Abstract: Calibration requires that predictions are conditionally unbiased and, therefore, reliably interpretable as probabilities. A calibration measure quan

A Sentence Relation-Based Approach to Sanitizing Malicious Instructions

arXiv:2605.01078v1 Announce Type: cross Abstract: Retrieval-augmented generation and tool-integrated LLM agents increasingly depend on external textual sources. This reliance broadens the available at

A Target-Free Harmonization Method for MRI

arXiv:2605.01282v1 Announce Type: cross Abstract: In MRI, variations in scan parameters, sequence, or hardware can lead to discrepancies in image appearance, even for the same subject. These inconsist

A User-Centric Analysis of Explainability in AI-Based Medical Image Diagnosis

arXiv:2605.02903v1 Announce Type: cross Abstract: In recent years, AI systems in the medical domain have advanced significantly. However, despite outperforming humans, they are rarely used in practice

Accelerating Inference of Discrete Autoregressive Normalizing Flows by Selective Jacobi Decoding

arXiv:2505.24791v2 Announce Type: replace Abstract: Discrete normalizing flows are promising generative models with advantages such as analytical log-likelihood computation and end-to-end training. Ho

Adaptive Data Compression and Reconstruction for Memory-Bounded EEG Continual Learning

arXiv:2605.03085v1 Announce Type: new Abstract: Electroencephalography (EEG) signals provide millisecond-level temporal resolution but their analysis is limited by remarkable noise and inter-subject v

Adaptive Estimation and Optimal Control in Offline Contextual MDPs without Stationarity

arXiv:2605.03393v1 Announce Type: cross Abstract: Contextual MDPs are powerful tools with wide applicability in areas from biostatistics to machine learning. However, specializing them to offline data

Adaptive graph-based algorithms for conditional anomaly detection and semi-supervised learning

arXiv:2605.03495v1 Announce Type: new Abstract: We develop graph-based methods for semi-supervised learning based on label propagation on a data similarity graph. When data is abundant or arrive in a

AI agents often struggle to plan movements because their internal representations of the physical world can be overly tangled. CDS PhD stude…

AI agents often struggle to plan movements because their internal representations of the physical world can be overly tangled. CDS PhD student Ying Wang (@yingwww_) shows how straightening these pathw

researchyann-lecun--x

AMSnet-q: Unsupervised Circuit Identification and Performance Labeling for AMS Circuits

arXiv:2605.01404v1 Announce Type: cross Abstract: Analog and mixed-signal (AMS) circuit design remains heavily reliant on expert knowledge. While recent AI-driven automation tools can generate candida

An End-to-End Framework for Building Large Language Models for Software Operations

arXiv:2605.02906v1 Announce Type: new Abstract: In the field of software operations, Large Language Models (LLMs) have attracted increasing attention. However, existing research has not yet achieved e

An ERP Study of Recursive Possessive Parsing in ASD Children and Its Cognitive Neuro Mechanisms

arXiv:2605.03447v1 Announce Type: new Abstract: Recursive structures are a core property of human language, yet little is known about how children with autism spectrum disorder (ASD) process complex r

Analysis and Explainability of LLMs Via Evolutionary Methods

arXiv:2605.02930v1 Announce Type: cross Abstract: Evolutionary methods have long been useful for analysis and explanation in genetics, biology, ecology, and related fields. In this work, we extend the

Analytic Bridge Diffusions for Controlled Path Generation

arXiv:2605.02961v1 Announce Type: new Abstract: Most modern bridge-diffusion methods achieve finite-time transport by specifying an interpolation, Schrodinger-bridge, or stochastic-control objective a

APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music

arXiv:2605.03395v1 Announce Type: cross Abstract: Music popularity prediction has attracted growing research interest, with relevance to artists, platforms, and recommendation systems. However, the ex

Are we Doomed to an AI Race? Why Self-Interest Could Drive Countries Towards a Moratorium on Superintelligence

arXiv:2605.01297v1 Announce Type: cross Abstract: This paper uses game theory to argue that, contrary to the prevailing view, a moratorium on Artificial Superintelligence (ASI) can be in a state's sel

AsymK-Talker: Real-Time and Long-Horizon Talking Head Generation via Asymmetric Kernel Distillation

arXiv:2605.02948v1 Announce Type: new Abstract: Recent advances in diffusion models have markedly enhanced the visual fidelity of audio-driven talking head generation. Nevertheless, existing methods a