Search: “models”

Bloom Filter Encoding for Machine Learning

arXiv:2512.19991v2 Announce Type: replace Abstract: We present a method that uses a Bloom filter transform to preprocess data for machine learning. Each sample is encoded into a compact bit-array repr

researcharxiv-cs-lg

Bridging Textual Profiles and Latent User Embeddings for Personalization

arXiv:2605.06981v1 Announce Type: cross Abstract: Personalized systems rely on user representations to connect behavioral history with downstream recommendation applications. Existing methods typicall

researcharxiv-cs-cl

11 May 2026

CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization

arXiv:2605.07162v1 Announce Type: new Abstract: Personalized LLMs can significantly enhance user experiences by tailoring responses to preferences such as helpfulness, conciseness, and humor. However,

researcharxiv-cs-cl

Agents

Conformal Agent Error Attribution

arXiv:2605.06788v1 Announce Type: new Abstract: When multi-agent systems (MAS) fail, identifying where the decisive error occurred is the first step for automated recovery to an earlier state. Error a

agentsarxiv-cs-lg

Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding

arXiv:2603.05687v3 Announce Type: replace Abstract: Contact-rich dexterous manipulation with multi-finger hands remains an open challenge in robotics because task success depends on multi-point contac

safetyarxiv-cs-ro

Agents

Cool paper from PwC. 'Earlier is always better' is the default intuition for agent clarification. New paper claims that's mostly wrong. Goal…

Cool paper from PwC. 'Earlier is always better' is the default intuition for agent clarification. New paper claims that's mostly wrong. Goal clarification loses nearly all of its value after just 10%

agentsdair-ai--x

Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training

arXiv:2605.07063v1 Announce Type: cross Abstract: Data selection methods address a critical challenge in LLM post-training: effectively leveraging scarce, high-fidelity target data alongside abundant

safetyarxiv-cs-ai

DReS: Dual Reconstruction Smoothing for Functional Regularization

arXiv:2510.00253v2 Announce Type: replace Abstract: Smoothness is a key inductive bias in machine learning and is closely related to generalization. Existing smoothness-inducing methods typically rely

safetyarxiv-cs-lg

DualResolution Residual Architecture with Artifact Suppression for Melanocytic Lesion Segmentation

arXiv:2508.06816v3 Announce Type: replace Abstract: Lesion segmentation, in contrast to natural scene segmentation, requires handling subtle variations in texture and color, frequent imaging artifacts

local-aiarxiv-cs-cv

Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey

arXiv:2605.06714v1 Announce Type: cross Abstract: Edge deep learning, a paradigm change reconciling edge computing and deep learning, facilitates real-time decision making attuned to environmental fac

EditTransfer++: Toward Faithful and Efficient Visual-Prompt-Guided Image Editing

arXiv:2605.07455v1 Announce Type: new Abstract: Visual-prompt-guided edit transfer aims to learn image transformations directly from example pairs, offering more precise and controllable editing than

safetyarxiv-cs-cv

Ensemble Distributionally Robust Bayesian Optimisation

arXiv:2605.07565v1 Announce Type: cross Abstract: We study zeroth-order optimisation under context distributional uncertainty, a setting commonly tackled using Bayesian optimisation (BO). A prevailing

Federated Spatiotemporal Graph Learning for Passive Attack Detection in Smart Grids

arXiv:2510.02371v2 Announce Type: replace-cross Abstract: Smart grids are exposed to passive eavesdropping, where attackers listen silently to communication links. Although no data is actively altered

FiSMiness: A Finite State Machine Based Paradigm for Emotional Support Conversations

arXiv:2504.11837v2 Announce Type: replace-cross Abstract: Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations. Although large l

Flow Matching for Count Data

arXiv:2605.07746v1 Announce Type: cross Abstract: High-dimensional count data arise in applications such as single-cell RNA sequencing and neural spike trains, where mapping between distributions acro

researcharxiv-cs-lg

Fortifying Time Series: DTW-Certified Robust Anomaly Detection

arXiv:2605.07690v1 Announce Type: new Abstract: Time-series anomaly detection is critical for ensuring safety in high-stakes applications, where robustness is a fundamental requirement rather than a m

safetyarxiv-cs-lg

From Pixels to Primitives: Scene Change Detection in 3D Gaussian Splatting

arXiv:2605.07203v1 Announce Type: new Abstract: Scene change detection methods built on Gaussian splatting universally follow a render-then-compare paradigm: the pre-change scene is rendered into 2D a

tutorialsarxiv-cs-cv

GASim: A Graph-Accelerated Hybrid Framework for Social Simulation

arXiv:2605.07692v1 Announce Type: new Abstract: Large-scale social simulators are essential for studying complex social patterns. Prior work explores hybrid methods to scale up simulations, combining

safetyarxiv-cs-ai

Generating training datasets for legal chatbots in Korean

arXiv:2605.07432v1 Announce Type: new Abstract: Chatbots are robots that can communicate with humans using text or voice signals. Legal chatbots improve access to justice, since legal representation a

local-aiarxiv-cs-cl

GESR: Graph-Based Edge Semantic Reconstruction for Stealthy Communication Detection with Benign-Only Training

arXiv:2605.07536v1 Announce Type: cross Abstract: Detecting stealthy malicious communications from flow logs under benign-only training remains a critical challenge in network security. Malicious comm

local-aiarxiv-cs-lg

HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning

arXiv:2605.07211v1 Announce Type: cross Abstract: Mobile devices face diverse resource constraints and non-IID data class distributions, requiring fast on-device inference for local in-distribution (I

Hybrid TF--IDF Logistic Regression and MLP Neural Baseline for Indonesian Three-Class Sentiment Analysis on Social Media Text

arXiv:2605.07793v1 Announce Type: new Abstract: This paper presents a compact three-class sentiment analysis study for Indonesian social media text. The task is formulated with positive, negative, and

applicationsarxiv-cs-cl

ICDAR 2026 Competition on Writer Identification and Pen Classification from Hand-Drawn Circles

arXiv:2605.07816v1 Announce Type: new Abstract: This paper presents CircleID, a large-scale ICDAR 2026 competition on writer identification and pen classification from scanned hand-drawn circles. The

researcharxiv-cs-cv

Implicit Multi-Camera System Calibration Using Gaussian Processes

arXiv:2605.07491v1 Announce Type: new Abstract: This paper proposes a novel framework for implicit multi-camera system calibration utilizing Gaussian Process (GP) regression. Conventional explicit cal

tutorialsarxiv-cs-cv

Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention

arXiv:2605.07010v1 Announce Type: new Abstract: Identifying vulnerable transmission lines in power grids before a cascading failure occurs is challenging: existing methods can learn inter-line failure

tutorialsarxiv-cs-lg

Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization

arXiv:2512.23032v2 Announce Type: replace-cross Abstract: Recent work, using the Biasing Features metric, labels a CoT as unfaithful if it omits a prompt-injected hint that affected the prediction. We

Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection

arXiv:2605.06955v1 Announce Type: cross Abstract: Denoising score matching (DSM) provides a way to learn data distributions by training a neural network to recover the score function, defined as the g

Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning

arXiv:2605.07026v1 Announce Type: cross Abstract: Functional connectivity (FC) derived from resting-state fMRI is widely used to characterize large-scale brain network alterations in neurological and

safetyarxiv-cs-ai

Learning Image-Adaptive Scale Fields for Metric Depth Recovery

arXiv:2605.07418v1 Announce Type: new Abstract: Monocular depth estimation (MDE) typically produces depth estimations that are defined up to an unknown scale or shift. When only sparse metric anchors

researcharxiv-cs-cv

Learning Multi-Relational Graph Representations for DNA Methylation-Based Biological Age Estimation

arXiv:2605.07175v1 Announce Type: cross Abstract: Aging clocks aim to estimate biological age, a measure of physiological state distinct from chronological age, from observable biomarkers, and are wid

Masks Can Talk: Extracting Structured Text Information from Single-Modal Images for Remote Sensing Change Detection

arXiv:2605.07178v1 Announce Type: new Abstract: Remote sensing change detection is pivotal for urban monitoring, disaster assessment, and environmental resource management. Yet, unimodal deep learning

safetyarxiv-cs-cv

Mind the Gap: Geometrically Accurate Generative Reconstruction from Disjoint Views

arXiv:2605.07550v1 Announce Type: new Abstract: 3D vision systems are fundamentally constrained by their reliance on visual overlap: reconstruction methods require it for geometric alignment, while ge

safetyarxiv-cs-cv

MinMax Recurrent Neural Cascades

arXiv:2605.06384v2 Announce Type: replace-cross Abstract: We show that the MinMax algebra provides a form of recurrence that is expressively powerful, efficiently implementable, and most importantly i

applicationsarxiv-cs-ai

Multi-Dimensional Evaluation of LLMs for Grammatical Error Correction

arXiv:2605.07635v1 Announce Type: new Abstract: Automated assistants for Grammatical Error Correction are now embedded in educational platforms serving millions of learners, yet three critical gaps re

researcharxiv-cs-cl

Multi-Stage Prototype Learning for Interpretable Time Series Classification

arXiv:2106.09636v2 Announce Type: replace Abstract: Deep learning methods are powerful tools in classifying multivariate time series data. Despite their high performance, these methods are hard to int

applicationsarxiv-cs-lg

Multimodal Stepwise Clinically-Guided Attention Learning for Pathological Complete Response Prediction in Breast Cancer

arXiv:2605.07561v1 Announce Type: new Abstract: Pathological complete response (pCR) is a key prognostic factor in breast cancer patients undergoing neoadjuvant therapy, strongly associated with long-

researcharxiv-cs-cv

New episode of the @chain_ofthought Podcast with @jerryjliu0 of @llama_index coming soon! https://chainofthought.show/

New episode of the @chain_ofthought Podcast with @jerryjliu0 of @llama_index coming soon! https://chainofthought.show/ We just wrapped recording with @jerryjliu0 of @llama_index 👀 His thesis: the AI f

applicationsjerry-liu--x

Nurnberg NLP at PsyDefDetect: Multi-Axis Voter Ensembles for Psychological Defence Mechanism Classification

arXiv:2605.07606v1 Announce Type: cross Abstract: Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous. In the PsyDefDetect shared task at BioNLP 20

One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators

arXiv:2605.06873v1 Announce Type: cross Abstract: Probabilistic conditioning is concerned with the identification of a distribution of a random variable X given a random variable Y. It is a cornerston

tutorialsarxiv-cs-lg

Parallel Lifted Planning via Semi-Naive Datalog Evaluation

arXiv:2605.07584v1 Announce Type: new Abstract: Lifted classical planners operate directly on first-order planning tasks to avoid the computationally demanding grounding step. However, lifted planning

Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning

arXiv:2605.06756v1 Announce Type: new Abstract: Real-time supervisory control of thermal energy distribution systems requires digital twins that are accurate, interpretable, and uncertainty-aware, yet

researcharxiv-cs-lg

RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion

arXiv:2602.16548v2 Announce Type: replace Abstract: The inverse design of RNA three-dimensional (3D) structures is crucial for engineering functional RNAs in synthetic biology and therapeutics. While

safetyarxiv-cs-lg

Robust and Reliable AI for Predictive Quality in Semiconductor Materials Manufacturing with MLOps and Uncertainty Quantification

arXiv:2605.07752v1 Announce Type: new Abstract: Semiconductor materials manufacturing presents unique challenges for machine learning deployment due to evolving process conditions, equipment degradati

applicationsarxiv-cs-lg

SAGE: Hierarchical LLM-Based Literary Evaluation through Ontology-Grounded Interpretive Dimensions

arXiv:2605.07102v1 Announce Type: new Abstract: Evaluating literary quality requires assessing interpretive dimensions such as cultural representation, emotional depth, and philosophical sophisticatio

safetyarxiv-cs-cl

SIMI: Self-information Mining Network for Low-light Image Enhancement

arXiv:2605.07767v1 Announce Type: new Abstract: Poor lighting conditions significantly impact image quality, posing substantial challenges for image editing and visualization. Many existing enhancemen

applicationsarxiv-cs-cv

SoftSAE: Dynamic Top-K Selection for Adaptive Sparse Autoencoders

arXiv:2605.06610v2 Announce Type: replace-cross Abstract: Sparse Autoencoders (SAEs) have become an important tool in mechanistic interpretability, helping to analyze internal representations in both

local-aiarxiv-cs-cv

Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer

arXiv:2605.07870v1 Announce Type: cross Abstract: We study the evolution of hidden-weight spectra in wide neural networks trained by (stochastic) gradient descent. We develop a two-level dynamical mea

Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature

arXiv:2408.11065v2 Announce Type: replace-cross Abstract: Physics seeks to uncover the laws of Nature and express them through mathematical equations. Despite the vast diversity of natural phenomena,

researcharxiv-cs-cl