Search: “research”

Why Are Linear RNNs More Parallelizable?

arXiv:2603.03612v3 Announce Type: replace-cross Abstract: The community is increasingly exploring linear RNNs (LRNNs) as language models, motivated by their expressive power and parallelizability. Whi

3 Jun 2026

Worth Remembering: Surprise-Gated Robot Episodic Memory

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arXiv:2606.03787v1 Announce Type: new Abstract: Robots solving generalist tasks need to be able to ground instructions in their past experience, since humans may refer to notable past events when givi

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3 Jun 2026

X-RAY: Mapping LLM Reasoning Capability via Formalized and Calibrated Probes

arXiv:2603.05290v2 Announce Type: replace Abstract: Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood. Existing evaluations largely emph

3 Jun 2026

ZX-Calculus:Trace-Indexed Dependent Types and Epistemic Semantics

arXiv:2606.03063v1 Announce Type: cross Abstract: We propose ZX-Calculus (Knowledge Evolution Calculus), a conservative extension of Martin-Lof Dependent Type Theory (MLTT) integrating trace-indexed t

3 Jun 2026

21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables

arXiv:2606.00219v1 Announce Type: cross Abstract: We are witnessing a surge in observations of the cosmic dawn (CD) and epoch of reionisation (EoR), driving an increasing demand for fast and robust th

A Direct Approach for Handling Contextual Bandits with Latent State Dynamics

arXiv:2604.08149v2 Announce Type: replace Abstract: We consider a linear contextual bandit model where contexts and rewards are governed by a finite hidden Markov chain. We first revisit the simplifie

A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering

arXiv:2606.00402v1 Announce Type: cross Abstract: We propose a distribution-free statistical framework that converts arbitrary rewrite-based detectors into detectors with finite-sample FDR guarantees

A Doeblin-Anchored Contrastive Chart for Learning Markov Transition Kernels

arXiv:2606.02232v1 Announce Type: new Abstract: Learning a Markov transition model is not merely conditional density estimation: the learned object must be a valid transition kernel before it is itera

A Fiber Criterion for Representation Identifiability in Supervised Learning

arXiv:2606.01092v1 Announce Type: cross Abstract: Supervised learning evaluates predictors through their input-output behavior. When a predictor is implemented as a composition f=circ h, supervised ev

A Framework for Graph-Conditioned Hierarchical Shapley Attribution in Patent Valuation

arXiv:2606.01632v1 Announce Type: cross Abstract: Estimating the economic contribution of a single patent inside a product that embodies tens of thousands of patents is a long-standing unsolved proble

A Grammar of Machine Learning Workflows: Rejecting Data Leakage at Call Time

arXiv:2603.10742v4 Announce Type: replace Abstract: Data leakage has been identified in 648 published papers across 30 scientific fields. The knowledge to prevent it has existed for over a decade; the

A Kinetic Theory of Encounter-Based Information Propagation in Multi-Robot Systems

arXiv:2606.02296v1 Announce Type: new Abstract: Multi-robot systems cannot assume persistent network connectivity. We study this problem through target tracking, where performance depends on how quick

A Lightweight Context-Driven Training-Free Network for Scene Text Segmentation and Recognition

arXiv:2503.15639v2 Announce Type: replace-cross Abstract: Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively exp

A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features

arXiv:2606.00889v1 Announce Type: cross Abstract: Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information. Traditional blacklist and rule-ba

A Mathematical Conflict Framework for Contextual Data Modulation

arXiv:2606.02381v1 Announce Type: new Abstract: In this study, a generalized operator-based mathematical conflict framework is presented to explicitly represent structural discrepancies between raw da

A Multiscale Network with Supervised Contrastive Learning for Real-Time Facial Emotion Recognition

arXiv:2606.01069v1 Announce Type: new Abstract: Real-time emotion recognition from facial expressions is a challenging task, particularly in video-based scenarios where multiple emotional states may o

A Note on Stability for Orthogonalized Matrix Momentum with Client Sampling

arXiv:2606.01720v1 Announce Type: new Abstract: We study finite-sample generalization for a client-sampled distributed optimization scheme with matrix-valued parameters and orthogonalized momentum upd

A passive universal grasping mechanism based on an everting shell

arXiv:2606.00470v1 Announce Type: new Abstract: A passive monolithic compliant grasping mechanism that works based on the eversion of an elastically deformable bistable shell is conceptualized. It com

A phenomenon of AI-conformity: how algorithms change human moral decision-making

arXiv:2606.00013v1 Announce Type: cross Abstract: Social conformity is a well-documented phenomenon in which individuals shift their opinions towards those of a social majority. As artificial intellig

A Pre-Training Analogue of Grokking in Language Models: Tracing Delayed Grammatical Generalization

arXiv:2606.00230v1 Announce Type: new Abstract: Grokking, the phenomenon in which neural networks generalize long after fitting their training data, has been studied in supervised settings on many epo

A Primer in Post-Training Reasoning Data: What We Know About How It Works

arXiv:2606.02113v1 Announce Type: cross Abstract: Post-training has become a primary driver of recent progress in large reasoning models, and reasoning data are often the key variable determining whet

A Registry-Bound LLM Pipeline for Evidence-Grounded Trait Extraction across Tropical Plants, Aquatic Species, and Exotic Pets

arXiv:2606.00994v1 Announce Type: new Abstract: We describe a registry-bound large-language-model extraction pipeline producing evidence-grounded structured trait records at scale, on cultivated tropi

A Task-Centric Theory for Iterative Self-Improvement with Easy-to-Hard Curricula

arXiv:2602.10014v3 Announce Type: replace Abstract: Iterative self-improvement fine-tunes an autoregressive large language model (LLM) on reward-verified outputs generated by the LLM itself. In contra

A Theoretical Framework for Self-Play Theorem Proving Algorithms

arXiv:2606.01861v1 Announce Type: new Abstract: Self-play, a type of training algorithm that enables a model to self-improve, has recently shown promising empirical results in the context of formal th

A Theoretical Framework for Statistical Evaluability of Generative Models

arXiv:2604.05324v2 Announce Type: replace Abstract: Statistical evaluation aims to estimate the generalization performance of a model using held-out i.i.d. test data sampled from the ground-truth dist

A Unified Evaluation-Instructed Framework for Query-Dependent Prompt Optimization

arXiv:2511.19829v2 Announce Type: replace Abstract: Most prompt-optimization methods refine a single static template, making them ineffective in complex and dynamic user scenarios. Existing query-depe

A Unified Framework for Probabilistic Dynamic-, Trajectory- and Vision-based Virtual Fixtures

arXiv:2506.10239v3 Announce Type: replace Abstract: Probabilistic Virtual Fixtures (VFs) enable the adaptive selection of the most suitable haptic feedback for each phase of a task, based on learned o

A unifying Bayesian framework for adversarial robustness

arXiv:2510.09288v2 Announce Type: replace-cross Abstract: The vulnerability of machine learning models to adversarial attacks remains a critical societal security challenge. Traditional defenses, such

Ablating Archetypes: The Stability of Archetypal SAEs is an Artifact of Initialization and Metric Design

arXiv:2606.02061v1 Announce Type: new Abstract: Dictionary learning with sparse autoencoders (SAEs) produces overcomplete bases from neural network activations that are often interpretable and reduces

Accelerating Min-Max Optimization via Power-Law Stepsizes

arXiv:2606.01764v1 Announce Type: cross Abstract: We revisit the convergence guarantees of the Extragradient (EG) method for unconstrained biaffine min-max optimization. It is known that EG with a fix

Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo

arXiv:2606.00293v1 Announce Type: new Abstract: Tuning algorithms such as stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD) for approximate sampling and uncertainty qu

Acoustic and perceptual differences between standard and accented speech and their voice clones

arXiv:2604.01562v2 Announce Type: replace-cross Abstract: Voice cloning is often evaluated in terms of overall quality, but less is known about accent preservation and its perceptual consequences. We

ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning

arXiv:2603.09692v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become the standard for aligning Large Language Models (LLMs), yet its efficacy is bottl

ActMVS: Active Scene Reconstruction with Monocular Multi-View Stereo

arXiv:2606.01367v1 Announce Type: cross Abstract: Active scene reconstruction enables robots/UAVs to autonomously plan trajectories and reconstruct environments without costly manual data acquisition.

AdaCodec: A Predictive Visual Code for Video MLLMs

arXiv:2606.02569v1 Announce Type: cross Abstract: Video is temporally redundant: adjacent frames usually share most objects, background, and layout. Yet existing video multimodal large language models

Adaptive data selection improves wearable prediction under low baseline performance

arXiv:2606.00141v1 Announce Type: cross Abstract: Adaptive sensing strategies that selectively sample data are increasingly used in wearable health systems to improve prediction performance under limi

Adaptive Dense Evidence Refinement for Video Relational Reasoning for VRR-QA Challenge

arXiv:2606.01104v1 Announce Type: new Abstract: VRR-QA evaluates whether video-language systems can infer spatial, temporal, viewpoint, depth, and visibility relations that are not always resolved by

Adaptive Exploration for Latent-State Bandits

arXiv:2602.05139v3 Announce Type: replace Abstract: We study bandits whose rewards depend on an unobserved Markov state that evolves independently of the learner's actions. The optimal arm can change

Adaptive PD Gains for Energy-Conscious Control in Physical Human-Robot Interaction

arXiv:2606.00459v1 Announce Type: new Abstract: Compliant force or torque control are approaches often investigated to achieve safe physical human-robot interaction (pHRI). However, these approaches h

Adaptive Sharpness-Aware Minimization with a Polyak-type Step size: A Theory-Grounded Scheduler

arXiv:2606.01827v1 Announce Type: cross Abstract: Sharpness-Aware Minimization (SAM) has established itself as a powerful and widely adopted optimizer for training machine learning models. By explicit

Advancing Local Clustering on Graphs via Compressive Sensing: Semi-supervised and Unsupervised Methods

arXiv:2504.19419v3 Announce Type: replace Abstract: Local clustering aims to identify specific substructures within a large graph without any additional structural information of the graph. These subs

Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why

arXiv:2606.00093v1 Announce Type: new Abstract: Validating an LLM judge against human annotations usually means reporting several agreement statistics: accuracy, precision, recall, F_1, Cohen's kappa,

Algebraic anti-unification

arXiv:2407.15510v2 Announce Type: replace Abstract: Abstraction is key to human and artificial intelligence as it allows one to identify common structure in otherwise distinct objects or situations. A

An Abstract Worlds Semantic Framework for Belief Change Operators

arXiv:2606.02163v1 Announce Type: new Abstract: This article proposes a set-theoretic framework for belief change, called Abstract Worlds Semantics, in which no logical syntax is assumed. Inspired by

An Attribute-Based Measure of Video Complexity

arXiv:2606.00640v1 Announce Type: new Abstract: A new framework for the estimation of the complexity posed by video-question pairs to video-LLMs, Video Attribute-Based Complexity (VideoABC), is propos

An Effective Solution for the CVPR 2026 8th UG2+ Challenge Track 3: Dynamic Object Segmentation in Turbulence

arXiv:2606.00522v1 Announce Type: new Abstract: In this work, we present our solution for the 8th UG2+ Challenge (CVPR 2026) Track 3: Dynamic Object Segmentation in Turbulence (DOST). Our method is bu

Analysis of Ethnic Disparities in Autism Spectrum Disorder among Toddlers

arXiv:2606.01217v1 Announce Type: new Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by challenges in communication and behavior. This study examines the relat

AnyEdit++: Adaptive Long-Form Knowledge Editing via Bayesian Surprise

arXiv:2606.01053v1 Announce Type: new Abstract: Editing complex, long-form knowledge in Large Language Models remains a significant challenge due to the difficulty of maintaining generation coherence.