Search: “papers”

Rethinking LLM Ensembling from the Perspective of Mixture Models

arXiv:2605.00419v1 Announce Type: cross Abstract: Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the

Revealing graph bandits for maximizing local influence

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DGX agent

arXiv:2605.00489v1 Announce Type: new Abstract: We study a graph bandit setting where the objective of the learner is to detect the most influential node of a graph by requesting as little information

researcharxiv-cs-lg

4 May 2026

Reward Modeling from Natural Language Human Feedback

arXiv:2601.07349v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable reward (RLVR) on preference data has become the mainstream approach for training Generative Reward Models (GR

Applications

Scaling Federated Linear Contextual Bandits via Sketching

arXiv:2605.00500v1 Announce Type: new Abstract: In federated contextual linear bandits, high data dimensionality incurs prohibitive computation and communication costs: local agents perform O(d^3)-tim

applicationsarxiv-cs-lg

Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification

arXiv:2605.00218v1 Announce Type: cross Abstract: Mobile remote identity verification (RIdV) systems are exposed to attacks that manipulate or replace the facial video stream, including presentation a

Smart Profit-Aware Crop Advisory System: Kisan AI

arXiv:2605.00133v1 Announce Type: new Abstract: Modern crop advisory systems exhibit a critical limitation termed extit{economic blindness}. These systems primarily optimize for biological yield, ofte

Soft Graph Diffusion Transformer for MIMO Detection

arXiv:2605.00449v1 Announce Type: cross Abstract: Learning-based MIMO detection has shown strong empirical performance, yet existing methods typically rely on fixed-depth architectures without explici

Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation

arXiv:2505.18875v4 Announce Type: replace Abstract: Diffusion Transformers (DiTs) are essential for video generation but suffer from significant latency due to the quadratic complexity of attention. B

Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium

arXiv:2503.10990v2 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with diverse human preferences is critical for ensuring fairness and informed outcomes when deploying th

Structure Liberates: How Constrained Sensemaking Produces More Novel Research Output

arXiv:2605.00557v1 Announce Type: new Abstract: Scientific discovery is an extended process of ideation--surveying prior work, forming hypotheses, and refining reasoning--yet existing approaches treat

Applications

The Power of Order: Fooling LLMs with Adversarial Table Permutations

arXiv:2605.00445v1 Announce Type: new Abstract: Large Language Models have achieved remarkable success and are increasingly deployed in critical applications involving tabular data, such as Table Ques

applicationsarxiv-cs-lg

Industry

There's a clay tablet with the founding charter of a 12-partner company on it. Twelve merchants pooled 33 pounds of gold to start the firm. …

There's a clay tablet with the founding charter of a 12-partner company on it. Twelve merchants pooled 33 pounds of gold to start the firm. The contract has the partner names, the starting capital, th

industryelon-musk--x

Thought Graph Traversal for Test-time Scaling in Chest X-ray VLLMs

arXiv:2506.11989v3 Announce Type: replace Abstract: Test-time scaling offers a promising way to improve the reasoning performance of vision-language large models (VLLMs) without additional training. I

Agents

Time-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking

arXiv:2605.00362v1 Announce Type: new Abstract: Multi-object tracking (MOT) is critical in numerous real-world applications, including surveillance, autonomous driving, and robotics. Accurately predic

agentsarxiv-cs-cv

Token Sparse Attention: Efficient Long-Context Inference with Interleaved Token Selection

arXiv:2602.03216v2 Announce Type: replace Abstract: The quadratic complexity of attention remains the central bottleneck in long-context inference for large language models. Prior acceleration methods

TURBOTEST: Learning When Less is Enough through Early Termination of Internet Speed Tests

arXiv:2510.21141v2 Announce Type: replace-cross Abstract: Internet speed tests are indispensable for users, ISPs, and policymakers, but their static flooding-based design imposes growing costs: a sing

Understanding Cognitive States from Head & Hand Motion Data

arXiv:2509.24255v2 Announce Type: replace-cross Abstract: As virtual reality (VR) becomes widespread, head and hand motion data captured by consumer systems has become substantially more common. Howev

VGR: Visual Grounded Reasoning

arXiv:2506.11991v3 Announce Type: replace-cross Abstract: In the field of multimodal chain-of-thought (CoT) reasoning, existing approaches predominantly rely on reasoning on pure language space, which

safetyarxiv-cs-cl

Syntheses

Wiki Lint Report — 2026-05-03

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Automated lint: 45 errors, 11 warnings, 3 info

3 May 2026

A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching

arXiv:2604.27307v1 Announce Type: cross Abstract: Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data. However, identifyin

A Real-time Scale-robust Network for Glottis Segmentation in Nasal Transnasal Intubation

arXiv:2604.27383v1 Announce Type: cross Abstract: Nasotracheal intubation (NTI) is a critical clinical procedure for establishing and maintaining patient airway patency. Machine-assisted NTI has emerg

A Short Note on Batch-efficient Divide-and-Conquer Algorithm for EigenDecomposition

arXiv:2604.27325v1 Announce Type: new Abstract: EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications. One crucial bottleneck limiting its usage is the expensive

A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion

arXiv:2501.07451v4 Announce Type: replace Abstract: Model compression is essential in the deployment of large Computer Vision models on embedded devices. However, static optimization techniques (e.g.

Agent Name Service (ANS): A Proof-of-Concept Trust Layer for Secure AI Agent Discovery, Identity, and Governance in Kubernetes

arXiv:2604.26997v1 Announce Type: cross Abstract: Autonomous AI agent ecosystems require stronger mechanisms for secure discovery, identity verification, capability attestation, and policy governance.

Agents

AgentEconomist: An End-to-end Agentic System Translating Economic Intuitions into Executable Computational Experiments

arXiv:2604.27725v1 Announce Type: cross Abstract: A long-standing challenge in economics lies not in the lack of intuition, but in the difficulty of translating intuitive insights into verifiable rese

agentsarxiv-cs-ai

Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy

model-releasesarxiv-cs-ai

arXiv:2602.11897v3 Announce Type: replace-cross Abstract: Cybersecurity decision-making increasingly occurs in environments characterized by uncertainty, partial observability, and adversarial manipul

Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents

arXiv:2601.01885v2 Announce Type: replace Abstract: Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows, making effective memory mana

safetyarxiv-cs-cl

AI Models for Depressive Disorder Detection and Diagnosis: A Review

arXiv:2508.12022v2 Announce Type: replace Abstract: Major Depressive Disorder is one of the leading causes of disability worldwide, yet its diagnosis still depends largely on subjective clinical asses

An adaptive wavelet-based PINN for problems with localized high-magnitude source

arXiv:2604.28180v1 Announce Type: new Abstract: In recent years, physics-informed neural networks (PINNs) have gained significant attention for solving differential equations, although they suffer fro

An Experimental Modular Instrument With a Haptic Feedback Framework for Robotic Surgery Training

arXiv:2604.27385v1 Announce Type: new Abstract: Robotic-assisted surgery offers significant clinical advantages but largely eliminates direct haptic feedback, increasing the risk of excessive tool-tis

researcharxiv-cs-ro

An Extended Evaluation Split for DeepSpaceYoloDataset

arXiv:2604.27593v1 Announce Type: cross Abstract: Recent technological advances in astronomy, particularly the growing popularity of smart telescopes for the general public, make it possible to develo

Analytical Correction for Subsampling Bias in Drifting Models

arXiv:2604.27239v1 Announce Type: new Abstract: Drifting models are capable one-step generative models trained to follow a drifting field. The field combines attractive and repulsive softmax-weighted

Are DeepFakes Realistic Enough? Exploring Semantic Mismatch as a Novel Challenge

arXiv:2604.28022v1 Announce Type: new Abstract: Current DeepFake detection scenarios are mostly binary, yet data manipulation can vary across audio, video, or both, whose variability is not captured i

Automated Detection of Mutual Gaze and Joint Attention in Dual-Camera Settings via Dual-Stream Transformers

arXiv:2604.27105v1 Announce Type: new Abstract: Analyzing mutual gaze (MG) and joint attention (JA) is critical in developmental psychology but traditionally relies on labor-intensive manual coding. A

Agents

AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models

arXiv:2507.12414v2 Announce Type: replace-cross Abstract: Training of autonomous driving systems requires extensive datasets with precise annotations to attain robust performance. Human annotations su

agentsarxiv-cs-ai

Belief-Guided Inference Control for Large Language Model Services via Verifiable Observations

arXiv:2604.27536v1 Announce Type: new Abstract: In black-box large language model (LLM) services, response reliability is often only partially observable at decision time, while stronger inference pat

Hardware

Benchmarking Deep Learning Models for Object Detection on Edge Computing Devices

arXiv:2409.16808v1 Announce Type: cross Abstract: Modern applications, such as autonomous vehicles, require deploying deep learning algorithms on resource-constrained edge devices for real-time image

hardwarearxiv-cs-lg

Beyond Accuracy: LLM Variability in Evidence Screening for Software Engineering SLRs

model-releasesarxiv-cs-ai

arXiv:2604.27006v1 Announce Type: cross Abstract: Context: Study screening in systematic literature reviews is costly, inconsistency-prone, and risk-asymmetric, since false negatives can compromise va

Beyond Semantics: Measuring Fine-Grained Emotion Preservation in Small Language Model-Based Machine Translation

model-releasesarxiv-cs-ai

arXiv:2604.27920v1 Announce Type: cross Abstract: Preserving affective nuance remains a challenge in Machine Translation (MT), where semantic equivalence often takes precedence over emotional fidelity

ChipLingo: A Systematic Training Framework for Large Language Models in EDA

arXiv:2604.27415v1 Announce Type: new Abstract: With the rapid advancement of semiconductor technology, Electronic Design Automation (EDA) has become an increasingly knowledge-intensive and document-d

Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models

arXiv:2604.27251v1 Announce Type: cross Abstract: Large Language Models (LLMs) are known to acquire reasoning capabilities through shared inference patterns in pre-training data, which are further eli

researcharxiv-cs-ai

Computing Equilibrium beyond Unilateral Deviation

arXiv:2604.28186v1 Announce Type: cross Abstract: Most familiar equilibrium concepts, such as Nash and correlated equilibrium, guarantee only that no single player can improve their utility by deviati

researcharxiv-cs-ai

Contextual Online Uncertainty-Aware Preference Learning for Human Feedback

arXiv:2504.19342v3 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm in artificial intelligence to align large models with human pr

Culture-inspired Multi-modal Color Palette Generation and Colorization: A Chinese Youth Subculture Case

arXiv:2102.05231v1 Announce Type: cross Abstract: Color is an essential component of graphic design, acting not only as a visual factor but also carrying cultural implications. However, existing resea

researcharxiv-cs-ai

Debiasing Reward Models via Causally Motivated Inference-Time Intervention

arXiv:2604.27495v1 Announce Type: cross Abstract: Reward models (RMs) play a central role in aligning large language models (LLMs) with human preferences. However, RMs are often sensitive to spurious

Tutorials

Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders

arXiv:2604.28176v1 Announce Type: cross Abstract: Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as b

tutorialsarxiv-cs-lg

Design Structure Matrix Modularization with Large Language Models

arXiv:2604.28018v1 Announce Type: cross Abstract: Design Structure Matrix (DSM) modularization, the task of partitioning system elements into cohesive modules, is a fundamental combinatorial challenge

Designing Ethical Learning for Agentic AI: Toegye Yi Hwang's Ethical Emotion Regulation Framework

arXiv:2604.26958v1 Announce Type: cross Abstract: Agentic AI systems capable of autonomous goal setting and proactive intervention introduce new challenges for regulating moral-emotional processes in