Search: “models”

Towards a more realistic evaluation of machine learning models for bearing fault diagnosis

arXiv:2509.22267v4 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery. While recent advances

18 May 2026

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Why are language models less surprised than humans? Testing the Parse Multiplicity Mismatch Hypothesis

DGX agent

arXiv:2605.15440v1 Announce Type: new Abstract: Surprisal theory posits that the processing difficulty of a word is determined by its predictability in context, offering a potential link between human

researcharxiv-cs-cl

18 May 2026

Natural Synthesis: Outperforming Reactive Synthesis Tools with Large Reasoning Models

arXiv:2605.15131v1 Announce Type: new Abstract: Reactive synthesis, the problem of automatically constructing a hardware circuit from a logical specification, is a long-standing challenge in formal ve

15 May 2026

Robust Inference-Time Steering of Protein Diffusion Models via Embedding Optimization

arXiv:2602.05285v2 Announce Type: replace Abstract: A core challenge in structural biophysics is generating biomolecular conformations that are both physically plausible and consistent with experiment

15 May 2026

Do Fair Models Reason Fairly? Counterfactual Explanation Consistency for Procedural Fairness in Credit Decisions

arXiv:2605.12701v1 Announce Type: cross Abstract: Machine learning algorithms in socially sensitive domains (e.g., credit decisions) often focus on equalizing predictive outcomes. However, satisfying

14 May 2026

Few-Shot Synthetic Data Generation with Diffusion Models for Downstream Vision Tasks

arXiv:2605.11898v1 Announce Type: new Abstract: Class imbalance is a persistent challenge in visual recognition, particularly in safety-critical domains where collecting positive examples is expensive

safetyarxiv-cs-cv

Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence

arXiv:2605.11348v1 Announce Type: new Abstract: During disasters, extracting causal relations from social media can strengthen situational awareness by identifying factors linked to casualties, physic

Leveraging Multimodal Large Language Models for All-in-One Image Restoration via a Mixture of Frequency Experts

arXiv:2605.11444v1 Announce Type: new Abstract: All-in-one image restoration seeks to recover clean images from inputs affected by diverse and unknown degradations using a unified framework. Recent me

safetyarxiv-cs-cv

LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models

arXiv:2605.11011v1 Announce Type: new Abstract: Looped computation shows promise in improving the reasoning-oriented performance of LLMs by scaling test-time compute. However, existing approaches typi

Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models

arXiv:2605.11102v1 Announce Type: new Abstract: Neural warm starts can sharply reduce the number of Newton-Raphson iterations required to solve the AC power flow problem, but existing supervised appro

ANCHOR: Abductive Network Construction with Hierarchical Orchestration for Reliable Probability Inference in Large Language Models

arXiv:2605.10328v1 Announce Type: new Abstract: A central challenge in large-scale decision-making under incomplete information is estimating reliable probabilities. Recent approaches leverage Large L

Can Language Models Identify Side Effects of Breast Cancer Radiation Treatments?

arXiv:2605.08439v1 Announce Type: new Abstract: Accurately communicating the side effects of cancer treatments to cancer survivors is critical, particularly in settings such as informed consent, where

Coarsening Linear Non-Gaussian Causal Models with Cycles

arXiv:2605.10163v1 Announce Type: cross Abstract: Recent work on causal abstraction, in particular graphical approaches focusing on causal structure between clusters of variables, aims to summarize a

Geometric Flood Depth Estimation: Fusing Transformer-Based Segmentation with Digital Elevation Models

arXiv:2605.08521v1 Announce Type: new Abstract: Post-disaster situational awareness relies heavily on understanding both the extent and the volume of floodwaters. While 2D semantic segmentation provid

LENS: LLM-Enabled Narrative Synthesis for Mental Health by Aligning Multimodal Sensing with Language Models

arXiv:2512.23025v2 Announce Type: replace-cross Abstract: Multimodal health sensing offers rich behavioral signals for assessing mental health, yet translating these numerical time-series measurements

Rethinking Entropy Minimization in Test-Time Adaptation for Autoregressive Models

arXiv:2605.08186v1 Announce Type: cross Abstract: Test-Time Adaptation (TTA) via entropy minimization (EM) has proven effective for classification tasks, yet its application to generative autoregressi

Tutorials

Bringing Multimodal Large Language Models to Infrared-Visible Image Fusion Quality Assessment

arXiv:2605.06969v1 Announce Type: new Abstract: Infrared-Visible image fusion (IVIF) aims to integrate thermal information and detailed spatial structures into a single fused image to enhance percepti

tutorialsarxiv-cs-cv

Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response

arXiv:2603.02274v2 Announce Type: replace-cross Abstract: Precision oncology is currently limited by the small-N, large-P paradox, where high-dimensional genomic data is abundant but pharmacological r

agentsarxiv-cs-ai

DCGL: Dual-Channel Graph Learning with Large Language Models for Knowledge-Aware Recommendation

arXiv:2605.07314v1 Announce Type: cross Abstract: Knowledge Graphs (KGs) have proven highly effective for recommendation systems by capturing latent item relationships, while recent integration of Lar

On Privacy Leakage in Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics

arXiv:2605.06835v1 Announce Type: cross Abstract: Tabular data plays an important role in many fields and industries, including those with elevated privacy considerations and risks. As such, there is

Applications

Uncovering and Shaping the Latent Representation of 3D Scene Topology in Vision-Language Models

arXiv:2605.07148v1 Announce Type: new Abstract: Decades of cognitive science establish that humans navigate environments by forming cognitive maps, defined as allocentric and topology-preserving repre

applicationsarxiv-cs-cv

Tutorials

Uncovering Hidden Systematics in Neural Network Models for High Energy Physics

arXiv:2605.07470v1 Announce Type: new Abstract: Neural networks (NNs) are inherently multidimensional classifiers that learn complex, non-linear relationships among input observables. While their flex

tutorialsarxiv-cs-lg

Anticipating Innovation Using Large Language Models

arXiv:2605.04875v1 Announce Type: new Abstract: Forecasting innovation, intended as the emergence of new technological combinations, is a fundamental challenge for science and policy. We show that for

safetyarxiv-cs-cl

7 May 2026

IDRBench: Understanding the Capability of Large Language Models on Interdisciplinary Research

arXiv:2507.15736v2 Announce Type: replace Abstract: Innovation is a key driving force of human civilization. As the body of knowledge has grown considerably, bridging knowledge across different discip

7 May 2026

Agentic AI-Based Joint Computing and Networking via Mixture of Experts and Large Language Models

arXiv:2605.02911v1 Announce Type: new Abstract: Future sixth-generation (6G) mobile networks are envisioned to be equipped with a diverse set of powerful, yet highly specialized, optimization experts.

Can Blockchains Reliably Train Machine Learning Models?

arXiv:2307.07066v2 Announce Type: replace-cross Abstract: Large proof of work (PoW) networks allow anyone to earn rewards by running computation-intensive hash puzzles for profit, yet they typically c

Applications

Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models

arXiv:2508.13881v2 Announce Type: replace Abstract: Existing driving style recognition systems largely depend on low-level sensor-derived features for training, neglecting the rich semantic reasoning

applicationsarxiv-cs-ro

Applications

InsTraj: Instructing Diffusion Models with Travel Intentions to Generate Real-world Trajectories

arXiv:2604.04106v2 Announce Type: replace Abstract: The generation of realistic and controllable GPS trajectories is a fundamental task for applications in urban planning, mobility simulation, and pri

applicationsarxiv-cs-ai

Metadata, Wavelet, and Time Aware Diffusion Models for Satellite Image Super Resolution

arXiv:2506.23566v2 Announce Type: replace Abstract: The acquisition of high-resolution satellite imagery is often constrained by the spatial and temporal limitations of satellite sensors, as well as t

Poly-EPO: Training Exploratory Reasoning Models

arXiv:2604.17654v3 Announce Type: replace Abstract: Exploration is a cornerstone of learning from experience: it enables agents to find solutions to complex problems, generalize to novel ones, and sca

Quantifying the human visual exposome with vision language models

arXiv:2605.03863v1 Announce Type: cross Abstract: The visual environment is a fundamental yet unquantified determinant of mental health. While the concept of the environmental exposome is well establi

agentsarxiv-cs-cv

The Counterexample Game: Iterated Conceptual Analysis and Repair in Language Models

arXiv:2605.03936v1 Announce Type: new Abstract: Conceptual analysis -- proposing definitions and refining them through counterexamples -- is central to philosophical methodology. We study whether lang

Combining Trained Models in Reinforcement Learning

arXiv:2605.02159v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) has delivered strong results in domains such as Atari and Go, but it still suffers from high sample cost and weak tran

5 May 2026

Fine-Tuning Pre-Trained Code Models for AI-Generated Code Detection

arXiv:2605.01596v1 Announce Type: new Abstract: This paper describes the system submitted by team extbf{Archaeology} to SemEval-2026 Task~13 on AI-generated code detection. The shared task consists of

5 May 2026

Adaptive Equilibrium: Dynamic Weighting Framework for Generalized Interruption of DeepFake Models

arXiv:2605.00443v1 Announce Type: cross Abstract: The advancement of generalized deepfake disruption is constrained by the interruption imbalance, a fundamental bottleneck inherent to the generation o

safetyarxiv-cs-cv

How Language Models Process Out-of-Distribution Inputs: A Two-Pathway Framework

arXiv:2605.00269v1 Announce Type: new Abstract: Recent white-box OOD detection methods for LLMs -- including CED, RAUQ, and WildGuard confidence scores -- appear effective, but we show they are struct

safetyarxiv-cs-cl

Leveraging Vision-Language Models as Weak Annotators in Active Learning

arXiv:2605.00480v1 Announce Type: new Abstract: Active learning aims to reduce annotation cost by selectively querying informative samples for supervision under a limited labeling budget. In this work

Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models

arXiv:2510.04378v2 Announce Type: replace Abstract: Identifying the structure of a partially observed causal system is essential to various scientific fields. Recent advances have focused on constrain

Binary Spiking Neural Networks as Causal Models

arXiv:2604.27007v1 Announce Type: new Abstract: We provide a causal analysis of Binary Spiking Neural Networks (BSNNs) to explain their behavior. We formally define a BSNN and represent its spiking ac

Local Ai

Can Tabular Foundation Models Guide Exploration in Robot Policy Learning?

arXiv:2604.27667v1 Announce Type: cross Abstract: Policy optimization in high-dimensional continuous control for robotics remains a challenging problem. Predominant methods are inherently local and of

local-aiarxiv-cs-lg

Flying by Inference: Active Inference World Models for Adaptive UAV Swarms

arXiv:2604.27935v1 Announce Type: new Abstract: This paper presents an expert-guided active-inference-inspired framework for adaptive UAV swarm trajectory planning. The proposed method converts multi-

agentsarxiv-cs-ro

ImagineNav++: Prompting Vision-Language Models as Embodied Navigator through Scene Imagination

arXiv:2512.17435v3 Announce Type: replace Abstract: Visual navigation is a fundamental capability for autonomous home-assistance robots, enabling long-horizon tasks such as object search. While recent

agentsarxiv-cs-ro

Revealing the Impact of Visual Text Style on Attribute-based Descriptions Produced by Large Visual Language Models

arXiv:2604.27553v1 Announce Type: new Abstract: When the visual style of text is considered, a wide variety can be observed in font, color, and size. However, when a word is read, its meaning is indep

Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators

arXiv:2503.06778v3 Announce Type: replace Abstract: Event annotation is important for identifying market changes, monitoring breaking news, and understanding sociological trends. Although expert annot

29 Apr 2026

Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health

arXiv:2410.09635v2 Announce Type: replace Abstract: Early detection of intrapartum risks enables timely interventions to prevent or mitigate adverse labor outcomes such as cerebral palsy. However, acc

29 Apr 2026

Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies

arXiv:2509.00081v2 Announce Type: replace-cross Abstract: Effective Cyber Threat Intelligence (CTI) relies upon accurately structured and semantically enriched information extracted from cybersecurity

agentsarxiv-cs-ai

28 Apr 2026

Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective

arXiv:2604.22569v1 Announce Type: cross Abstract: Machine learning-based malware detectors are increasingly vulnerable to adversarial examples. Traditional defenses, such as one-shot adversarial train

27 Apr 2026

Attention-based multiple instance learning for predominant growth pattern prediction in lung adenocarcinoma wsi using foundation models

arXiv:2604.21530v1 Announce Type: cross Abstract: Lung adenocarcinoma (LUAD) grading depends on accurately identifying growth patterns, which are indicators of prognosis and can influence treatment de