An argument paper reframes LLM explainability as an embodied, situated practice based on Dourish and enactivist cognition, identifying ontological obstacles in internal explanations and advocating affordance-based designs.
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cs.LG 27 cs.CV 9 cs.AI 6 cs.CL 3 eess.SP 3 physics.ao-ph 3 q-bio.NC 3 quant-ph 3 astro-ph.IM 2 astro-ph.SR 2polarities
background 15representative citing papers
ffortissimo is a JAX-based freeform forward-modeling pipeline that fits complex dust distributions and infers scattering properties in KLIP-reduced images of circumstellar disks such as HR 4796A.
eCNNTO applies an element-wise CNN with residual connections and final-stage training data to accelerate density-based topology optimization while generalizing across boundary conditions, loads, geometries, and mesh sizes.
Optimizing training data via a differentiable SCM yields climate emulators that outperform those trained on six standard ScenarioMIP pathways while using less data and isolating distinct forcing responses.
Spatial multiplexing in optical neural networks is repurposed as a trainable representational coordinate, demonstrated in multi-layer architectures for image classification, regression, and hybrid vision-language captioning with over one million optical phase parameters.
A U-Net surrogate with multigroup attention pooling is trained on OpenMC sensitivity data and combined with gradient optimization to generate grid-based critical experiment geometries that achieve c_k values up to 0.97757 for HALEU fuel validation.
CAML meta-learns a progressively refined inductive bias from active-learning queries to improve robustness to spurious correlations, reporting accuracy gains on minority groups across several benchmarks.
In a combinatorial toy setting, winning lottery tickets preserve families of compatible feature locations in early feature space that balance proximity to final codes with low interference, rather than specific weight subnetworks.
DualTCN is the first deep-learning model for time-domain marine CSEM inversion that regresses four earth parameters, achieves high accuracy on simulated data, and runs up to 21,000 times faster than classical optimizers.
Broximal Alignment is a novel condition under which the Ball Proximal Point Method converges to global minima in non-convex settings, generalizing quasiconvexity, star convexity, and related frameworks.
Neural decompositionality is defined via decision-boundary semantic preservation, and language transformers largely satisfy it under SAVED while vision models often do not.
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
A machine learning model called neural quantum propagator is introduced to efficiently solve non-Markovian quantum dynamics described by HEOM and applied to simulate spectra of the FMO complex.
Local 2- and 3-cycles enhance RNN computational capacity for Boolean functions, predicted by structural statistics, while adding interneurons boosts large networks.
Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
Hybrid neural-process model derives biokinetic parameters from genomic traits for soil organic matter turnover, with ecological constraints, and outperforms baselines on synthetic and real data.
Introduces the directional linear separability measure (LSM) as an asymmetric diagnostic for one-sided affine separability of neural representations.
Tabular foundation models applied to PHM via signal-to-table conversion achieve the best average ranks across prognostic and diagnostic tasks and remain competitive in low-data regimes.
A parameter-efficient dual-encoder model with differentiable Choquet integral fusion improves underwater acoustic classification accuracy over single-encoder baselines on DeepShip and ShipsEar datasets.
Observation of ultrafast large dynamic magnetic soliton formation inside the linear spin-wave band in garnet films, extending tens of microns and collapsing into short-wavelength spin waves at large distances.
Models composed from bilinear factor, exponential link, Gamma prior, Gaussian likelihood, and equality node admit closed-form variational message passing under mean-field factorization.
Picid is a new modular evaluation infrastructure that enforces deterministic, leakage-safe dataset construction and unified protocols for fault detection, diagnostics, and prognostics across twelve datasets and thirteen models.
Equivariant neural networks for 2D Q-tensor prediction in nematic liquid crystals achieve lower errors and better generalization than non-equivariant models while satisfying symmetry constraints.
Symmetrizing cross-entropy produces the unique convex multi-class unhinged loss, which locally approximates other symmetric losses, and enables new interpolating losses SGCE and alpha-MAE with competitive performance on noisy-label benchmarks.
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Determination of Nanoparticle and Microdroplet Parameters in Levitating Microdroplets of Suspension by Speckle Image Analysis Using Convolutional Neural Networks
CNNs trained on speckle images from levitating TiO2 suspension microdroplets classify droplet diameter with better than 6% accuracy and provide useful discrimination for nanoparticle concentration and diameter, including simultaneous three-parameter classification.
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Operator-Theoretic Energy Functionals for Impulse-Excited Nonstationary Signal Analysis
An operator-based Energy Concentration Index yields the IMRED detector that identifies defect-induced changes in impulse responses with AUC 0.908, outperforming standard Fourier and wavelet energy measures.
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Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression
VCON is a unified framework for smooth iterative DNN compression that uses parallel execution and an affine combination to progressively replace the original model with its compressed form during fine-tuning.
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Bayesian Reasoning for Physics Informed Neural Networks
Introduces Laplace-approximated Bayesian PINNs for automatic loss-weight optimization when solving PDEs such as heat, wave, and Burgers equations.
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General Inverse Design of Thin-Film Metamaterials With Convolutional Neural Networks
Convolutional neural networks are shown to perform inverse design of thin-film metamaterial stacks by learning the mapping from structure to ellipsometric and reflectance/transmittance spectra, with efficiency gains over traditional optimization as layer count increases.
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MAPE: Defending Against Transferable Adversarial Attacks Using Multi-Source Adversarial Perturbations Elimination
MAPE combines a channel-attention U-Net (SAPE) trained on multi-model adversarial examples scheduled by PPSA to eliminate perturbations, reporting over 95.1% average defense on CIFAR-10 and 71.5% on Mini-ImageNet against black-box transferable attacks.
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From Sentiment to Actionable Insights: A Data-Driven Public Sentiment Analysis of Advanced Air Mobility
Applies standard sentiment classifiers and topic modeling to a large AAM discussion corpus, identifies six clusters of public concern, and lists strategies to address them.
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Learning Entropy and Spatial Adaptation Dynamics of Multilayer Perceptrons for Structural Point Extraction
Spatial Learning Entropy Maps derived from MLP weight adaptations during spatial pixel prediction tasks highlight image points with high learning impact.
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Business World Model
This paper introduces the Business World Model, a conceptual architecture that encodes business states, dynamics, and actions using semantic representations to support autonomous planning.
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Learning to model pediatric asthma exacerbation from multiple risk factors: a case study in coastal Virginia
A case study develops a sparse dictionary learning approach to model pediatric asthma exacerbations from multiple risk factors and reports consensus on relative risks across statistical and machine learning models.
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Machine-Learning-Enhanced Non-Invasive Testing for MASLD Fibrosis: Shallow-Deep Neural Networks Versus FIB-4, Tabular Foundation Models, and Large Language Models
A 354-parameter shallow-deep neural network using age, AST, ALT, platelets and FIB-4 achieved external ROC-AUCs of 0.77 and 0.67 for advanced MASLD fibrosis, slightly above FIB-4's 0.75 and 0.60 on Malaysian and Indian cohorts.
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The New Associationism: Lessons from Deep Learning
Supervised learning across AI systems vindicates a uniform error-driven associationism for cognition, though operating inside advanced computational structures beyond classical associationist models.
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Joint sparse coding and temporal dynamics support context reconfiguration
Joint sparse coding and temporal dynamics in mPFC and computational networks reduce cross-context interference and enhance separability, enabling better retention in lifelong learning without extra heuristics.
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Single-Cycle Multidirectional EOG Classification Faster than Human Reaction Time for Wearable Human-Computer Interactions
Cascaded neural networks classify 10 eye-movement classes from single-cycle EOG signals at 99% accuracy with sub-83 ms latency below human reaction time.
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Using Deep Learning Models Pretrained by Self-Supervised Learning for Protein Localization
DINO-based ViT models pretrained on HPA FOV achieve macro F1 of 0.822 zero-shot and 0.860 after fine-tuning for protein localization on OpenCell, demonstrating effective transfer from SSL pretraining.
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The ZTF-ULTRASAT experiment: Characterizing the non-transients in ULTRASAT's high cadence survey
ZTF high-cadence data shows RR Lyrae stars and flaring sources can mimic UV transients, with pre-existing ML catalogs offering a concrete mitigation approach.
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Machine Learning-Based Cluster Classification to Suppress Background in a Prototype RPC Detector
Machine learning classifiers using fifteen cluster-level descriptors from time and ADC distributions effectively separate signal from background hits in prototype RPC detectors.
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Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI
Experts rate AI scenarios as more likely, less risky, more beneficial, and more valuable than the public, applying different weightings to risk versus benefit.
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Multiple Object Detection and Tracking in Panoramic Videos for Cycling Safety Analysis
A framework segments panoramic video into sub-images for detection, modifies multi-object tracking for boundary continuity, and applies it to vehicle overtaking detection in real cycling videos, reporting gains in precision and an F-score of 0.82.
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The Mathematics of AI Winters: The mathematical Taxonomy of Paradigm Fragility in AI Winter
Established mathematical bottlenecks in representation, optimization, complexity, and high-dimensional learning aligned with the central disappointments of early AI research periods.
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A Proof-of-Concept Simulation-Driven Digital Twin Framework for Decision-Aware Diabetes Modeling
A simulation-driven digital twin framework is shown to generate interpretable diabetes trajectories for decision-aware analysis by combining benchmark data with controlled synthetic scenarios.
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A Specialized Importance-Aware Quantum Convolutional Neural Network with Ring-Topology (IA-QCNN) for MGMT Promoter Methylation Prediction in Glioblastoma
IA-QCNN applies quantum principles via ring-topology convolution and importance weighting to achieve claimed high-accuracy MGMT methylation prediction from MRI with fewer parameters and noise robustness than classical models.
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Supplementary Materials to Graph Convolutional Branch and Bound
Supplementary results on 1-tree relaxation performance inside a GCN-augmented branch-and-bound solver for TSP.
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Statistical Properties of Training & Generalization
Neural scaling laws in deep learning interact with physics constraints and inductive biases beyond classical statistics.
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Software Platform for Hybrid Pseudo-Random Sequence Generation and Predictability Analysis Based on LFSR and Mersenne Twister
Software platform for hybrid LFSR-MT PRNG generation and ML-based predictability analysis, reporting inherent limitations in classical generators versus quantum randomness.
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MiniGPT: Rebuilding GPT from First Principles
MiniGPT is a self-contained PyTorch implementation of standard GPT autoregressive modeling that reaches 1.478 validation loss on Tiny Shakespeare with a 10.77M-parameter model and produces recognizable Shakespeare-style text.
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AI-Powered Surrogate Modelling for Multiscale Combustion: A Critical Review and Opportunities
A critical review of AI surrogate models for multiscale combustion that compares supervised, unsupervised, and physics-guided methods, identifies transferability and consistency challenges, and outlines future opportunities.
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Enhancing Laser Surface Texturing through Advanced Machine Learning Techniques
Neural networks and random forests predict surface roughness from laser parameters and material data with high accuracy, speeding up optimization and reducing experimental effort.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
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A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation
A survey that categorizes deep learning models for point cloud tasks by backbone architecture, evaluates benchmark performance, and outlines challenges and future research directions.
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Deep Learning in the Automotive Industry: Recent Advances and Application Examples
An overview of deep learning applications and challenges in the automotive industry, covering ADAS, automated driving, virtual sensing, and data-driven development.