Formal Sciences

100 papers

Every entry carries a structured overview and the reproduction evidence collected so far. A paper is listed here when it carries the category as its primary field or as a secondary classification. Both are shown on the paper page.

Latest in Formal Sciences

Showing 81–100 of 100

Apr 20, 2026Formal Sciences260421.0068

ProtoCLIP: Prototype-Aligned Latent Refinement for Robust Zero-Shot Chest X-Ray Classification

Florian Kittler, Sheethal Bhat, Andreas Maier

ProtoCLIP refines CLIP-style vision-language models for chest X-ray classification by using curated training data and prototype-aligned distillation to reduce co-occurrence bias and improve zero-shot performance. The method achieves 2-10 percentage point AUC improvements over baseline CLIP on unseen chest X-ray datasets without large-scale retraining.

Apr 20, 2026Formal Sciences260421.0069

Scalable Physics-Informed Neural Differential Equations and Data-Driven Algorithms for HVAC Systems

Hanfeng Zhai, Hongtao Qiao, Hassan Mansour, Christopher Laughman

This paper develops a hybrid simulation framework for large HVAC systems that combines physics-informed neural networks with traditional differential-algebraic equation solvers, achieving multi-fold speedups over high-fidelity simulations while maintaining low errors. The approach scales to systems with 32+ compressor-condenser pairs by learning component-level dynamics and enforcing system-level physical constraints.

Apr 20, 2026Formal Sciences260421.0070

Spectral bandits for smooth graph functions

Michal Valko, Rémi Munos, Branislav Kveton, Tomáš Kocák

This paper introduces a bandit algorithm for learning smooth functions over graphs, where neighboring nodes have similar payoffs. The approach achieves regret bounds that scale with an effective dimension rather than the total number of nodes, enabling efficient online learning for applications like content recommendation with thousands of items from just tens of evaluations.

Apr 20, 2026Formal Sciences260421.0071

Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning

Hen Davidov, Nachshon Cohen, Oren Kalinsky, Yaron Fairstein +3 more

This paper develops a principled reinforcement learning framework for deciding when language models should stop generating text mid-response to avoid wasting compute on incorrect outputs. The approach uses value function estimation to determine optimal stopping points, improving accuracy-compute tradeoffs compared to existing methods.

Apr 20, 2026Formal Sciences260421.0072

Bridge-Centered Metapath Classification Using R-GCN-VGAE for Disaster-Resilient Maintenance Decisions

Takato Yasuno

This paper develops a graph neural network approach to classify bridges based on their disaster-resilience roles by analyzing metapaths connecting highways, bridges, and critical buildings. The method helps prioritize bridge maintenance budgets by identifying which bridges are essential for supply chains, medical access, or residential protection during disasters.

Apr 20, 2026Formal Sciences260421.0073

Randomly Initialized Networks Can Learn from Peer-to-Peer Consensus

Esteban Rodríguez-Betancourt, Edgar Casasola-Murillo

This paper shows that randomly initialized neural networks can learn useful representations through simple peer-to-peer consensus (self-distillation) alone, without projectors, predictors, or pretext tasks. The findings suggest that self-distillation itself is a key mechanism driving learning in self-supervised methods, independent of other architectural components.

Apr 20, 2026Formal Sciences260421.0074

Learning from Less: Measuring the Effectiveness of RLVR in Low Data and Compute Regimes

Justin Bauer, Thomas Walshe, Derek Pham, Harit Vishwakarma +3 more

This paper investigates how well small language models can learn reasoning tasks through reinforcement learning when training data and compute are limited. The study finds that mixing easy and hard problems during training provides up to 5x better sample efficiency than training on easy problems alone.

Apr 20, 2026Formal Sciences260421.0075

Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning

Mert Can Turkmen, Eng Leong Tan, Yee Hui Lee

This paper introduces IonoDGNN, a dynamic graph neural network that forecasts ionospheric irregularities by modeling satellite pierce points as graph nodes with time-varying connectivity, conditioned on predictable future satellite positions. The approach outperforms persistence baselines by 35-52% and enables forecasting on lines of sight that don't yet exist in the observation period.

Apr 20, 2026Formal Sciences260421.0076

Parkinson's Disease Detection via Self-Supervised Dual-Channel Cross-Attention on Bilateral Wrist-Worn IMU Signals

Meheru Zannat

This paper presents a self-supervised deep learning method using bilateral wrist-worn IMU sensors to detect Parkinson's disease, achieving over 93% accuracy for distinguishing PD from healthy controls and demonstrating effective transfer learning with only 20% labeled data. The model is lightweight enough to run in real-time on a Raspberry Pi, making it practical for clinical deployment.

Apr 20, 2026Formal Sciences260421.0077

Tight Auditing of Differential Privacy in MST and AIM

Georgi Ganev, Meenatchi Sundaram Muthu Selva Annamalai, Bogdan Kulynych

This paper introduces a GDP-based auditing framework that provides the first tight privacy audits for state-of-the-art differentially private synthetic data generators MST and AIM. The audits show a small gap between theoretical guarantees and empirical measurements in strong-privacy regimes.

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Apr 20, 2026Formal Sciences260421.0078

Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems

Runhao Jiang, Renchi Yang, Donghao Wu

BACO is a framework that compresses embedding tables in recommender systems by clustering similar users and items to share embeddings, achieving over 75% parameter reduction with minimal accuracy loss. It outperforms existing methods by being up to 346X faster while maintaining recommendation quality.

Apr 20, 2026Formal Sciences260421.0079

Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference

Jonas Arruda, Sophie Chervet, Paula Staudt, Andreas Wieser +5 more

This paper develops a simulation-based Bayesian inference method that corrects for selection bias by embedding the selection mechanism directly into the generative model, enabling accurate parameter estimation in complex models where traditional likelihood-based approaches fail. The approach allows both debiased estimation and explicit testing for bias presence without requiring tractable likelihoods.

Apr 20, 2026Formal Sciences260421.0080

Predictive Modeling of Natural Medicinal Compounds for Alzheimer Disease Using Cheminformatics

Hafiza Syeda Yusra Tirmizi, Syed Ibad Hasnain, Muhammad Faris, Rabail Khowaja +1 more

This study develops a machine learning model using molecular descriptors to screen over 7,000 natural compounds for potential anti-Alzheimer activity, identifying 73 promising candidates. The cheminformatics approach demonstrates moderate predictive performance and highlights key molecular features associated with therapeutic potential.

Apr 20, 2026Formal Sciences260421.0081

Scale-free adaptive planning for deterministic dynamics & discounted rewards

Peter L. Bartlett, Victor Gabillon, Jennifer Healey, Michal Valko

Platypoos is a planning algorithm for deterministic environments with stochastic rewards that automatically adapts to unknown reward scales and smoothness without requiring prior knowledge of discount factors or reward bounds. It achieves optimal sample complexity with matching upper and lower bounds.

Apr 20, 2026Formal Sciences260421.0082

Symmetry Guarantees Statistic Recovery in Variational Inference

Daniel Marks, Dario Paccagnan, Mark van der Wilk

This paper develops a general mathematical theory explaining how symmetries in target distributions and variational families guarantee that variational inference can accurately recover certain statistics, even when the approximation is imperfect. The framework unifies existing results and enables derivation of new recovery guarantees across diverse settings including directional statistics.

Apr 20, 2026Formal Sciences260421.0083

CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting

Etienne Tajeuna, Patrick Asante Owusu, Armelle Brun, Shengrui Wang

CAARL uses large language models to forecast co-evolving time series by converting temporal dependencies into narrative form, enabling interpretable chain-of-thought reasoning for predictions. The approach decomposes series into autoregressive segments and builds dependency graphs that LLMs can process as text, achieving competitive accuracy with enhanced transparency.

Apr 20, 2026Formal Sciences260421.0084

Dissipative Latent Residual Physics-Informed Neural Networks for Modeling and Identification of Electromechanical Systems

Youyuan Long, Gokhan Solak, Arash Ajoudani

DiLaR-PINN is a physics-informed neural network that learns unmodeled dissipative effects in electromechanical systems by constraining residual terms to be energy-dissipating rather than energy-injecting. The method combines first-principles models with data-driven components that operate on latent states and guarantee physically consistent energy behavior.

Apr 20, 2026Formal Sciences260421.0085

Block-encodings as programming abstractions: The Eclipse Qrisp BlockEncoding Interface

Matic Petrič, René Zander

This paper introduces the BlockEncoding interface in the Eclipse Qrisp framework, which makes block-encoding techniques accessible as high-level programming abstractions for implementing advanced quantum algorithms. The interface simplifies the practical implementation and resource estimation of algorithms like QSVT, QSP, and Hamiltonian simulation.

Apr 20, 2026Formal Sciences260421.0086

Incremental learning for audio classification with Hebbian Deep Neural Networks

Riccardo Casciotti, Francesco De Santis, Alberto Antonietti, Annamaria Mesaros

This paper applies Hebbian learning principles to audio classification in a continual learning setting, introducing a kernel plasticity approach that selectively updates network weights to balance learning new sounds while retaining knowledge of previously learned ones. On the ESC-50 dataset, the method achieves 76.3% accuracy across five incremental learning steps, significantly outperforming a baseline approach.

Apr 20, 2026Formal Sciences260421.0087

Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling

Fei Wang, Li Shen, Liang Ding, Chao Xue +2 more

AdaLeZO accelerates zeroth-order optimization for fine-tuning large language models by intelligently sampling layers based on their sensitivity rather than uniformly perturbing all parameters. This adaptive approach achieves 1.7-3.0x speedup over existing methods while maintaining memory efficiency and acting as a universal plug-in for existing ZO optimizers.