Computer Science

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 Computer Science

Showing 61–80 of 100

Apr 20, 2026Formal Sciences260421.0048

Physics-Informed Neural Networks for Biological $2\mathrm{D}{+}t$ Reaction-Diffusion Systems

William Lavery, Jodie A. Cochrane, Christian Olesen, Dagim S. Tadele +2 more

This paper extends biologically-informed neural networks (BINNs) from 1D to 2D spatial domains for learning reaction-diffusion equations from data, combining neural network training with symbolic regression to discover closed-form equations. The method is demonstrated on real lung cancer cell microscopy data, successfully recovering interpretable 2D+time reaction-diffusion models.

Apr 20, 2026Formal Sciences260421.0049

FUSE: Ensembling Verifiers with Zero Labeled Data

Joonhyuk Lee, Virginia Ma, Sarah Zhao, Yash Nair +3 more

FUSE is a method that combines multiple imperfect AI verifiers to better judge model outputs without needing any labeled training data. It matches or beats semi-supervised methods across diverse benchmarks by controlling how verifiers depend on each other using spectral algorithms.

Apr 20, 2026Formal Sciences260421.0050

Wasserstein Distributionally Robust Risk-Sensitive Estimation via Conditional Value-at-Risk

Feras Al Taha, Eilyan Bitar

This paper develops a distributionally robust estimation method that minimizes worst-case conditional value-at-risk (CVaR) of estimation error when the true distribution is uncertain but lies within a Wasserstein ball. The method can be computed via tractable semidefinite programming and outperforms existing approaches on electricity price forecasting.

Apr 20, 2026Formal Sciences260421.0051

Duality for the Adversarial Total Variation

Leon Bungert, Lucas Schmitt

This paper establishes a mathematical duality framework for adversarial total variation, showing that adversarial training of binary classifiers can be understood through nonlocal calculus of variations. The work provides rigorous characterizations of subdifferentials using dual representations and integration by parts formulas in both metric and Euclidean spaces.

Apr 20, 2026Formal Sciences260421.0052

IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem

Aniruddha Adiga, Jingyuan Chou, Anshul Chiranth, Bryan Lewis +6 more

IDOBE is a standardized benchmark dataset containing over 10,000 infectious disease outbreak segments from a century of surveillance data across 13 diseases, designed to evaluate and compare epidemic forecasting methods. The authors test 11 baseline models and find MLP-based methods perform most robustly, with statistical methods excelling in pre-peak phases.

Apr 20, 2026Formal Sciences260421.0053

Learning the Riccati solution operator for time-varying LQR via Deep Operator Networks

Jun Chen, Umberto Biccari, Junmin Wang

This paper uses Deep Operator Networks (DeepONets) to learn a surrogate for the Riccati differential equation solution operator in finite-horizon LQR problems, enabling fast approximate optimal control without repeated numerical integration. The approach includes theoretical guarantees on stability and performance, and demonstrates significant computational speedups while maintaining high accuracy.

Apr 20, 2026Formal Sciences260421.0054

Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data

Zhenwen Liang, Yujun Zhou, Sidi Lu, Xiangliang Zhang +2 more

This paper addresses a critical problem in reinforcement learning for large language models: when base models are already very accurate on training benchmarks, standard RL methods fail because there aren't enough errors to learn from, causing models to collapse into repetitive solutions. The authors propose CUTS, a novel sampling strategy that maintains solution diversity even when models are highly accurate, improving generalization on challenging out-of-domain math problems by up to 15.1%.

Apr 20, 2026Formal Sciences260421.0055

Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting

Worachit Amnuaypongsa, Yotsapat Suparanonrat, Pana Wanitchollakit, Jitkomut Songsiri

This paper presents a neural network framework that simultaneously generates point forecasts and prediction intervals for multi-step time series forecasting, using multi-objective optimization to automatically balance forecast accuracy and interval sharpness while guaranteeing non-crossing intervals and target coverage. The method eliminates manual hyperparameter tuning and demonstrates superior performance on solar irradiance forecasting compared to existing approaches.

Apr 20, 2026Formal Sciences260421.0056

Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD

Nicholas Thumiger, Andrea Bartezzaghi, Mattia Rigotti, Cezary Skura +4 more

This paper introduces GIST, a neural network surrogate that predicts race-car aerodynamics 10,000× faster than traditional CFD simulations while maintaining accuracy suitable for early-stage design. The work includes a new high-fidelity dataset of LMP2 race-car aerodynamics validated by industry experts at Dallara, enabling interactive design exploration in motorsport.

Apr 20, 2026Formal Sciences260421.0057

Safe Control using Learned Safety Filters and Adaptive Conformal Inference

Sacha Huriot, Ihab Tabbara, Hussein Sibai

This paper introduces Adaptive Conformal Filtering (ACoFi), which combines learned safety filters with adaptive conformal inference to provide soft safety guarantees for control systems. The method dynamically adjusts switching criteria between nominal and safe policies based on prediction uncertainty, achieving better safety performance than fixed-threshold approaches.

Apr 20, 2026Formal Sciences260421.0058

Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle

Abdeladhim Tahimi

This paper provides a complete, step-by-step manual derivation of how Physics-Informed Neural Networks (PINNs) are trained, including forward propagation, loss computation, and backpropagation with explicit numerical examples. It bridges the gap between automatic differentiation libraries and the underlying mathematical operations, making PINN training transparent and verifiable.

Apr 20, 2026Formal Sciences260421.0059

Multi-Scale Reversible Chaos Game Representation: A Unified Framework for Sequence Classification

Sarwan Ali, Taslim Murad

MS-RCGR is a new method that converts biological sequences (DNA/protein) into multi-resolution geometric images without losing information, enabling better classification through traditional ML, computer vision, or hybrid approaches. The method consistently improves performance across different analysis paradigms and achieves best results when combined with protein language models.

Apr 20, 2026Formal Sciences260421.0060

Train Separately, Merge Together: Modular Post-Training with Mixture-of-Experts

Jacob Morrison, Sanjay Adhikesaven, Akshita Bhagia, Matei Zaharia +2 more

BAR (Branch-Adapt-Route) trains separate domain experts independently and combines them via Mixture-of-Experts, enabling modular updates to language models without retraining everything or degrading existing capabilities. This approach matches monolithic retraining performance while scaling linearly instead of quadratically when adding new domains.

Apr 20, 2026Formal Sciences260421.0061

NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization

Enshu Liu, Xuefei Ning, Yu Wang, Zinan Lin

This paper introduces Neural Indicator Sampling (NI Sampling), a method that optimizes the token sampling order in discrete diffusion language models to achieve up to 14.3× speedup over standard sampling while maintaining accuracy. The approach uses a trained neural indicator to intelligently select which tokens to sample at each step, dramatically reducing the number of required sampling iterations.

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Apr 20, 2026Formal Sciences260421.0062

Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation

Tianshi Cao, Jiawei Ren, Yuxuan Zhang, Jaewoo Seo +11 more

Asset Harvester converts sparse, real-world object observations from autonomous vehicle driving logs into complete 3D assets suitable for simulation. The system combines large-scale data curation, geometry-aware preprocessing, and a novel sparse-view-conditioned model (SparseViewDiT) to generate simulation-ready 3D objects from limited viewing angles.

Apr 20, 2026Formal Sciences260421.0063

Semantic Step Prediction: Multi-Step Latent Forecasting in LLM Reasoning Trajectories via Step Sampling

Yidi Yuan

This paper shows that applying Semantic Tube Prediction (STP) at reasoning step boundaries instead of random token positions dramatically improves multi-step latent prediction in LLMs (168x vs 4x improvement), revealing that sampling position is critical for geometric regularization of reasoning trajectories.

Apr 20, 2026Formal Sciences260421.0064

Using large language models for embodied planning introduces systematic safety risks

Tao Zhang, Kaixian Qu, Zhibin Li, Jiajun Wu +3 more

This paper introduces DESPITE, a benchmark of over 12,000 tasks to evaluate safety in LLM-based robotic planning, revealing that even models with near-perfect planning ability produce dangerous plans 28% of the time. The study shows planning ability scales with model size while safety awareness remains relatively flat, creating a critical gap for deploying LLMs in robotics.

Apr 20, 2026Formal Sciences260421.0065

Learning Invariant Modality Representation for Robust Multimodal Learning from a Causal Inference Perspective

Sijie Mai, Shiqin Han

This paper proposes CmIR, a causal inference framework that separates multimodal data into stable causal features and spurious environment-specific features to improve robustness in affective computing. The method achieves state-of-the-art performance, especially on out-of-distribution and noisy data.

Apr 20, 2026Formal Sciences260421.0066

Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario

Florentin Coeurdoux, Grégoire Ferré, Jean-Philippe Bouchaud

This paper develops a random matrix theory model that explains why neural networks exhibit a transient learning window where signal is detectable before overfitting occurs. The key mechanism is that anisotropy in input data creates fast and slow learning directions, causing a learnable eigenvalue to temporarily separate from noise before being reabsorbed.

Apr 20, 2026Formal Sciences260421.0067

AutoPPA: Automated Circuit PPA Optimization via Contrastive Code-based Rule Library Learning

Chongxiao Li, Pengwei Jin, Di Huang, Guangrun Sun +13 more

AutoPPA is an automated framework for optimizing circuit performance, power, and area (PPA) that learns optimization rules by contrasting code pairs rather than relying on manual rules. It outperforms existing methods including manual optimization and state-of-the-art automated approaches.