AIBullisharXiv – CS AI · 3d ago7/10
🧠Researchers introduce Battery-Sim-Agent, an LLM-based framework that uses AI agents to estimate battery parameters by mimicking scientific reasoning rather than traditional black-box optimization. The system outperforms conventional methods like Bayesian optimization on benchmark tests and demonstrates practical applicability on real-world battery datasets, representing a novel approach to accelerating battery innovation through physics-informed AI reasoning.
AIBullisharXiv – CS AI · Apr 147/10
🧠Researchers introduce PnP-CM, a new method that reformulates consistency models as proximal operators within plug-and-play frameworks for solving inverse problems. The approach achieves high-quality image reconstructions with minimal neural function evaluations (4 NFEs), demonstrating practical efficiency gains over existing consistency model solvers and marking the first application of CMs to MRI data.
AINeutralarXiv – CS AI · 3d ago6/10
🧠Researchers introduce LLMSurgeon, a framework that reverse-engineers the pretraining data composition of Large Language Models by analyzing their generated text, addressing the opacity surrounding how foundation models are trained. The method estimates domain-level distributions across a predefined taxonomy without requiring access to actual training datasets, offering a practical auditing tool for understanding model behavior and capabilities.
AINeutralarXiv – CS AI · 4d ago5/10
🧠Researchers have developed a gradient-step plug-and-play algorithm that uses a trained denoiser model to reduce photon noise in dental cone-beam CT reconstructions. The method combines inverse problem formulation with machine learning, demonstrating effective denoising on synthetic data and promising generalization to real-world dental imaging applications.
AINeutralarXiv – CS AI · 4d ago5/10
🧠Researchers present a new diffusion posterior sampling method that improves inverse problem solving by replacing hand-tuned guidance weights with a mathematically principled damped Gauss-Newton correction. The approach demonstrates competitive or superior performance on image reconstruction tasks including accelerated MRI while reducing computational overhead compared to existing methods.
AINeutralarXiv – CS AI · 5d ago6/10
🧠Researchers propose DISS, a training-free framework that enhances diffusion-based image reconstruction by incorporating side information through inference-time search. The method demonstrates consistent quality improvements across multiple inverse problems (inpainting, super-resolution, deblurring) and diffusion solvers while supporting diverse side information types including reference images, text, and medical scans.
AINeutralarXiv – CS AI · 5d ago6/10
🧠Researchers propose Cascaded Sensing, a machine learning framework combining autoencoders and diffusion models to reconstruct physical fields from extremely sparse sensor measurements. The approach addresses the ill-posed problem of inferring complete spatial data from limited observations by first establishing global structural anchors through coarse-scale estimation, then refining details through conditional diffusion sampling.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers have developed an improved diffusion model-based approach for solving inverse problems that demonstrates robustness to outliers in real-world measurements. The method combines explicit noise estimation, Huber loss optimization, and conjugate gradient methods to outperform existing diffusion model techniques across linear and nonlinear tasks.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers present bifurcation models, a machine learning approach that uses weight-tied dynamical systems to learn multiple valid solutions for problems with set-valued outputs. Rather than forcing a single target label, the model represents an attractor landscape where different initializations converge to different stable equilibria, enabling discovery of diverse valid solutions without explicit branch labels.
AINeutralarXiv – CS AI · Mar 116/10
🧠Researchers developed tunable-complexity priors for generative models (diffusion models, normalizing flows, and variational autoencoders) that can dynamically adjust complexity based on the specific inverse problem. The approach uses nested dropout and demonstrates superior performance across compressed sensing, inpainting, denoising, and phase retrieval tasks compared to fixed-complexity baselines.
AIBullisharXiv – CS AI · Mar 36/1012
🧠Researchers developed FMCT/EFMCT, a new Flow Matching-based framework for CT medical imaging reconstruction that significantly improves computational efficiency over existing diffusion models. The method uses deterministic ordinary differential equations and velocity field reuse to reduce neural network evaluations while maintaining reconstruction quality.
AIBullisharXiv – CS AI · Mar 36/108
🧠Researchers propose FAST-DIPS, a new training-free diffusion prior method for solving inverse problems that achieves up to 19.5x speedup while maintaining competitive image quality metrics. The method replaces computationally expensive inner optimization loops with closed-form projections and analytic step sizes, significantly reducing the number of required denoiser evaluations.
AIBullisharXiv – CS AI · Mar 36/103
🧠Researchers propose EquiReg, a new framework that improves diffusion models for inverse problems like image restoration by keeping sampling trajectories on the data manifold. The method uses equivariance regularization to guide sampling toward symmetry-preserving regions, enabling high-quality reconstructions with fewer sampling steps.
AIBullisharXiv – CS AI · Feb 275/107
🧠Researchers have developed a self-supervised learning method that can reconstruct audio and images from clipped/saturated measurements without requiring ground truth training data. The approach extends self-supervised learning to non-linear inverse problems and performs nearly as well as fully supervised methods while using only clipped measurements for training.
AINeutralarXiv – CS AI · Mar 34/103
🧠Researchers introduce DAWN-FM, a new AI method using Flow Matching to solve inverse problems in fields like medical imaging and signal processing. The approach incorporates data and noise embedding to provide robust solutions even with incomplete or noisy observations, outperforming pretrained diffusion models in highly ill-posed scenarios.
AINeutralarXiv – CS AI · Mar 34/105
🧠Researchers published a study comparing traditional numerical methods with Physics-Informed Neural Networks (PINNs) for solving direct and inverse problems in differential equations. The work demonstrates that PINNs can effectively estimate solutions at competitive computational costs for complex systems like the Porous Medium Equation.
