arXiv subjects

Sarang Rajendra Patil

Publications and source records attributed to Sarang Rajendra Patil.

3 recordsLinked to original sources

arXiv2026

HyperGuide: Hyperbolic Guidance for Efficient Multi-Step Reasoning in Large Language Models

Searching over alternative continuations lets large language models (LLMs) compare the downstream consequences of a reasoning step before committing to it, but expanding and evaluating many branches makes inference expensive. Training value models to score intermediate steps does not avoid this cost, as they still rank candidates at inference time. We introduce HyperGuide, a framework that treats reasoning as movement through a tree of states embedded in hyperbolic space. HyperGuide first trains a state encoder on the Poincar'e ball, then trains a guidance head to predict the direction of a minimum-cost transition from the current state. The predicted direction is inserted into the decoder as a virtual token after each reasoning step, so inference follows a single autoregressive trajectory without expanding or reranking candidates. Because the supervision is ordinal rather than binary, it favors continuations that are both reliable and short, which steers generation toward successful, concise solutions. Experiments on competition mathematics and code generation show that HyperGuide matches or exceeds the accuracy of search- and verifier-based baselines while generating substantially fewer tokens than these baselines and about as many as few-shot prompting.

cs.AI↗

arXiv2026

Multilevel Graph Wavelet Compressed Sensing with Scale-Aware Neural Recovery

Scientific machine learning methods such as neural operators and physics-informed neural networks have advanced engineering applications and inverse problems, but their training typically requires large volumes of simulated data. This makes data preparation and model training expensive. We propose Graph Wavelet Compressed Sensing (GWCS), a learning-based framework for offline compression of graph signals by representing them as sparse, interpretable wavelet-domain representations using the spectral graph wavelet transform. The framework combines a nonparametric multilevel importance sampler, which retains high-energy wavelet coefficients within each scale for a given compression ratio, with a scale-aware graph neural network that reconstructs the signal from the sparse coefficients. We evaluate the proposed framework on synthetic approximately band-limited graph signals over random graphs and four PDE simulation datasets over meshes, which include Turbulent Radiative Layer, Viscoelastic Instability, Kolmogorov Flow, and Dynamic Stall. We compare against graph signal sampling methods and graph autoencoder baselines. Results demonstrate that the framework achieves high reconstruction fidelity and substantial data compression compared to existing benchmarks.

cs.LG↗

arXiv2026

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering

Electronic health record (EHR) question answering is often handled by LLM-based pipelines that are costly to deploy and do not explicitly leverage the hierarchical structure of clinical data. Motivated by evidence that medical ontologies and patient trajectories exhibit hyperbolic geometry, we propose HypEHR, a compact Lorentzian model that embeds codes, visits, and questions in hyperbolic space and answers queries via geometry-consistent cross-attention with type-specific pointer heads. HypEHR is pretrained with next-visit diagnosis prediction and hierarchy-aware regularization to align representations with the ICD ontology. On two MIMIC-IV-based EHR-QA benchmarks, HypEHR approaches LLM-based methods while using far fewer parameters. Our code is publicly available at https://github.com/yuyuliu11037/HypEHR.

cs.AI↗