arXiv subjects

Shiva Kaul

Publications and source records attributed to Shiva Kaul.

6 recordsLinked to original sources

arXiv2026

The Verbose Context Problem in Medical Records

The verbose context problem occurs when structured concepts have token-inefficient textual representations. This bottleneck is acute in population health: cohort-level analysis of longitudinal patient records requires reasoning over thousands of medically-coded events, often exceeding 400K tokens in total. We present PopMedQA, a benchmark isolating this problem through computational tasks on groups of longitudinal patient records. We construct the benchmark using neopatient, a new library for language-controlled generation of artificial patient records. Through extensive ablations -- including prompting strategies, prompt compression, and agentic decomposition -- we find that domain-independent methods fail to alleviate the verbose context problem. There remains significant opportunity to exploit domain-specific structure in language model inputs for population-scale reasoning.

cs.CL↗

arXiv2026

Position: Medical AI Neglects Real Treatment Outcomes

Medical AI has rapidly improved its ability to perform diagnostic and prognostic tasks that lead to treatment decisions. But understanding of treatment itself is still inadequately trained and evaluated, using human opinions and syntheses (especially texts such as biomedical publications and clinical practice guidelines) rather than actual underlying data on treatment outcomes. This neglect seriously limits the potential of medical AI, and is already causing deficiencies in both frontier models and major benchmarks, as argued in this position paper. Real treatment outcomes, drawn from sources such as observational databases and randomized experiments, should be substantially incorporated into both training and evaluation. Improving these outcomes should be reemphasized as the downstream goal of all medical AI.

cs.AI↗

arXiv2025

How Balanced Should Causal Covariates Be?

Covariate balancing is a popular technique for controlling confounding in observational studies. It finds weights for the treatment group which are close to uniform, but make the group's covariate means (approximately) equal to those of the entire sample. A crucial question is: how approximate should the balancing be, in order to minimize the error of the final estimate? Current guidance is derived from heuristic or asymptotic analyses, which are uninformative when the size of the sample is small compared to the number of covariates. This paper presents the first rigorous, nonasymptotic analysis of covariate balancing; specifically, we use PAC-Bayesian techniques to derive valid, finite-sample confidence intervals for the treatment effect. More generally, we prove these guarantees for a flexible form of covariate balancing where the regularization parameters weighting the tradeoff between bias (imbalance) and variance (divergence from uniform) are optimized, not fixed. This gives rise to a new balancing algorithm which empirically delivers superior adaptivity. Our overall contribution is to make covariate balancing a more reliable method for causal inference.

stat.ME↗

arXiv2024

Meta-Analysis with Untrusted Data

[See paper for full abstract] Meta-analysis is a crucial tool for answering scientific questions. It is usually conducted on a relatively small amount of ``trusted'' data -- ideally from randomized, controlled trials -- which allow causal effects to be reliably estimated with minimal assumptions. We show how to answer causal questions much more precisely by making two changes. First, we incorporate untrusted data drawn from large observational databases, related scientific literature and practical experience -- without sacrificing rigor or introducing strong assumptions. Second, we train richer models capable of handling heterogeneous trials, addressing a long-standing challenge in meta-analysis. Our approach is based on conformal prediction, which fundamentally produces rigorous prediction intervals, but doesn't handle indirect observations: in meta-analysis, we observe only noisy effects due to the limited number of participants in each trial. To handle noise, we develop a simple, efficient version of fully-conformal kernel ridge regression, based on a novel condition called idiocentricity. We introduce noise-correcting terms in the residuals and analyze their interaction with a ``variance shaving'' technique. In multiple experiments on healthcare datasets, our algorithms deliver tighter, sounder intervals than traditional ones. This paper charts a new course for meta-analysis and evidence-based medicine, where heterogeneity and untrusted data are embraced for more nuanced and precise predictions.

stat.ML↗

arXiv2015

Embedding parameters in ab initio theory to develop approximations based on molecular similarity

A means to take advantage of molecular similarity to lower the computational cost of electronic structure theory is explored, in which parameters are embedded into a low-cost, low-level (LL) ab initio model and adjusted to obtain agreement with results from a higher-level (HL) ab initio model. A parametrized LL (pLL) model is created by multiplying selected matrix elements of the Hamiltonian operators by scaling factors that depend on element types. Various schemes for applying the scaling factors are compared, along with the impact of making the scaling factors linear functions of variables related to bond lengths, atomic charges, and bond orders. The models are trained on ethane and ethylene, substituted with -NH2, -OH and -F, and tested on substituted propane, propylene and t-butane. Training and test datasets are created by distorting the molecular geometries and applying uniform electric fields. The fitted properties include changes in total energy arising from geometric distortions or applied fields, and frontier orbital energies. The impacts of including additional training data, such as decomposition of the energy by operator or interaction of the electron density with external charges, are also explored. The best-performing model forms reduce the root mean square (RMS) difference between the HL and LL energy predictions by over 85% on the training data and over 75% on the test data. The argument is made that this approach has the potential to provide a flexible and systematically-improvable means to take advantage of molecular similarity in quantum chemistry.

physics.chem-ph↗

arXiv2013

Embedding parameters in ab initio theory to develop well-controlled approximations based on molecular similarity

A means to take advantage of molecular similarity to lower the computational cost of electronic structure theory is proposed, in which parameters are embedded into a low-cost, low-level (LL) ab initio theory and adjusted to obtain agreement with a higher level (HL) ab initio theory. This approach is explored by training such a model on data for ethane and testing the resulting model on methane, propane and butane. The electronic distribution of the molecules is varied by placing them in strong electrostatic environments consisting of random charges placed on the corners of a cube. The results find that parameters embedded in HF/STO-3G theory can be adjusted to obtain agreement, to within about 2 kcal/mol, with results of HF/6-31G theory. Obtaining this level of agreement requires the use of parameters that are functions of the bond lengths, atomic charges, and bond orders within the molecules. The argument is made that this approach provides a well-controlled means to take advantage of molecular similarity in quantum chemistry.

physics.chem-ph↗