arXiv subjects

Anshul Verma

Publications and source records attributed to Anshul Verma.

12 recordsLinked to original sources

arXiv2026

Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications

Recent advances in Large Language Models (LLMs) and multi-agent systems have driven the rise of Agentic AI, showing promise for medical reasoning. However, open-ended conversational agents remain prone to two critical failure modes: premature diagnostic handoff and silent clinical hallucinations that may go undetected before reaching the patient. In this work, we propose a multi-agent framework that addresses both issues by replacing ``LLM-as-a-judge'' routing with deterministic orchestration constraints. The framework incorporates two safety mechanisms. First, a neuro-symbolic state-tracking gate enforces completeness of the OLDCARTS clinical protocol (Onset, Location, Duration, Character, Aggravating/Alleviating factors, Radiation, Timing, and Severity) by blocking diagnostic transitions until all required dimensions are collected. Second, an epistemic uncertainty quantification (UQ) gate computes semantic entropy (H) across K=5 independent diagnostic samples to identify and intercept divergent outputs before delivery. We evaluate the system using simulated patient agents powered by the llama-3.1-70b-instruct model on 150 test cases. The full architecture achieves 49.3% diagnostic precision, representing an absolute improvement of 11.3 percentage points over an unconstrained baseline. Additionally, we observe a statistically significant negative correlation (r = -0.181, p < 0.05) between OLDCARTS completeness (\sigma) and semantic entropy (H), suggesting that structured information gathering is associated with reduced diagnostic uncertainty.

cs.AI↗

arXiv2026

Predictive Autoscaling in Cloud-Native and Federated Cloud-Edge Computing Environments: A Taxonomy and Future Directions

Autoscaling is a key capability in cloud-native systems, where dynamic workloads, heterogeneous environments, and latency-sensitive applications require efficient and adaptive resource management. Traditional reactive approaches based on fixed thresholds often respond too late, leading to resource imbalance, performance degradation, and unstable scaling behavior. Recent advances in predictive models, Kubernetes Custom Resource Definitions (CRDs), Monitor-Analyse-Plan-Execute (MAPE) based control loops, and federated learning (FL) have enabled more proactive and autonomous autoscaling strategies. This paper presents a structured review of these developments. It first introduces a taxonomy of autoscaling techniques based on triggers, targets, prediction models, and evaluation metrics. It then examines predictive autoscaling approaches and CRD-based mechanisms, including Kubernetes operators and reconciliation workflows. Further, it analyses autoscaling in federated learning environments, highlighting reactive and proactive strategies alongside privacy-preserving techniques and container-level isolation. The paper also discusses drift-aware and uncertainty-aware autoscaling, incorporating concepts such as the Autoscaling Drift Index (ADI), feedback-driven correction, and stability control for heterogeneous workloads. Finally, it outlines open challenges and future research directions, providing a foundation for next-generation intelligent predictive autoscaling in cloud-edge environments.

cs.DC↗

arXiv2026

Evaluating Financial Intelligence in Large Language Models: Benchmarking SuperInvesting AI with LLM Engines

Large language models are increasingly used for financial analysis and investment research, yet systematic evaluation of their financial reasoning capabilities remains limited. In this work, we introduce the AI Financial Intelligence Benchmark (AFIB), a multi-dimensional evaluation framework designed to assess financial analysis capabilities across five dimensions: factual accuracy, analytical completeness, data recency, model consistency, and failure patterns. We evaluate five AI systems: GPT, Gemini, Perplexity, Claude, and SuperInvesting, using a dataset of 95+ structured financial analysis questions derived from real-world equity research tasks. The results reveal substantial differences in performance across models. Within this benchmark setting, SuperInvesting achieves the highest aggregate performance, with an average factual accuracy score of 8.96/10 and the highest completeness score of 56.65/70, while also demonstrating the lowest hallucination rate among evaluated systems. Retrieval-oriented systems such as Perplexity perform strongly on data recency tasks due to live information access but exhibit weaker analytical synthesis and consistency. Overall, the results highlight that financial intelligence in large language models is inherently multi-dimensional, and systems that combine structured financial data access with analytical reasoning capabilities provide the most reliable performance for complex investment research workflows.

cs.AI↗

arXiv2026

Thurston geometries and parameter constraints from SNIa data

Following the numerous evidence for large-scale cosmic isotropy violation with the advent of the `precision cosmology' era, we explore the possible advantages of extending the flat $\Lambda$CDM model to more general models in order to constrain anisotropies in the universe, otherwise absent in the standard model based on FLRW spacetime. Such extensions are offered by the topologically unique Thurston geometries, which are homogeneous but anisotropic spacetime models. In this work, we attempt to distinguish Thurston geometries from one another by introducing anisotropies via different scale factors in different directions, thereby introducing additional model parameters such as shear, eccentricity, curvature, and a preferred axis. We used the latest compilation of Pantheon+ \& SH0ES Type Ia supernova data for deriving model constraints, and found mild evidence of large-scale isotropy violation.

gr-qc↗

arXiv2025

Cosmology-informed Neural Networks to infer dark energy equation-of-state

We present a framework that combines physics-informed neural networks (PINNs) with Markov Chain Monte Carlo (MCMC) inference to constrain dynamical dark energy models using the Pantheon+ Type Ia supernova compilation. First, we train a physics-informed neural network to learn the solution of the Friedmann equation and accurately reproduce the matter density term x_m(z) = Omega_m,0 (1+z)^3 across a range of Omega_m,0. For each of five two-parameter equation-of-state (EoS) forms: Chevallier-Polarski-Linder (CPL), Barboza-Alcaniz (BA), Jassal-Bagla-Padmanabhan (JBP), Linear-z, and Logarithmic-z, we derive the analytic dark energy factor x_de(z), embed the trained surrogate within a GPU-accelerated likelihood pipeline, and sample the posterior of (h0, Omega_m,0, w0, wa, M0) using the emcee ensemble sampler with the full Pantheon+ covariance. All parameterizations remain consistent with a cosmological constant (w0 = -1, wa = 0) at the 95% credible level, with the tightest bounds from the CPL form. While the surrogate does not reduce computation time for a single run in simple models, it becomes advantageous for repeated analyses of the same EoS or for models with expensive likelihood evaluations, and can be shared as a reusable tool with different datasets within the training range of SNe redshifts. This flexibility makes the approach a scalable tool for future cosmological inference, especially in regimes where conventional ODE-based methods are computationally prohibitive.

astro-ph.CO↗

arXiv2025

Cosmographic constraints on a G\"odel-type rotating universe

We investigate the possibility of global cosmic rotation using a G\"odel-type rotating cosmological model, constrained through a cosmographic analysis of Type Ia supernovae (SNIa) from the Pantheon+ dataset. Employing a Taylor-expanded apparent magnitude--redshift relation derived via the Kristian-Sachs formalism, we analyze low-redshift SNIa data across five redshift bins (up to $Z \leq 0.5$). Our results reveal a mild but consistent preference for cosmic rotation, with the dimensionless rotation parameter $\Omega_0$ peaking at $0.29^{+0.21}_{-0.15}$ for $Z \leq 0.2$, and a broadly aligned anisotropy axis centered around equatorial coordinates $(243^\circ, -49^\circ)$. The inferred Hubble constant $h_0 \approx 0.73$ remains stable across all bins, while the deceleration parameter $q_0$ trends from near-zero to mildly negative values with increasing redshift. Model comparison using the Akaike Information Criterion (AIC) indicates a statistically significant preference for the rotating model over the standard $\Lambda$CDM cosmology at intermediate redshifts. These findings suggest that cosmic rotation, if present, may influence the late-time expansion history of the universe and warrants further investigation beyond the cosmographic regime.

astro-ph.CO↗

arXiv2024

Anisotropic universe with anisotropic dark energy

We investigate the anisotropic parameterization of the dark energy equation of state within the framework of an axisymmetric (planar) Bianchi-I universe. Using the latest Pantheon+ Type Ia Supernova dataset, augmented by SH0ES Cepheid distance calibrators, we constrain both the equation of state for anisotropic dark energy and other standard cosmological parameters. Additionally, we examine the presence of an underlying anisotropic axis. Our analysis yields a mean anisotropic dark energy equation of state of $\bar{w} = -0.86^{+0.15}_{-0.11}$ and a difference in the equation of states in and perpendicular to the plane of the axisymmetric Bianchi-I spacetime of $\delta_w = -0.129^{+0.090}_{-0.064}$. We also identify an axis of anisotropy at approximately $(272^{\circ}, 21^{\circ})$ in galactic coordinates. Through a comparative study of different cosmological models, we find that the data favor a Bianchi-I universe with anisotropic dark energy, where the equation of state deviates from ``-1'' along the axis of anisotropy (the $w_b$CDM model), over both other anisotropic models considered and the standard flat $\Lambda$CDM or $w$CDM models.

astro-ph.CO↗

arXiv2023

Constraints on Bianchi-I type universe with SH0ES anchored Pantheon+ SNIa data

We study the Bianchi-I cosmological model motivated by signals of statistical isotropy violation seen in cosmic microwave background (CMB) observations and others. To that end, we consider various kinds of anisotropic matter that source anisotropy in our model, specifically Cosmic strings, Magnetic fields, Domain walls and Lorentz violation generated magnetic fields. These anisotropic matter sources, taking one at a time, are studied for their co-evolution with standard model (isotropic) sources viz., dust-like (dark/normal) matter, and dark energy modelled as cosmological constant. We constrain the Hubble parameter, density fractions of anisotropic matter, cold dark matter (CDM), and dark energy ($\Lambda$) in a Bianchi-I universe with planar symmetry i.e., which has a global ellipsoidal geometry, and try to find signatures of a cosmic preferred axis if any. The latest compilation of Type Ia Supernova (SNIa) data from Pantheon+SH0ES collaboration is used in our analysis to obtain constraints on cosmological parameters and any preferred axis for our universe. In our analysis, we found mild evidence for a cosmic preferred axis. It is interesting to note that this preferred axis lies broadly in the vicinity of other prominent cosmic anisotropy axes reported in the literature from diverse data sets. Also we find some evidence for non-zero (negative) cosmic shear and eccentricity that characterize different expansion rates in different directions and deviation from an isotropic scale factor respectively. The energy density fractions of two of the sources considered are found to be non-zero at a $2\sigma$ confidence level. To be more conclusive, we require more SNIa host galaxy data for tighter constraints on distance and absolute magnitude calibration which are expected to be available from the future JWST observations and others.

astro-ph.CO↗

arXiv2020

A new set of cluster driven composite development indicators

Composite development indicators used in policy making often subjectively aggregate a restricted set of indicators. We show, using dimensionality reduction techniques, including Principal Component Analysis (PCA) and for the first time information filtering and hierarchical clustering, that these composite indicators miss key information on the relationship between different indicators. In particular, the grouping of indicators via topics is not reflected in the data at a global and local level. We overcome these issues by using the clustering of indicators to build a new set of cluster driven composite development indicators that are objective, data driven, comparable between countries, and retain interpretabilty. We discuss their consequences on informing policy makers about country development, comparing them with the top PageRank indicators as a benchmark. Finally, we demonstrate that our new set of composite development indicators outperforms the benchmark on a dataset reconstruction task.

econ.GN↗

arXiv2019

A memory-based method to select the number of relevant components in Principal Component Analysis

We propose a new data-driven method to select the optimal number of relevant components in Principal Component Analysis (PCA). This new method applies to correlation matrices whose time autocorrelation function decays more slowly than an exponential, giving rise to long memory effects. In comparison with other available methods present in the literature, our procedure does not rely on subjective evaluations and is computationally inexpensive. The underlying basic idea is to use a suitable factor model to analyse the residual memory after sequentially removing more and more components, and stopping the process when the maximum amount of memory has been accounted for by the retained components. We validate our methodology on both synthetic and real financial data, and find in all cases a clear and computationally superior answer entirely compatible with available heuristic criteria, such as cumulative variance and cross-validation.

q-fin.ST↗

arXiv2018

A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering

We introduce a new factor model for log volatilities that performs dimensionality reduction and considers contributions globally through the market, and locally through cluster structure and their interactions. We do not assume a-priori the number of clusters in the data, instead using the Directed Bubble Hierarchical Tree (DBHT) algorithm to fix the number of factors. We use the factor model and a new integrated non parametric proxy to study how volatilities contribute to volatility clustering. Globally, only the market contributes to the volatility clustering. Locally for some clusters, the cluster itself contributes statistically to volatility clustering. This is significantly advantageous over other factor models, since the factors can be chosen statistically, whilst also keeping economically relevant factors. Finally, we show that the log volatility factor model explains a similar amount of memory to a Principal Components Analysis (PCA) factor model and an exploratory factor model.

q-fin.ST↗

arXiv2012

Integrated Routing Protocol for Opportunistic Networks

In opportunistic networks the existence of a simultaneous path is not assumed to transmit a message between a sender and a receiver. Information about the context in which the users communicate is a key piece of knowledge to design efficient routing protocols in opportunistic networks. But this kind of information is not always available. When users are very isolated, context information cannot be distributed, and cannot be used for taking efficient routing decisions. In such cases, context oblivious based schemes are only way to enable communication between users. As soon as users become more social, context data spreads in the network, and context based routing becomes an efficient solution. In this paper we design an integrated routing protocol that is able to use context data as soon as it becomes available and falls back to dissemination based routing when context information is not available. Then, we provide a comparison between Epidemic and PROPHET, these are representative of context oblivious and context aware routing protocols. Our results show that integrated routing protocol is able to provide better result in term of message delivery probability and message delay in both cases when context information about users is available or not.

cs.NI↗