arXiv subjects

Aditya Sharma

Publications and source records attributed to Aditya Sharma.

At least 19 recordsLinked to original sources

arXiv2026

Interacting Galilean and Finite-Energy Carroll Fermions

We present a unified derivation of the Galilean and Carrollian limits of the massive Dirac action based on a similarity transformation depending on the mass and the speed of light. The two choices of Dirac conjugation, combined with different mass scalings, generate distinct families of limiting actions. This construction recovers known Galilei and Carroll fermion models and yields new systems, one in each regime. We classify a sufficient set of boost-compatible local (self-)interactions, which includes Nambu--Jona-Lasinio (NJL). The new Galilean free action possesses a local fermionic gauge symmetry that eliminates its local field content. A selected quartic interaction, preserving the Bargmann boost, explicitly breaks the original fermionic gauge symmetry so that the argument that eliminates the field content no longer applies. The NJL limit interaction merely deforms the realization of that gauge symmetry. We show that the energy parameter in several Carroll actions can be eliminated by a time-dependent phase redefinition; conversely, in the new Carrollian extension the energy cannot be removed.

hep-th↗

arXiv2026

When Prompts Ignore Structure: Graph-Based Attribute Reasoning for Calibrated VLMs

Reliable confidence estimation remains a key limitation of test-time adaptation in vision-language models (VLMs), where prompt tuning improves zero-shot accuracy but often degrades calibration due to entropy-driven overconfidence. Prior approaches mitigate this using LLM-derived class attributes and contrastive regularization, yet treat attributes independently, ignoring their relational structure. We propose ARGTCA, which represents (class, attribute) pairs as nodes in a Symbolic Attribute Graph and trains a Graph Attention Network (GAT) using contrastive objectives to produce structurally informed embeddings that capture inter-attribute dependencies. We introduce two attribute selection strategies: ARGTCA-DIV for intra-class diversity and ARGTCA-DISC for inter-class discrimination. Experiments across nine benchmarks show that ARGTCA-DIV reduces average Expected Calibration Error (ECE) by approximately ~37% over baselines, while ARGTCA-DISC consistently performs as the second-best variant, reducing average ECE by approximately ~17% over baselines. These results suggest that modeling symbolic attribute interactions provides a principled approach for reliable test-time adaptation in VLMs.

cs.CV↗

arXiv2026

Transmon Phase Gates Controlled by Superconducting Soliton DAC

We introduce a superconducting digital-to-analog converter (DAC) that filters control noise, provides native multiplexing, performs quantum gates in nanoseconds, and can be controlled by CMOS. This is achieved by transducing a trapezoidal drive pulse into a superconducting soliton, which is then held in the DAC load loop, applying flux to a mutually-coupled superconducting qubit or gate coupler. The analog flux output by the DAC can be easily controlled by varying the soliton hold time, or with a DC-biased tunable DAC-qubit coupler, allowing the DAC to perform a fixed-time, high-fidelity gate that's robust to fabrication variance or flux offsets in the quantum circuit. Our initial demonstration shows that the DAC can successfully perform 5.6 ns S-gates on transmons. We measure the DAC-induced quantum state excitation probability per gate to be 0.05%, and find that the DAC-induced relaxation rate from the qubit 1 state is below the intrinsic T1 rate limit of the transmon. Quantum simulations show qualitative agreement with the measured data, and predict that the DAC excitation rate can be lowered 10 times further by overdamping the Josephson junction (JJ) in the DAC load loop. may be limited by a Interleaved Randomized Benchmarking (IRB) sequences on an observer qubit reveal that, when scaling to many qubits, the DAC's performance may be limited by a non-local, DAC-induced phase error of 1.6% per gate, appearing in ancilla qubits that are not directly coupled to any of the 30 DACs on the chip. We discuss strategies for future layouts of multi-DAC chips that focus on mitigating the source of these non-local, high-frequency electromagnetic interactions (EMI), and how to incorporate a DC-tunable coupler for phase correction.

quant-ph↗

arXiv2026

Rotate2Think: Geometric Priming via Orthogonal Rotation to Improve Language Model Reasoning

Reasoning models achieve strong performance on challenging tasks by generating explicit intermediate reasoning traces before producing a final answer. Yet the internal structure of representation space when reasoning remains poorly understood: how do a model's hidden representations differ during thinking versus the embeddings of the input prompt, and can this structure be exploited to elicit stronger reasoning at inference time? We show that both input embeddings and thinking embeddings (mean-pooled last-layer hidden states over the prompt and reasoning trace, respectively) exhibit extremely high conicity, with all vectors clustering tightly around a single mean direction. Crucially, these mean input and thinking directions are non-collinear, with thinking embeddings occupying a geometrically distinct region of embedding space across many different models and benchmark tasks. This observation motivates casting the input-to-thinking transition as a rotation problem admitting a closed-form solution via orthogonal Procrustes analysis. We propose Rotate2Think, a training-free method that estimates this rotation from a small set of correctly solved examples and injects the resulting synthetic thinking vector between thinking delimiters at inference time, providing a geometric primer at the onset of the reasoning trace. Evaluated across multiple benchmarks and model families, Rotate2Think improves accuracy in 30 of 32 model-benchmark configurations across mathematics, science, and code tasks, and generalizes zero-shot to multimodal reasoning on MATH-Vision.

cs.LG↗

arXiv2026

The Radio--X-ray Correlation of High-Redshift AGN: A Numerical Study of Inverse-Compton Scattering of the CMB Photons in Relativistic Jets

Relativistic jets from active galactic nuclei are expected to exhibit strong redshift evolution in their radiative output due to the increasing energy density of the cosmic microwave background (CMB). We investigate the role of inverse Compton (IC) scattering of CMB photons in regulating the radio and X-ray emission from large-scale jets using three-dimensional relativistic magnetohydrodynamic simulations coupled with a hybrid Eulerian-Lagrangian particle framework. By keeping the jet dynamics and ambient medium properties fixed across redshifts, we are able to isolate the impact of the cosmological evolution of the CMB on the jet radiation. From our simulations, we construct synthetic spectral energy distributions and intensity maps considering synchrotron and IC/CMB losses along with particle acceleration from shocks. We are able to reproduce the weak redshift dependence of radio luminosity and the strong enhancement of X-ray emission toward high redshift that is observed in radio-loud quasars. At high redshift, the X-ray luminosity follows the expected $(1+z)^4$ scaling, confirming IC/CMB as the dominant mechanism driving the X-ray enhancement. The resulting X-ray-to-radio flux ratio increases systematically with redshift and is consistent with observational constraints. Finally, we show that slower jets exhibit a stronger redshift evolution of the X-ray enhancement than faster jets, highlighting the critical role of jet propagation length scales and particle energy evolution. The simulations also naturally reproduce the steepening of the radio spectral index with redshift - the $\alpha$-$z$ relation - thus providing a unified framework that allows to interpret the multiwavelength properties of high-redshift radio sources.

astro-ph.HE↗

arXiv2026

DeSQ: Decomposition-based SPARQL Query Generation

Dominant approaches to Knowledge Base Question Answering (KBQA) fall into two categories. First is the generation of a formal query that suffers from brittleness and limited explainability, and the second is direct answer retrieval through KB exploration that is computationally costly and prone to hallucination. To combine the strengths of both paradigms while mitigating their respective weaknesses, we introduce DeSQ (Decomposition-based SPARQL Query Generation), a KB-agnostic framework that operates in three stages. First, it decomposes complex questions into Atomic Constraints (ACs) that mirror the relational structure of the underlying KB. Second, it generates a two-part structured output: (a) Mapping of each AC to its corresponding SPARQL Fragment, using standardized variable and URIs placeholders, and (b) URIs Grounding block describing each placeholder. Third, it assembles these fragments into a complete SPARQL query. DeSQ surpasses state-of-the-art approaches on four out of five major benchmarks and demonstrates superior robustness to lexical variation. Beyond performance gains, our framework greatly simplifies evaluation by eliminating the need for a live KB endpoint, and its structured output enables fine-grained error analysis, allowing more targeted interventions for improvement.

cs.CL↗

arXiv2026

Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards

An AI system for professional floor plan design must precisely control room dimensions and areas while respecting the desired connectivity between rooms and maintaining functional and aesthetic quality. Existing generative approaches focus primarily on respecting the requested connectivity between rooms, but do not support generating floor plans that respect numerical constraints. We introduce a text-based floor plan generation approach that fine-tunes a large language model (LLM) on real plans and then applies reinforcement learning with verifiable rewards (RLVR) to improve adherence to topological and numerical constraints while discouraging invalid or overlapping outputs. Furthermore, we design a set of constraint adherence metrics to systematically measure how generated floor plans align with user-defined constraints. Our model generates floor plans that satisfy user-defined connectivity and numerical constraints and outperforms existing methods on Realism, Compatibility, and Diversity metrics. Across all tasks, our approach achieves at least a 94% relative reduction in Compatibility compared with existing methods. Our results demonstrate that LLMs can effectively handle constraints in this setting, suggesting broader applications for text-based generative modeling.

cs.CL↗

arXiv2026

Carroll fermions from null reduction: A case of good and bad fermions

We derive Carrollian fermionic actions using the null reduction method from Bargmann spacetimes. In the Lorentzian light-cone formulation, the Dirac spinor naturally decomposes into dynamical and constrained degrees of freedom $-$ the so-called `good' and `bad' fermions $\Psi_{(\pm)}$. These light-cone projections are intrinsically adapted to the null frame and, unlike the chiral decomposition into left- and right-handed spinors $\Psi_{L(R)}$, are valid in arbitrary spacetime dimensions, both even and odd. As in the case of bosons, the magnetic Carroll sector for fermions is governed by the dynamical modes of the parent theory, while the electric sector arises from the constrained modes. Upon deforming to a Bargmann spacetime, these constraints are removed, promoting the `bad' fermions to dynamical modes that describe the electric Carroll fermions. We construct the Clifford algebra on the Carroll manifold through its embedding in the ambient Bargmann manifold, and obtain both electric and magnetic Carroll fermion actions from a \textit{single} Bargmann-invariant Dirac action. We analyze the canonical structure of both theories, establish their invariance under Carroll transformations, and compute the corresponding two-point functions, which exhibit the expected behavior in both sectors. We conclude with some comments on the quantization of these Carrollian theories.

hep-th↗

arXiv2026

ModTGCN: Modularity-aware Graph Neural Networks for Text Classification

Graph-based text classification models typically rely on local neighborhood aggregation and overlook global community structure, despite semantic document graphs exhibiting strong class-consistent clustering. Ignoring this can blur class boundaries and lead to over-smoothing. We propose ModTGCN, a modularity-aware graph neural network for text classification that jointly optimizes cross-entropy and a modularity-based auxiliary objective to promote class-coherent document communities while preserving discriminative representations. The modularity term is computed on a document-document similarity graph derived from transformer embeddings (pretrained or fine-tuned). To improve scalability, we decouple the original heterogeneous TextGCN graph into separate document-word and word-word components, achieving 2x-10x faster training. We further study graph construction strategies, label-aware edge reweighting, and supervision choices for modularity optimization. Experiments on five benchmarks show consistent gains, with larger improvements on complex, low homophily datasets such as Ohsumed and 20NG.

cs.CL↗

arXiv2026

G-Loss: Graph-Guided Fine-Tuning of Language Models

Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language models such as BERT, operate only within local neighborhoods and fail to account for the global semantic structure. We present G-Loss, a graph-guided loss function that incorporates semi-supervised label propagation to use structural relationships within the embedding manifold. G-Loss builds a document-similarity graph that captures global semantic relationships, thereby guiding the model to learn more discriminative and robust embeddings. We evaluate G-Loss on five benchmark datasets covering key downstream classification tasks: MR (sentiment analysis), R8 and R52 (topic categorization), Ohsumed (medical document classification), and 20NG (news categorization). In the majority of experimental setups, G-Loss converges faster and produces semantically coherent embedding spaces, resulting in higher classification accuracy than models fine-tuned with traditional loss functions.

cs.CL↗

arXiv2026

Multi-messenger emission derived from relativistic magnetized jet dynamics using a multi-zone framework

Relativistic jets from Active Galactic Nuclei (AGN) are highly energetic and emit radiation across a wide range of frequencies. Despite several observational studies, their particle composition still remains a key open question. The detection of high-energy neutrinos from blazar sources such as TXS 0506+056 has highlighted the plausibility of hadronic/lepto-hadronic models for AGN jets. To understand the origin of high-energy neutrinos from such sources, it is imperative to capture the complex interplay between the jet dynamics, their composition, and the mechanism of particle acceleration and cooling in relativistic jets. In this pilot study, we have coupled a numerical multi-zone framework for lepto-hadronic modeling, with 3D relativistic magneto-hydrodynamic simulations of AGN jets, including external photon fields. Our framework provides synthetic multi-wavelength and neutrino flux by spatially sampling the simulated jet into multiple zones. We investigate the implications of such a framework in exploring the different intrinsic and extrinsic pathways for proton-enrichment in jets. Essentially, we find that for low proton-to-electron number density ratios, producing a substantial jet neutrino flux, requires the underlying proton energy distribution to have a relatively flat spectrum with a power-law index of $\simeq 2.0$. We further find that while intrinsic shocks triggered by kink-instabilities in the jet can accelerate electrons to high energies, they may not be sufficient to produce such flat particle energy distributions for the chosen set of parsec-scale jet parameters. Finally, to produce a significant jet neutrino emission, our simulations suggest the need to consider particle acceleration mechanisms through alternative pathways, either internal or external.

astro-ph.HE↗

arXiv2026

Adoption and Use of LLMs at an Academic Medical Center

While large language models (LLMs) can support clinical documentation needs, standalone tools struggle with "workflow friction" from manual data entry. We developed ChatEHR, a system that enables the use of LLMs with the entire patient timeline spanning several years. ChatEHR enables automations - which are static combinations of prompts and data that perform a fixed task - and interactive use in the electronic health record (EHR) via a user interface (UI). The resulting ability to sift through patient medical records for diverse use-cases such as pre-visit chart review, screening for transfer eligibility, monitoring for surgical site infections, and chart abstraction, redefines LLM use as an institutional capability. This system, accessible after user-training, enables continuous monitoring and evaluation of LLM use. In 1.5 years, we built 7 automations and 1075 users have trained to become routine users of the UI, engaging in 23,000 sessions in the first 3 months of launch. For automations, being model-agnostic and accessing multiple types of data was essential for matching specific clinical or administrative tasks with the most appropriate LLM. Benchmark-based evaluations proved insufficient for monitoring and evaluation of the UI, requiring new methods to monitor performance. Generation of summaries was the most frequent task in the UI, with an estimated 0.73 hallucinations and 1.60 inaccuracies per generation. The resulting mix of cost savings, time savings, and revenue growth required a value assessment framework to prioritize work as well as quantify the impact of using LLMs. Initial estimates are $6M savings in the first year of use, without quantifying the benefit of the better care offered. Such a "build-from-within" strategy provides an opportunity for health systems to maintain agency via a vendor-agnostic, internally governed LLM platform.

cs.CY↗

arXiv2025

Chern-Simons propagators in AdS$_3$

We introduce parity-odd spin-1 harmonic functions in AdS$_3$ and study their properties. We demonstrate that such parity-odd harmonics are related to their parity-even counterparts through the action of a `Chern-Simons operator', which we present as a novelty in this paper. This relation leads to the construction of simultaneous eigen-functions of the Laplacian and the Chern-Simons operators. Subsequently, these harmonic functions are employed to construct propagators in pure abelian Chern-Simons theory as well as Maxwell-Chern-Simons theory in a covariant gauge. We demonstrate the consistency of the Chern-Simons propagator with the expected two-point function of the boundary currents. Our results are built upon the embedding formalism, which we modify suitably to incorporate parity-odd structures. This formalism also readily helps us write down parity odd structures for the propagators of higher-spin fields. Finally, we construct a split representation for the parity-odd harmonic functions, which may be useful to compute Witten diagrams with loops. Our results are expected to be useful in perturbative studies of parity violating QFTs on AdS$_3$.

hep-th↗

arXiv2025

Inducing Causal World Models in LLMs for Zero-Shot Physical Reasoning

Large Language Models (LLMs), despite their advanced linguistic capabilities, fundamentally lack an intuitive understanding of physical dynamics, which limits their effectiveness in real-world scenarios that require causal reasoning. In this paper, we introduce Causal World Model Induction (CWMI), a novel framework designed to embed an explicit model of causal physics within an LLM. Our approach incorporates a dedicated Causal Physics Module (CPM) and a new training objective called Causal Intervention Loss, encouraging the model to learn cause-and-effect relationships from multimodal data. By training the model to predict the outcomes of hypothetical interventions instead of merely capturing statistical correlations, CWMI develops a robust internal representation of physical laws. Experimental results show that CWMI significantly outperforms state-of-the-art LLMs on zero-shot physical reasoning tasks, including the PIQA benchmark and our newly proposed PhysiCa-Bench dataset. These findings demonstrate that inducing a causal world model is a critical step toward more reliable and generalizable AI systems.

cs.LG↗

arXiv2025

Weak Links in LinkedIn: Enhancing Fake Profile Detection in the Age of LLMs

Large Language Models (LLMs) have made it easier to create realistic fake profiles on platforms like LinkedIn. This poses a significant risk for text-based fake profile detectors. In this study, we evaluate the robustness of existing detectors against LLM-generated profiles. While highly effective in detecting manually created fake profiles (False Accept Rate: 6-7%), the existing detectors fail to identify GPT-generated profiles (False Accept Rate: 42-52%). We propose GPT-assisted adversarial training as a countermeasure, restoring the False Accept Rate to between 1-7% without impacting the False Reject Rates (0.5-2%). Ablation studies revealed that detectors trained on combined numerical and textual embeddings exhibit the highest robustness, followed by those using numerical-only embeddings, and lastly those using textual-only embeddings. Complementary analysis on the ability of prompt-based GPT-4Turbo and human evaluators affirms the need for robust automated detectors such as the one proposed in this study.

cs.SI↗

arXiv2025

Automated Defect Identification and Categorization in NDE 4.0 with the Application of Artificial Intelligence

This investigation attempts to create an automated framework for fault detection and organization for usage in contemporary radiography, as per NDE 4.0. The review's goals are to address the lack of information that is sufficiently explained, learn how to make the most of virtual defect increase, and determine whether the framework is viable by using NDE measurements. As its basic information source, the technique consists of compiling and categorizing 223 CR photographs of airplane welds. Information expansion systems, such as virtual defect increase and standard increase, are used to work on the preparation dataset. A modified U-net model is prepared using the improved data to produce semantic fault division veils. To assess the effectiveness of the model, NDE boundaries such as Case, estimating exactness, and misleading call rate are used. Tiny a90/95 characteristics, which provide strong differentiating evidence of flaws, reveal that the suggested approach achieves exceptional awareness in defect detection. Considering a 90/95, size error, and fake call rate in the weld area, the consolidated expansion approach clearly wins. Due to the framework's fast derivation speed, large images can be broken down efficiently and quickly. Professional controllers evaluate the transmitted system in the field and believe that it has a guarantee as a support device in the testing cycle, irrespective of particular equipment cut-off points and programming resemblance.

cs.CV↗

arXiv2025

MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks

While large language models (LLMs) achieve near-perfect scores on medical licensing exams, these evaluations inadequately reflect the complexity and diversity of real-world clinical practice. We introduce MedHELM, an extensible evaluation framework for assessing LLM performance for medical tasks with three key contributions. First, a clinician-validated taxonomy spanning 5 categories, 22 subcategories, and 121 tasks developed with 29 clinicians. Second, a comprehensive benchmark suite comprising 35 benchmarks (17 existing, 18 newly formulated) providing complete coverage of all categories and subcategories in the taxonomy. Third, a systematic comparison of LLMs with improved evaluation methods (using an LLM-jury) and a cost-performance analysis. Evaluation of 9 frontier LLMs, using the 35 benchmarks, revealed significant performance variation. Advanced reasoning models (DeepSeek R1: 66% win-rate; o3-mini: 64% win-rate) demonstrated superior performance, though Claude 3.5 Sonnet achieved comparable results at 40% lower estimated computational cost. On a normalized accuracy scale (0-1), most models performed strongly in Clinical Note Generation (0.73-0.85) and Patient Communication & Education (0.78-0.83), moderately in Medical Research Assistance (0.65-0.75), and generally lower in Clinical Decision Support (0.56-0.72) and Administration & Workflow (0.53-0.63). Our LLM-jury evaluation method achieved good agreement with clinician ratings (ICC = 0.47), surpassing both average clinician-clinician agreement (ICC = 0.43) and automated baselines including ROUGE-L (0.36) and BERTScore-F1 (0.44). Claude 3.5 Sonnet achieved comparable performance to top models at lower estimated cost. These findings highlight the importance of real-world, task-specific evaluation for medical use of LLMs and provides an open source framework to enable this.

cs.CL↗

arXiv2025

Thermal Product Formula for Shear Modes

We investigate the validity of the thermal product formula proposed in [arXiv:2304.12339], for the shear channel fluctuations of R-charged black branes in five-dimensional AdS where the shear mode is coupled with charge diffusion mode at non-zero momentum. When these modes are suitably decoupled, we are able to obtain an exact formula for the two point functions of the boundary current and energy-momentum tensor in terms of the quasinormal modes of this channel. This exact formula is a simple modification of the previous version of the product formula. We also obtain a similar formula for the case involving a boundary global R-symmetry anomaly, when we have a bulk Chern-Simons term which introduces additional couplings in the shear channel. Also based on insights from the quasinormal mode spectrum, we report on an instability as well as the presence of high momentum long-lived modes associated with large values of the anomaly coefficient.

hep-th↗