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Rajesh Kumar

Publications and source records attributed to Rajesh Kumar.

At least 19 recordsLinked to original sources
arXiv2026

Fano Resonance of a Magnonic Ring Resonator with a Nano-Scatterer Embedded in a Magnonic Conduit

We present micromagnetic simulation of a nanoscale magnonic ring resonator coupled to a magnonic waveguide, with and without a hole embedded in the waveguide (H-NRR and NRR respectively), for realizing transformation into a Fano resonance frequency spectral lineshape from a Lorentzian one. Transmission and reflection spectra of the H-NRR and NRR have been analyzed for different structural parameters (embedded hole diameter and gap between the ring and the waveguide) and material parameters (damping factor of the ring) to achieve maximum spectral transmission magnitude variation. The obtained micromagnetic simulation results are supported by an analytical model that consists of two-tracks of wave propagation, forwards and backwards. Our findings show that H-NRR system demonstrates a monotonic dependence of spectral transmission magnitude variation against the aforementioned design parameters (gap and damping) which should provide better engineering controllability and tolerance when compared with the original NRR system. Overall, our proposed structure and study may help in building unconventional magnonic integrated circuits for sensing, data processing systems and computing.

cond-mat.other↗
arXiv2026

Fabry-Pérot Cavity formed by Magnonic waveguide using holes and notches

In this paper, we have investigated a spin-wave-based Fabry-Pérot (FP) cavity using two air holes and notches embedded in a magnonic waveguide. Through micromagnetic simulations, we have demonstrated the spin-wave profile, as well as the transmission and reflection spectra of the FP type magnonic waveguide, by varying the distance between the center of holes and notches. We have introduced an analysis of the reflection spectra in an FP type magnonic waveguide, which is essential and considerable for the performance of magnonic integrated circuits. We have also presented a theoretical model to describe the transmission and reflection characteristics of the FP-type magnonic waveguide. Furthermore, active tuning of the resonance peak of the FP type magnonic wavguide by varying the external magnetic field has been demonstrated. Our proposed structure and its study may contribute to the development of magnonic integrated circuits for low-power computing and data processing in next-generation spintronic technologies.

cond-mat.other↗
arXiv2026

AutoResearch: An Execution-Grounded Multi-Agent Framework for Reliable Research Workflow Automation

Automated research agents increasingly generate code, retrieve literature, and draft scientific artifacts, but they often fail to verify whether generated experiments execute correctly or whether cited sources support generated claims. We present AutoResearch, an execution-grounded multi-agent framework for reliable research workflow automation. AutoResearch couples sandboxed Python/PyTorch execution, iterative code repair, citation verification, claim-support auditing, decision control, and structured \LaTeX{} artifact generation. The system treats runtime errors, citation-verification failures, and review-agent feedback as practical filtering signals for generated research artifacts. In controlled evaluations on HumanEval, MBPP, a SciCode subset, citation-validation tasks, claim-support auditing, and small end-to-end workflow stress tests, AutoResearch improves execution success, citation validity, local claim support, and workflow completion relative to directly comparable baselines. Code-oriented agents are reported separately as partial comparisons. AutoResearch is intended as a reliability-oriented research assistant, not as a fully autonomous scientist or a standalone manuscript-quality benchmark. Source Code: https://github.com/raja21068/AutoResearch

cs.CY↗
arXiv2026

Detecting LLM-Assisted Vietnamese Writing via Keystrokes under Behavioral Manipulation

We study the robustness of keystroke dynamics for detecting large language model (LLM)-assisted writing. We introduce a Vietnamese keystroke dataset capturing realistic writing modes, including bona fide composition, transcription, and paraphrasing. We also define a behaviorally grounded threat model in which users deliberately alter typing patterns. To implement the threat model, we create behaviorally manipulated variants of the data designed to evade keystroke-based detection. We evaluate four keystroke modeling approaches: temporal and rhythmic representations, and sequential representations modeled with a one-dimensional convolutional neural network (1D-CNN) and TypeNet, under user-independent and context-independent settings. The results show that sequential models outperform feature-based approaches in most cases and that keystroke signals encode discriminative information about the writing process. However, detection is not uniformly robust: transcription is reliably identified, while paraphrasing and adversarially manipulated samples are frequently misclassified as bona fide when not explicitly modeled. To address this, we incorporate adversarial training using behaviorally manipulated data, which substantially improves separability and robustness. These results suggest that keystroke-based detection depends critically on exposure to diverse writing behaviors, and that strong performance under limited conditions does not generalize to realistic or adversarial settings without targeted modeling.

cs.CL↗
arXiv2026

Detecting GPT-Assisted Writing Using Interpretable Stylometric Features

Distinguishing GPT-assisted from independently authored student writing has become a critical challenge in academia. This paper evaluates the discriminative capability of interpretable stylometric features extracted solely from submitted text. Using data from 90 participants who wrote both independently and with ChatGPT assistance, we evaluate eight machine learning classifiers while keeping data from the same participant together during validation. On the held-out test set, Random Forest achieved an ROC-AUC of 0.87 and an F1-score of 0.84, with False Positive and False Negative rates of 22.2% and 11.1%, respectively. SHAP analysis shows that lexical and grammatical characteristics drive the resulting predictions. The findings suggest that transparent, text-intrinsic features provide measurable signal for detecting GPT-assisted writing.

cs.CL↗
arXiv2026

Including Thermal Mesons and Meson Resonances in the Chiral Mean-Field Equation of State

The Chiral Mean-Field (CMF) model describes dense matter in terms of baryons and quarks interacting through scalar and vector meson mean fields. In the mean-field approximation, the meson fields are replaced by their expectation values. Their role in generating interactions and in-medium properties is retained, but explicit thermal mesonic excitations are absent. This becomes increasingly problematic at high temperature, where mesons contribute significantly to the thermodynamics of hadronic matter. Here, we keep the original CMF description of dense matter and add the thermal mesonic degrees of freedom using the mesonic sector of the Hadron Resonance Gas (HRG) module in the MUSES framework. Ground-state pseudoscalar and vector mesons account for the missing thermal excitations, while mesonic resonances provide, within the HRG picture, an effective description of mesonic interactions through resonance formation. We study how these contributions affect the pressure, entropy density, energy density, and baryon density, and compare the resulting equation of state with continuum-extrapolated lattice-QCD calculations.

nucl-th↗
arXiv2026

An Extended Chiral Mean-Field Model with Medium-Modified Thermal Mesons for Hot and Dense Hadronic Matter

In this conference proceeding we review an extension of the Chiral Mean Field (CMF) model that consistently incorporates interacting thermal mesons with self-consistent in-medium masses. In this approach, the in-medium masses of pseudoscalar and vector mesons are evaluated through the explicit chiral symmetry-breaking and vector-interaction terms in the Lagrangian respectively, before applying the mean-field approximation. These medium-modified meson properties introduce additional feedback into the CMF equations of motion, leading to a revised equation of state. The impact of this refinement is analyzed by comparing the hadronic model predictions with recent lattice QCD data and other hadronic descriptions, such as the hadron resonance gas model. The modified CMF framework, featuring an improved meson treatment (mCMF),demonstrates enhanced consistency with lattice-QCD results for thermodynamic observables across a broad range of temperatures and baryon chemical potentials.

nucl-th↗
arXiv2026

Probing Nonlinear Logarithmic Kalb-Ramond Black Holes: Particle Dynamics, Epicyclic Oscillations and Thermodynamic Signatures

In contrast to conventional linear coupling frameworks, the proposed work investigated the nonlinear effects that become more significant in the strong field regime of a black hole. We have investigated a new class of Kalb Ramond black holes generated by a nonlinear logarithmic coupling of the field, referred to as a Logarithmic Kalb Ramond black hole. The logarithmic coupling introduces strong-field modifications to the spacetime geometry, leading to significant departures from the Schwarzschild and Reissner Nordstrom BHs. We analyze the motion of test particles using the effective potential formalism and derive the conserved energy and angular momentum for circular equatorial geodesics. The stability of circular orbits and the location of the innermost stable circular orbit are examined, revealing a strong dependence on the model parameters Q, l, and $β$. The epicyclic frequencies (radial, vertical, and azimuthal) together with the associated periastron precession demonstrate that nonlinear logarithmic corrections can substantially modify quasi periodic oscillation observables. We further investigate the Hawking temperature and energy emission rate, which show that the nonlinear coupling also impacts distinct imprints on the thermodynamic behavior and evaporation characteristics of the black hole. Our results also identify the BH parameters as key regulators of the orbital dynamics, oscillatory properties, and thermal evolution, providing a unified framework for probing nonlinear KR gravity through strong-field astrophysical phenomena.

gr-qc↗
arXiv2026

Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation

Large language models (LLMs) are beginning to reshape how organic-synthesis workflows are represented, queried, planned, and connected to experimental automation. Assessing their contribution is not straightforward because reaction-specific transformers, chemistry-adapted LLMs, tool-using agents, optimizers, and autonomous laboratories are often discussed under the same broad terminology despite operating at different levels of the synthesis workflow. This Review traces the progression from reaction prediction and retrosynthetic planning to reaction development, literature-to-protocol translation, and robotic execution, with representative numerical claims verified against primary sources available through 4 September 2026. We examine where language models provide genuine added value, where established specialist methods remain the principal source of performance, and how retrieval, uncertainty-aware optimization, deterministic chemistry tools, robotics, sensing, and human oversight alter system behavior. The evidence shows a clear transition from isolated language-model demonstrations toward modular scientific systems that combine broad language priors with specialized computation and experimentally grounded feedback. Emerging work on multimodal reaction understanding, standardized tool interoperability, provenance-aware execution, and programmable laboratory infrastructure extends this transition further. Despite this progress, reliable deployment continues to depend on generalization beyond historical reaction corpora, calibrated uncertainty, transparent component attribution, safety controls, reproducible system state, and prospective experimental validation.

cs.AI↗
arXiv2026

How strange: Phase diagrams with 3 critical points

We show that the Chiral Mean-Field model (CMF) can produce a phase diagram with three critical points: the nuclear liquid-gas transition, quark deconfinement, and a strangeness driven transition. The strangeness driven transition separates a mainly nucleonic phase from one dominated by hyperons and baryon resonances. We discuss the compositional change in these transitions and their possible signatures in heavy-ion collisions and neutron star mergers.

nucl-th↗
arXiv2026

One Diffusion Model, Two Roles: Guided Trajectory Planning and Safety-Critical Scenario Generation in Closed-Loop Simulation

Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future trajectories in driving scenes. We show that a single pretrained diffusion traffic model can serve two complementary roles in the autonomous driving development loop: as an ego motion planner, and as a controllable generator of safety-critical scenarios for stress-testing the planners. On the planning side, we introduce a Single-Stream Dual-Stream (SSDS) diffusion-transformer decoder that fuses scene context via joint attention rather than late cross-attention, improving closed-loop performance on nuPlan. We further propose Decoupled Annealing Posterior Sampling with Energy (DAPSE), a training-free guidance scheme that injects arbitrary energy functions at the clean-sample level, avoiding the first-order approximation errors while requiring no auxiliary networks. Beyond planning, we leverage the same diffusion model as a controllable scenario generator to create realistic long-tail driving interactions for closed-loop evaluation. Through inference-time guidance, selected agents are steered toward safety-critical behaviors, including aggressive cut-ins, lead-vehicle braking, and combined longitudinal-lateral interactions, while preserving realistic traffic behaviors. Evaluated in closed-loop nuPlan simulations with independent black-box planners, the generated scenarios expose failure modes that remain hidden under standard benchmarks. Although the SSDS-based planner achieves stronger nominal performance, it experiences larger degradation under these challenging scenarios, demonstrating that benchmark superiority does not necessarily translate to robustness. These results demonstrate that a single learned traffic prior can simultaneously improve motion planning and provide a realistic framework for systematic planner robustness evaluation.

cs.CV↗
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

Phase Stability in the 3-Dimensional Open-source Code for the Chiral mean-field Model

In this paper we explore independently for the first time three chemical potentials (baryon $μ_B$, charged $μ_Q$, and strange $μ_S$) in the Chiral Mean Field (CMF) model. We designed and implemented \texttt{CMF++}, a new version of the CMF model rewritten in \texttt{C++} that is optimized, modular, and well-documented. \texttt{CMF++} has been integrated into the MUSES Calculation Engine as a free and open-source software module. The runtime improved in more than 4 orders of magnitude across all 3 chemical potentials, when compared to the legacy code. Here we focus on the zero temperature case and study stable, as well as metastable and unstable, vacuum, hadronic, and quark phases, showing how phase boundaries vary with the different chemical potentials. Due to the significant numerical improvements in \texttt{CMF++}, we can now for the first time sweep the entire $μ_B$, $μ_S$, $μ_Q$ phase space, investigate metastable phases, and calculate high-order susceptibilities within the CMF framework. This allows us to find phases of matter that include a light hadronic phase, a strangeness-dominated hadronic phase, and a quark phase. The numerical improvements also allow us to identify the order of the transitions among these phases, finding a first-order chiral symmetry restoration phase transition among the hadronic phases (favored for negative $μ_Q$ and/or $μ_S$ for some coupling schemes), in addition to third-order phase transitions. In particular, we identify for the first time triple points in the CMF model, where both chiral symmetry restoration and deconfinement phase transitions meet in the chemical potential phase space. Such points could potentially be identified in low-energy heavy-ion collisions.

nucl-th↗
arXiv2026

Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models

With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately. Convolutional neural networks have given promising results when used for diagnosing diseases using imaging tests. In this study, we investigate the potential of applying deep learning algorithms such as VGG16, VGG19, and ResNet50 for classification of lung ailments based on X-ray images. A detailed analysis of the aforementioned models' performances was conducted to assess how well they can classify various types of lung ailments, including pneumonia, tuberculosis, lung cancer, and normal lungs. In order to do that, these deep learning models were trained on a vast amount of X-ray images. The results of our study show that while all three models provide good results, ResNet-50 performs best in comparison with other models due to its efficiency and high level of accuracy. We believe that these deep learning models can be successfully implemented in the practice of diagnosing pulmonary diseases in the future. It helps with early disease detection and improves patient outcomes.

cs.CV↗
arXiv2026

Massive Compact Stars Beyond the General Relativity Limit in Finslerian Gravity: A Possible Explanation for the GW190814 Secondary

The existence of an upper mass limit for compact stars is one of the fundamental predictions of general relativity (GR), with important implications for the outcome of compact binary mergers and the nature of the proposed neutron star black hole mass gap. The LIGO Virgo collaboration announced the discovery of a compact binary merger, GW190814, containing a compact star with mass 2.5 to 2.67 $M_\odot$ [R. Abbott et al.(2020) ApJ Lett., 896, L44], which provided an exciting new stimulus to the ongoing debate on whether a gap exists between the maximum mass of NS and the minimum mass of black hole. Such GW detection has also challenged conventional stellar models and renewed interest in exploring whether modified theories of gravity can accommodate such ultra-massive compact objects without invoking black hole formation. The present work investigated the structure and physical properties of compact stars within the framework of Finslerian gravity, employing the Heintzmann IIa gravitational potentials and showed that the our model can substantially enhance the maximum mass of compact stars upto $2.67 M_{\odot}$, thereby offering a plausible explanation for massive compact objects such as the secondary component of GW190814. In addition, the moment of inertia is found to increase, indicating stronger rotational support and a redistribution of the internal mass profile. The analysis focuses on key astrophysical observables, including the mass radius and moment of inertia mass relations, and a detailed comparison with the GR counterpart is performed. Assuming a linear equation of state, we construct a class of physically viable anisotropic stellar models and analyze their behavior under the influence of Finslerian corrections. The physical viability of the model is rigorously tested through stability criteria.

gr-qc↗
arXiv2026

Study of GeSn Selective Area Growth with Demonstration of SWIR Light Detection

As germanium-tin (GeSn) epitaxial growth quality continuously improves, the search for an efficient integration strategy of GeSn optoelectronics devices into complementary metal-oxide-semiconductor (CMOS) manufacturing line also accelerates. Selective area growth (SAG) on patterned substrate emerges as a promising approach for this quest, with locally controlled growth of GeSn laser/detector suitable for either co-integration with silicon-based waveguide structure or stand-alone module like focal plane array. In this work, we report successful GeSn SAG with Sn content ranging from 3.2% to 8.7% of good optical quality, with demonstration of tunable GeSn SAG photoluminescence and GeSn SAG photoconductor device, the latter with detection cutoff wavelength up to 2 um. In addition, we present a comprehensive study of GeSn SAG condition at different window sizes, from 2 um to 100 um, and shapes: circle, square, octagon, and rectangle. Presence of loading effect is revealed, where GeSn growth rate increases as pattern fill factor and window size shrink. It introduces a different growth condition compared to thin film growth, which can weaken or inhibit Sn incorporation at very small window size and induce Sn segregation in high Sn content SAG growth.

physics.app-ph↗
arXiv2026

SleepLM: Natural-Language Intelligence for Human Sleep

We present SleepLM, a family of sleep-language foundation models that enable human sleep alignment, interpretation, and interaction with natural language. Despite the critical role of sleep, learning-based sleep analysis systems operate in closed label spaces (e.g., predefined stages or events) and fail to describe, query, or generalize to novel sleep phenomena. SleepLM bridges natural language and multimodal polysomnography, enabling language-grounded representations of sleep physiology. To support this alignment, we introduce a multilevel sleep caption generation pipeline that enables the curation of the first large-scale sleep-text dataset, comprising over 100K hours of data from more than 10,000 individuals. Furthermore, we present a unified pretraining objective that combines contrastive alignment, caption generation, and signal reconstruction to better capture physiological fidelity and cross-modal interactions. Extensive experiments on real-world sleep understanding tasks verify that SleepLM outperforms state-of-the-art in zero-shot and few-shot learning, cross-modal retrieval, and sleep captioning. Importantly, SleepLM also exhibits intriguing capabilities including language-guided event localization, targeted insight generation, and zero-shot generalization to unseen tasks. All code and data will be open-sourced.

cs.AI↗
arXiv2026

Studying the QCD Matter produced in Heavy-Ion Collisions using the MUSES Calculation Engine

The equation of state of hot and dense matter is essential for describing heavy-ion collisions at all collision energies. Here, we explore the capabilities of the latest version of the MUSES Calculation Engine, $\textit{Calliope}$, focusing on software modules and workflows that compute the equation of state and observable properties of the matter produced in heavy-ion collisions. These include several equations of state, ranging from first-principles lattice QCD to phenomenological approaches, with or without a critical point, and with phase-space dimensionality ranging from two dimensions defined by temperature $T$ and baryon chemical potential $μ_B$, to four dimensions after the addition of strangeness and electric-charge chemical potentials $μ_S$ and $μ_Q$. We also discuss modules that provide additional thermodynamic quantities and observables relevant for heavy-ion modeling, including elements of the pressure Hessian matrix and transport coefficients. Workflow examples are constructed that merge two equations of state thermodynamically consistently to extend phase-diagram coverage, and feed the results into an equation of state inverter to produce inputs suitable for hydrodynamic simulations. Finally, we apply this framework to perform a relativistic viscous hydrodynamic simulation with equations of state with an extended $T$ and $μ_B$ coverage and a movable critical point, including effects from transport coefficients that phenomenologically encode critical scaling, at collision energies $\sqrt{s_{NN}}=7.7, 19.6$, and $39$ GeV.

nucl-th↗