arXiv subjects

Raghav Sharma

Publications and source records attributed to Raghav Sharma.

12 recordsLinked to original sources

arXiv2026

Energy-efficient spin Hall nano-oscillators using near-compensated CoGd ferrimagnets

Conventional spin Hall nano-oscillators (SHNOs) based on ferromagnets face practical limitations due to high threshold current densities and large external magnetic field requirements. Ferrimagnets provide an attractive alternative due to their unique magnetic dynamics and potential for energy-efficient spintronic devices. In this study, we report rare-earth-transition-metal (RE-TM) ferrimagnetic SHNOs utilizing Co1-xGdx alloys, in which compositional tuning enables high-performance operation near the magnetization compensation. The optimized SHNO operates at a low current density (1.43*10^7 A/cm^2), a small magnetic field (5 mT), and exhibits a narrow linewidth (0.61 MHz) simultaneously, showing an order-of-magnitude improvement over its ferromagnetic counterparts. This enhanced performance arises from high spin-orbit torque efficiency, low magnetic anisotropy, reduced effective magnetization, and minimized nonlinearity near the compensation point. These results establish RE-TM ferrimagnets as a promising material platform for next-generation spintronic devices and offer new strategies for realizing energy-efficient, high-performance spintronic oscillators.

cond-mat.mtrl-sci↗

arXiv2026

Fractional parametric resonance in spintronic diodes

Parametric pumping is a powerful tool for the excitation, amplification, and processing of oscillations and waves of different nature. In general, parametric resonance can occur when the pumping frequency $f_p$ and eigenfrequency of a linear mode (or wave) $f_0$ satisfy the relation $f_p$=2$f_0$/n (n=1,2,3,...). While such parametric resonance is well known in mechanical, superconductive, and quantum systems, in magnetic and spintronic systems only the lowest (n=1) parametric resonance at double the spin wave mode frequency $f_p$=2$f_0$ was thoroughly studied and explored. Here, using a theoretical analysis based on both micromagnetic simulations and an analytical model, we show the emergence of resonances at fractional frequencies $f_p$=2$f_0$/n (with n>10) in spintronic diodes driven by the simultaneous action of ac spin-transfer torque (STT, current densities < $10^6$ A/cm2) and voltage-controlled magnetic anisotropy (VCMA, effective anisotropy fields < 50 mT). The analytical model shows that parametric magnetization dynamics is irreducible to the standard Mathieu model of a parametric oscillator and demonstrates the crucial role of VCMA-driven mode frequency modulation: together with parametric coupling, it results in higher-order odd (n=3,5,7,...) fractional resonances, observed above certain VCMA pumping threshold, while simultaneous action with linear STT drive produces thresholdless even (n=4,6,8,...) resonances. This higher-order parametric dynamics is not restricted to VCMA pumping and opens new directions for the application of spintronic diodes for nonlinear signal processing and electromagnetic energy harvesting.

cond-mat.mes-hall↗

arXiv2026

NavTrust: Benchmarking Trustworthiness for Embodied Navigation

There are two major categories of embodied navigation: Vision-Language Navigation (VLN), where agents navigate by following natural language instructions; and Object-Goal Navigation (OGN), where agents navigate to a specified target object. However, existing work primarily evaluates model performance under nominal conditions, overlooking the potential corruptions that arise in real-world settings. To address this gap, we present NavTrust, a unified benchmark that systematically corrupts input modalities, including RGB, depth, and instructions, in realistic scenarios and evaluates their impact on navigation performance. To our best knowledge, NavTrust is the first benchmark that exposes embodied navigation agents to diverse RGB-Depth corruptions and instruction variations in a unified framework. Our extensive evaluation of seven state-of-the-art approaches reveals substantial performance degradation under realistic corruptions, which highlights critical robustness gaps and provides a roadmap toward more trustworthy embodied navigation systems. Furthermore, we systematically evaluate four distinct mitigation strategies to enhance robustness against RGB-Depth and instruction corruptions. Our base models include Uni-NaVid and ETPNav. We deployed them on a real mobile robot and observed improved robustness to corruptions. The project website is: https://navtrust.github.io/.

cs.RO↗

arXiv2025

RLHF: A comprehensive Survey for Cultural, Multimodal and Low Latency Alignment Methods

Reinforcement Learning from Human Feedback (RLHF) is the standard for aligning Large Language Models (LLMs), yet recent progress has moved beyond canonical text-based methods. This survey synthesizes the new frontier of alignment research by addressing critical gaps in multi-modal alignment, cultural fairness, and low-latency optimization. To systematically explore these domains, we first review foundational algo- rithms, including PPO, DPO, and GRPO, before presenting a detailed analysis of the latest innovations. By providing a comparative synthesis of these techniques and outlining open challenges, this work serves as an essential roadmap for researchers building more robust, efficient, and equitable AI systems.

cs.LG↗

arXiv2025

CLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure Federated Learning

Secure federated learning (FL) preserves data privacy during distributed model training. However, deploying such frameworks across heterogeneous devices results in performance bottlenecks, due to straggler clients with limited computational or network capabilities, slowing training for all participating clients. This paper introduces the first straggler mitigation technique for secure aggregation with deep neural networks. We propose CLIP, a client-side invariant neuron pruning technique coupled with network-aware pruning, that addresses compute and network bottlenecks due to stragglers during training with minimal accuracy loss. Our technique accelerates secure FL training by 13% to 34% across multiple datasets (CIFAR10, Shakespeare, FEMNIST) with an accuracy impact of between 1.3% improvement to 2.6% reduction.

cs.LG↗

arXiv2025

Small Language Models for Agentic Systems: A Survey of Architectures, Capabilities, and Deployment Trade offs

Small language models (SLMs; 1-12B params, sometimes up to 20B) are sufficient and often superior for agentic workloads where the objective is schema- and API-constrained accuracy rather than open-ended generation. We synthesize recent evidence across open and proprietary SLMs (Phi-4-Mini, Qwen-2.5-7B, Gemma-2-9B, Llama-3.2-1B/3B, Ministral-3B/8B, Apple on-device 3B, DeepSeek-R1-Distill) and connect it to modern evaluations (BFCL v3/v4, StableToolBench) and serving stacks (vLLM, SGLang, TensorRT-LLM) paired with guided decoding libraries (XGrammar, Outlines). We formalize SLM-default, LLM-fallback systems with uncertainty-aware routing and verifier cascades, and propose engineering metrics that reflect real production goals: cost per successful task (CPS), schema validity rate, executable call rate, p50/p95 latency, and energy per request. Guided decoding, strict JSON Schema outputs, and validator-first tool execution close much of the capability gap with larger models and often let SLMs match or surpass LLMs on tool use, function calling, and RAG at 10x-100x lower token cost with materially better latency and energy. We provide design patterns for agent stacks that prioritize SLMs: schema-first prompting, type-safe function registries, confidence scoring with verifier rollups, and lightweight adaptation via LoRA/QLoRA. We also delineate limits where fallback remains valuable (open-domain reasoning and some long-horizon planning). The result is a practical blueprint for building fast, inexpensive, and reliable agents that default to SLMs while preserving headroom with targeted LLM assistance. Keywords: small language models, agents, function calling, structured outputs, JSON Schema, guided decoding, LoRA/QLoRA, routing, energy efficiency, edge inference

cs.AI↗

arXiv2025

Adaptive and Explainable AI Agents for Anomaly Detection in Critical IoT Infrastructure using LLM-Enhanced Contextual Reasoning

Ensuring that critical IoT systems function safely and smoothly depends a lot on finding anomalies quickly. As more complex systems, like smart healthcare, energy grids and industrial automation, appear, it is easier to see the shortcomings of older methods of detection. Monitoring failures usually happen in dynamic, high dimensional situations, especially when data is incomplete, messy or always evolving. Such limits point out the requirement for adaptive, intelligent systems that always improve and think. LLMs are now capable of significantly changing how context is understood and semantic inference is done across all types of data. This proposal suggests using an LLM supported contextual reasoning method along with XAI agents to improve how anomalies are found in significant IoT environments. To discover hidden patterns and notice inconsistencies in data streams, it uses attention methods, avoids dealing with details from every time step and uses memory buffers with meaning. Because no code AI stresses transparency and interpretability, people can check and accept the AI's decisions, helping ensure AI follows company policies. The two architectures are put together in a test that compares the results of the traditional model with those of the suggested LLM enhanced model. Important measures to check are the accuracy of detection, how much inaccurate information is included in the results, how clearly the findings can be read and how fast the system responds under different test situations. The metaheuristic is tested in simulations of real world smart grid and healthcare contexts to check its adaptability and reliability. From the study, we see that the new approach performs much better than most existing models in both accuracy and interpretation, so it could be a good fit for future anomaly detection tasks in IoT

cs.AI↗

arXiv2024

Rovibrational Spectroscopy of Diatomic Molecules in a Modified Morse Potential using Nikiforov-Uvarov Functional Analysis

The radial time-independent Schr\"odinger equation is solved for the diatomic molecules: H2, LiH, HCl, CO, VH, CrH, CuLi, TiC, NiC, and ScN using the recently developed Nikiforov-Uvarov Functional Analysis (NUFA) method. A modified Morse potential is considered and the Pekeris approximation is used to accommodate the centrifugal term. Accurate energy eigenvalues and eigenfunction solutions are obtained for vibrational ($\mathit{n}$) and rotational ($\ell$) states. For H2, LiH, HCl, and CO, excellent agreement is observed between present values and literature, provided that the Pekeris approximation remains valid. For other molecules, a collection of low and high-lying states not found in literature are reported. The NUFA method is a simple, general and accurate approach that may be applied to other interatomic potentials.

physics.chem-ph↗

arXiv2024

Nanoscale spin rectifiers for harvesting ambient radiofrequency energy

Radiofrequency harvesting using ambient wireless energy could be used to reduce the carbon footprint of electronic devices. However, ambient radiofrequency energy is weak (less than -20 dBm), and thermodynamic limits and high-frequency parasitic impedance restrict the performance of state-of-the-art radiofrequency rectifiers. Nanoscale spin rectifiers based on magnetic tunnel junctions have recently demonstrated high sensitivity, but suffer from a low a.c.-to-d.c. conversion efficiency (less than 1%). Here, we report a sensitive spin rectifier rectenna that can harvest ambient radiofrequency signals between -62 and -20 dBm. We also develop an on-chip co-planar waveguide-based spin rectifier array with a large zero-bias sensitivity (around 34,500 mV/mW) and high efficiency (7.81%). Self-parametric excitation driven by voltage-controlled magnetic anisotropy is a key mechanism that contributes to the performance of the spin-rectifier array. We show that these spin rectifiers can wirelessly power a sensor at a radiofrequency power of -27 dBm.

cond-mat.mtrl-sci↗

arXiv2021

Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations

In digital pathology, different staining procedures and scanners cause substantial color variations in whole-slide images (WSIs), especially across different laboratories. These color shifts result in a poor generalization of deep learning-based methods from the training domain to external pathology data. To increase test performance, stain normalization techniques are used to reduce the variance between training and test domain. Alternatively, color augmentation can be applied during training leading to a more robust model without the extra step of color normalization at test time. We propose a novel color augmentation technique, HistAuGAN, that can simulate a wide variety of realistic histology stain colors, thus making neural networks stain-invariant when applied during training. Based on a generative adversarial network (GAN) for image-to-image translation, our model disentangles the content of the image, i.e., the morphological tissue structure, from the stain color attributes. It can be trained on multiple domains and, therefore, learns to cover different stain colors as well as other domain-specific variations introduced in the slide preparation and imaging process. We demonstrate that HistAuGAN outperforms conventional color augmentation techniques on a classification task on the publicly available dataset Camelyon17 and show that it is able to mitigate present batch effects.

eess.IV↗

arXiv2020

Room temperature nonlinear Hall effect and wireless RF rectification in Weyl semimetal TaIrTe4

The nonlinear Hall effect (NLHE), which can produce a transverse voltage without any magnetic field, is a potential alternative for rectification or frequency doubling. However, the low temperature detection of NLHE limits its applications. Here, we report the room-temperature NLHE in a type-II Weyl semimetal TaIrTe4, which hosts a robust NLHE due to substantial broken inversion symmetry and large band overlapping at the Fermi level. We also observe a temperature-induced sign inversion of NLHE in TaIrTe4. Our theoretical calculations suggest that the observed sign inversion is a result of temperature-induced shift in the chemical potential indicating a direct correlation of NLHE with the electronic structure at the Fermi surface. Finally, the room-temperature NLHE in TaIrTe4 is exploited to demonstrate the wireless RF rectification with zero external bias and magnetic field. This work opens a door to realizing room temperature applications based on the NLHE in Weyl semimetals.

cond-mat.mtrl-sci↗

arXiv2017

Time-domain stability of parametric synchronization in a spin-torque nano-oscillator based on a magnetic tunnel junction

We report on a time-domain study of parametric synchronization in a magnetic tunnel junction based spin torque nano-oscillator (STNO). Time-domain measurements of the instantaneous frequency ($f_{i}$) of a parametrically synchronized STNO show random short-term unlocking of the STNO signal for low injected radio-frequency (RF) power, which cannot be revealed in time-averaged frequency domain measurements. Macrospin simulations reproduce the experimental results and reveal that the random unlocking during synchronization is driven by thermal fluctuations. We show that by using a high injected RF power, random unlocking of the STNO can be avoided. However, a perfect synchronization characterized by complete suppression of phase noise, so-called phase noise squeezing, can be obtained only at a significantly higher RF power. Our macrospin simulations suggest that a lower temperature and a higher positive ratio of the field-like torque to the spin transfer torque reduce the threshold RF power required for phase noise squeezing under parametric synchronization.

cond-mat.mes-hall↗