arXiv subjects

Ashutosh Srivastava

Publications and source records attributed to Ashutosh Srivastava.

17 recordsLinked to original sources

arXiv2026

Comparative Assessment of Thermal Transport Theories: Dual-Channel Mechanism Dictates Heat Transport in Ultralow-$κ$ Materials

Anomalous heat transport in strongly anharmonic crystalline solids poses both a fundamental challenge to the theoretical understanding and an opportunity for thermoelectric and thermal barrier coating applications. Although Green-Kubo theory reproduces experimental thermal conductivity ($κ$) at high temperatures, it lacks microscopic insight and neglects the Bose-Einstein statistics of lattice vibrations. On the other hand, the conventional Boltzmann transport equation (BTE) framework, based on a phonon-gas picture, fails due to strong anharmonicity-induced overdamped phonons. Herein, the thermal transport properties in TlAgSe, a metal chalcogenide, and Cs$_2$PbI$_2$C$_2$, an all-inorganic layered Ruddlesden-Popper perovskite, are investigated by explicitly accounting for temperature-dependent lattice dynamics through machine learning interatomic potentials and employing the Wigner transport equation (WTE) framework. Crucially, heat conduction is governed not only by higher-order phonon scattering-dominated populations' transport channel described within the BTE, but also by a coherences' channel in the WTE framework arising from wave-like interbranch coherence between eigenstates. Incorporating four-phonon scattering, WTE predicts average room-temperature $κ$ values of 0.31 Wm$^{-1}$K$^{-1}$ (TlAgSe) and 0.38 Wm$^{-1}$K$^{-1}$ (Cs$_2$PbI$_2$C$_2$), in excellent agreement with experiments. Phonon scattering-rate analysis reveals strong coherences' contributions and prevalent overdamped phonon modes, demonstrating the breakdown of the conventional BTE framework based on the phonon quasiparticle picture with only first-order anharmonic perturbation. This computational approach provides a unified description of heat transport in ultralow-$κ$ materials, offering a basis for the rational design of phononic and thermoelectric devices.

cond-mat.mtrl-sci↗

arXiv2026

On-the-Fly Machine-Learned Force Fields for High-Fidelity Polymer Glass Transition Simulations

Predicting polymer glass transition temperatures (Tg) with first-principles fidelity has long remained out of reach, as cooling multi-thousand-atom systems over a broad temperature range at acceptable rates exceeds the computational limits of ab initio molecular dynamics (AIMD). Here we employ a hybrid scheme that merges AIMD with accelerated on-the-fly (OTF) machine-learned force-field (MLFF) construction, enabling Tg prediction at quantum-mechanical accuracy with near-classical computational cost. The OTF protocol to construct MLFFs adaptively triggers first-principles calculations only when newly encountered configurations lie outside the current model's domain of confidence, allowing robust, parameter-free MLFFs to be built from merely 1000 AIMD-sampled configurations per polymer. These MLFFs are then utilized to perform long-time cooling simulations on amorphous supercells containing several thousand atoms. Applied across twelve polymers spanning aromatic, aliphatic, heteroatomic, and branched chemistries, the method yields predictions in excellent accord with experiment while reducing computational cost by approximately six orders of magnitude relative to AIMD. This work establishes a new paradigm for predictive polymer modeling, demonstrating that OTF-MLFFs provide a generalizable, accurate, and scalable route to simulating the thermophysical behavior of complex disordered materials at near quantum-mechanical fidelity.

cond-mat.mtrl-sci↗

arXiv2026

MEMENTO: Leveraging Web as a Learning Signal for Low-Data Domains

Real-world tasks often lack large labeled datasets, motivating extensive work on learning in low-data regimes. However, existing approaches such as few-shot prompting, instruction tuning, and synthetic data generation, continue to treat labeled or pseudo-labeled data as the primary learning signal. In contrast, human practitioners acquire expertise through repeated, self-directed interaction with the open web, progressively refining both domain knowledge and search strategies. We propose MEMENTO, a framework that treats the web as a learning signal rather than a stateless retrieval interface. MEMENTO operates at two levels: within each session, it conducts iterative web exploration via an Adaptive Exploration Tree (AET) that decomposes tasks into evolving questions and reflects on intermediate findings; across sessions, it accumulates experience through dual-channel memory, separating declarative knowledge (facts) from procedural knowledge (search strategies). This design enables agents to learn reusable research strategies and domain expertise from trajectories of web interaction without additional model training. We evaluate MEMENTO on two low-data professional domains: sales automation and legal research. Our empirical results show consistent improvements in performance over ReAct based baselines (+25.6% on sales automation and 36.5% on legal research), demonstrating that the web can serve as a scalable learning source for acquiring task-specific expertise in data-scarce settings.

cs.AI↗

arXiv2026

Freezing of the tetrahedral amorphous network in supercooled water triggers crystallization towards LDA ice

In this work, we provide mechanistic insight into the initial stages of formation of ice across the limit of stability of supercooled water. Such an analysis is particularly important since crystal nucleation is not a relevant mechanism under these conditions. Using molecular dynamics simulation with the TIP4P/2005 potential, water is cooled at a constant pressure with cooling rates of 5 to 10 K per nanosecond. As the liquid is cooled across the temperature of maximum density (T_0 = 277 K), we find that there is a continuous increase in the tetrahedrality of the system. As the cooling continues across the limit of stability of water (T_s $\approx$ 235 K), large scale thermal fluctuations dissipate while the thermal equilibration is achieved through small scale fluctuations. This phenomenon, known as the dynamical crossover [Goutam et. al. in J. Stat. Phys., 168: 1302--1318 (2017)], ends the existence of the liquid state. Subsequently, we find that the tetrahedral network drives the decrease of energy and density. This process terminates when the network undergoes `freezing' (i.e., the bonds of the network acquire sufficient rigidity), due to which the network evolution, as a whole, stops. This triggers a qualitative change in the relaxation mechanism: subsequent relaxation occurs through crystallization, i.e., an increase in the structural order. In particular, we find that the cubic and hexagonal crystalline motifs, which possess medium range order, increase rapidly across the freezing point. In the resulting LDA ice states, cubic ice is found to have a significant contribution in the overall extent of crystallization, which is consistent with the experimental findings. Overall, our work provides the specific mechanism by which crystallization (leading to LDA ice) is initiated across the limit of stability of supercooled water.

cond-mat.stat-mech↗

arXiv2026

Benchmarking the Personalization Capabilities of Large Language Models

Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradition in psychology and marketing as a two-party problem in which sender and receiver have independent objectives. Large language models remove the bounded-inventory constraint of classical retrieval-and-ranking approaches by generating a continuum of message variants conditioned on inferred receiver state, raising the question of how well current models perform personalization in the classical sense. Existing LLM personalization benchmarks measure sender-side adaptation, in which the receiver is the same user the model is serving. The two-party question, whether a generated message induces its intended action in a third party, has been investigated only through A/B tests and small-scale human studies that cannot be re-run against a new model on demand. We adapt the Bayesian Persuasion framework of Kamenica and Gentzkow (2011) to generative agents and instantiate the formulation in sales, where receiver actions are routinely logged against the outreach that induced them. We release SDR-Bench, a public corpus of 6,279 customer success stories spanning 22 industries and approximately 200 enterprises, served through a temporally constrained simulation that prevents future-data leakage. Across frontier LLMs and deep-research agents, we observe a consistent personalization plateau and on a Fortune 100 tech cohort no model statistically separates successful from unsuccessful outreach. A field deployment with 12 professional sales representatives validates the framework, with 48 percent of model-generated content rated immediately useful and senior-expert agreement at Pearson 0.82. We release SDR-Arena and SDR-Bench publicly to support reproducible study of generative personalization at scale.

cs.AI↗

arXiv2026

Phonon Band Center: A Robust Descriptor to Capture Anharmonicity

Understanding anharmonicity is crucial for designing materials with desired lattice thermal conductivity. Designing a material descriptor that effectively captures anharmonicity while being cost-effective remains a significant challenge. This work proposes a simple metric that helps explain the diversity in lattice thermal conductivity (kl) among materials by quantifying their anharmonic effects. This descriptor "phonon band center" (PBC) encapsulates the critical factors associated with the physics of phonon scattering, revealing a simple inverse relationship with the Gruneisen parameter, the response of phonons with changing volume, and strong correlation with lattice thermal conductivity. This metric has been established using the chalcopyrite class of materials and subsequently validated across various classes of materials using experimental kl. Our approach effectively differentiates materials based on PBC, thereby streamlining the identification of candidates with desirable kl.

cond-mat.mtrl-sci↗

arXiv2025

Anisotropic in-plane lattice thermal conductivity in bilayer ReS2

The significantly weak interlayer coupling strength and puckered structure provide the novel layer-tolerant and anisotropic features in two-dimensional (2D) ReS2. These unique features offer an opportunity to modulate the optoelectronic, vibrational, and transport properties along different lattice directions in ReS2. Here, using first-principles density functional theory (DFT), we investigated the thermal transport properties of ReS2 in AA and AB stacking orders. The anisotopic ratios for lattice thermal conductivities (\k{appa}) are found to be 1.08 and 1.12 for AA and AB stacking, respectively. This anisotropic nature remains intact even at higher temperatures up to 1000K, demonstrating anisotropic robustness. Lower symmetry in AB stacking leads to higher phonon scattering, which results in lower group velocity, smaller phonon lifetime, and thereby lower \k{appa} along both directions as compared to AA stacking. The strong breathing and shear Raman modes in AB stacking indicate stronger layer coupling, further confirming the dominant contribution of acoustic modes towards thermal transport. The findings underscore that the stacking-order-driven preferential heat flow in ReS2 and opens up a new dimension for optimizing device performance.

cond-mat.mtrl-sci↗

arXiv2025

Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarks

Recent claims of strong performance by Large Language Models (LLMs) on causal discovery are undermined by a key flaw: many evaluations rely on benchmarks likely included in pretraining corpora. Thus, apparent success suggests that LLM-only methods, which ignore observational data, outperform classical statistical approaches. We challenge this narrative by asking: Do LLMs truly reason about causal structure, and how can we measure it without memorization concerns? Can they be trusted for real-world scientific discovery? We argue that realizing LLMs' potential for causal analysis requires two shifts: (P.1) developing robust evaluation protocols based on recent scientific studies to guard against dataset leakage, and (P.2) designing hybrid methods that combine LLM-derived knowledge with data-driven statistics. To address P.1, we encourage evaluating discovery methods on novel, real-world scientific studies. We outline a practical recipe for extracting causal graphs from recent publications released after an LLM's training cutoff, ensuring relevance and preventing memorization while capturing both established and novel relations. Compared to benchmarks like BNLearn, where LLMs achieve near-perfect accuracy, they perform far worse on our curated graphs, underscoring the need for statistical grounding. Supporting P.2, we show that using LLM predictions as priors for the classical PC algorithm significantly improves accuracy over both LLM-only and purely statistical methods. We call on the community to adopt science-grounded, leakage-resistant benchmarks and invest in hybrid causal discovery methods suited to real-world inquiry.

cs.LG↗

arXiv2025

Towards Efficient Exemplar Based Image Editing with Multimodal VLMs

Text-to-Image Diffusion models have enabled a wide array of image editing applications. However, capturing all types of edits through text alone can be challenging and cumbersome. The ambiguous nature of certain image edits is better expressed through an exemplar pair, i.e., a pair of images depicting an image before and after an edit respectively. In this work, we tackle exemplar-based image editing -- the task of transferring an edit from an exemplar pair to a content image(s), by leveraging pretrained text-to-image diffusion models and multimodal VLMs. Even though our end-to-end pipeline is optimization-free, our experiments demonstrate that it still outperforms baselines on multiple types of edits while being ~4x faster.

cs.CV↗

arXiv2025

BBR's Sharing Behavior with CUBIC and Reno

TCP BBR's behavior has been explained by various theoretical models, and in particular those that describe how it co-exists with other types of flows. However, as new versions of the BBR protocol have emerged, it remains unclear to what extent the high-level behaviors described by these models apply to the newer versions. In this paper, we systematically evaluate the most influential steady-state and fluid models describing BBR's coexistence with loss-based flows over shared bottleneck links. Our experiments, conducted on a new experimental platform (FABRIC), extend previous evaluations to additional network scenarios, enabling comparisons between the two models and include the recently introduced BBRv3. Our findings confirm that the steady-state model accurately captures BBRv1 behavior, especially against single loss-based flows. The fluid model successfully captures several key behaviors of BBRv1 and BBRv2 but shows limitations, in scenarios involving deep buffers, large numbers of flows, or intra-flow fairness. Importantly, we observe clear discrepancies between existing model predictions and BBRv3 behavior, suggesting the need for an updated or entirely new modeling approach for this latest version. We hope these results validate and strengthen the research community's confidence in these models and identify scenarios where they do not apply.

cs.NI↗

arXiv2025

Robust Root Cause Diagnosis using In-Distribution Interventions

Diagnosing the root cause of an anomaly in a complex interconnected system is a pressing problem in today's cloud services and industrial operations. We propose In-Distribution Interventions (IDI), a novel algorithm that predicts root cause as nodes that meet two criteria: 1) **Anomaly:** root cause nodes should take on anomalous values; 2) **Fix:** had the root cause nodes assumed usual values, the target node would not have been anomalous. Prior methods of assessing the fix condition rely on counterfactuals inferred from a Structural Causal Model (SCM) trained on historical data. But since anomalies are rare and fall outside the training distribution, the fitted SCMs yield unreliable counterfactual estimates. IDI overcomes this by relying on interventional estimates obtained by solely probing the fitted SCM at in-distribution inputs. We present a theoretical analysis comparing and bounding the errors in assessing the fix condition using interventional and counterfactual estimates. We then conduct experiments by systematically varying the SCM's complexity to demonstrate the cases where IDI's interventional approach outperforms the counterfactual approach and vice versa. Experiments on both synthetic and PetShop RCD benchmark datasets demonstrate that \our\ consistently identifies true root causes more accurately and robustly than nine existing state-of-the-art RCD baselines. Code is released at https://github.com/nlokeshiisc/IDI_release.

cs.LG↗

arXiv2024

Enhanced heat dissipation and lowered power consumption in electronics using two-dimensional hexagonal boron nitride coatings

Miniaturization of electronic components has led to overheating, increasing power consumption and causing early circuit failures. Conventional heat dissipation methods are becoming inadequate due to limited surface area and higher short-circuit risks. This study presents a fast, low-cost, and scalable technique using 2D hexagonal boron nitride (hBN) coatings to enhance heat dissipation in commercial electronics. Inexpensive hBN layers, applied by drop casting or spray coating, boost thermal conductivity at IC surfaces from below 0.3 W/m-K to 260 W/m-K, resulting in over double the heat flux and convective heat transfer. This significantly reduces operating temperatures and power consumption, as demonstrated by a 17.4% reduction in a coated audio amplifier circuit board. Density functional theory indicates enhanced interaction between 2D hBN and packaging materials as a key factor. This approach promises substantial energy and cost savings for large-scale electronics without altering existing manufacturing processes.

cond-mat.mtrl-sci↗

arXiv2024

ReEdit: Multimodal Exemplar-Based Image Editing with Diffusion Models

Modern Text-to-Image (T2I) Diffusion models have revolutionized image editing by enabling the generation of high-quality photorealistic images. While the de facto method for performing edits with T2I models is through text instructions, this approach non-trivial due to the complex many-to-many mapping between natural language and images. In this work, we address exemplar-based image editing -- the task of transferring an edit from an exemplar pair to a content image(s). We propose ReEdit, a modular and efficient end-to-end framework that captures edits in both text and image modalities while ensuring the fidelity of the edited image. We validate the effectiveness of ReEdit through extensive comparisons with state-of-the-art baselines and sensitivity analyses of key design choices. Our results demonstrate that ReEdit consistently outperforms contemporary approaches both qualitatively and quantitatively. Additionally, ReEdit boasts high practical applicability, as it does not require any task-specific optimization and is four times faster than the next best baseline.

cs.CV↗

arXiv2024

To Switch or Not to Switch to TCP Prague? Incentives for Adoption in a Partial L4S Deployment

The Low Latency, Low Loss, Scalable Throughput (L4S) architecture has the potential to reduce queuing delay when it is deployed at endpoints and routers throughout the Internet. However, it is not clear how TCP Prague, a prototype scalable congestion control for L4S, behaves when L4S is not yet universally deployed. Specifically, we consider the question: in a partial L4S deployment, will a user benefit by unilaterally switching from the status quo TCP to TCP Prague? To address this question, we evaluate the performance of a TCP Prague flow when sharing an L4S or non-L4S bottleneck queue with a non-L4S flow. Our findings suggest that the L4S congestion control, TCP Prague, has less favorable throughput or fairness properties than TCP Cubic or BBR in some coexistence scenarios, which may hinder adoption.

cs.NI↗

arXiv2023

Can 5G NR Sidelink communications support wireless augmented reality?

Smart glasses that support augmented reality (AR) have the potential to become the consumer's primary medium of connecting to the future internet. For the best quality of user experience, AR glasses must have a small form factor and long battery life, while satisfying the data rate and latency requirements of AR applications. To extend the AR glasses' battery life, the computation and processing involved in AR may be offloaded to a companion device, such as a smartphone, through a wireless connection. Sidelink (SL), i.e., the D2D communication interface of 5G NR, is a potential candidate for this wireless link. In this paper, we use system-level simulations to analyze the feasibility of NR SL for supporting AR. Our simulator incorporates the PHY layer structure and MAC layer resource scheduling of 3GPP SL, standard 3GPP channel models, and MCS configurations. Our results suggest that the current SL standard specifications are insufficient for high-end AR use cases with heavy interaction but can support simpler previews and file transfers. We further propose two enhancements to SL resource allocation, which have the potential to offer significant performance improvements for AR applications.

cs.NI↗

arXiv2023

Probing angle dependent thermal conductivity in twisted bilayer MoSe2

Twisted bilayer (t-BL) transition metal dichalcogenides (TMDCs) attracted considerable attention in recent years due to their distinctive electronic properties, which arise due to the moire superlattices that lead to the emergence of flat bands and correlated electron phenomena. Also, these materials can exhibit interesting thermal properties, including a reduction in thermal conductivity. In this article, we report the thermal conductivity of monolayer (1L) and t-BL MoSe2 at some specific twist angles around two symmetric stacking AB (0 degree) and AB' (60 degree) and one intermediate angle 31 (degree) using the optothermal Raman technique. The observed thermal conductivity values are found to be 13, 23, and 30 W m-1K-1 for twist angle = 58 (degree), 31 (degree) and, 3 (degree) respectively, which is well supported by our first-principles calculation results. The reduction in thermal conductivity in t-BL MoSe2 compared to monolayer (38 W m-1K-1) can be explained by the occurrence of phonon scattering caused by the formation of a moire super-lattice. Herein, the emergence of multiple folded phonon branches and modification in the Brillouin zone caused by in-plane rotation are also accountable for the decrease in thermal conductivity observed in t-BL MoSe2. The theoretical phonon lifetime study and electron localization function (ELF) analysis further reveals the origin of angle-dependent thermal conductivity in t-BL MoSe2. This work paves the way towards tuning the angle-dependent thermal conductivity for any bilayer TMDCs system.

cond-mat.mtrl-sci↗

arXiv2019

Tethered Balloon Technology for Green Communication in Smart Cities and Healthy Environment

The development and adopting of advanced communication technologies provide mobile users more convenience to connect any wireless network anytime and anywhere. Therefore, a large number of base stations (BS) are demanded keeping users connectivity, enhancing network capacity, and guarantee a sustained users Quality of Experiences (QoS). However, increasing the number of BS leads to an increase in the ecological ad radiation hazards. In order to green communication, many factors should be taken into consideration, i.e., saving energy, guarantee QoS, and reducing pollution hazards. Therefore, we propose tethered balloon technology that can replace a large number of BS and reduce ecological and radiation hazards due to its high altitude and feasible green and healthy broadband communication. The main contribution of this paper is to deploy tethered balloon technology at different altitude and measure the power density. Furthermore, we evaluate the measurement of power density from different height of tethered balloon comparison with traditional wireless communication technologies. The simulation results showed that tethered balloon technology can deliver green communication effectively and efficiently without any hazardous impacts.

eess.SP↗