arXiv subjects

Sayan Ghosh

Publications and source records attributed to Sayan Ghosh.

At least 19 recordsLinked to original sources

arXiv2026

Dispersive-phonon-driven room-temperature Ni1+-Ni2+ polaron hopping in spin-charge coupled rutile niobate

Understanding how lattice dynamics mediate polaron hopping is essential for designing multifunctional correlated oxides. Here, we demonstrate room-temperature dispersive phonon excitations and elucidate the Ni1+-Ni2+ polaron-hopping mechanism and the presence of rare spin-charge-phonon coupling even in a magnetically short-range-ordered state in rutile niobate, a rare room-temperature magnetodielectric system. We reveal room-temperature dispersive phonon excitations using inelastic neutron scattering (INS), complemented by machine-learning-based phonon calculations, to establish the microscopic origin of the polaron-hopping mechanism. Experimental evidence of dispersive phonon-driven polaron hopping is scarce. INS measurements show significant dispersive phonon excitations at 21, 33, and 47 meV, implying collective lattice dynamics that enable delocalized polaron propagation via coupled charge-spin-phonon interactions. Dispersive phonons couple to charge carriers and promote correlated NiO6 lattice distortions, facilitating delocalized polaron hopping. Low-energy magnetic excitations at 4 and 8 meV indicate the presence of local short-range magnetic correlations or spin-orbit-coupling-induced anisotropy in deformed NiO6 octahedra, which are thoroughly discussed. Machine-learning phonon calculations replicate the experimentally observed phonon excitations and demonstrate lattice instability, which is compatible with dynamic local distortions caused by polaron production. These findings provide microscopic evidence for a coupled charge-spin-phonon mechanism that mediates polaron hopping in rutile oxide systems.

cond-mat.mtrl-sci↗

arXiv2026

Interplay of Spin Waves, Crystal-Field Excitations, and Phonons in Multiferroic Ba3HoRu2O9 revealed by Inelastic Neutron Scattering, Crystal-Field Analysis, and Machine-Learned Phonon Calculations

Understanding the microscopic origin of spin-dipole coupling and high-energy excitations in correlated 4d-4f multiferroic oxides is challenging because magnetic, crystal-field, and lattice excitations frequently overlap in energy. The hexagonal 6H perovskite Ba3HoRu2O9 provides an ideal platform to investigate this interplay owing to the coexistence of Ru2O9 molecular units and localized Ho3+ moments. To identify the contributions from these different excitations, we combine inelastic neutron scattering (INS) with linear spin-wave calculations, crystal-field analysis, Raman spectroscopy, and machine-learned force field (MLFF) phonon calculations. A dispersive magnetic excitation below 6.2 meV is accurately reproduced by linear spin-wave theory, establishing its origin as a collective spin-wave excitation of the coupled Ru-Ho magnetic network. At higher energies, broad excitations centered near 20, 39, 70, and 90 meV is observed that are present far above magnetic ordering temperature. Crystal-field calculations based on the Stevens formalism place the strongest Ho3+ transitions within the experimentally observed energy window, while Raman spectroscopy and MLFF phonon calculations identify optical phonons with comparable energies. Together, these complementary results show that the broad INS feature near 39 meV is consistent with overlapping contributions from Ho3+ crystal-field excitations, lattice vibrations, and previously reported Ru2O9 molecular magnetic excitations. These findings establish a microscopic framework for understanding the interplay between spin, crystal-field, and lattice degrees of freedom in this multiferroic 4d-4f compound.

cond-mat.str-el↗

arXiv2026

State Localization and Selective Charge Filtering Near a Null Point

Null points in synthetically tunable molecular aggregates are predicted to generate flat energy bands analogous to those known in strongly correlated condensed-matter physics. For chemistry, null points provide a powerful design principle for photovoltaic materials with selective charge filtering similar to photosynthesis. However, null points have never been experimentally verified because their defining prediction - state localization with selective electron or hole transfer - has remained unobserved. Here, using a donor-acceptor dyad as a minimal model, we provide the first experimental observation of a null point. Impulsive pump-probe measurements reveal charge separation through a near-instantaneously generated locally excited-charge transfer (LE-CT) intermediate that emerges upon solvent stabilization of CT states. Polarization anisotropy directly reveals state localization and selective charge-filtering, spanning balanced electron-hole transfer to selective hole filtering consistent with synthetic design. A generalized vibronic theory of null points explains these observations and identifies the ideal synthetic parameters for achieving null points which are protected from the vibrational bath.

physics.chem-ph↗

arXiv2026

Prominent Signatures of Energy Transfer in Action-Detected Spectra of a Cyanobacterial Photosynthetic Protein

Action-detected two-dimensional electronic spectroscopy (A-2DES) could potentially be a versatile chemical tool with applicability across a range of photophysical observables such as photocurrent, photoionization, or fluorescence. However, a prominent absence of excited state energy/charge transfer dynamics signals in archetypal photosynthetic proteins has suggested severe limitations of A-2DES in probing large aggregates where sensitivity to excited state dynamics is proposed to go down as 1/N, where N is the aggregate size. We report measurements of energy transfer dynamics in a cyanobacterial protein through both conventional and fluorescence 2DES (F-2DES), where the dynamics reported by F-2DES is quite prominent and comparable to that measured by conventional 2DES. Analysis of our experiments combined with coarse-grained simulations of the spectra suggest that the 1/N limit argument, which assumes infinitely fast intra-exciton manifold equilibration, is modified in case of cyanobacterial proteins because of slow annihilation. Our results suggest that action detection may in fact be well-suited to probe exciton diffusion across weakly coupled systems.

physics.chem-ph↗

arXiv2026

Non-Vanishing of Cubic Twists of $GL_n(\mathbb{Q})$ $L$-functions

Let $\pi$ be an irreducible, cuspidal automorphic representation of $GL_n(\mathbb{A}_\mathbb{Q})$ ($n\geq 3$), which is tempered only for $n=3$. Let $s$ be a complex number such that $\Re(s)\notin \left[1/n, 1-1/n\right]$ if $n\neq 4$; $\Re(s)\notin\left[1/5, 4/5\right]$ if $n=4$, then we show that there are infinitely many primitive cubic Dirichlet characters $\chi$ such that $L(s,\pi\times \chi)\neq 0$. Similar results were previously known only for primitive Dirichlet characters without any restriction on the order and quadratic Dirichlet characters.

math.NT↗

arXiv2026

ContextWeaver: Selective and Dependency-Structured Memory Construction for LLM Agents

Large language model (LLM) agents often struggle in long-context interactions. As the agent accumulates more interaction history, context management approaches such as sliding window and prompt compression may omit earlier structured information that later steps rely on. Recent retrieval-based memory systems surface relevant content but still overlook the causal and logical structure needed for multi-step reasoning. We introduce ContextWeaver, a selective and dependency-structured memory framework that organizes an agent's interaction trace into a graph of reasoning steps and selects the relevant context for future actions. Unlike prior context management approaches, ContextWeaver supports: (1) dependency-based construction and traversal that link each step to the earlier steps it relies on; (2) compact dependency summarization that condenses root-to-step reasoning paths into reusable units; and (3) a lightweight validation layer that incorporates execution feedback. On the SWE-Bench Verified and Lite benchmarks, ContextWeaver improves performance over a sliding-window baseline in pass@1, while reducing reasoning steps and token usage. Our observations suggest that modeling logical dependencies provides a stable and scalable memory mechanism for LLM agents that use tools.

cs.CL↗

arXiv2026

NOMAD: Generating Embeddings for Massive Distributed Graphs

Successful machine learning on graphs or networks requires embeddings that not only represent nodes and edges as low-dimensional vectors but also preserve the graph structure. Established methods for generating embeddings require flexible exploration of the entire graph through repeated use of random walks that capture graph structure with samples of nodes and edges. These methods create scalability challenges for massive graphs with millions-to-billions of edges because single-node solutions have inadequate memory and processing capabilities. We present NOMAD, a distributed-memory graph embedding framework using the Message Passing Interface (MPI) for distributed graphs. NOMAD implements proximity-based models proposed in the widely popular LINE (Large-scale Information Network Embedding) algorithm. We propose several practical trade-offs to improve the scalability and communication overheads confronted by irregular and distributed graph embedding methods, catering to massive-scale graphs arising in web and science domains. NOMAD demonstrates median speedups of 10/100x on CPU-based NERSC Perlmutter cluster relative to the popular reference implementations of multi-threaded LINE and node2vec, 35-76x over distributed PBG, and competitive embedding quality relative to LINE, node2vec, and GraphVite, while yielding 12-370x end-to-end speedups on real-world graphs.

cs.LG↗

arXiv2026

Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training

Graph neural networks (GNNs) are widely used for learning on graph datasets derived from various real-world scenarios. Learning from extremely large graphs requires distributed training, and mini-batching with sampling is a popular approach for parallelizing GNN training. Existing distributed mini-batch approaches have significant performance bottlenecks due to expensive sampling methods and limited scaling when using data parallelism. In this work, we present ScaleGNN, a 4D parallel framework for scalable mini-batch GNN training that combines communication-free distributed sampling, 3D parallel matrix multiplication (PMM), and data parallelism. ScaleGNN introduces a uniform vertex sampling algorithm, enabling each process (GPU device) to construct its local mini-batch, i.e., subgraph partitions without any inter-process communication. 3D PMM enables scaling mini-batch training to much larger GPU counts than vanilla data parallelism with significantly lower communication overheads. We also present additional optimizations to overlap sampling with training, reduce communication overhead by sending data in lower precision, kernel fusion, and communication-computation overlap. We evaluate ScaleGNN on five graph datasets and demonstrate strong scaling up to 2048 GPUs on Perlmutter, 2048 GCDs on Frontier, and 1024 GPUs on Tuolumne. On Perlmutter, ScaleGNN achieves 3.5x end-to-end training speedup over the SOTA baseline on ogbn-products.

cs.LG↗

arXiv2026

Rudder: Steering Prefetching in Distributed GNN Training using LLM Agents

Large-scale Graph Neural Networks (GNNs) are typically trained by sampling a vertex's neighbors to a fixed distance. Because large input graphs are distributed, training requires frequent irregular communication that stalls forward progress. Moreover, fetched data changes with graph, graph distribution, sample and batch parameters, and caching polices. Consequently, any static prefetching method will miss crucial opportunities to adapt to different dynamic conditions. In this paper, we introduce Rudder, a software module embedded in the state-of-the-art AWS DistDGL framework, to autonomously prefetch remote nodes and minimize communication. Rudder's adaptation contrasts with both standard heuristics and traditional ML classifiers. We observe that the generative AI found in contemporary Large Language Models (LLMs) exhibits emergent properties like In-Context Learning (ICL) for zero-shot tasks, with logical multi-step reasoning. We find this behavior well-suited for adaptive control even with substantial undertraining. Evaluations using standard datasets and unseen configurations on the NERSC Perlmutter supercomputer show up to 91% improvement in end-to-end training performance over baseline DistDGL (no prefetching), and an 82% improvement over static prefetching, reducing communication by over 50%. Our code is available at https://github.com/aishwaryyasarkar/rudder-llm-agent.

cs.LG↗

arXiv2026

Spin-Chain Incipient Magnetocaloric Effect and Rare-Earth Controlled Switching in the Haldane-Chain System, R2BaNiO5

We have experimentally investigated the magnetocaloric effect (MCE) of a prototype spin-frustrated one-dimensional spin-chain system, the famous Haldane-chain system, R2BaNiO5 (R = Nd, Gd, Er, Dy). The significant MCE is observed far above long-range ordering, even in the paramagnetic region, which is attributed to the change in magnetic entropy due to short-range spin correlation arising from (low-dimensional) magnetic frustration. Such a spin-chain incipient MCE above long-range ordering is rarely reported. Interestingly, multiple magnetocaloric switching from conventional to inverse MCE (and vice versa) are observed below long-range magnetic ordering, as a function of temperature and magnetic field, for the R = Nd, Dy, and Er members. However, such MCE switching is absent in the Gd member, which is an S-state atom (orbital moment L = 0). Our systematic investigation of this series demonstrates that the interplay between crystal-electric field (CEF), strong spin-orbit coupling (SOC) and rare earth anisotropy of R-ions play an important role in spin reorientation, leading to multiple MCE switching due to intriguing changes in magnetic and lattice entropy. The maximum change of entropy for Er, Gd, Dy and Nd is 7.8, 6.8, 4.0 and 1.0 J Kg-1 K-1 respectively. Our study presents a pathway for tuning MCE switching and the MCE effect over large temperature regions in d-f coupled spin-frustrated and spin-chain oxide systems.

cond-mat.str-el↗

arXiv2026

BladeSDF : Unconditional and Conditional Generative Modeling of Representative Blade Geometries Using Signed Distance Functions

Generative AI has emerged as a transformative paradigm in engineering design, enabling automated synthesis and reconstruction of complex 3D geometries while preserving feasibility and performance relevance. This paper introduces a domain-specific implicit generative framework for turbine blade geometry using DeepSDF, addressing critical gaps in performance-aware modeling and manufacturable design generation. The proposed method leverages a continuous signed distance function (SDF) representation to reconstruct and generate smooth, watertight geometries with quantified accuracy. It establishes an interpretable, near-Gaussian latent space that aligns with blade-relevant parameters, such as taper and chord ratios, enabling controlled exploration and unconditional synthesis through interpolation and Gaussian sampling. In addition, a compact neural network maps engineering descriptors, such as maximum directional strains, to latent codes, facilitating the generation of performance-informed geometry. The framework achieves high reconstruction fidelity, with surface distance errors concentrated within $1\%$ of the maximum blade dimension, and demonstrates robust generalization to unseen designs. By integrating constraints, objectives, and performance metrics, this approach advances beyond traditional 2D-guided or unconstrained 3D pipelines, offering a practical and interpretable solution for data-driven turbine blade modeling and concept generation.

cs.LG↗

arXiv2026

Spin-correlation Driven Ferroelectric Quantum Criticality in a Perovskite Quantum Spin-liquid System, Ba3CuSb2O9

Here we have experimentally demonstrated spin-correlation-driven ferroelectric quantum criticality in a prototype quantum spin-liquid system, Ba3CuSb2O9, a quantum phenomenon rarely observed. The dielectric constant follows a clear T2 scaling, showing that the material behaves as a quantum paraelectric without developing ferroelectric order. Magnetically, the system avoids long-range order down to 1.8 K and instead displays a T3/2 dependence in its inverse susceptibility, a hallmark of antiferromagnetic quantum critical fluctuations. Together with known spin-orbital-lattice entanglement in this compound, these signatures point to a strong interplay between spin dynamics and the polar lattice. Our pioneering work places this perovskite spin-liquid family at the forefront of this domain and suggest the flexibility of this family in a suitable environment by tuning chemical/ external pressure.

cond-mat.str-el↗

arXiv2025

Intriguing Magnetocaloric Effect in Multiferroic Ba3RRu2O9 (R=Ho, Gd, Tb, Nd) with Strong 4d-4f Correlations

Here we demonstrate the magnetocaloric effect (MCE) of a 4d-4f correlated system, namely Ba3RRu2O9 (R= Ho, Gd, Tb, Nd). The compound Ba3HoRu2O9 antiferromagnetically orders at 50 K where both the Ho and Ru-moments order, followed by another phase transition ~ 10 K. Whereas, the compound Ba3GdRu2O9 and Ba3TbRu2O9 orders at 14.5 and 10.5 K respectively, where the ordering of both R and Ru moments are speculated. Our results reveal robust MCE around low-T magnetic phase transition for all the heavy rare-earth members (Ho, Gd, Tb) in this family. The heavy rare-earth members exhibit an intriguing MCE behavior switching from conventional to non-conventional MCE. Interestingly, the light R-member, Ba3NdRu2O9, orders ferromagnetically below 24 K where Nd-moments order, followed by Ru-ordering below 18 K, exhibits a positive MCE below and above FM-ordering. The compelling MCE are attributed to temperature dependent complex spin-reorientations for different R-members and anisotropy.

cond-mat.str-el↗

arXiv2025

Quasiparticle Dynamics in the 4d-4f Ising-like Double Perovskite Ba2DyRuO6 studied using Neutron Scattering and Machine-Learning Framework

Double perovskites containing 4d--4f interactions provide a platform to study complex magnetic phenomena in correlated systems. Here, we investigate the magnetic ground state and quasiparticle excitations of the fascinating double perovskite system, Ba$_2$DyRuO$_6$, through Time of flight (TOF) neutron diffraction (TOF), inelastic neutron scattering (INS), and theoretical modelling. The compound Ba$_2$DyRuO$_6$ is reported to exhibit a single magnetic transition, in sharp contrast to most of the other rare-earth (R) members in this family, A$_2$RRuO$_6$ (A = Ca/Sr/Ba), which typically show magnetic ordering of the Ru ions, followed by R-ion ordering. Our neutron diffraction results confirm that long-range antiferromagnetic order emerges at $T_\mathrm{N} \approx 47$~K, primarily driven by 4d--4f Ru$^{5+}$--Dy$^{3+}$ exchange interactions, where both Dy and Ru moments start to order simultaneously. The ordered ground state is a collinear antiferromagnet with Ising character, carrying ordered moments of $\mu_{\mathrm{Ru}} = 1.6(1)~\mu_\mathrm{B}$ and $\mu_{\mathrm{Dy}} = 5.1(1)~\mu_\mathrm{B}$ at 1.5~K. Low-temperature INS reveals well-defined magnon excitations below 10~meV. SpinW modelling of the INS spectra evidences complex exchange interactions and the presence of magnetic anisotropy, which governs the Ising ground state and accounts for the observed magnon spectrum. Combined INS and Raman spectroscopy reveal crystal-electric-field (CEF) excitations of Dy$^{3+}$ at 46.5 and 71.8~meV in the paramagnetic region. The observed CEF levels are reproduced by point-charge calculations consistent with the $O_h$ symmetry of Dy$^{3+}$. A complementary machine-learning approach is used to analyse the phonon spectrum and compare with INS data. Together, these results clarify the origin of phonon and magnon excitations and their role in the ground-state magnetism of Ba$_2$DyRuO$_6$.

cond-mat.str-el↗

arXiv2025

Basis Adaptive Algorithm for Quantum Many-Body Systems on Quantum Computers

We introduce a Basis Adaptive (BA) algorithm for hybrid quantum-classical simulation of correlated quantum many-body systems. Starting from a small set of physically motivated bitstrings, the algorithm iteratively applies a single-step first-order Trotterized circuit on a quantum processor, filters the sampled configurations by enforcing $U(1)$ spin conservation and lattice reflection symmetry, and classically diagonalizes the Hamiltonian in the resulting reduced Hilbert space. This design avoids the variational optimization overhead of VQE, the deep coherent circuits required by QPE, and the symmetry-violating subspaces that arise in SKQD. The ground-state energy error is bounded analytically by $\sqrt{8}\,\|H\|\left(1-\sqrt{\alpha_{D_T}}\right)^{1/2}$, where $\alpha_{D_T}$ is the probability weight captured by the $D_T$ sampled basis states. This bound connects algorithm performance directly to ground-state sparsity and explains the observed accuracy hierarchy across different phases. Benchmarked on the spin-$1/2$ Heisenberg XXZ chain (up to $N=62$ qubits on the IBM Heron processor), the algorithm achieves a $3.5\%$ energy error in the gapped Neel phase ($\Delta=2.0$) and below $0.5\%$ at the ferromagnetic boundary ($\Delta=-1.0$). The accuracy degrades to $28.7\%$ in the strongly quasi-long-range-ordered regime ($\Delta=0.5$). Spin-spin correlation functions are reproduced across all regimes, confirming that symmetry-filtered real-time sampling provides a practical and noise-resilient pathway to ground-state properties on near-term quantum hardware.

cond-mat.str-el↗

arXiv2025

MixLM: High-Throughput and Effective LLM Ranking via Text-Embedding Mix-Interaction

Large language models (LLMs) excel at capturing semantic nuances and therefore show impressive relevance ranking performance in modern recommendation and search systems. However, they suffer from high computational overhead under industrial latency and throughput requirements. In particular, cross-encoder ranking systems often create long context prefill-heavy workloads, as the model has to be presented with the user, query and item information. To this end, we propose MixLM, a novel LLM-based ranking framework, which significantly improves the system throughput via reducing the input context length, while preserving the semantic strength of cross-encoder rankers. In contrast to a standard ranking system where the context is presented to the model as pure text, we propose to use mix-interaction, a mixture of text and embedding tokens to represent the input. Specifically, MixLM encodes all items in the catalog into a few embedding tokens and stores in a nearline cache. The encoded item descriptions are used during online inference, effectively reducing the item length from a few thousand text tokens to a few embedding tokens. We share insights from deploying our MixLM framework to a real-world search application at LinkedIn, including a detailed discussion of our training pipelines, as well as a thorough analysis of our online serving infrastructure optimization. With the same latency budget and on-par relevance metrics, MixLM increased throughput by 10.0x comparing with strong baselines, 75.9x over full-text LLM rerankers. The efficiency gains delivered by MixLM enabled full-traffic deployment of LLM-powered search, which resulted in a significant 0.47\% increase in Daily Active Users (DAU) in online A/B tests.

cs.IR↗

arXiv2025

Martingale Characterizations of Non-Homogeneous Counting Processes and Their Fractional Variants

This paper investigates the martingale characterizations of non-homogeneous counting processes and their fractional generalizations. We show that the weighted sum of non-homogeneous Poisson processes (NPPs) is the non-homogeneous generalized counting process (NGCP). Both the compensated and exponential forms of martingale characterization for NGCP are obtained, and are shown to be equivalent. Moreover, we provide martingale characterizations for various time-changed variants of the NGCP and their Skellam versions using stable and/or inverse stable subordinators.

math.PR↗

arXiv2025

Scaling Up Efficient Small Language Models Serving and Deployment for Semantic Job Search

Large Language Models (LLMs) have demonstrated impressive quality when applied to predictive tasks such as relevance ranking and semantic search. However, deployment of such LLMs remains prohibitively expensive for industry applications with strict latency and throughput requirements. In this work, we present lessons and efficiency insights from developing a purely text-based decoder-only Small Language Model (SLM) for a semantic search application at LinkedIn. Particularly, we discuss model compression techniques such as pruning that allow us to reduce the model size by up to $40\%$ while maintaining the accuracy. Additionally, we present context compression techniques that allow us to reduce the input context length by up to $10$x with minimal loss of accuracy. Finally, we present practical lessons from optimizing the serving infrastructure for deploying such a system on GPUs at scale, serving millions of requests per second. Taken together, this allows us to increase our system's throughput by $10$x in a real-world deployment, while meeting our quality bar.

cs.IR↗