arXiv subjects

Rahul Ghosh

Publications and source records attributed to Rahul Ghosh.

At least 19 recordsLinked to original sources

arXiv2026

PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling

Accurate modeling of environmental systems is fundamental to scientific understanding and decision-making, yet remains challenging because observations are limited and physical dynamics vary across systems. Retrieval-augmented approaches offer a natural path to transfer knowledge across systems, but standard embedding-based retrieval does not guarantee consistency of underlying physical processes, since scenarios with similar embeddings may arise from different underlying mechanisms. We propose Physics-Informed Environmental Retrieval (PIER), a model-agnostic framework that augments embedding-based retrieval with a physics-aware stream that scores candidates by flux-response consistency with the target, using local verifiers trained on physics-derived flux features. A weight adjustment mechanism then learns per-scenario weights that adaptively balance the two retrieval streams based on diagnostic features summarizing physics-stream reliability. Experiments on 356 lakes across the Midwestern United States spanning 41 years show that PIER consistently outperforms baselines for water temperature and dissolved oxygen prediction, and serves as a general augmentation strategy across diverse backbones.

cs.LG↗

arXiv2026

Correlation-Induced Topological Reconstruction in a Periodically Driven Kagome Mott Insulator

We study the interplay between electronic correlations and circularly polarized periodic driving in the Hubbard model on a Kagome lattice. Using Brillouin-Wigner perturbation theory, we derive an effective Floquet Hamiltonian in which the drive renormalizes the bare hopping and generates chiral nearest- and next-nearest-neighbor terms, producing repeated topological transitions and a flat band whose position is continuously tunable across the spectrum. Within slave-rotor mean-field theory, we show that the resulting Mott transition is strongly non-monotonic in the drive amplitude, yielding repeated metal-insulator transitions, and that the spinon excitations inside the Mott phase acquire a band topology distinct from that of the non-interacting Floquet bands. This correlation-driven topological reconstruction produces emergent flat-band spinon insulators inaccessible to either driving or interactions alone. Our results establish periodically driven Kagome systems as a platform for engineering correlated topological flat-band physics out of equilibrium, with proposed realizations in ultracold atomic lattices.

cond-mat.str-el↗

arXiv2026

Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization

As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG and Agentic RAG. We provide implementation for 9 standardized RAG scenarios, and conduct experiments for a comprehensive comparison. These scenarios are designed for real use cases regarding data and domain restrictions, spanning from simple document-based retrieval to advanced features such as hybrid text-graph retrieval, integration with computed or pre-defined domain knowledge graphs, agentic multi-step planning, and agent-graph integration. Besides, we present a novel context engineering method for GraphRAG and Agentic RAG, addressing the context/memory overflow issues, efficiently managing text and graph retrievals with new representations and agentic loop design, leading to 19%-53% reduction on token usage. Moreover, further analysis identifies a retrieval-generation gap where expanded retrieval does not proportionally improve generation quality, suggesting retrieval-oriented metrics overstate advanced retrieval benefits. This work provides data-driven insights on when and how to use them for building production-ready intelligent RAG systems.

cs.CL↗

arXiv2026

Quantifying Consistency in LLM Logical Reasoning via Structural Uncertainty

Large language models can arrive at the same answer through reasoning paths that are unstable, contradictory, or difficult to rank consistently -- a failure mode especially prevalent in multi-step deductive reasoning. Existing methods assess reliability primarily through output dispersion -- measuring how much sampled answers differ -- but this discards a complementary signal: whether the model can consistently rank competing reasoning candidates. We propose structural uncertainty, a consistency-aware framework derived from the stability of self-preference-induced rankings over sampled reasoning solutions. Given a query, we generate multiple candidate solutions and ask the model to judge pairwise preferences among its own outputs. We aggregate self-preferences into ranking distributions via Bradley-Terry modeling with PageRank, and decompose the signal into two entropy-based components: across-trial ranking instability and within-trial candidate ambiguity. Across five LLMs and eight benchmarks, structural signals provide information complementary to answer dispersion: on logical and mathematical reasoning tasks, the combination improves identification of unreliable instances, while on factual retrieval the structural signal collapses toward uniformity, diagnosing a regime boundary where reasoning-level consistency evaluation is uninformative. The two components relate differently to accuracy: within-trial ambiguity correlates positively with correctness -- consistent with settings where multiple plausible solution paths remain competitive -- while across-trial instability correlates negatively, signaling unreliable reasoning. Structural uncertainty is best understood not as a universal confidence estimator, but as a regime-sensitive evaluator of logical reasoning consistency.

cs.AI↗

arXiv2026

Learning Flow Distributions via Projection-Constrained Diffusion on Manifolds

We present a generative modeling framework for synthesizing physically feasible two-dimensional incompressible flows under arbitrary obstacle geometries and boundary conditions. Whereas existing diffusion-based flow generators either ignore physical constraints, impose soft penalties that do not guarantee feasibility, or specialize to fixed geometries, our approach integrates three complementary components: (1) a boundary-conditioned diffusion model operating on velocity fields; (2) a physics-informed training objective incorporating a divergence penalty; and (3) a projection-constrained reverse diffusion process that enforces exact incompressibility through a geometry-aware Helmholtz-Hodge operator. We derive the method as a discrete approximation to constrained Langevin sampling on the manifold of divergence-free vector fields, providing a connection between modern diffusion models and geometric constraint enforcement in incompressible flow spaces. Experiments on analytic Navier-Stokes data and obstacle-bounded flow configurations demonstrate significantly improved divergence, spectral accuracy, vorticity statistics, and boundary consistency relative to unconstrained, projection-only, and penalty-only baselines. Our formulation unifies soft and hard physical structure within diffusion models and provides a foundation for generative modeling of incompressible fields in robotics, graphics, and scientific computing.

physics.flu-dyn↗

arXiv2025

TelcoAI: Advancing 3GPP Technical Specification Search through Agentic Multi-Modal Retrieval-Augmented Generation

The 3rd Generation Partnership Project (3GPP) produces complex technical specifications essential to global telecommunications, yet their hierarchical structure, dense formatting, and multi-modal content make them difficult to process. While Large Language Models (LLMs) show promise, existing approaches fall short in handling complex queries, visual information, and document interdependencies. We present TelcoAI, an agentic, multi-modal Retrieval-Augmented Generation (RAG) system tailored for 3GPP documentation. TelcoAI introduces section-aware chunking, structured query planning, metadata-guided retrieval, and multi-modal fusion of text and diagrams. Evaluated on multiple benchmarks-including expert-curated queries-our system achieves $87\%$ recall, $83\%$ claim recall, and $92\%$ faithfulness, representing a $16\%$ improvement over state-of-the-art baselines. These results demonstrate the effectiveness of agentic and multi-modal reasoning in technical document understanding, advancing practical solutions for real-world telecommunications research and engineering.

cs.LG↗

arXiv2025

Exactly solvable Stuart-Landau models in arbitrary dimensions

We use Clifford's geometric algebra to extend the Stuart-Landau system to dimensions $D >2$ and give an exact solution of the oscillator equations in the general case. At the supercritical Hopf bifurcation marked by a transition from stable fixed-point dynamics to oscillatory motion, the Jacobian matrix evaluated at the fixed point has $N=\lfloor{D/2}\rfloor$ pairs of complex conjugate eigenvalues which cross the imaginary axis simultaneously. For odd $D$ there is an additional purely real eigenvalue that does the same. Oscillatory dynamics is asymptotically confined to a hypersphere $\mathbb{S}^{D-1}$ and is characterised by extreme multistability, namely the coexistence of an infinite number of limiting orbits each of which has the geometry of a torus $\mathbb{T}^N$ on which the motion is either periodic or quasiperiodic. We also comment on similar Clifford extensions of other limit cycle oscillator systems and their generalisations.

nlin.CD↗

arXiv2025

CompAgent: An Agentic Framework for Visual Compliance Verification

Visual compliance verification is a critical yet underexplored problem in computer vision, especially in domains such as media, entertainment, and advertising where content must adhere to complex and evolving policy rules. Existing methods often rely on task-specific deep learning models trained on manually labeled datasets, which are costly to build and limited in generalizability. While recent Multimodal Large Language Models (MLLMs) offer broad real-world knowledge and policy understanding, they struggle to reason over fine-grained visual details and apply structured compliance rules effectively on their own. In this paper, we propose CompAgent, the first agentic framework for visual compliance verification. CompAgent augments MLLMs with a suite of visual tools-such as object detectors, face analyzers, NSFW detectors, and captioning models-and introduces a planning agent that dynamically selects appropriate tools based on the compliance policy. A compliance verification agent then integrates image, tool outputs, and policy context to perform multimodal reasoning. Experiments on public benchmarks show that CompAgent outperforms specialized classifiers, direct MLLM prompting, and curated routing baselines, achieving up to 76% F1 score and a 10% improvement over the state-of-the-art on the UnsafeBench dataset. Our results demonstrate the effectiveness of agentic planning and robust tool-augmented reasoning for scalable, accurate, and adaptable visual compliance verification.

cs.CV↗

arXiv2025

Quantum sensing with discrete time crystals in the Lipkin-Meshkov-Glick Model

Quantum phase transitions have been shown to be highly beneficial for quantum sensing, owing to diverging quantum Fisher information close to criticality. In this work we consider a periodically modulated Lipkin-Meshkov-Glick model to show that discrete time crystal (DTC) phase transition in this setup can enable us to achieve quantum-enhanced high-precision sensing of field strength. We employ a detailed finite-size scaling analysis, a time-averaged Inverse Participation Ratio analysis, and mean-field analysis in the thermodynamic limit, to determine the critical properties of this second-order phase transition. Our studies provide a comprehensive understanding of how quantum criticality in DTCs involving long-range interactions can be harnessed for advanced quantum sensing applications.

quant-ph↗

arXiv2025

Learning to Retrieve for Environmental Knowledge Discovery: An Augmentation-Adaptive Self-Supervised Learning Framework

The discovery of environmental knowledge depends on labeled task-specific data, but is often constrained by the high cost of data collection. Existing machine learning approaches usually struggle to generalize in data-sparse or atypical conditions. To this end, we propose an Augmentation-Adaptive Self-Supervised Learning (A$^2$SL) framework, which retrieves relevant observational samples to enhance modeling of the target ecosystem. Specifically, we introduce a multi-level pairwise learning loss to train a scenario encoder that captures varying degrees of similarity among scenarios. These learned similarities drive a retrieval mechanism that supplements a target scenario with relevant data from different locations or time periods. Furthermore, to better handle variable scenarios, particularly under atypical or extreme conditions where traditional models struggle, we design an augmentation-adaptive mechanism that selectively enhances these scenarios through targeted data augmentation. Using freshwater ecosystems as a case study, we evaluate A$^2$SL in modeling water temperature and dissolved oxygen dynamics in real-world lakes. Experimental results show that A$^2$SL significantly improves predictive accuracy and enhances robustness in data-scarce and atypical scenarios. Although this study focuses on freshwater ecosystems, the A$^2$SL framework offers a broadly applicable solution in various scientific domains.

cs.LG↗

arXiv2025

Quantum Phases in a Two-Dimensional Generalized interacting SSH Model

We study interaction-driven quantum phases in a two-dimensional generalized Su-Schrieffer-Heeger (SSH) defined on a square lattice with inequivalent nearest-neighbor hopping, next-nearest-neighbor hopping and a staggered on-site potential. In the non-interacting limit, the model hosts either a quadratic band touching (QBT) at the Brillouin-zone center or symmetry-protected Dirac nodes, depending on microscopic parameters. In the parameter regime with a QBT, our self-consistent Hartree-Fock analysis shows that weak to intermediate interactions can spontaneously break time-reversal symmetry and stabilize a quantum anomalous Hall (QAH) insulating phase with a finite Chern number. Interestingly, this QAH phase is found to weakly break lattice-symmetries, leading to a small but finite nematic bond order. This is in contrast to the standard QAH phase in checkerboard lattice, which preserves all lattice symmetries. Additionally, we find an enhanced bond-nematic Dirac semimetallic (BNDS) phase due to asymmetric hopping, which is thought to be absent in the Hartree-Fock approach. In the parameter regimes where QBT splits into two Dirac nodes, the QAH phase survives up to a finite staggered on-site potential. However, as the staggered potential increases, the QAH phase is suppressed while the BNDS phase grows stronger.

cond-mat.str-el↗

arXiv2025

RAG for Geoscience: What We Expect, Gaps and Opportunities

Retrieval-Augmented Generation (RAG) enhances language models by combining retrieval with generation. However, its current workflow remains largely text-centric, limiting its applicability in geoscience. Many geoscientific tasks are inherently evidence-hungry. Typical examples involve imputing missing observations using analog scenes, retrieving equations and parameters to calibrate models, geolocating field photos based on visual cues, or surfacing historical case studies to support policy analyses. A simple ``retrieve-then-generate'' pipeline is insufficient for these needs. We envision Geo-RAG, a next-generation paradigm that reimagines RAG as a modular retrieve $\rightarrow$ reason $\rightarrow$ generate $\rightarrow$ verify loop. Geo-RAG supports four core capabilities: (i) retrieval of multi-modal Earth data; (ii) reasoning under physical and domain constraints; (iii) generation of science-grade artifacts; and (iv) verification of generated hypotheses against numerical models, ground measurements, and expert assessments. This shift opens new opportunities for more trustworthy and transparent geoscience workflows.

cs.ET↗

arXiv2025

Stabilizing boundary time crystals through Non-markovian dynamics

We study Boundary time crystals (BTCs) in the presence of non-Markovian dynamics. In contrast to BTCs observed in earlier works in the Markovian regime, we show that non-Markovian dynamics can be highly beneficial for stabilizing BTCs over a wide range of parameter values, even in the presence of intermediate rates of dissipation. Notably, we also observe the emergence of higher-order limit cycles (HO-LCs) for some parameter regimes. We analyze the effect of non-Markovian dynamics on BTCs and HO-LCs using quantum Fisher information, time-averaged magnetization, a measure of non-Markovianity, and a dynamical phase diagram, all of which show complex behaviors with changing non-Markovianity parameters. Our studies can pave the way for stabilizing time crystals in dissipative systems, as well as lead to studies on varied dissipative dynamics on time translational symmetry breaking.

quant-ph↗

arXiv2025

Generating discrete time crystals through optimal control

In this work we use optimal control to generate Discrete Time Crystals (DTC) in generic many-body quantum systems. We define appropriate cost functions, which, when optimized, result in the formation of DTCs. This hitherto unexplored method represents DTCs as an optimization problem, and allows us to find non-trivial realistic periodic control pulses and parameter regimes which result in spontaneous breaking of time-translational symmetry in quantum systems. We exemplify our approach using many-body quantum systems in the presence, as well as absence of dissipation. We also discuss possible experimental realization of the control protocol for generating DTCs.

quant-ph↗

arXiv2025

Spin-imbalance induced buried topological edge currents in Mott \& topological insulator heterostructures

We theoretically investigate the heterostructure between a ferrimagnetic Mott insulator and a time-reversal invariant topological band insulator on the two-dimensional Lieb lattice with periodic boundary conditions. Our Hartree-Fock and slave-rotor mean-field results incorporate long-range Coulomb interactions. We present charge and magnetic reconstructions at the two edges of the heterostructure and reveal how \textit{buried} topological edge modes adapt to these heterostructure edge reconstructions. In particular, we demonstrate that the interface magnetic field induces a spin imbalance in the edge modes while preserving their topological character and metallic nature. We show that this imbalance leads to topologically protected buried spin and charge currents. The inherent spin-momentum locking ensures that left and right movers contribute to the current at the two buried interfaces in opposite directions. We show that the magnitude of the spin-imbalance induced charge and spin current can be tuned by adjusting the spin-orbit coupling of the bulk topological insulator relative to the correlation strength of the bulk Mott insulator. Thus, our results demonstrate a controlled conversion of a spin Hall effect into an analog of a charge Hall effect driven by band topology and interaction effects. These topologically protected charge and spin currents pave the way for advances in low-energy electronics and spintronic devices.

cond-mat.mes-hall↗

arXiv2024

Hierarchical Conditional Multi-Task Learning for Streamflow Modeling

Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning models have achieved state-of-the-art results of streamflow prediction, their end-to-end single-task learning approach often fails to capture the causal relationships within these systems. To address this, we propose Hierarchical Conditional Multi-Task Learning (HCMTL), a hierarchical approach that jointly models soil water and snowpack processes based on their causal connections to streamflow. HCMTL utilizes task embeddings to connect network modules, enhancing flexibility and expressiveness while capturing unobserved processes beyond soil water and snowpack. It also incorporates the Conditional Mini-Batch strategy to improve long time series modeling. We compare HCMTL with five baselines on a global dataset. HCMTL's superior performance across hundreds of drainage basins over extended periods shows that integrating domain-specific causal knowledge into deep learning enhances both prediction accuracy and interpretability. This is essential for advancing our understanding of complex hydrological systems and supporting efficient water resource management to mitigate natural disasters like droughts and floods.

cs.LG↗

arXiv2024

Signatures of topology in generic transport measurements for Rarita-Schwinger-Weyl semimetals

We investigate how the signatures of the topological properties of the bandstructures for nodal-point semimetals are embedded in the response coefficients, arising in two distinct experimental set-ups, by taking the Rarita-Schwinger-Weyl (RSW) semimetal as an example. The first scenario involves the computation of third-rank tensors representing second-order response coefficients, relating the charge/thermal current densities to the combined effects of the gradient of the chemical potential and an external electric field/temperature gradient. On the premises that internode scatterings can be ignored, the relaxation-time approximation leads to a quantized value for the nonvanishing components of each of these nonlinear response tensors, characterizing a single untilted RSW node. Furthermore, the final expressions turn out to be insensitive to the specific values of the chemical potential and the temperature. The second scenario involves computing the magnetoelectric conductivity under the action of collinear electric ($\mathbf E$) and magnetic ($\mathbf B$) fields, representing a planar Hall set-up. In particular, our focus is in bringing out the dependence of the linear-in-$|\mathbf B|$ parts of the conductivity tensor on the intrinsic topological properties of the bandstructure, which are nonvanishing only in the presence of a nonzero tilt in the energy spectrum.

cond-mat.mes-hall↗

arXiv2024

Linear response in planar Hall and thermal Hall setups for Rarita-Schwinger-Weyl semimetals

We investigate the nature of the linear-response tensors in planar Hall and planar thermal Hall setups, where we subject a Rarita-Schwinger-Weyl (RSW) semimetal to the combined influence of an electric field $\mathbf E $ (and/or temperature gradient $\nabla_{\mathbf r } T$) and a weak (i.e., nonquantizing) magnetic field $\mathbf B $. For computing the in-plane transport components, we have added an elastic deformation which gives rise to a chirality-dependent effective magnetic field $ \mathbf B^{\text{tot}} = \mathbf B + \chi \, \mathbf B_5 $, where $\chi $ is the chirality of an RSW node. We have included the effects of orbital magnetic moment (OMM), in conjunction with the Berry curvature (BC), both of which appear as a consequence of nontrivial topology of the bandstructure. Due to the presence of four bands, RSW semimetals provide a richer structure for obtaining the linear-response coefficients, compared to the Weyl semimetals. In particular, we have found that the OMM-contributed terms may oppose or add up to the BC-only parts, depending on which band we are considering. Last, but not the least, we have determined the out-of-plane response comprising the intrinsic anomalous-Hall and the Lorentz-force-contributed currents, whose nature corroborates the findings of some recent experimental results.

cond-mat.mes-hall↗