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Aditya Kumar

Publications and source records attributed to Aditya Kumar.

At least 19 recordsLinked to original sources
arXiv2026

The Agentic AI Kerr Lab: LLM Agents for Kerr Microscopy Experiments with Magnetic Skyrmions

Magnetic skyrmions are topological spin textures that can be observed directly by magnetic microscopy and driven by fields and currents, which makes them a model system for fundamental physics and candidates for spintronic devices. Magneto-optical Kerr microscopy is a method to image them in real time; however, most experiments use an inefficient cycle of consecutive imaging, analysis and excitation in which every step requires the judgement of the experimentalist operating the setup. Here we present the Agentic AI Kerr Lab (AgentKerr), in which an agent based on a large language model operates this cycle automatically and efficiently on a Kerr microscope. Rather than relying on pattern detection by a language model, a convolutional neural network trained on Kerr images turns live images into skyrmion observables such as position, size and shape, on which the agent bases its decisions. The architecture lets agents operate the instruments safely, as every hardware action is checked deterministically against an action policy, independently of the agent that issues it. With no procedure given, the agent determines by itself how to create, tune and move skyrmions, and writes its own analyses. The approach can be transferred to other spin textures and points towards magnetic imaging experiments in which the researcher poses the question and the agent finds the way to realize the experiments that yield the answer.

cond-mat.mes-hall↗
arXiv2026

Spin and scalar curvature on punctured four manifolds

We show that a closed spin four-manifold with nonzero signature, even after being punctured along a finite graph, does not admit a complete metric with uniformly positive scalar curvature. We also show that this implies Schoen's conjecture on $L^{\infty}$-metrics with non-negative scalar curvature for these manifolds. This is in contrast to higher dimensions. A key new observation, which could be of independent interest, is that Gromov's $μ$-bubbles are a convenient class of boundaries for the APS boundary value problem for the Dirac operator.

math.DG↗
arXiv2026

Time-reversal-based quantum metrology in a central-spin architecture: from SATIN to butterfly echo

We investigate quantum-enhanced time-reversal-based metrology using the central spin model, in which a single ancilla spin of magnitude $S_{\rm cen}$ mediates interactions between $N$ bath spins through a star-graph topology. We demonstrate that the SATIN (Signal Amplification Through Time-reversal Interaction) echo protocol can be implemented in this setup to achieve a Fisher information of $N^2/e$ with the collective $\langle J_y\rangle$ readout, corresponding to a $4.34\,\mathrm{dB}$ offset from the Heisenberg limit. Furthermore, we establish that this platform provides a pathway to perform the butterfly echo protocol by conjugating each evolution step with a random collective bath rotation, thereby achieving axis-agnostic Heisenberg-scaled sensing. Crucially, with a small $N-$independent number of random steps, the butterfly echo reaches a Fisher information of $N^2/4$ ($6\,\mathrm{dB}$ below the Heisenberg limit) for any unknown encoding axis, with the plateau gain tracking the Haar limit. Finally, we demonstrate that the central-spin magnitude $S_{\rm cen}$ provides a powerful knob to reduce the optimal protocol timescale without altering the metrological ceiling or the circuit depth required to reach isotropy. The proposed scheme is relevant to cavity QED and NMR quantum processors, where the central spin architecture arises naturally.

quant-ph↗
arXiv2026

Decision Making and Ride Comfort Evaluation Based on Lane-Change Prediction for Autonomous Vehicles

Predicting surrounding vehicles' lane-change intentions enables autonomous vehicles to anticipate potential hazards and respond proactively rather than relying solely on reactive maneuvers. This study investigates the benefits of integrating lane-change prediction, based on knowledge graph embeddings and Bayesian inference, into autonomous vehicle decision-making. Three driving strategies are evaluated in risky merging scenarios simulated in CARLA: reactive driving, prediction-based longitudinal action, and prediction-based lateral action. The proposed prediction model anticipates the surrounding vehicle's lane change approximately 3-4 seconds before its occurrence, allowing the ego vehicle to adjust its behavior accordingly. In the representative simulation records, reactive driving results in a collision accompanied by aggressive deceleration. In contrast, prediction-based longitudinal action initiates early braking to establish a safe gap, while prediction-based lateral action performs a lane change when a safe adjacent lane is available. Both predictive strategies avoid collisions in the evaluated records. Compared with reactive driving, peak deceleration before impact is reduced by 31.5% with longitudinal action and 91.0% with lateral action. Additionally, an ISO 2631-1-based horizontal vibration estimate is used to provide a ride comfort assessment. Overall, the results highlight the potential of lane-change prediction to support safer and smoother driving through proactive decision-making, with additional insights into ride comfort provided by vibration-based estimates.

cs.RO↗
arXiv2026

6.5% of the Neuro-Symbolic Literature Can Be Reproduced from Its Published Artifacts, a Six-Stage Audit Framework and First Instantiation

We present a six-stage framework for auditing the reproducibility of scientific claims across a research literature within the computer science domain, and instantiate our framework for the neuro-symbolic AI (NSAI) subdomain. Instantiating the framework on the NSAI subdomain produced a multi-year audit. Stage one retrieved 5,497 records and removed 3,018 duplicates. Stage two screened the 2,479 unique records at title and abstract, identifying 1,365 self-identified NSAI records, then removed a further 61 at full text for off-topic, non-research, no-quantitative-evaluation, or inaccessible-full-text reasons. Stage three sought a verifiable public code artifact for each of the 1,304 eligible records and found none for 849, leaving 455 to enter the artifact inventory and bounded rerun of stages four and five. We fully or partially reproduced 85 studies, 6.52% of the eligible corpus and 18.68% of attempted reruns. We found that 321 attempted reruns were blocked by missing non- code artifacts and 42 by missing or unusable code repositories. These figures quantify a persistent reproducibility deficit that survives even nominal "code available" declarations, and signal the need for enforced, versioned, and permanently archived artifact bundles in future NSAI publications. We argue that empirical NSAI papers should be required at submission time to provide complete, versioned, and permanently archived artifact bundles.

cs.AI↗
arXiv2026

Adaptive Crack-Tip Localization of Phase-Field Fracture Computations

Phase-field models of fracture provide a unified framework for simulating crack nucleation and propagation, but their computational cost remains high because the governing equilibrium and phase-field equations are typically iteratively solved over the entire domain at every staggered iteration. We present an extension of the recently developed adaptive mesh refinement and coarsening framework that localizes not only spatial resolution but also repeated solution effort to a small active patch around the crack tip for linear elastic brittle materials. The inactive exterior is eliminated through a Schur complement and condensed onto the patch interface, and its factorization is reused as long as the patch remains fixed. The phase field is frozen in the exterior, introducing only a small approximation in the crack wake. The method is implemented in FEniCSx and evaluated for quasi-static crack propagation, dynamic crack branching, three-dimensional mixed-mode crack growth, and coalescence of multiple cracks in a unit cell. Across these problems, the condensed formulation reproduces the reference solutions with differences in global quantities below 0.5%, while substantially reducing the number of active degrees of freedom. In the quasi-static surfing problem, wall time speedups increase from 6.9 to 22.8 as the domain size is doubled, demonstrating increasing computational advantage for large-scale phase-field fracture simulations.

cond-mat.mtrl-sci↗
arXiv2026

Spacetime Sentinels: a Qiskit game that exposes the competition between spacetime checks and magic in low-overhead quantum error detection

We present Spacetime Sentinels, a game in which the player protects a noisy circuit with spacetime Pauli checks and is scored on the post-selected fidelity and the fraction of shots that survive, with every number computed in Qiskit. The player spends a budget of ancilla qubits on checks found by the qiskit-paulice add-on, must inject a quota of $T$ gates into the protected circuit, and chooses whether to place them where they commute with every check or to ignore the checks. Each move is evaluated exactly with Qiskit Aer density-matrix simulation against a stabilizer or statevector reference, and the evaluation agrees with 50,000-shot runs of the SamplerV2 primitive. We score moves with a fidelity rate $R=F_{\mathrm{ps}}κ^{1/2}$, whose inverse square is the sampling overhead of detection followed by rescaling. Exhaustive evaluation of all 5,412 legal moves under two noise models, together with sweeps over five random payloads, reveals three trade-offs that are not obvious before playing. Checks pay for themselves only above a break-even depth: at depth 6 adding checks lowers $R$, while at depth 18 the best number of checks raises it by a factor of 1.103 and lifts the fidelity from 0.629 to 0.886. $T$ gates add no noise on hardware, yet each check removes spacetime locations where a $T$ gate can be placed safely, from all 90 at depth 18 with no checks to about 14 with four, so the optimal number of checks falls as the magic quota grows. Finally, a check broken by a $T$ gate is worse than no check: with sixteen $T$ gates at depth 18, blind placement lowers the post-selected fidelity to 0.158, and retiring the broken checks lowers it further to 0.102, below the 0.628 of the unprotected circuit. The game runs in the browser and in the terminal, and all code and data are open source.

physics.pop-ph↗
arXiv2026

Coding LLMs Can Be Harnessed to Learn Interpretable Models from Data

Since compact, interpretable classifiers correspond to simple programs, it is natural to ask if LLM-based coding agents can be used to learn interpretable models from data. In this paper we present a simple harness that repeatedly prompts a coding LLM to produce the next rule in a rule set, given a sample of labeled examples. We show that this approach is competitive with, and often superior to, state-of-the-art rule-learning methods on tabular data, especially with small training sets, and wins by large margins on text, including against another LLM-based rule learner; that this approach outperforms standard harnesses, and that the approach immediately leads to new kinds of interpretable models, since coding agents are not restricted to search a highly restricted hypothesis space. In addition to the advances made in learning interpretable models, these results are of interest because they introduce a new class of coding benchmarks - construction of interpretable classifiers - which includes quantitative measurements of output quality (i.e., test-set error) and existing mature ``specialist coding algorithms'' (i.e., existing learning methods).

cs.LG↗
arXiv2026

Adaptive Mesh Coarsening for Efficient Phase-Field Fracture Simulations

Phase-field models provide a versatile framework for simulating fracture nucleation and propagation without explicit crack tracking. Their principal computational challenge is the need to resolve a small regularization length with a sufficiently fine finite element mesh. This requirement can render uniform-mesh simulations prohibitively expensive, particularly for three-dimensional problems, problems involving distributed crack nucleation, and soft nearly incompressible materials. Adaptive mesh refinement offers a natural means of reducing this cost, but existing approaches often rely on heuristic refinement indicators, are primarily designed for problems containing pre-existing cracks, and retain increasingly large refined regions as cracks grow in size. This work presents an adaptive mesh coarsening framework along with adaptive mesh refinement for phase-field fracture. The coarsening strategy replaces the fractured region in the crack wake with a coarse crack band that preserves the essential mechanical behavior of a crack, making it necessary to have a refined mesh only in a small region near the tip of the growing crack. The method introduces a physics-based refinement indicator derived from the violation of the material strength surface, which is a necessary condition for fracture evolution. The indicator therefore robustly identifies regions where crack nucleation or propagation is imminent and can be applied across arbitrary materials, geometries, and loading conditions. The framework is implemented in parallel within FEniCSx; the supporting finite element codes are made available. Its generality and substantial computational benefit are demonstrated through benchmark problems involving crack propagation and nucleation under quasi-static and dynamic loading, for soft and hard materials, and for thermomechanical fracture.

cond-mat.mtrl-sci↗
arXiv2026

Distilling Foundation Models for Agentic What-If Reasoning:Cost, Latency, and Governance in a Hybrid LLM+SLM Architecture

Tabular foundation models deliver strong zero-training predictive performance via in-context learning, but their high inference latency makes them impractical as hot-path decision backends in interactive agentic loops. We distill a TabPFN teacher into a compact feed-forward student across a business-decision simulation on UCI Adult and five OpenML benchmarks: the classification head compresses 53.2M parameters to 8,546 (6,220x); the deployed two-head loan pipeline compresses 111.4M parameters to 17,059 (6,532x). The student retains 95.4-100.5% accuracy and 96.8-100.0% AUC, with the lowest accuracy retention on credit-g at 95.4%; an alpha = 0 hard-label control shows that the teacher's soft targets provide a 2.1-7.0 AUC point gain.

cs.LG↗
arXiv2026

Localizing and Correcting Errors for LLM-based Planners

Large language models (LLMs) have demonstrated strong reasoning capabilities on math and coding, but frequently fail on symbolic classical planning tasks. Our studies, as well as prior work, show that LLM-generated plans routinely violate domain constraints given in their instructions (e.g., walking through walls). To address this failure, we propose iteratively augmenting instructions with Localized In-Context Learning (L-ICL) demonstrations: targeted corrections for specific failing steps. Specifically, L-ICL identifies the first constraint violation in a trace and injects a minimal input-output example giving the correct behavior for the failing step. Our proposed technique of L-ICL is much effective than explicit instructions or traditional ICL, which adds complete problem-solving trajectories, and many other baselines. For example, on an 8x8 gridworld, L-ICL produces valid plans 89% of the time with only 60 training examples, compared to 59% for the best baseline, an increase of 30%. L-ICL also shows dramatic improvements in other domains (gridworld navigation, mazes, Sokoban, and BlocksWorld), and on several LLM architectures.

cs.AI↗
arXiv2026

Learning to Construct Practical Agentic Systems

Automated design and optimization of agentic LLM-based systems leads to sophisticated systems that substantially improve result quality over off-the-shelf agentic patterns. However, studies of fielded agentic systems show that production systems focus much more on issues such as simplicity, controllability, and predictability of inference costs. In this paper we propose principled approaches to designing and optimizing practical agentic systems. We describe an agent framework that enables designers to enforce modularity in agentic systems, by defining "pseudo-tools" that call LLMs recursively on a restricted context. Using this framework we hand-engineer agents for a diverse set of tasks, and show that relative to dynamically-planned workflows, hand-constructed fixed workflows are generally cheaper and more accurate. We then propose novel learning methods for the agentic components required by this framework, namely pseudo-tools and fixed workflows. These learning methods generally outperform hand-engineered agents. We also exploit the modularity of the framework to apply multi-objective optimization methods to jointly optimize cost and response quality and blend the results of multiple learning systems.

cs.LG↗
arXiv2026

Rational Mechanics of Material Strength in Brittle Solids

Material strength is a classical concept with renewed importance in fracture mechanics, particularly in crack nucleation in brittle solids. We formulate material strength in finite elasticity and examine its geometric, constitutive, and symmetry-theoretic foundations. Spatial covariance requires a strength function to depend on both stress and the corresponding strain measure, so that strength is governed by the pair (stress,strain), not stress alone, and only then can representations based on different stress measures be consistently related, with classical stress-based criteria recovered as a special case. We analyze covariance under spatial diffeomorphisms and relate formulations based on the first Piola--Kirchhoff, second Piola--Kirchhoff, and Cauchy stresses. For stress-based criteria, we define the strength hypersurface as a subset of the constitutively admissible stress manifold and study the associated safe domain. Under standard regularity assumptions and the requirement that sufficiently large stresses are inadmissible, the strength surface is a smooth compact hypersurface of this manifold. For isotropic solids, we show that the safe domain is star-shaped under a proportional-reduction hypothesis. We extend the formulation to anelastic brittle solids, showing that residual stresses and eigenstrains modify the strength surface through the material metric, and discuss anisotropic strength via material symmetry.

cond-mat.mtrl-sci↗
arXiv2026

Dynamic Entanglement-Weighted Pruning for Quantum Federated Unlearning in Supply-Chain Risk Prediction

Federated deployments of variational quantum classifiers are attractive for cross-organisation risk prediction in supply chains, because raw data never leaves the client, yet data-protection regulations such as the GDPR grant clients a right to request that their contribution be removed from a trained model after the fact. Retraining a federated model from scratch to honour such a request is correct but wasteful, and it is not obvious which quantum circuit parameters actually carry a given client's influence. We introduce Entanglement-Weighted Pruning (EWP), an unlearning procedure for quantum federated learning that scores every trainable circuit parameter with the product of two signals: the diagonal entry of the quantum Fisher information matrix estimated on the target client's data via the parameter-shift rule, and a structural entanglement weight associated with the parameter's gate. Parameters with the lowest scores are pruned, optionally followed by a short fine-tuning pass on the retained clients. We implement the full pipeline in Qiskit for a four-qubit data-re-uploading ansatz trained with FedAvg across five simulated supply-chain-risk clients, and benchmark EWP against full retraining, fine-tuning alone, random pruning, Fisher-only pruning, and entanglement-only pruning, over three random seeds. EWP attains a mean post-unlearning accuracy statistically indistinguishable from the full-retraining oracle, while producing a lower forgetting score and requiring roughly 16 times less wall-clock time. Ablations over pruning threshold, client count, and non-IID strength show that combining the two signals is necessary, as entanglement-only and Fisher-only pruning each substantially degrade accuracy relative to EWP.

quant-ph↗
arXiv2026

Large bias-tunable magnetoresistance from spin-dependent interlayer hybridization in van der Waals antiferromagnet CrSBr-based heterostructures

We explore the large magnetoresistance (MR) in \ce{hBN}/few-layer-graphene/\ce{CrSBr}/few-layer-graphene heterostructures and reveal the mechanism behind its non-monotonic bias dependence. Using bias voltage and temperature as independent tuning knobs, we achieve MR up to \SI{350}{\percent} at \SI{20}{K}, characterized by symmetric M-shaped maxima around $\pm 0.5\,\mathrm{V}$. Continuous tuning of the magnetization angle $θ$ via a hard-axis magnetic field shows that the barrier band-edge offset varies linearly with $\cos(θ/2)$, a first-order signature of spin-dependent interlayer hybridization. This linear relationship rules out the Jullière model and a spin-filter projection. We conclude that the magnetic-configuration-dependent band edge, rather than electrode spin polarization, dictates the large magnetoresistance in \ce{CrSBr} junctions.

cond-mat.mes-hall↗
arXiv2026

Finite-Time Analysis of Discounted Exponential-Utility Reinforcement Learning

Discounted exponential utility provides a principled criterion for risk-sensitive sequential decision-making, but its nonlinear structure complicates reinforcement learning. A recent work \citep{thoppe2026reinforcement} addressed this difficulty by introducing a Bellman-compatible surrogate and two model-free fixed-point algorithms for optimizing it over stationary policies. However, their main convergence results are asymptotic. In this work, we establish finite-time rates of $\tilde{O} (1/\sqrt{n})$ for the aforementioned two algorithms under asynchronous Markovian sampling, where $n$ is the iteration index and $\tilde{O}$ hides logarithmic expressions. Importantly, we employ parameter-free choices for the stepsize parameter to derive these rate results. For the algorithmically simpler one-timescale method, the main challenge is that its update equation is not directly aligned with the contraction geometry of its underlying power-law operator. We overcome this mismatch by exploiting the boundedness, monotonicity, and homogeneity of the operator to obtain a local pseudo-contraction property for the relative-error dynamics. We then use a Moreau-envelope-based Lyapunov function and Polyak--Ruppert averaging to obtain the stated convergence rate with parameter-free stepsizes. For the two-timescale method, the main challenge is to control a tracking error on the faster timescale. These results provide the first finite-time guarantees for model-free discounted exponential-utility reinforcement learning.

cs.LG↗
arXiv2026

Dynamic phase-field model for brittle fracture in grounded glaciers

Fracture and calving of glaciers are key contributors to ice-mass loss and sea-level rise, yet predictive modeling remains challenging. Fracture in grounded glaciers is driven by gravitational forces and is typically studied within the framework of quasi-static linear elastic fracture mechanics. In this work, we show that purely quasistatic brittle fracture simulations within the phase field fracture framework under fixed self-weight can become strongly overdriven after crevasse initiation, producing unphysical thickening of the diffusive crack band and diffuse damage patterns. This pathology arises because gravity drives a growing region ahead of the crack tip beyond the strength surface. To resolve this, we show that the post-nucleation propagation is fundamentally a dynamic instability rather than a quasistatic process and propose the use of dynamic formulations of fracture. We demonstrate that accounting for inertia results in sharp, localized cracks that propagate through the ice thickness. As a second objective, this paper introduces a new dynamic formulation of the phase-field fracture model of Kumar et al. (J. Mech. Phys. Solids 2018) in which elastic, inertial, and gravitational contributions are degraded consistently in fractured regions.

cond-mat.mtrl-sci↗
arXiv2026

Why planar cracks fragment into echelon cracks

Predicting the path and shape of growing cracks is fundamental to understanding of fracture. Under out-of-plane shear loading, an initially planar crack may spontaneously fragment into multiple cracks, forming a striking echelon crack pattern. Explaining this crack morphogenesis in brittle materials has been a long-standing open problem essential to developing a complete theory of crack growth. Here, through comparison with classical experiments, we show that a strength-constrained minimization of the sum of elastic and surface energies explains echelon crack formation. Results are presented for both soft and hard materials, confirming the model's general applicability to any brittle material. As a corollary, we show that, contrary to prevailing views, a purely energetic minimization model is insufficient to predict the growth of large cracks. We identify two key non-dimensional parameters governing crack fragmentation and orientation, and demonstrate that these reconcile the various energy-based and stress-based empirical criteria proposed in the literature for crack path.

cond-mat.mtrl-sci↗