arXiv subjects

Amit Agarwal

Publications and source records attributed to Amit Agarwal.

At least 37 records · Page 2Linked to original sources

arXiv2026

Longitudinal Nonreciprocal Charge Transport with Time Reversal Symmetry

Longitudinal nonreciprocal charge transport is usually associated with broken time-reversal symmetry, either from magnetic order or an external magnetic field. Here, we show that it can also arise in nonmagnetic conductors preserving time-reversal symmetry through disorder-induced asymmetric scattering. Within a semiclassical Boltzmann theory, skew-scattering and side-jump processes generate a finite longitudinal current quadratic in the electric field. Our symmetry analysis identifies 42 point groups that allow this longitudinal nonreciprocal response. As a concrete example, gated Bernal-stacked bilayer graphene shows a gate-tunable nonreciprocal response with clear enhancement near its Lifshitz transition. These results identify disorder-driven asymmetric scattering as a route to bulk longitudinal nonreciprocal charge transport in crystalline conductors.

cond-mat.mes-hall↗

arXiv2026

Correlation-Driven Orbital Order Realizes 2D Metallic Altermagnetism

Two-dimensional metallic altermagnets are rare, and no correlated 2D material has been established to host large nonrelativistic spin splitting. Here we show that spontaneous orbital order, driven by electronic correlations and Fermi surface nesting, provides a general microscopic route to two-dimensional metallic altermagnetism. Antiferro-orbital ordering between the d$_{xz}$ and d$_{yz}$ orbitals breaks the equivalence of magnetic sublattices with opposite spins and generates a symmetry-enforced altermagnetic spin texture. As a concrete realization, we identify monolayer YbMn$_2$Ge$_2$ as a stable correlated metallic altermagnet exhibiting giant nonrelativistic spin splitting of order 1 eV. The resulting phase supports an exceptionally large and gate-tunable transverse spin conductivity. These results establish correlation-driven orbital order as a robust and general mechanism for designing correlated altermagnets with large spin splitting.

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arXiv2026

Think Twice Before You Write -- an Entropy-based Decoding Strategy to Enhance LLM Reasoning

Decoding strategies play a central role in shaping the reasoning ability of large language models (LLMs). Traditional methods such as greedy decoding and beam search often suffer from error propagation, while sampling-based approaches introduce randomness without adequate robustness. Self-consistency improves reliability by aggregating multiple rollouts, but incurs significant computational overhead. We propose an entropy-guided decoding framework that introduces token-level adaptivity into generation. At each step, the model computes the entropy of the token distribution, identifies high-uncertainty positions, and selectively branches on these vulnerable points. A dynamic pool of partial rollouts is maintained and expanded until solutions are completed, concentrating computation where uncertainty is greatest and avoiding unnecessary exploration in confident regions. To enable efficient termination, we apply a rollout-level Entropy After (EAT) stopping criterion by performing entropy evaluation after the full reasoning trace, rather than incrementally at every step. Experiments on GSM8K, AMC2023, and their perturbed variants demonstrate that our method achieves consistently strong accuracy. Notably, on smaller LLMs, performance is comparable to GPT-5 while operating at a fraction of the cost.

cs.CL↗

arXiv2026

Electric-Polarization Probe of the Magnon Orbital Moment Current in Altermagnet

Efficient transport of spin and orbital moments, and their electrical detection, are among the main challenges in spintronics and orbitronics. In magnetic insulators, these currents are mediated by magnons. In addition to carrying spin and orbital moment, the orbital motion of a magnon combined with its magnetic moment, generates an effective electric dipole moment. Here, we develop a theoretical framework for Seebeck- and Nernst-type transport of the magnon orbital moment (MOM) and its associated electric dipole moment (EDM). We identify a Drude-like scattering contribution and an intrinsic component governed by the generalized Berry curvatures of magnon bands. We show that a measurable transverse voltage generated by the EDM current provides a direct electrical detection scheme for magnon orbital transport. Applying our theory to an hexagonal altermagnet, we obtain an experimentally accessible voltage of approximately $0.4~μ$V. Our results establish a concrete electrical probe of magnon orbital transport and highlight magnons as potential low-dissipation information carriers for orbitronics.

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arXiv2026

Valleytronics in 2D Materials Roadmap

Valleytronics exploits non-equivalent energy extrema in the electronic band structure of crystalline solids -- the valley degree of freedom -- to encode, manipulate, and read out information. The advent of 2D materials, first graphene and then transition-metal dichalcogenides, made valley control practical through optical, electrical, and magnetic routes. This foundation has enabled remarkable progress in recent years spanning established frontiers, such as valley exciton physics and valley Hall effects, as well as emerging directions including lightwave valleytronics, nanophotonic integration, flat-band valleytronics, and spin-valley qubits. In parallel, there are sustained efforts to scale up valleytronic materials and to predict new valleytronic platforms. This Roadmap brings together perspectives from leading experts to chart the key opportunities and challenges at the forefront of 2D material valleytronics. Each section captures a snapshot of progress in a key research area, identifies critical open challenges, and outlines pathways toward future valleytronics breakthroughs.

cond-mat.mes-hall↗

arXiv2026

Extrinsic Spin Splitter Currents in Altermagnets

Altermagnets exhibit momentum-dependent spin splitting despite having zero net magnetization. This enables a spin-splitter effect in which an external electric field generates transverse spin currents by separating oppositely polarized carriers. Here, we develop a unified semiclassical theory of linear extrinsic spin-splitter currents, incorporating impurity-induced side-jump and skew-scattering contributions, and apply it to the $d$-wave altermagnet \ch{FeSb2}. We demonstrate that asymmetric impurity scattering provides a dominant channel for spin-splitter currents. Remarkably, the resulting extrinsic spin conductivity is time-reversal even, in contrast to previously studied spin-splitter responses arising from symmetric scattering.

cond-mat.mes-hall↗

arXiv2026

Judging What We Cannot Solve: A Consequence-Based Approach for Oracle-Free Evaluation of Research-Level Math

Recent progress in reasoning models suggests that generating plausible attempts for research-level mathematics may be within reach, but verification remains a bottleneck, consuming scarce expert time. We hypothesize that a meaningful solution should contain enough method-level information that, when applied to a neighborhood of related questions, it should yield better downstream performance than incorrect solutions. Building on this idea, we propose \textbf{Consequence-Based Utility}, an oracle-free evaluator that scores each candidate by testing its value as an in-context exemplar in solving related yet verifiable questions. Our approach is evaluated on an original set of research-level math problems, each paired with one expert-written solution and nine LLM-generated solutions. Notably, Consequence-Based Utility consistently outperforms reward models, generative reward models, and LLM judges on ranking quality. Specifically, for GPT-OSS-120B, it improves Acc@1 from 67.2 to 76.3 and AUC from 71.4 to 79.6, with similarly large AUC gains on GPT-OSS-20B (69.0 to 79.2). Furthermore, compared to LLM-Judges, it also exhibits a larger solver-evaluator gap, maintaining a stronger correct-wrong separation even on instances where the underlying solver often fails to solve.

cs.CL↗

arXiv2026

Asymmetric Scattering Drives Large Nonlinear Nernst and Seebeck Effects

The nonlinear Nernst and Seebeck effects (NNE and NSE) offer promising routes for thermoelectric energy conversion in non-magnetic systems. While intrinsic mechanisms such as the nonlinear Drude and Berry-curvature-dipole terms are well established, extrinsic contributions to thermoelectric responses arising from disorder-induced asymmetric scattering remain comparatively less explored, despite growing experimental evidence of their dominance. Here, we develop a unified semiclassical theory of NNE and NSE that incorporates skew scattering and side-jump processes, identifying four distinct extrinsic contributions to NNE and two for NSE. A systematic symmetry analysis shows that these responses are allowed in time-reversal-symmetric non-magnets, PT-symmetric antiferromagnets, and non-centrosymmetric magnetic systems such as altermagnets. As a case study, we demonstrate that ABA-stacked trilayer graphene hosts large nonlinear Nernst and Seebeck responses dominated by extrinsic scattering, in excellent agreement with recent experiments. Our results establish the microscopic origin of these effects and provide guiding principles for designing high-efficiency nonlinear thermoelectric devices.

cond-mat.mes-hall↗

arXiv2026

DAIQ: Auditing Demographic Attribute Inference from Question in LLMs

Recent evaluations of Large language models (LLMs) audit social bias primarily through prompts that explicitly reference demographic attributes, overlooking whether models infer sensitive demographics from neutral questions. Such inference constitutes epistemic overreach and raises concerns for privacy. We introduce Demographic Attribute Inference from Questions (DAIQ), a diagnostic audit framework for evaluating demographic inference under epistemic uncertainty. We evaluate 18 open- and closed-source LLMs across six real-world domains and five demographic attributes. We find that many models infer demographics from neutral questions, defaulting to socially dominant categories and producing stereotype-aligned rationales. These behaviors persist across model families, scales and decoding settings, indicating reliance on learned population priors. We further show that inferred demographics can condition downstream responses and that abstention oriented prompting substantially reduces unintended inference without model fine-tuning. Our results suggest that current bias evaluations are incomplete and motivate evaluation standards that assess not only how models respond to demographic information, but whether they should infer it at all.

cs.CL↗

arXiv2026

Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought

Recent frontier models employ long chain-of-thought reasoning to explore solution spaces in context and achieve stonger performance. While many works study distillation to build smaller yet capable models, most focus on English and little is known about language-specific reasoning. To bridge this gap, we first introduct **Language-Mixed CoT**, a reasoning schema that switches between English and a target language, using English as an anchor to excel in reasoning while minimizing translation artificats. As a Korean case study, we curate **Yi-Sang**: 5.79M native-Korean prompts from web Q&A, exams, STEM, and code; 3.7M long reasoning traces generated from Qwen3-32B; and a targeted 260k high-yield subset. We train ninve models (4B-35B) across six families (Qwen2.5, Llama-3.1, Gemma-3, etc). Our best model, **KO-REAson-35B**, achieves state-of-the-art performance, with the highest overall average score (64.0 \pm 25), ranking first on 5/9 benchmarks and second on the remainder. Samller and mid-sized models also benefit substantially, with an average improvement of +18.6 points across teh evaluated nine benchmarks. Ablations show **Language-Mixed CoT** is more effective than monolingual CoT, also resulting in cross-lingual and mult-modal performance gains. We release our data-curation pipeline, evaluation system, datasets, and models to advance research on language-specific reasoning. Data and model collection: https://huggingface.co/KOREAson.

cs.CL↗

arXiv2026

Observation of Time-Reversal Symmetry Breaking in the Type-I Superconductor YbSb$_2$

The spontaneous breaking of time-reversal symmetry is a hallmark of unconventional superconductivity, typically observed in type-II superconductors. Here, we report evidence of time-reversal symmetry breaking in the type-I superconductor YbSb$_2$. Zero-field $μ$SR measurements reveal spontaneous internal magnetic fields emerging just below the superconducting transition, while transverse-field $μ$SR confirms a fully gapped type-I superconducting state. Our first-principles calculations identify YbSb$_2$ as a ${\mathbb Z}_2$ topological metal hosting a Dirac nodal line near the Fermi level. Symmetry analysis within the Ginzburg Landau framework indicates an internally antisymmetric nonunitary triplet (INT) state as the most probable superconducting ground state. Calculations based on an effective low-energy model further demonstrate that this INT state hosts gapless Majorana surface modes, establishing YbSb$_2$ as a topological superconductor. Our results highlight YbSb$_2$ as a unique material platform where type-I superconductivity coexists with triplet-pairing and nontrivial topology.

cond-mat.supr-con↗

arXiv2026

RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark

Existing benchmarks treat multi-turn conversation and reasoning-intensive retrieval separately, yet real-world information seeking requires both. To bridge this gap, we present a benchmark for reasoning-based conversational information retrieval comprising 707 conversations (2,971 turns) across eleven domains. To ensure quality, our Decomposition-and-Verification framework transforms complex queries into fact-grounded multi-turn dialogues through multi-level validation, where atomic facts are verified against sources and explicit retrieval reasoning is generated for each turn. Comprehensive evaluation reveals that combining conversation history with reasoning doubles retrieval performance (Baseline .236 $\rightarrow$ History+Reasoning .479 nDCG@10), while reasoning-specialized models substantially outperform dense encoders. Despite these gains, further analysis highlights that implicit reasoning remains challenging, particularly when logical connections are not explicitly stated in the text.

cs.IR↗

arXiv2026

Intrinsic Gyrotropic Magnetic Current of Orbital Origin

In gyrotropic crystals, an oscillating magnetic field induces a charge response known as the gyrotropic magnetic current. While its conventional origin is attributed to magnetic field modified band energy and shift in the Fermi-surface, a recent study identified an additional spin-driven magnetic displacement contribution. Here, we complete the picture by identifying the orbital counterpart of the magnetic displacement current. Using a density-matrix formulation that incorporates both minimal coupling and spin-Zeeman interactions, we derive the electronic equations of motion in the presence of an oscillating magnetic field and uncover a previously unexplored orbital contribution to the wavepacket velocity. Physically, this contribution arises from the time variation of the magnetic-field induced charge polarization. In the low frequency transport regime, this mechanism becomes purely intrinsic. We illustrate this intrinsic gyrotropic current of orbital origin in the ${\cal P}{\cal T}$-symmetric antiferromagnet CuMnAs. We show that the intrinsic gyrotropic magnetic current reverses sign upon Néel vector reversal, establishing it as a direct probe of antiferromagnetic order in CuMnAs and other $\mathcal{PT}$-symmetric antiferromagnets.

cond-mat.mes-hall↗

arXiv2026

LLM-Guided Lifecycle-Aware Clustering of Multi-Turn Customer Support Conversations

Clustering customer chat data is vital for cloud providers handling multi service queries. Traditional methods struggle with overlapping concerns and create broad, static clusters that degrade over time. Reclustering disrupts continuity, making issue tracking difficult. We propose an adaptive system that segments multi turn chats into service specific concerns and incrementally refines clusters as new issues arise. Cluster quality is tracked via DaviesBouldin Index and Silhouette Scores, with LLM based splitting applied only to degraded clusters. Our method improves Silhouette Scores by over 100\% and reduces DBI by 65.6\% compared to baselines, enabling scalable, real time analytics without full reclustering.

cs.AI↗

arXiv2025

Modulation of quantum geometry and its coupling to pseudo-electric field by dynamic strain

Two-dimensional materials are a fertile ground for exploring quantum geometric phenomena, with Berry curvature and its first moment, the Berry curvature dipole, playing a central role in their electronic response. These geometric properties influence electronic transport and result in the anomalous and nonlinear Hall effects, and are typically controlled using static electric fields or strain. However, the possibility of modulating quantum geometric quantities in real-time remains unexplored. Here, we demonstrate the dynamic modulation of Berry curvature and its moments, as well as the generation of a pseudo-electric field using time-dependent strain. By placing heterostructures on a membrane, we introduce oscillatory strain together with an in-plane AC electric field and measure Hall signals that are modulated at linear combinations of the frequencies of strain and electric field. Our measurements reveal modulation of Berry curvature and its first moment. Notably, we provide direct experimental evidence of pseudo-electric field that results in an unusual dynamic strain-induced Hall response. This approach opens up a new pathway for controlling quantum geometry on demand, moving beyond conventional static perturbations. The pseudo-electric field provides a framework for external electric field-free anomalous Hall response and opens new avenues for probing the topological properties.

cond-mat.mes-hall↗

arXiv2025

World in a Frame: Understanding Culture Mixing as a New Challenge for Vision-Language Models

In a globalized world, cultural elements from diverse origins frequently appear together within a single visual scene. We refer to these as culture mixing scenarios, yet how Large Vision-Language Models (LVLMs) perceive them remains underexplored. We investigate culture mixing as a critical challenge for LVLMs and examine how current models behave when cultural items from multiple regions appear together. To systematically analyze these behaviors, we construct CultureMix, a food Visual Question Answering (VQA) benchmark with 23k diffusion-generated, human-verified culture mixing images across four subtasks: (1) food-only, (2) food+food, (3) food+background, and (4) food+food+background. Evaluating 10 LVLMs, we find consistent failures to preserve individual cultural identities in mixed settings. Models show strong background reliance, with accuracy dropping 14% when cultural backgrounds are added to food-only baselines, and they produce inconsistent predictions for identical foods across different contexts. To address these limitations, we explore three robustness strategies. We find supervised fine-tuning using a diverse culture mixing dataset substantially improve model consistency and reduce background sensitivity. We call for increased attention to culture mixing scenarios as a critical step toward developing LVLMs capable of operating reliably in culturally diverse real-world environments.

cs.CV↗

arXiv2025

Odd-parity longitudinal magnetoconductivity in time-reversal symmetry broken materials

Magnetotransport measurements are a sensitive probe of symmetry and electronic structure in quantum materials. While conventional metals exhibit longitudinal magnetoconductivity that is even in a magnetic field ($B$) for small $B$, we show that magnetic materials which intrinsically break time-reversal symmetry (TRS) show an {\it odd-parity magnetoconductivity} (OMC), with a leading linear-$B$ response. Using semiclassical transport theory, we derive explicit expressions for the longitudinal and transverse conductivities and identify their origin in Berry curvature and orbital magnetic moment. Crystalline symmetry analysis shows that longitudinal OMC follows the same point-group constraints as the anomalous Hall effect, while transverse OMC obeys distinct rules, providing an independent probe of TRS breaking. In the large $B$ quantum oscillation regime, we uncover both odd- and even-$B$ contributions, demonstrating OMC beyond the semiclassical picture. Explicit calculations in valley-polarized gapped graphene show that OMC peaks near the band edges, vanish in the band gap and follow the temperature dependence of the magnetic order parameter. Our results explain the odd-parity magnetoresistance recently observed in magnetized graphene and establish OMC as a robust transport signature of intrinsic TRS breaking in metals.

cond-mat.mes-hall↗

arXiv2025

Quantum Transport Spectroscopy of Pseudomagnetic Field in Graphene

Nonuniform strain in graphene acts as a valley-dependent gauge field, generating pseudomagnetic fields (PMFs) that mimic real magnetic fields but preserve global time-reversal symmetry. While local probes have visualized such fields, their quantitative detection via macroscopic transport has remained elusive. Here, we demonstrate that high-mobility graphene exhibits distinct beating patterns in Shubnikov-de Haas oscillations, arising from valley-resolved Landau quantization under different effective magnetic fields. Systematic analysis of these beats reveals universal quadratic and linear scaling of the node carrier density and Landau level filling factor with the applied magnetic field, enabling the extraction of PMFs as small as a few millitesla. Our results establish quantum oscillation spectroscopy as a robust and broadly applicable probe of strain-induced gauge fields in Dirac materials, opening avenues for mechanically tunable valleytronic and straintronic devices.

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