Searcharxiv⌕ Search

arXiv subjects

Abhishek Kumar

Publications and source records attributed to Abhishek Kumar.

At least 19 recordsLinked to original sources
arXiv2026

A 39pJ/b 7.3Gbps 1.3mm$^2$ Multi-Subcarrier Massive MU-MIMO-OFDM Detector Exploiting Beamspace Sparsity and Frequency-Domain Correlation in 22FDX

We present the first multi-subcarrier massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) data detector reported in the open literature. By exploiting the channel's beamspace sparsity and frequency-domain (FD) correlation, we achieve up to 3x area and power reduction. Our design supports U=8 user equipments and B=64 basestation antennas, computes soft outputs for QPSK to 256-QAM, and processes 16 subcarriers in parallel. The fabricated 22FDX ASIC has a core cell area of 1.3mm$^2$, consumes 286mW, and delivers a throughput of 7.3Gbps at 0.8V core voltage, achieving best-in-class energy efficiency of 39pJ/b.

eess.SP↗
arXiv2026

SE-MoLoRA: Shared-Expert LoRA Adapters for Domain-Specific Photographic Assessment

Vision-language models can describe images fluently, but they often fail to provide actionable photographic critique because semantic content and aesthetic judgment remain entangled. We propose SE-MoLoRA, a modular parameter-efficient adaptation framework for domain-specific photographic assessment. The method separates general photographic knowledge from specialist residual judgments using an always-active shared LoRA expert and routed adapters for composition, lighting, and technical quality. A lightweight query router selects the relevant specialist, enabling targeted critique without training separate full models. A rank-64 shared adapter captures broad photographic vocabulary, while rank-32 specialists learn domain-specific residuals with an orthogonal regularization penalty that encourages disentangled representations. Training data is obtained by distilling the Reddit Photo Critique Dataset into domain-labeled critique samples. On held-out critique generation, SE-MoLoRA improves BERTScore-F1 from 0.2317 to 0.4215 over monolithic LoRA and is preferred in 84.6\% of pairwise comparisons, while using fewer active parameters than separate specialist models. SVD-based ablation study shows that shared-specialist decomposition and orthogonal regularization reduce expert overlap. These results demonstrate that modular adaptation improves controllability and specificity in multimodal photographic critique.

cs.CV↗
arXiv2026

Symmetry Breaking by Interfacial Dead Layers: Observation of Forbidden Self-Induced Spin-Orbit Torque in Symmetric Ferromagnets

Conventionally, spin-orbit torques (SOTs) in ferromagnets require heavy-metal layers or engineered structural asymmetry to break inversion symmetry. In this work, we report the observation of robust, self-generated SOTs in a nominally symmetric, heavy-metal-free MgO/NiFe/MgO trilayer - a geometry where such torques are theoretically forbidden. By combining harmonic Hall measurements with SQUID magnetometry and X-ray photoelectron spectroscopy, we identify the symmetry-breaking origin: a 1.8 nm magnetic dead layer at the bottom interface. Crucially, we demonstrate a quantitative agreement between our data and the drift-diffusion theory predicted by Kim and Lee, yielding a theoretically extracted dead-layer thickness (1.2 nm) which matches structural characterization. Furthermore, density-functional calculations confirm that NiFe possesses sufficient intrinsic spin Hall conductivity to support the observed spin currents. These results reframe the parasitic dead layer as a functional spintronic component, establishing a universal, all-ferromagnetic route to SOTs in standard magnetic heterostructures.

cond-mat.mes-hall↗
arXiv2026

Universal crossovers in weakly-monitored quantum critical states

We study post-measurement ensembles of ground states of tricritical and critical 1D quantum Ising Hamiltonians subjected, respectively, to weak energy and spin measurements without post-selection. These measurements act as relevant perturbations about the unmeasured critical ground states. Using finite-size renormalization group (RG) crossover analyses, we characterize their universal properties through the entanglement effective central charge, effective Affleck-Ludwig boundary entropy, and signatures of multifractality from moments of measurement-averaged correlation functions. In both cases, we find evidence for "measurement-dominated" or "measurement-altered" fixed points governed by the underlying Born-rule randomness. For critical Ising, we find a direct RG flow to a projective-measurement fixed point with area-law entanglement, whereas for the tricritical Ising model, we find evidence for a weak-measurement fixed point with logarithmic entanglement. These results clarify the RG-flow structure of weakly measured multicritical Ising ground states and show how intrinsic measurement-induced randomness can generate complex and rich universal long-distance scaling behavior in the post-measurement ensembles, accessible to controlled analytical RG and numerical finite-size RG crossover analyses.

cond-mat.stat-mech↗
arXiv2026

Harnessing Native Chromium Oxidation for Giant Orbital Torque and Field-Free Magnetization Switching in NiFe/Cr Bilayers

Orbital currents offer charge-to-spin conversion beyond the efficiency limit of conventional heavy-metal Spin Hall sources. However, harnessing them has so far required either thick orbital-Hall materials or additional heavy-metal conversion layers. Here, we show that the native oxide of chromium, typically regarded as parasitic, transforms a simple NiFe\Cr bilayer into a self-contained dual-channel orbital-current source without the need for any conversion layer. First-principles calculations predict a nearly threefold enhancement of the orbital Hall conductivity upon surface oxygenation, driven by Cr(3d)-O(2p) hybridization. Experimentally, naturally oxidized NiFe\Cr heterostructures exhibit a giant damping-like torque efficiency of $3.9 \times 10^{6}$ $\Omega^{-1}$ m$^{-1}$, exceeding Pt (Ta) by one (two) orders of magnitude. The torque depicts a non-monotonic Cr-thickness dependence which cannot be explained by a conventional model. We have developed a drift-diffusion model with an oxidation-gated interfacial source which quantitatively reproduces the data, revealing that the Cr-CrO$_x$ interface generates orbital currents over an order of magnitude stronger than the bulk orbital Hall channel with an orbital transport length of $\approx 4$ nm. The enhanced torque enables field-free magnetization switching at $1.58 \times 10^{11}$ A m$^{-2}$, outperforming heavy-metal and CuO$_x$ benchmarks. These results establish native oxidation as a scalable strategy for realizing efficient orbital-torque devices.

cond-mat.mes-hall↗
arXiv2026

Element-Specific Visualization of Layer-Parity and Twist-Dependent Magnetism in CrSBr

Van der Waals (vdW) based antiferromagnets (AFMs) are an ideal platform for probing and understanding thickness- and twist-angle-dependent emergent spin phenomena. However, element-specific nanoscale characterization of the spin structure in atomically thin vdW-based AFMs systems and layer-parity effects remain elusive, making them crucial for both fundamental insight into low-dimensional magnetism and the rational design of spintronic devices based on these materials. Here, we utilize X-ray magnetic circular and linear dichroisms paired with photoemission electron microscopy to resolve the magnetic order in atomically thin CrSBr. Our comprehensive measurements reveal CrSBr magnetic structure at the nanoscale and its dependence on the layer number, surface encapsulation, temperature, and applied field. Moreover, in the orthogonally twisted bilayer configuration, obtained by twisting two CrSBr ferromagnetic monolayers by 90$^\circ$, the magnetic easy axis fundamentally differs from the individual monolayers, unlocking a new pathway for moir\'e magnetism.

cond-mat.mes-hall↗
arXiv2026

LinkRank: A Learning-to-Rank Framework for One-to-Many Issue-Commit Traceability

Recovering traceability links between issues and commits is important for software maintenance, debugging, impact analysis, and project understanding. However, most existing approaches assume a one-to-one relationship, where each issue is linked to a single commit. In practice, many issues are resolved through multiple commits, and ignoring this one-to-many nature can lead to incomplete traceability. This paper presents LinkRank, a learning-to-rank framework for recovering one-to-many issue--commit links. Unlike existing methods that mainly judge issue--commit pairs independently, LinkRank considers the set of candidate commits for an issue and identifies the commits that are most likely to contribute to its resolution. To support realistic evaluation, we construct a new dataset from six open-source GitHub repositories. LinkRank follows an iterative pick--remove--renormalize strategy: it selects the highest-ranked commit, removes it from the candidate pool, renormalizes the remaining scores, and repeats the process until the stopping criterion is met. We evaluate LinkRank under two settings: Known-K, where the true number of linked commits is available, and Unknown-K, where the model must infer when to stop selecting commits using ABS and REL stopping rules. Across six projects, LinkRank achieves an average Known-K F1 score of 74.54%, compared with 48.39% for the strongest baseline. In the Unknown-K setting, LinkRank achieves 68.84% F1 with ABS and 67.02% F1 with REL, outperforming the strongest baselines under both automatic stopping rules. Overall, the findings suggest that one-to-many issue--commit traceability is better addressed as an issue-centric ranking and iterative selection problem than as independent pairwise classification.

cs.SE↗
arXiv2026

Annealing-enhanced spin-orbit effects in non-centrosymmetric superconducting NbRe films

$\text{Nb}_{0.18}\text{Re}_{0.82}$ (NbRe) is a non-centrosymmetric superconductor with a transition temperature $T_\mathrm{c}$ reaching $9\text{ K}$ in bulk form. While bulk and single-crystalline NbRe exhibit signatures of multigap superconductivity, thin films generally display a single-gap superconducting state due to structural disorder and reduced crystallite dimensions. Here, we investigate the impact of thermal annealing on the superconducting and normal-state magnetotransport properties of NbRe films. The temperature dependence of the upper critical field, $B_{\mathrm{c2}}(T)$, is analyzed within the microscopic Werthamer--Helfand--Hohenberg (WHH) framework, while the normal-state magnetoconductivity is described using the three-dimensional Kawabata weak-localization/weak-anti-localization model. Annealing drives a pronounced change in the electronic response, manifested by a strong weak anti-localization behavior in the normal state and an upper critical field that surpasses both the conventional orbital-limiting field and the Pauli paramagnetic limit. The microscopic analysis reveals a strong intrinsic increase in the relative spin--orbit scattering strength, with the annealed film showing a significantly enhanced spin--orbit-to-dephasing field ratio. These findings provide direct, independent evidence that thermal modification of the NbRe microstructure successfully amplifies spin--orbit-mediated quantum transport, which acts as the key mechanism protecting the non-centrosymmetric superconducting state against paramagnetic pair-breaking well beyond conventional theoretical boundaries.

cond-mat.supr-con↗
arXiv2026

SlimPer: Make Personalization Model Slim and Smart

Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the task: generative models rely on per-token autoregressive prediction, which justifies maintaining large intermediate tensors that scale with sequence length. In contrast, recommendation systems produce a single set of relevance scores for each pair without token-level supervision. Leveraging this observation, we propose SlimPer, which reformulates personalized ranking as iterative refinement of a compact, unified knowledge base. At each layer, the model selectively queries raw multi-modal user-side tokens, computes explicit relevance matching scores, and refines the knowledge base, all in O(N) per-layer cost with a fixed-size intermediate representation. As a result, model depth is decoupled from user history length, enabling deeper relevance understanding without proportional growth in compute or memory; request-only optimization further trims memory by sharing a single copy of user-side tokens across all candidate items. SlimPer unifies sparse, dense, and sequence features within a single backbone and provides inherent interpretability through its attention mechanism. Deployed on Instagram Reels and Feed, SlimPer yields measurable improvements in user engagement while streamlining the overall system and enabling effective modeling of 10k+ fine-grained user history events.

cs.IR↗
arXiv2026

An LLM-powered Agentic Recommendation System for Connected TV Content Discovery

Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse contextual signals, such as trending topics, breaking news, cultural events, and cross-surface user activities, into their ranking pipelines. These systems are designed to consume structured behavioral signals with consistent schemas, and lack the reasoning capability to naturally process unstructured or heterogeneously formatted contextual information. Incorporating such signals typically requires feature engineering, bespoke data pipelines, and carefully tuned heuristics. In this paper, we present an LLM-powered agentic recommendation system designed for Connected TV (CTV) content discovery that addresses these limitations. Our system leverages the reasoning capabilities of large language models to naturally process and synthesize diverse signals across varying schemas and structures, eliminating much of the manual integration inherent in traditional ranking and retrieval systems. Recognizing that current LLM-based solutions still fall short of traditional machine learning models in several recommendation tasks, including retrieval efficiency, personalization precision, and scalability, we adopt an agentic architecture that orchestrates specialized components, allowing each sub-task to be handled by the most suitable method, whether LLM-based or traditional ML. The main contribution of this work is our engineering approach to successfully overcoming the practical limitations of enabling LLM for recommendation, particularly inference latency. We share insights from our work and discuss the trade-offs and lessons learned in building a hybrid system that combines the flexibility of LLMs with the performance of established recommendation techniques.

cs.IR↗
arXiv2026

Refused in Chat, Written in Code: Workflow-Level Jailbreak Construction in IDE Coding Agents

Large language models are increasingly deployed as IDE-integrated coding agents that decompose tasks, generate and edit files, run code, and refine outputs over many turns. Yet their safety is still often evaluated as if they were chatbots: one harmful prompt, one response, judged in isolation. We introduce workflow-level jailbreak construction, a failure mode in which a harmful objective is assembled across ordinary stages of a software-development workflow rather than generated through a single direct prompt. Using GitHub Copilot in Visual Studio Code, we study four closed-weight backends: Claude Sonnet 4.6, Claude Haiku 4.5, Gemini 3.1 Pro, and Gemini 3.5 Flash. Across 204 prompts from Hammurabi's Code, HarmBench, and AdvBench , the models show near-complete refusal under direct chat, CSV-read, and single-step code-fix baselines, with only 8/816 successful responses in each baseline condition. Under the full workflow, however, the same prompts and backends produce 816/816 unsafe teaching-shot completions, all independently confirmed by two expert evaluators under a strict rubric. These results show that conversational refusal benchmarks can substantially overstate the safety of deployed coding agents and motivate defenses that reason about safety across multi-turn IDE workflows and their generated artifacts, not only individual chat turns.

cs.SE↗
arXiv2026

A Post-Quantum Secure Lattice-Based Forward-Secure Identity Based Encryption with Applications to Internet of Things Architecture

The rapid expansion of the Internet of Things (IoT) has led to an unprecedented scale of data exchange across heterogeneous and resource-constrained devices. Ensuring confidentiality and secure key management in such environments is challenging. Traditional public-key infrastructures require heavy certificate-handling overhead. Identity-Based Encryption (IBE) offers a lightweight alternative by deriving public keys directly from device identities, making it attractive for IoT deployments. However, IoT devices are highly vulnerable to side-channel and key-extraction attacks, motivating the need for Forward-Secure IBE(FS-IBE), where the compromise of a current secret key does not threaten past communications. Existing FS-IBE constructions based on classical hardness assumptions are not secure in the era of post-quantum, while the lattice-based (LWE-based) forward-secure scheme suffer from large key and ciphertext sizes, limiting their suitability for constrained IoT systems. Here, we propose a new lattice-based fs-IBE scheme in the ring setting, relying on the RLWE assumption to achieve post-quantum security and significant efficiency gains. Our design uses trapdoor delegation with a minimal-cover mechanism over a binary tree. It results in compact public parameters and efficient per-epoch key updates. Compared to prior LWE-based constructions, our scheme reduces public key, secret key, and ciphertext sizes, and thus, making it better suited for practical IoT environments.

cs.CR↗
arXiv2026

Enginuity: A Dataset and Benchmark for Vision-Language Understanding of Engineering Diagrams

Engineering diagrams pose a distinct challenge for vision-language models: unlike natural images or general documents, they encode information through dense spatial layouts, domain-specific symbols, and cross-references between visual callouts and structured parts tables. Despite their centrality to service, repair, and design workflows, there is no public benchmark for measuring VLM capabilities in this domain; existing datasets primarily focus on flowcharts, scientific figures, or business documents. To address this gap, we introduce Enginuity, the first open dataset and benchmark for evaluating VLMs on complex engineering diagrams. We define two tasks over a corpus of U.S. military service and repair manuals: structured parts-table extraction (Task 1) and free-form visual diagram question answering (VQA)(Task 2) for benchmarking. We evaluate four frontier VLMs (GPT-5.2 Chat, Claude Opus 4.7, Gemma 4, Qwen3-VL-32B-Instruct) under zero-shot and chain-of-thought prompting. On Task 1, models reach Recall@all of 0.61-0.87 but Token F1pen of only 0.03-0.18, exposing a systematic gap between part identification and description fidelity. Task 2 reveals a consistent factual-reasoning gap across all models. A supporting analysis shows that token-overlap metrics under-report model capability on technical descriptions by 2-6x relative to semantic similarity, motivating LLM-as-judge calibration for domain-specific evaluation. We release the dataset, annotations, evaluation harness, and per-sample model outputs to support a reproducible study of VLM capability on engineering content.

cs.CV↗
arXiv2026

Room-Temperature Electric-Field Control of Anomalous Hall Effect in Py/BTO/LSMO Heterostructures

We demonstrate room temperature electric field control of the anomalous Hall effect in epitaxial Ni80Fe20 (Py) BaTiO3 (BTO) La0.7Sr0.3MnO3 (LSMO) thin film heterostructures grown on MgO and LaAlO3 substrates. Substrate induced strain states generate distinct magnetic anisotropies, enabling voltage driven tuning between anomalous and topological Hall contributions. Robust ferroelectric polarization in BTO, confirmed by piezoresponse force microscopy, couples strongly to interfacial orbital reconstruction and carrier redistribution. As a result, Hall resistivity exhibits giant low voltage tunability, with up to nearly 93 percent modulation at operating voltages of only 0.5 tand 2 V. Density functional theory calculations further reveal polarization controlled Rashba spin splitting, establishing a direct link between ferroelectric order and emergent quantum transport. These findings establish Py/BTO/LSMO heterostructures as promising candidates for low-power multifunctional spintronic devices, where substrate engineering enables control over emergent quantum transport phenomena.

cond-mat.mtrl-sci↗
arXiv2026

CoMIC: Collaborative Memory and Insights Circulation for Long-Horizon LLM Agents in Cloud-Edge Systems

Deploying lightweight Large Language Model (LLM) agents on edge servers can reduce latency and move agentic services closer to users, but resource-constrained edge models often struggle with long-horizon tasks that require persistent memory, subgoal tracking, and reflection. Fine-tuning edge models after deployment is costly and difficult to scale across heterogeneous nodes, while purely local memory leaves agents with isolated experience and growing prompt context. We propose \textsc{CoMIC}, a parameter-update-free cloud-edge framework for Collaborative Memory and Insights Circulation. \textsc{CoMIC} follows a \textit{Centralized Reflection, Decentralized Execution} design: edge agents execute locally using subgoal-oriented hierarchical memory and selective re-expansion of relevant histories, while a cloud-side LLM critic asynchronously evaluates completed trajectories, filters reusable experience, and aggregates cross-agent guidance keyed by semantic subgoal identifiers. Across five long-horizon agent tasks spanning symbolic planning and text interaction, \textsc{CoMIC} improves progress rate and action grounding for weak edge agents and yields task-dependent success-rate gains without updating model parameters.

cs.AI↗
arXiv2026

Autonomic Federated-Market Orchestration for the Edge-Cloud Continuum

The edge-cloud computing continuum demands self-management mechanisms that scale across autonomous administrative domains while honouring tenant- and operator-specified data sovereignty. We present Neural Pub/Sub, a federated-broker autonomic substrate whose self-organising behaviour emerges from market-based price signals rather than centralised control. Its MAPE-K control loop closes over per-broker health and load monitoring, marginal-cost clearing-price analysis, placement planning over a polymatroidal feasibility region, federated cross-domain dispatch, and shared peer subscription summaries with bounded-staleness price signals. The Plan step is anchored in a Walrasian convergence proposition: under gross-substitutes valuations on tree and series-parallel service-dependency DAGs, decentralised price-based allocation matches the welfare of a centralised oracle. We evaluate the substrate on a 4-VM, 4-domain, 48-worker federated edge-cloud testbed (single data centre, 50 ms emulated WAN) in a 1005-run campaign augmented by a fair-process-count sharded-oracle comparator. The federated market dominates a single-process oracle by 2-4% with 45 of 45 per-seed wins (sign-test p ~ 2.8e-14, Hodges-Lehmann median -39.6 ms); against a four-shard centralised orchestrator at equal process count the gap stays within +/-1.5% across all nine (pipeline, load) cells. Round-robin completion rate collapses 98.8% -> 22.4% -> 3.3% across arrival rates 5/10/15 pps while the market preserves completion; the advantage decomposes into three Walrasian properties (information completeness, admission control, price discovery). Federation withstands broker death and network partition (completion rate >= 98.7% across 75 cells), and sovereignty enforcement adds no measurable runtime overhead across 60 governance-grid runs. Heterogeneous-domain stressors and cross-site WAN deployment remain future work.

cs.DC↗
arXiv2026

Nitrogen-doped W0.75Re0.25 Superconducting Nanowire Single Photon Detectors

Nitrogen-doped Tungsten-Rhenium superconducting alloys were recently proposed as a promising material platform for superconducting nanowire single-photon detectors (SNSPDs), offering a favorable balance between high normal state resistivity and tunable superconducting properties. In this work, we report on the fabrication and characterization of SNSPDs based on thin W0.75Re0.25 films deposited by reactive DC magnetron sputtering in a mixed Ar/N2 atmosphere. Meander detectors with 70 nm linewidth exhibit saturated internal detection efficiency (IDE) up to 1310 nm and 85.3% IDE at 1550 nm at 2.5 K, with sub-nanosecond rise times, decay times of the order of a few nanoseconds, and timing jitter of 73.2 ps measured with room temperature amplifiers.

cond-mat.supr-con↗
arXiv2026

Neural Router: Semantic Content Matching for Agentic AI

Large language models (LLMs) can serve as the semantic-matching engine of a content-based publish/subscribe broker for agentic AI across the edge-cloud computing continuum, bridging the vocabulary and modality gaps that defeat keyword and embedding filters. Framed as offline multi-label retrieval over three public datasets spanning social-media, legal, and smart-home sensor domains (six LLMs, seven baselines), our central contribution is a two-crossover cost-accuracy characterisation: an analytical context-window crossover below which a CoverAndMerge compression pipeline reduces LLM invocations, and an empirical discrimination-capacity crossover above which matching accuracy collapses independently of context budget, by a model-dependent factor of parameter count and training generation. Two findings carry practical weight: above the discrimination crossover, compression cannot recover accuracy and only frontier-scale models clear large subscription sets; and there backend choice dominates configuration choice, so model selection, not pipeline tuning, is the primary operator lever. We accompany this with three composable algorithms and a per-cluster Quality-of-Experience framework for autonomic LLM-tier selection.

cs.DC↗