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cs.ET: explore 91 source-linked works published from 2025 to 2026, with original documents and citations.

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Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.

Sources: arxiv. Collection updated 2026-09-16. Counts describe this index, not the complete source archives.

arXiv2026

multi-Stochastic Core Architecture for Scaling Probabilistic Ising Machines

Ising Machines offer vast potential to solve NP-hard optimization problems efficiently that are intractable to solve using conventional computing architecture. A lot of these optimization problems fall into statistical learnability and involve finding an optimal solution among many possible, near-analogous configurations, by searching in a non-convex energy landscape. In this context, the probabilistic Boltzmann machine architecture especially PASS (Parallel Asynchronous Stochastic Sampler), explores and models the complex probability landscape pertaining to all possible configurations and excels in finding the ground-state energy solution of these intractable problems. Additionally, the noise-based neuron architecture addresses the limitation of conventional annealing methods, which may get stuck around local minima. Here, we demonstrate a stochastic sampling approach based on Block Gibbs Sampling to integrate multiple asynchronous PASS chips (four in this work), enabling improved scalability. Further, we demonstrate the scaling by mapping 784 nodes Max-Cut problem integrating 256 nodes PASS accelerator manufactured in 14 nm CMOS FinFET technology. PASS-enabled system with Block Gibbs Sampling protocol shows approximately 1000 times speedup for Max-Cut optimization compared to state-of-the-art methods implemented on CPUs and GPUs. The general applicability of this approach is further illustrated by solving a quantum spin chain Transverse Ising system and accurately representing complex probability landscapes. Moreover, our results demonstrate the change in the scaling law to constant in the scaled-PASS accelerator as compared to exponential on GPUs enabling at least 4 orders of magnitude improvement in time-to-solution. Hence, the presented methodology enables the pathway for scaling of asynchronous brain-like dynamics systems that do not follow any clock for its operation.

cs.ET↗
arXiv2026

Differentiable Partitioning With Dual-Max 3D Wirelength Model and Hybrid Bonding Terminal Awareness for Optimized 3D Placement

Research on 3D integrated circuits (3D-ICs) physical design has expanded rapidly in recent years, driven by the increasing adoption of hybrid bonding and the demand for higher integration density. Continued scaling of hybrid-bonding pitch supports higher vertical interconnect density and finer-grained circuit partitioning in 3D-ICs. Hybrid bonding-enabled 3D-ICs offer substantial benefits in interconnect scaling and system integration, yet tier assignment remains challenging because it jointly determines 3D wirelength and hybrid bonding terminal (HBT) assignment. This paper presents a differentiable partitioning framework that directly optimizes placement-aware tier assignment for 3D-ICs through gradient-based optimization. Discrete tier assignment is relaxed to continuous probabilities, and a Dual-Max 3D wirelength model is introduced as a placement-aware surrogate for tier-specific half-perimeter wirelength (HPWL). In addition, a terminal-aware cutsize penalty gives higher weights to cross-die nets with predicted HBT overlaps, and a local balance penalty discourages regional area overflow on each tier. On ten OpenROAD benchmarks, our method reduces final die-to-die (D2D) HPWL by 8.05% and 14.81% on average relative to hMETIS and TritonPart, respectively. On the seven benchmarks with available CoPlace results, the average HPWL reduction is 10.33%. The source code for the differentiable partitioner and the integrated 3D placement flow is publicly available to support reproducibility.

cs.ET↗
arXiv2026

Phase-cycled randomized benchmarking of quantum processors: recovering hidden classical noise correlations

Randomized benchmarking can hide classical temporal correlations because its Clifford-twirled response is even in the noise phase. For a stationary symmetric telegraph fluctuator, we show that continuous evolution and independent stationary resets at slot boundaries yield identical mean responses for arbitrary fixed idle modulations. We construct an eight-setting phase-cycle measurement of the connected sine-phase covariance under ideal Clifford twirling and classical idle dephasing. This observable vanishes for independent slot noise and fixed detuning without a weak-phase or Gaussian approximation. A closed telegraph response, independent circuit calculations and 800 simulation trials validate the construction and quantify the empirical coverage of a paired bootstrap estimator. A separate conservative confidence set states its finite-sample assumptions. Two acquisitions on an IBM processor compare engineered shared-sign and independently reset phases with identical marginals. Their primary contrasts are 0.254 and 0.211, with empirical 95% intervals [0.177, 0.331] and [0.136, 0.285], respectively; all negative-control intervals include zero. At equal shot and sensingwindow budgets, an ideal Ramsey/echo estimator is more precise in every tested class. The result supplies an explicit connection between a benchmarking identifiability limitation and a controlled correlation measurement. No native or quantum-memory detection is claimed.

cs.ET↗
arXiv2026

Forward-only learning in memristor arrays with month-scale stability

Turning memristor arrays from efficient inference engines into systems capable of on-chip learning has proved difficult. Weight updates have a high energy cost and cause device wear, analog states drift, and backpropagation requires a backward pass with reversed signal flow. Here we experimentally demonstrate learning on standard filamentary HfOx/Ti arrays that addresses these challenges with two design choices. First, we rely on forward-only training algorithms in the Forward-Forward family that use only inference-style operations. Second, we use sub-1 V reset-only, single-pulse updates that cut energy and yield stable analog states. We first validate fully in-hardware single-layer learning, where both analog MAC operations and sub-1 V programming updates are performed on chip. We then train two-layer classifiers on an ImageNet-resolution four-class task using a hardware-in-the-loop protocol on arrays up to 8,064 devices: all conductance reads and programming updates are physical, and multilayer MACs are emulated in software from the measured conductances. Two forward-only variants, two-pass supervised Forward-Forward and a single-pass competitive rule, achieve test accuracies of 89.5% and 89.6%, respectively; a reference experiment using backpropagation reaches 90.0%. Across five independent runs per method, these accuracies are indistinguishable within statistical uncertainty. Trained models retain accuracy for at least one month under ambient conditions and remain stable under an aggressive 25 h read-disturb protocol, whereas a matched set-based Write&Verify baseline loses nearly three percentage points of test accuracy. At the array level, sub-1 V reset updates use 460 times less energy than conventional program-and-verify programming and require just 46% more energy than inference-only operation.

cs.ET↗
arXiv2026

Towards Enabling Distance-Based Memory Addressing

Approximate Nearest-Neighbor Search (ANNS) in high dimensional vector datasets is an application of significant prevalence across different AI applications. However, such an operation is significantly bandwidth limited at large workingset sizes owing to the curse of dimensionality. Traditional indices used to accelerate ANNS rely on search-space pruning as a preprocessing step to alleviate such bandwidth requirement, but such optimization occurs either at the cost of increased bandwidth-inefficiency and/or degradation of search quality. This paper proposes a data-parallel hardware/software mechanism for performing large-scale similarity search in-memory. We propose a novel algorithm to simplify the computation requirement for similarity search across various distance metrics through lightweight primitives to perform a fast and approximate data-parallel brute-force search on the entire vector space. We further build a memory system capable of executing the required operations to generate a distance metric per datapoints, which is then used to enable pruning as a post-processing step. We offer adequate software support for user control over the proposed system. By enabling such search-space pruning as a post-processing step, we achieve near-perfect recall across representative workloads while achieving orders of magnitude performance and energy improvement over state-of-the-art algorithmic approaches on million and billion-scale workloads.

cs.AR↗
arXiv2026

An FPGA-ASIC Co-Design Framework for Capacity-Constrained Physics-Based Ising Chips

When a problem exceeds an analog Ising machine's spin capacity it cannot be solved in one shot: a digital orchestration layer must iteratively decompose the graph, clamp boundary spins, and deliver hardware-sized subproblems to the solver. As per-subproblem solve times reach the us-scale regime, this digital layer (not the analog core) becomes the primary scalability bottleneck. On a 28 nm CMOS coupled-oscillator Ising chip (T_core ~ 77.5 us, 24 mW), a CPU orchestrator leaves the solver idle for over 98% of every iteration at N=750, and the gap survives OpenMP and AVX2 optimization because it is dominated by data-dependent memory access rather than arithmetic throughput. We argue that the orchestration layer is a first-class design object and propose a sizing methodology for hybrid analog-digital Ising systems: given a solver's core time, clock frequency, and target problem class, it gives an analytical baseline for hardware parallelism and memory-bandwidth dimensioning. Guided by the sizing laws, an FPGA orchestration layer co-located with the chip satisfies the pipeline condition with substantial margin. Across graph coloring, MaxCut, and SAT benchmarks it delivers 9.8x-46.5x raw end-to-end time-to-solution (TTS) improvements (14.8x-65.9x on per-repeat runtime) over the optimized CPU orchestrator, with device-level power reductions of 85x-116x.

cs.ET↗
arXiv2026

Improving Federated Graph Recommendation with Semantic Guidance

Graph-based recommendation models effectively capture high-order collaborative signals from user--item interaction graphs. Federated learning (FL) enables privacy-preserving training across distributed clients. However, directly aggregating graph representations under FL is challenging: locally learned structural embeddings are not globally aligned under non-IID data distributions, and naive parameter averaging fails to recover cross-client relational structure. Existing federated graph-based approaches primarily rely on structural aggregation, yet overlook the global semantic knowledge encoded in large language models (LLMs). In this work, we propose a semantic--structural federated graph recommendation framework that leverages LLM embeddings to guide cross-client alignment. Each client learns user representations from its local interaction graph and summarizes typical interaction patterns into compact semantic vectors using a frozen LLM encoder. These vectors are sent to the server, which identifies semantically related patterns across different clients and combines their structural representations accordingly. The updated representations are then returned to clients to refine subsequent local training. This design enables collaboration guided by shared semantic understanding without exposing raw interaction data, preserving both recommendation accuracy and privacy. Experiments on benchmark datasets demonstrate consistent improvements over existing federated graph-based baselines.

cs.IR↗
arXiv2026

Hidden In Plain Gaze: Gaze Representations as Privacy Controls for Utility and Re-identification Risk in XR

Intelligent extended reality (XR) systems increasingly use eye and head tracking to infer user intent, task, and attention, but the same signals can also reveal biometric identity. We study whether gaze data representation choice can serve as a lightweight privacy control at feature extraction, before adding perturbation or formal privacy mechanisms. Using the egocentric HoloAssist dataset, we compare three gaze representations under matched model capacity: raw gaze, spatial attention heatmaps, and engineered eye-movement features. We evaluate each representation on action recognition as task utility and closed-set user re-identification as privacy leakage. Representation choice substantially changes the privacy-utility tradeoff. Engineered features retain roughly 85% of raw gaze's action-recognition accuracy while reducing re-identification by about an order of magnitude, to roughly four times the chance rate across 206 identities. This reduction attenuates rather than eliminates identity leakage, and the differences across representations show that abstraction alone does not guarantee privacy. Engineered features expose interpretable and auditable structure, giving designers a transparent privacy lever that complements mechanisms such as differential privacy.

cs.CV↗
arXiv2026

Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks

As quantum hardware scales to larger devices, the classical software layers that interface with it must evolve in step. Postprocessing routines developed and tested primarily in simulator settings can encode assumptions that no longer hold on utility-scale devices, leading to data loss that can be difficult to detect from high-level model outputs alone. We present a case study of \texttt{SamplerQNN}, the sampling-based quantum neural network class in the Qiskit Machine Learning library. Here, the postprocessing method applies a filter that assumes measurement bit-strings are in virtual qubit space. On our quantum hardware runs, where bit-strings span over 100 physical qubits, this filter led to the loss of 85 to 99.6\% of valid measurement shots, depending on the transpiler's qubit placement. The resulting probability vector is unnormalised, allowing distorted prediction and loss values to propagate through the model without an API-level warning. We demonstrate the impact across five experiments on two IBM backends: for inference, accuracy drops from 0.94 to 0.39 on the same raw measurements; for training, the loss signal is compressed by 22 to 27$\times$, substantially reducing the sensitivity of the optimiser to the objective landscape. The behaviour arises in all released versions of the library (0.8.4 to 0.9.0). We implemented a layout-based marginalisation fix, merged into the GitHub codebase as Pull Request \#1041, that makes \texttt{SamplerQNN} postprocessing forward-compatible with current and upcoming hardware.

quant-ph↗
arXiv2026

Beyond Maintenance Manual Multimodal RAG: Suggesting What Tool

Aircraft technicians are required to consult the maintenance manual (MM) for nearly every task, and locating the relevant procedure across hundreds of pages remains time-consuming. Multimodal retrieval augmented generation (MRAG) has been proposed to address this, allowing technicians to retrieve procedures, together with the accompanying figures, through natural-language queries. However, retrieval alone does not tell the technicians which tools the task requires. The MM identifies special tools only when the corresponding step is reached, and it does not state hand tool requirements at all; to select hand tools, technicians are required to find the hardware dimension from the illustrated parts catalog (IPC) and infer the right tool from it. We therefore propose MRAG-SWAT, an extension of the MRAG pipeline that returns the required hand tools and special tools alongside the retrieved procedure. The framework was implemented for the Lycoming IO-360-N1A engine and demonstrated on eight test queries. By presenting the correct tools together with the procedure, MRAG-SWAT may help reduce repeated trips to the tool crib, prevent damage to aircraft caused by improper tool selection, and thereby avoid additional maintenance tasks and support continued airworthiness.

cs.ET↗
arXiv2026

Hatebench in the era of safer LLMs

As Large Language Models (LLMs) lower the barrier for au- tomated content generation, the potential for producing hate speech poses a significant challenge for digital safety. This paper presents a reproducibility study of the HateBench paper by Shen et al., investigating whether existing hate speech detectors, typically trained on human-authored data, generalize to LLM-generated hateful content, and evaluating whether their reported weaknesses are stable over time and robust to evolving components. We independently reconstruct the original dataset genera- tion pipeline using modern LLMs and extend the benchmark to include recently released models and updated detector versions. Our independent assessment under current con- ditions finds that for newer LLMs, safeguards have been put into place to prevent the generation of harmful content. We also replicate the results for two sophisticated types of hate campaigns. While the original findings seem to have been overestimated slightly due to bias in the datasets, the overall findings can be confirmed. Finally, we compare text- Moderation against the newer omni-Moderation and find that its robustness against adversarial hate campaigns has improved slightly. By clarifying which detector vulnerabil- ities persist, this study informs the community about the longevity of content moderation measurements.

cs.ET↗
arXiv2026

Proton Irradiation Characterization of an Open-Source ML Accelerator on a Zynq UltraScale+ MPSoC

As spaceborne computing systems increasingly rely on neural network (NN) accelerators, the opacity of commercial, black-box architectures severely restricts the development of verifiable radiation mitigation strategies. Open-source, register-transfer level (RTL)-accessible accelerators resolve this limitation by enabling user-defined instrumentation, yet few have empirical radiation-response baselines. This work establishes a foundational system-level proton-irradiation baseline for an unmitigated open-source Tensil NN accelerator deployed on a Zynq UltraScale+ SoC executing ResNet-20 inference. Under 20 to 58 MeV proton irradiation, we delivered $4.29 \times 10^{10}$ p/cm$^{2}$ within monitored operational windows. Seven workload interruptions required two restarts of the notebook process, four reboots or board resets, and one power-cycle sequence. Two output-corruption events returned incorrect CIFAR-10 classes without loss of service. In the longer event, the accelerator returned a class absent from the ten-image CIFAR-10 pool for 39 consecutive inputs at normal cadence. The process remained alive, while the kernel log, limited memory test, and sampled power showed no anomaly. Observation of the stuck-class sequence ended with scheduled bitstream reconfiguration. All nine onsets occurred under the nominal 4 cm beam, which exposed the SoC, LPDDR4, and additional board circuitry; none occurred under the 2 cm SoC-centered field. This pattern shows a field association but does not establish LPDDR4 as the cause because field size was confounded with run order and dose. Linux-managed accelerators require end-to-end content checks and recovery that reaches the state in which corruption can persist. This baseline documents availability loss and silent output corruption, supporting future software hardening of COTS FPGA-SoCs for neural-network inference in space systems.

cs.AR↗
arXiv2026

Towards Scaling Quantum Fine-Tuning of Foundational Time Series Models for Classification

Time-series foundation models produce rich embeddings, but whether quantum models can exploit them, and how far hybrid classical-quantum architectures scale, remains unclear. We address this by fine-tuning Chronos for power-grid event classification (PSML-5) with a quantum head on the model's embeddings. Grouping embeddings by physical sensor type before summarization already surpasses the best published baseline built for this benchmark, and with finer-grained features the quantum head outperforms a larger classical multilayer perceptron on identical inputs by 1.7-2.0 percentage points of balanced accuracy. Yet the gains saturate: past a point, feeding more information to the same fixed-width register yields no improvement. We show the bottleneck is neither the supply of information nor circuit expressiveness, but the bandwidth of the data intake. To overcome this limitation, we introduce the wing module, a self-contained few-qubit circuit that feeds additional information into the core circuit through a sparse, one-way coupling. Under a preregistered four-seed protocol, we attach wings to a fixed 12-qubit core with fixed features. Balanced accuracy increases with each added wing, from 83.6% with no wings (13 qubits, including a post-selection qubit) to 85.2% with two (19 qubits). Ablations establish that a circuit enlarged without new information gains nothing, while a wing fed information from the wrong sample harms accuracy. These results reframe scaling for quantum fine-tuning: added qubits help when they carry added inputs, not merely more parameters. Wings offer a modular and stable route to widening that bandwidth.

quant-ph↗
arXiv2026

Temperature Scaling Attack Disrupting Model Confidence in Federated Learning

Predictive confidence serves as a foundational control signal in mission-critical systems, directly governing risk-aware logic such as escalation, abstention, and conservative fallback. While prior federated learning attacks predominantly target accuracy or implant backdoors, we identify confidence calibration as a distinct attack objective. We present the Temperature Scaling Attack (TSA), a training-time attack that degrades calibration while preserving accuracy. By injecting temperature scaling with learning rate-temperature coupling during local training, TSA shifts model confidence while keeping predictive accuracy and common optimization signals close to benign training. We provide a convergence analysis under non-IID settings, showing that the coupling controls the primary update scale while leaving a bounded temperature-induced residual, yielding the standard non-convex FL convergence structure with an additional residual term. Across three benchmarks, TSA substantially shifts calibration (e.g., 145% error increase on CIFAR-100) with <2% accuracy change, and remains effective under robust aggregation and post-hoc calibration defenses. Case studies further show up to a 7.2x increase in missed verifications in healthcare and severe confidence-gating failures in autonomous driving, even when accuracy is unchanged. Overall, our results establish calibration integrity as a critical attack surface in federated learning.

cs.LG↗
arXiv2026

GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models

Environmental scientists spend disproportionate effort on data wrangling rather than analysis. New AI agents can be a helpful tool, but no benchmark exists to evaluate AI agents that automate environmental geospatial workflows through structured tool calling against real APIs. We introduce the GeoNatureAgent Benchmark, the first benchmark for environmental analysis agents that operate via structured tool calls to a production-style geospatial API. The benchmark comprises 93 tasks across 18 categories. Tasks are evaluated against an open, self-hostable geospatial API that serves three environmental indicators across Spain and Portugal via sixteen tools. We evaluate nine frontier and open-weight LLMs, reporting capability and per-case cost as orthogonal axes. Results manifest that (1) Claude Sonnet 4 achieves the highest capability at 60.8% +/- 0.8%, followed closely by DeepSeek V3.2 at 56.3% +/- 3.1%, while no other model exceeds 51%; (2) the cost-accuracy Pareto frontier is occupied mostly by open-weight models, with DeepSeek V3.2 offering 93% of Claude's capability at 11.6x lower cost; and (3) structured tool calling against a real API provides a more discriminative measure of real-world agent capability, with mean accuracies 25-35 percentage points below those reported on general-purpose GIS benchmarks.

cs.AI↗
arXiv2026

Beyond the Trust Boundary: A Critical Reassessment of the FIDO2 Threat Model

FIDO2/WebAuthn has been widely deployed as a phishing-resistant authentication scheme. Because FIDO2 relies on public-key cryptography and hardware-backed authenticators, its security is often assumed to be guaranteed by design, provided that the cryptographic implementation is correct. In this work, we critically reassess the FIDO2 threat model and show that several commonly assumed security properties do not hold under realistic deployment conditions. We extend the threat model beyond the cryptographic layer to examine eight attack vectors across the FIDO2 stack: malicious browser extensions, platform-handler malware, passive sniffing, virtual device drivers, CTAP2-specific malware, USB/hardware implants, malicious USB hubs/docks/extenders, and NFC relay attacks. Our analysis shows that FIDO2 depends on environmental assumptions that may not hold in practice. We demonstrate how AAGUID and timing information can enable user profiling and targeted attacks, and how compromise of the browser, operating system, or hardware can undermine FIDO2 security even when the underlying cryptographic primitives remain uncompromised. We further show that attack chains spanning multiple layers can bypass the intended security guarantees of FIDO2. These findings indicate that the primary weakness in a FIDO2 deployment is often not the cryptographic layer, but the surrounding trusted environment. We also examine how these attack vectors can undermine device attestation by targeting the FIDO Metadata Service (MDS3), which serves as a root of trust for authenticator metadata. Finally, we characterize the attacks according to privilege, skill, and resource requirements. We conclude that effective FIDO2 security requires layered mitigations covering the browser, operating system, hardware, protocol stack, and metadata infrastructure.

cs.CR↗
arXiv2026

STEMPix: A Phase-Transition-Material-Based Pixel Sensor for Resolving Edge-Movement Direction

This paper proposes a spatio-temporal edge-movement direction pixel (STEMPix) for generating compact direction-aware edge movement information inside a CMOS-compatible image sensor array. The proposed design targets specialized sensing applications where local boundary movement is more important than full-frame intensity reconstruction. Instead of transferring full multi-bit frames for external processing, STEMPix generates a 3-bit local edge direction code (LEDC) by combining pixel-level temporal change information with neighboring-pixel spatial edge information. We design the architecture using a two-tier organization, where the photodiode layer is separated from the computation layer to preserve light-collection area while accommodating the additional in-array processing circuitry. The proposed circuit is evaluated through HSPICE transient simulations. The estimated implementation achieves a horizontal pitch of 1.73 μm, a vertical pitch of 2.36 μm, and a geometric fill factor of 95.47%. The average active switching energy is 0.465 fJ per LEDC operation across representative edge-movement cases. The proposed STEMPix operation also supports global-shutter capture and dynamic thresholding. These results indicate that STEMPix can provide a compact and scalable front-end representation for edge-movement-aware sensing systems.

cs.ET↗
arXiv2026

Differential Space-Time Block Coding for Phase-Unsynchronized Cell-Free MIMO Downlink

In the downlink of CF-mMIMO systems, spectral efficiency gains critically rely on joint coherent transmission, as all APs must align their transmitted signals in phase at the UE. Achieving such phase alignment is challenging, as it requires tight synchronization among geographically distributed APs. In this paper, we address this issue by introducing a DSTBC approach that bypasses the need for AP phase synchronization. We first provide analytic bounds to the achievable spectral efficiency of CF-mMIMO with phase-unsynchronized APs. Then, we propose a DSTBC-based transmission scheme tailored to CF-mMIMO, which operates without CSI and phase synchronization among the APs. We derive a closed-form expression for the resulting SINR, enabling quantitative comparisons among different DSTBC schemes. Numerical simulations confirm that phase misalignments can significantly impair system performance. In contrast, the proposed DSTBC scheme can mitigate these effects, achieving performance comparable to that of fully synchronized systems. However, when more than two APs jointly serve a UE, the code rate of DSTBC schemes can limit their SE gains. Hence, we also investigate DQO-STBC schemes that achieve full code rate by relaxing the orthogonality constraints.

cs.IT↗
Compare source metadata on this page
WorkPublishedSource identifierSource
multi-Stochastic Core Architecture for Scaling Probabilistic Ising Machines2026-09-062609.06365arxiv
Differentiable Partitioning With Dual-Max 3D Wirelength Model and Hybrid Bonding Terminal Awareness for Optimized 3D Placement2026-10-092609.06408arxiv
Phase-cycled randomized benchmarking of quantum processors: recovering hidden classical noise correlations2026-09-062609.06448arxiv
Forward-only learning in memristor arrays with month-scale stability2026-09-052601.09903arxiv
Towards Enabling Distance-Based Memory Addressing2026-10-052609.06270arxiv
An FPGA-ASIC Co-Design Framework for Capacity-Constrained Physics-Based Ising Chips2026-09-042602.15985arxiv
Improving Federated Graph Recommendation with Semantic Guidance2026-09-042606.15277arxiv
Hidden In Plain Gaze: Gaze Representations as Privacy Controls for Utility and Re-identification Risk in XR2026-09-042609.04592arxiv
Impact of Data Loss in Postprocessing on Training and Inference of Quantum Neural Networks2026-09-042609.05060arxiv
Beyond Maintenance Manual Multimodal RAG: Suggesting What Tool2026-09-042609.05116arxiv
Hatebench in the era of safer LLMs2026-09-042609.05169arxiv
Proton Irradiation Characterization of an Open-Source ML Accelerator on a Zynq UltraScale+ MPSoC2026-09-042609.05249arxiv
Towards Scaling Quantum Fine-Tuning of Foundational Time Series Models for Classification2026-09-042609.05408arxiv
Temperature Scaling Attack Disrupting Model Confidence in Federated Learning2026-09-032602.06638arxiv
GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models2026-09-032606.12821arxiv
Beyond the Trust Boundary: A Critical Reassessment of the FIDO2 Threat Model2026-09-032609.03789arxiv
STEMPix: A Phase-Transition-Material-Based Pixel Sensor for Resolving Edge-Movement Direction2026-09-032609.04435arxiv
Differential Space-Time Block Coding for Phase-Unsynchronized Cell-Free MIMO Downlink2026-09-022511.06510arxiv

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