Peer Revenue Publish or profit

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Computer Science Emerging Technologies

cs.ET Watch Emerging Technologies

Sunday 16 August

Emerging Technologies · Cross-listed from quant-ph

Rand-SEMI-QAOA: Finite-Budget Depth-One MaxCut Ensembles on Compressed Quantum Registers

Emilio Semre and Steven Frankel

We introduce Rand-SEMI-QAOA, a finite-budget QRAO–QAOA ensemble for MaxCut based on a $(3,1)$-QRAC relaxation. The method samples labels uniformly without replacement from the product-$X$ family of an implemented Galois stabilizer mutually unbiased basis (MUB) system. Each selected state–mixer pair is optimized independently for relaxed QRAO energy and then evaluated by deterministic Pauli-sign decoding. Exhaustive scans of the implemented noncomputational MUB catalog place the product-$X$ family first in 18 of 20 validated family-mean cells and in every tested cell for $r=5,6,7$. On a complete cohort of $2{,}400$ random connected 3-regular MaxCut instances with $n\in\{18,20,22\}$, the capped matched-cardinality schedule attains a graph-mean decoded best-of-set approximation ratio of $0.9421$ with one QAOA layer, while the $K=r^2$ schedule attains $0.9319$. These statistics are conditional on the disclosed frozen selector pools and do not estimate variability over selector seeds. An exact gauge identity shows that product-family labels generate the orbit of the QRAO Hamiltonian under an $r$-dimensional sign-gauge group. For common angles and relaxed energy, deterministic syndrome analysis proves branchwise dephasing, while an anisotropic Gaussian surrogate controls the coherent label-averaged response on the scale $\beta=b/r$. The theory does not order independently optimized decoded maxima. The results are finite-size and resource-explicit and do not establish quantum advantage.
Read the full abstract

Emerging Technologies · Cross-listed from cs.SE

Kozuchi Agent: A Language-Agnostic Open-Weight Agent for Software Repair

Mehdi Bahrami, Kosaku Kimura, Satoshi Munakata et al. (+24 more)

Industrial software-engineering teams increasingly need LLM agents that turn bug reports into correct patches, yet benchmark-scale operation adds long horizons, tool-use discipline, context persistence, heterogeneous clusters, and evaluation reuse. We present Kozuchi Agent, a language-agnostic open-weight repair agent and CI-operated evaluation pipeline. Explicit phases, persistent state, deterministic tools, a model-independent action interface, and cross-agent test-time selection make runs auditable and repeatable. With locally hosted Qwen3.5-27B, no fine-tuning, and TTS@8, Kozuchi resolves 374/500 SWE-bench Verified instances on the official evaluator. Unchanged on Multi-SWE-bench Java, the same 27-billion-parameter agent resolves 41/128 instances (32.03%), ranking first among strict open-weight submissions and fourth of 42 overall; on Python it ranks 12th of 135 and first among open-weight systems. Per-phase behavior remains within +/-5 percentage points across languages. Remaining failures mainly reflect semantic correctness, Java-specific harness issues, and selection errors. Across both tracks, results compare favorably with open/local peers by parameter count. Analysis of candidate diversity, selector regret, and patch reliability shows that the remaining gap is primarily semantic correctness and selection rather than edit formatting or proprietary-model access. Operationally, reusable CI stages reduce operator touch-points from five to one across heterogeneous internal clusters.
Read the full abstract

All authors Mehdi Bahrami, Kosaku Kimura, Satoshi Munakata, Satoshi Nakashima, Yu Ishikawa, Kosuke Maeda, Nao Soma, Kenichi Kobayashi, Keisuke Miyazaki, Keizo Kato, Shigeki Fukuta, Tatsuo Kumano, Nobutaka Imamura, Kevin Musgrave, Shahbaz Abdul Khader, Kwun Ho Ngan, Joe Townsend, Fayas Asharindavida, Matthieu Parizy, Akira Sakai, Yuma Ichikawa, Yang Zhao, Michiaki Takizawa, Taku Fukui, Hiroki Ohtsuji, Wei-Peng Chen, Hiromichi Kobashi

Saturday 15 August

Emerging Technologies · Cross-listed from cs.NI

ST-NDT: A Topological Framework for Reducing Communication Overhead in Network Digital Twins

John Sengendo and Fabrizio Granelli

Next-generation networks (NGN) are becoming increasingly complex, especially with the increasing size of topologies. To manage this complexity requires the adoption of frameworks that enable real-time monitoring, optimization, and “what-if” case scenario analysis. Network Digital Twins (NDTs) have emerged as a key enabler technology for supporting these capabilities because of their ability to provide digital replicas of physical networks operations, enabling scenario testing without interfering with the live network. Although NDTs are considered a key enabler technology, maintaining continuous synchronization with the Physical Twin (PT) introduces considerable communication overhead and excessive bandwidth utilization, limiting sustainability in resource-constrained networks. This paper proposes a topological signal processing framework for sparse network monitoring in NDTs. We represent network edge flows as signals defined on a cell complex, and exploit the Hodge spectral structure of the graph to identify a minimal set of maximally informative sensor edges, thereby reducing the measurement overhead between the physical network and its digital twin replica. Results show that the proposed Sparse Topological Network Digital Twin (ST-NDT) framework consistently outperforms all four baseline frameworks across most of the edge monitoring budgets. For example, at a 20% monitoring budget (48 of 243 edges monitored), ST-NDT achieves improvements of 10.85%, 11.24%, 7.36%, and 6.15% over degree-based, core-number, betweenness centrality, and random-selection frameworks, respectively.
Read the full abstract

Emerging Technologies

Conditional Dynamical Systems for Image Generation

Lianlong Sun, Chuan Liu, Yongchao Liu et al. (+3 more)

Image generation is dominated by deep generative models on GPUs, whose computational and energy costs raise sustainability concerns. Emerging non-von Neumann substrates, including quantum, compute-in-memory, photonic, and thermodynamic platforms, promise greater efficiency, yet most work ports conventional neural network architectures onto them and mainly accelerates operations such as matrix multiplication. This does not fully exploit a native capability of many hardware substrates: relaxation toward low-energy states can itself compute at little cost. Building on this primitive, we develop dynamical systems for image generation based on Ising spins and Kuramoto oscillators. The internal states of both models evolve under dynamics with an explicit Lyapunov energy, and a compact decoder renders the final states as images. For conditional generation, we introduce local conditioning: programmed interactions remain fixed, and one class-dependent bias per node reshapes the landscape, avoiding additional reprogramming and calibration costs. On CIFAR-10, the Ising and Kuramoto models reach clean-FID scores of $6.40$ and $5.37$, respectively, with $4096$ nodes. With a CMOS-compatible hardware realization, the dynamical-system core is projected to cost ${\sim}110.6$~nJ per image, and the full computation, even with the decoder on a GPU, outperforms all GPU baselines in latency and energy. These results suggest that energy-descending dynamics on non-von Neumann substrates can serve as generative computation, offering an efficient path beyond GPUs.
Read the full abstract

All authors Lianlong Sun, Chuan Liu, Yongchao Liu, Hui Wu, Tony Geng, Michael Huang

Friday 14 August

Emerging Technologies · Cross-listed from cs.CR

A Throughput-Oriented Analytical Model for Post-Quantum Security Protocols

Ignazio Pedone and Stefano Pirandola

Growing awareness of the impact of quantum threat on classical cryptography directly translates into a growing demand for accurate network simulation tools capable of estimating the integration effects of quantum-safe cryptography in current systems. In particular, the adoption of Post-Quantum Cryptography (PQC) has a direct impact on the performance of network endpoints and transmission overhead. This also affects the scalability of widely adopted security protocols such as TLS and SSH. In this paper, we present a throughput-oriented analytical model that provides a tight upper bound on the maximum sustainable rate of post-quantum secure connection establishment in TLS and SSH. This model takes into account both endpoint and network capacity constraints, decomposing the handshake process into dominant cryptographic operation time and network transmission time. Identifying the bottleneck allows us to derive the achievable throughput in terms of handshakes per second. The experimental results provided show the accuracy of the model against the data obtained from an experimental testbed using, among others, NIST standard primitives from FIPS 203, 204, and 205, including ML-KEM and ML-DSA. Finally, we integrate our model into a network environment and demonstrate how it can be leveraged to enable efficient resource allocation among multiple endpoints, optimizing PQC traffic in multiple-unicast scenarios.
Read the full abstract

Emerging Technologies

PRISM-UDE: Physics-Regularized Iterative Symbolic Modeling of 3nm FinFETs via Universal Differential Equation

Pranavanath Balamurali, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar et al. (+2 more)

Compact transistor models are the mathematical backbone of circuit simulation. However, at advanced nodes such as 3nm, transport physics becomes too complex for traditional hand-derived equations to capture accurately. Purely data-driven neural surrogates, on the other hand, are numerically unstable inside circuit solvers and offer no physical insight into their own predictions. We introduce PRISM-UDE (Physics-Regularized Iterative Symbolic Modeling via Universal Differential Equations), a framework that embeds a small neural network inside a physics-based transistor model, using the network only to learn the transport behavior that the analytical baseline misses, rather than replacing the physics altogether. Once trained, this neural correction is distilled into a single, interpretable closed-form expression via symbolic regression, making the final model fully analytical and simulator-ready. Applied to a 3nm FinFET benchmark dataset, PRISM-UDE reduces prediction error more than sixfold (70.33% to 11.01%) relative to the standard physics-only baseline. The distilled expression preserves this accuracy almost exactly while eliminating the neural network entirely. We further validate the extracted expression directly inside a SPICE circuit simulator, confirming stable, physically consistent behavior under both static bias sweeps and dynamic switching conditions.
Read the full abstract

All authors Pranavanath Balamurali, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar, Rajat Dandekar, Sreedath Panat

Emerging Technologies · Cross-listed from cs.DC

Porting and Benchmarking Chapel on Emerging RISC-V Hardware: an HPC Viability Study

Ian Henriksen, Chris Taylor, Patrick Diehl et al. (+4 more)

The Chapel programming language recently added support for the RISC-V architecture. Here we discuss what changes were needed for Chapel to work on RISC-V as well as lessons learned from the porting process. We use some of Chapel's extensive benchmark suite to gain further insight into the suitability of the RISC-V architecture for future HPC use. We compare performance on SiFive P550 and Unmatched boards and a Sophon SG2042 with a variety of other recent HPC CPU platforms. Various portability and performance anomalies arose on different architectures and will be discussed. While the RISC-V machines represent smaller preliminary offerings and not HPC-class hardware, these results still provide hope that RISC-V hardware can become viable for HPC in the near future.
Read the full abstract

All authors Ian Henriksen, Chris Taylor, Patrick Diehl, Jade Abraham, Palmer Cox, Bradford L. Chamberlain, Stephen L. Olivier

Emerging Technologies · Cross-listed from cs.LG

Hybrid Quantum-inspired Kolmogorov-Arnold Networks for Privacy-Aware Federated Biosignal Learning

Chun-Hua Lin, Samuel Yen-Chi Chen, Yu-Chao Hsu et al. (+7 more)

Electrocardiogram (ECG) recordings are sensitive biomedical data, limiting the ability of hospitals and wearable devices to share raw signals for centralized model training. Federated learning addresses this practical privacy constraint by enabling collaborative model training while keeping raw biosignal data at their respective sources. However, federated ECG classification remains challenging due to limited client-side samples, imbalanced arrhythmia labels, and non-independent and identically distributed (non-IID) data across clients. These constraints require classifiers that are both communication-efficient and robust to cross-client distribution shifts. In this work, we evaluate a hybrid quantum-inspired Kolmogorov-Arnold network (HQKAN) against a multilayer perceptron (MLP) for five-class arrhythmia classification on the MIT-BIH dataset and three-class classification on the INCART dataset under federated averaging (FedAvg). Across multiple client configurations, HQKAN improves most aggregate and minority-class metrics while using 37.35% fewer trainable parameters and reducing communication cost by 24.89% on MIT-BIH; on INCART, it achieves corresponding reductions of 44.81% and 36.41%. These results indicate that HQKAN offers a compact, communication-efficient and robust alternative to the MLP baseline for privacy-aware federated learning on biosignal data.
Read the full abstract

All authors Chun-Hua Lin, Samuel Yen-Chi Chen, Yu-Chao Hsu, Kuo-Chung Peng, Jiun-Cheng Jiang, Chi-Sheng Chen, Tai-Yue Li, Nan-Yow Chen, En-Jui Kuo, Hsi-Sheng Goan

Emerging Technologies · Cross-listed from cs.SE

Engineering Signals of Human-AI Collaboration in the Agentic Coding Era: A Longitudinal Analysis of 33,228 Pull Requests from vLLM and SGLang with Implications for Biomedical AI Agents and Bioinformatics Pipeline Developmen

Jiada Li, Xuesong Ye and Olamide Olowoniyi

The rapid adoption of AI coding assistants and autonomous agentic development systems has coincided with major changes in the pace and structure of open-source software engineering. Yet empirical longitudinal evidence of these changes at the team level remains limited. We present a descriptive longitudinal analysis of seven engineering metrics: pull request (PR) throughput, cycle time, contributor diversity, PR comment density, merge rate, new-author participation, and PR size. Metrics were computed from all merged PRs in two high-velocity AI infrastructure repositories, vLLM (February 2023-June 2026; 18,290 PRs) and SGLang (January 2024-June 2026; 14,938 PRs). We segment development into four eras aligned with major changes in AI-assisted software development and examine human- and bot-authored activities. Both projects show substantial increases in development velocity and AI-developer collaboration signals. PR throughput increased 21x in vLLM and 17.9x in SGLang, while bot-authored PRs accounted for less than 0.2% of this growth, indicating that the increase was overwhelmingly human-driven. In the latest era, median cycle time was 1.04 days for vLLM and 0.62 days for SGLang, while P90 cycle times reached 16.8 and 14.3 days, respectively. Monthly unique authors increased steadily in both projects, suggesting broader contributor participation. PR comment density increased 4.2x in vLLM and 3.8x in SGLang, with bot comments contributing an estimated 15-20% of the increase. In contrast, PR size remained relatively stable across eras. Overall, AI-assisted development is associated with higher throughput, broader contributor participation, and increased AI-developer collaboration signals in high-velocity open-source software development.
Read the full abstract

Thursday 13 August

Emerging Technologies · Cross-listed from quant-ph

How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection

Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Shahid Munir Shah et al. (+2 more)

Quantum machine learning (QML) for network intrusion detection (NIDS) is routinely reported to reach near-perfect accuracy, yet the most rigorous studies find that well-tuned classical models remain competitive, and that apparent quantum gains may be artefacts of classical dimensionality reduction and implicit regularisation rather than genuine quantum effects. We ask not whether a quantum model can post a high accuracy, but how quantum the advantage really is. We present a unified, reproducible QML-IDS benchmark evaluating hybrid variational quantum circuits and quantum-kernel SVMs against five honestly-tuned classical baselines across four standard NIDS datasets (NSL-KDD, UNSW-NB15, CICIDS2017, NF-ToN-IoT-v2) under one leakage-controlled protocol, with an equal-budget feature view, imbalance- and calibration-aware metrics with significance testing, and a simulated NISQ noise sweep. We introduce a quantum-attribution audit (parameter-matched classical controls, a random-feature kernel, and a regularisation sweep) that quantifies how much of any gain is genuinely attributable to the quantum component. Tuned classical models (Random Forest, XGBoost) match or exceed the quantum models on aggregate detection on every dataset, and the audit attributes this to classical preprocessing and regularisation rather than quantum effects. Two advantages survive false-discovery-rate correction: the quantum-kernel SVM out-ranks its direct classical surrogate (a random-feature kernel) on AUPRC and ROC-AUC, and a small four-qubit hybrid out-detects the best classical baseline at the 1% false-positive operating point on the distribution-shifted NSL-KDD task (p = 0.005, BH q = 0.030). Code, seeds, and splits are released; our contribution stands whether quantum wins, ties, or loses.
Read the full abstract

All authors Syeda Anshrah Gillani, Mirza Samad Ahmed Baig, Shahid Munir Shah, Asher Ali, Hamzah Siddiqui

Emerging Technologies

RIVERPlace: Repairing Interconnect Violations with Efficient Retiming and Incremental Placement for AQFP Circuits

Robert S. Aviles, Ziyu Liu, Sasan Razmkhah et al. (+2 more)

The Adiabatic Quantum-Flux-Parametron (AQFP) offers near-Landauer-limit energy efficiency but faces significant scalability challenges due to strict path balancing and limited drive strength. To address this, we propose RIVERPlace, a framework that integrates long-wire pipelining, retiming, and incremental placement to resolve interconnect violations with minimal disruption. RIVERPlace first applies placement-aware retiming to repair violations without increasing logical depth. When depth increases are necessary, we introduce Buffer Cut Insertion (BCI), which formulates violation resolution as a constrained global edge-selection problem reducible to a maximum topological cut, thereby enabling an exact polynomial-time solution. By selectively pipelining edges across multiple rows, BCI avoids excessive buffer insertion while resolving interconnect violations. Experimental results demonstrate that RIVERPlace consistently outperforms prior AQFP placement approaches, reducing placement overhead by more than an order of magnitude in inserted buffers, 3x in placement-induced depth, and over 2x in circuit area, while also reducing runtime by more than an order of magnitude and latency by 38%. These improvements enable the first post-routing, timing-closed implementations of the complete open-source AQFP benchmark suite, including larger circuits like alu32.
Read the full abstract

All authors Robert S. Aviles, Ziyu Liu, Sasan Razmkhah, Massoud Pedram, Peter A. Beerel

Emerging Technologies · Cross-listed from cs.SE

Fine-Tuning Qwen3-27B for C-to-Rust Code Translation: A Three-Stage Curriculum of Pretraining, Debugging-Aware SFT, and Task-Specific SFT

Pu Zhao, Changdi Yang, Yixiao Chen et al. (+4 more)

Translating C code into safe, idiomatic Rust is a longstanding software-engineering goal because it can eliminate entire classes of memory-safety vulnerabilities while preserving the functional behavior of legacy systems. Large language models (LLMs) have shown promise for this task but typically underperform when applied off-the-shelf, since general-purpose pretraining rarely emphasizes idiomatic Rust generation, cross-language semantic equivalence, or the ability to reason about and repair compiler/runtime feedback. In this report we describe a three-stage fine-tuning curriculum applied to Qwen3-27B that is designed to progressively specialize the model for the C-to-Rust (C2Rust) translation task: (1) continued pretraining on Rust-centric corpora to strengthen the model's prior over idiomatic Rust syntax and standard-library usage; (2) supervised fine-tuning (SFT) on the microsoft/Verus_Training_Data dataset to instill debugging and self-repair behavior over Rust code; and (3) task-specific SFT on paired C/Rust solutions derived from LeetCode problems to teach direct semantic translation. We evaluate the resulting model using the agentic, static-analysis-guided verification framework of SACTOR, which performs structure-aware, two-phase (unidiomatic to idiomatic) translation with foreign-function-interface (FFI)-based end-to-end (E2E) testing. We report success rate, idiomaticity (Clippy lint counts, unsafe-code fraction), and failure-mode analyses, and compare our fine-tuned model against baseline Qwen3-27B and other LLMs evaluated under the same framework.
Read the full abstract

All authors Pu Zhao, Changdi Yang, Yixiao Chen, Yi Gao, Yifan Cao, Haochen Zeng, Yanzhi Wang

Emerging Technologies · Cross-listed from cs.IT

Energy-Aware Compression-Computation Co-Adaptation for Latency Minimization in Multi-User Semantic Communication

Loc X. Nguyen, Yumin Park, Avi Deb Raha et al. (+4 more)

Deep joint source-channel coding-enabled (DeepJSCC) semantic communication (SemCom) has excelled at delivering high perceptual quality at low channel-bandwidth ratios, which positions it as a pillar for next-generation wireless networks. However, the existing works have difficulty accommodating user heterogeneity in terms of communication channel quality, expected quality-of-service (QoS) targets, and the available local energy. Therefore, in this paper, we explicitly reflect the heterogeneity of user devices in terms of the differences in expected QoS, channel condition, and local energy, and then mathematically formulate the problem. Next, we propose an energy-aware compression-computation co-adaptation (CoCo) framework, in which the base station can meet the expected user QoS by transmitting a longer signal or offloading the task to a local device. The user has to dedicate energy to denoising the signal to recover higher-fidelity latent features before feeding it to the semantic decoder. To solve the formulated problem, we first decompose it into two sub-problems: parameter optimization and resource allocation problems. Specifically, we propose a robust codec that effectively works under a diversity of compression rates and channel noise without re-training, while the greedy sub-carrier allocation lowers the communication time. Finally, we present simulation results on standard image datasets over additive white Gaussian noise to demonstrate the effectiveness of CoCo, which reduces total latency relative to rate-only adaptive DeepJSCC or denoising-only, thereby ensuring the demands of each individual user are met.
Read the full abstract

All authors Loc X. Nguyen, Yumin Park, Avi Deb Raha, Huy Q. Le, Zhu Han, Eui-Nam Huh, Choong Seon Hong

Emerging Technologies · Cross-listed from quant-ph

Quantum-Inspired Phase Bicoherence Spectroscopy: A Framework for Detecting Universal Textural Angular Order Across Multi-Modal Complex Datasets

Zheng Xing, Chan-Tong Lam and Xiaochen Yuan

Classical image analysis routinely discards structurally meaningful orientation signatures encoded within Fourier phase, which are easily corrupted by local cellular rotation. Although quantum-inspired data processing offers new avenues for complex signal characterization, practical tools for directly extracting gauge-invariant angular correlations without explicit phase reconstruction remain scarce. Here we introduce Quantum Phase Bicoherence (QPBC) spectroscopy, a novel quantum-interferometric framework for capturing gauge-invariant angular order. The method embeds image angular sectors into a nine-qubit entangled state and probes three-body bicoherence via an ancilla, yielding 16 interpretable readout channels. We validate our framework on three independent public multi-modal imaging datasets covering fluorescence (BBBC021), bright-field (BBBC041) and histopathology (PathMNIST). QPBC consistently resolves angular-phase order and discriminates distinct biological phenotypes with high statistical significance. After principal-axis alignment, the optimal probing frequency universally converges, driven by Fourier directional sensitivity; negative-control experiments fully eliminate discriminative capacity, demonstrating frequency tuning acts as an on-off switch. Cross-dataset benchmarks confirm QPBC outperforms conventional Fourier-phase statistics, where inherent inversion symmetry serves as a built-in pipeline self-check. QPBC delivers a universal, classically unachievable quantitative texture observable, establishes interpretable quantum morphometry, and broadens the toolbox for quantum-inspired analysis applicable to diverse multi-modal microscopic measurements.
Read the full abstract

Emerging Technologies

Enabling Ultra-Low-Power Always-On Feedforward Leakage Suppression Logic Circuits with FDSOI

Clément Choné, Leslie Xu, Filippo Quadri et al. (+5 more)

The growing deployment of real-time applications on wearable and Internet of Things (IoT) edge devices has intensified the need for energy-efficient, high-performance systems that meet stringent timing and energy constraints. Events-driven architectures leverage the sparsity of real-time to further improve system energy efficiency by employing an always-on (AO) domain to monitor inputs and activate a high-performance (HP) domain only when relevant events occur. However, for low-duty-cycle applications, the energy bottleneck shifts toward the AO domain, where leakage power dominates overall consumption. To mitigate this issue, AO circuits are typically implemented using high voltage threshold (HVT) or ultra-high voltage threshold (UHVT) transistors, thereby avoiding sub- and near-threshold operation, which is highly sensitive to process, voltage, and temperature (PVT) variations. In this context, feedforward leakage suppression logic (FLSL) has recently emerged as a promising candidate, offering reduced leakage compared to conventional. However, previous studies report a significant degradation in FLSL leakage performance in technology nodes below 90 nm, primarily due to increased gate and junction leakage currents. FDSOI technology, with its ability to effectively suppress junction leakage, provides an opportunity to overcome this limitation and restore FLSL efficiency in advanced nodes. Therefore, we demonstrate in this work that FLSL implemented in a 22 nm FDSOI technology can significantly reduce the energy consumption of small AO circuits with low-frequency inputs compared to state-of-the-art ultra-low-power CMOS designs. Silicon measurements on an FIR filter and an AES cryptographic core show reduced operating voltage and up to 9.8 x and 1.83 x reductions in leakage power compared to equivalent HVT and UHVT CMOS implementations, respectively.
Read the full abstract

All authors Clément Choné, Leslie Xu, Filippo Quadri, Pasquale Davide Schiavone, Alexandre Levisse, Jean-Luc Naguel, David Atienza, Andreas Burg

Emerging Technologies

ProbSplat: Efficient Probabilistic Hardware for Gaussian Splatting in 3D Scene Reconstruction

Siddarth Gottumukkula, M P Samartha, Vedant Pahariya et al. (+3 more)

This paper presents ProbSplat, a Compute-in-Memory (CIM)-inspired architecture based on programmable and energy efficient floating-gate inverter columns for probabilistic computing. Improving upon our prior work, ProbSplat programs and stores both means and variances of Gaussian mixture components, and evaluates log-likelihood for gaussian splatting during scene reconstruction with high energy efficiency, suitable for robotics and augmented/virtual reality (AR/VR) at the edge. Our proposed scheme enables independent control of both mean and variance via deterministic adjustment of floating-gate MOSFET threshold voltages, increasing the fidelity of hardware to program probability distributions. The design is simulated in 180nm CMOS on 1.8 V at 50 MHz and achieves mean-variance independence with <2.4% deviation during 3-D Gaussian mixture modeling. Compared to conventional digital implementations, ProbSplat significantly reduces compute complexity, memory footprint, and power consumption. The scalable framework consumes 18pJ energy per log-likelihood inference with 4-bit precision while operating for 500 mixture functions in a 3-D GMM. Scene reconstruction with ProbSplat's characteristics gave satisfactory fidelity of 21.99 PSNR (dB) at 8-bit precision.
Read the full abstract

All authors Siddarth Gottumukkula, M P Samartha, Vedant Pahariya, Priyanshi Jain, Amit Ranjan Trivedi, Priyesh Shukla

Emerging Technologies · Cross-listed from cs.AI

BoardroomAI: Dependency-Aware Human-Steerable Multi-Agent Deliberation through Evolving Decision Graphs

Sanjeev Manivannan

Organizational decisions are co-created while evidence, constraints, and human priorities continue to evolve. In conventional transcript-based multi-agent systems, humans typically provide an initial problem, agents deliberate internally, and the system returns a final response. BoardroomAI instead treats the human as a persistent participant who can intervene by challenging assumptions, modifying constraints, changing priorities, introducing evidence, or redirecting the decision process. We operationalize this human–agent coexistence through four components: (i) a typed decision graph representing evidence, assumptions, constraints, claims, objections, alternatives, risks, decisions, semantic dependencies, and specialist responsibility; (ii) an intervention compiler that converts confirmed human actions into explicit graph updates; (iii) dependency-aware propagation that identifies affected subgraphs, preserves unaffected artifacts, and selectively reactivates relevant specialists; and (iv) an evaluation framework measuring intervention impact, repair coverage, preservation, recomputation, and decision validity. Across 600 generated decision-DAG interventions, propagation matched exhaustive impact computation while inspecting only 14.59% of nodes. In a 12-case exploratory pilot, selective repair recomputed 62.11% of canonical nodes, preserved all gold-unaffected nodes, and produced valid updated decisions in six cases while abstaining in the remaining six. These abstentions show that correct intervention routing may still provide insufficient context for synthesis, motivating a decision-sufficient context closure for human-steered multi-agent deliberation. All results are synthetic and prototype-level.
Read the full abstract

Emerging Technologies

Exponential multi-graylevel computational-weighted dithering for high-quality binarized Fourier single-pixel imaging

Qigao Zhu, Haojia Jiang, Guan Wang et al. (+4 more)

Binarized Fourier single-pixel imaging (FSI) takes full advantage of the high modulation speed of digital micromirror devices by applying Floyd-Steinberg spatial dithering to binarize grayscale Fourier patterns. However, the use of the spatial dithering introduces substantial quantization errors, leading to decreasing imaging quality. Here, we propose a binarization method for grayscale Fourier patterns based on exponential multi-graylevel computational-weighted dithering, aimed at reducing quantization errors and then enhancing the imaging quality of binarized FSI. The proposed method quantizes Fourier patterns into $2^{R}$ values $\{0, 1/(R-1), 2/(R-1)... 1\}$ and then decomposes them into binarized patterns. Both simulation and experimental results demonstrate that the method significantly reduces quantization errors in Fourier coefficients acquisition and improves imaging quality. The mean absolute percentage error of Fourier coefficients decreases from $194\%$ to $28\%$ and the structural similarity of reconstructed images ($256\times256$ pixels) improves from 0.430 to 0.971, a $126\%$ enhancement compared to the conventional method. Lateral resolution of this proposed method almost approaches the theoretical lateral resolution limit calculated by Rayleigh Criterion.
Read the full abstract

All authors Qigao Zhu, Haojia Jiang, Guan Wang, Lianhao Zhang, Hanlei Gong, Huaxia Deng, Xinglong Gong

Emerging Technologies · Cross-listed from quant-ph

AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization

Harshkumar Oza, Aritra Sarkar, Syed Naqi Abbas et al. (+4 more)

As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks. Quantum resource estimation (QRE) plays a central role in this transition, yet existing approaches remain largely compilation-heavy or domain-knowledge-guided symbolic annotations, and tightly coupled to long-term fault-tolerant assumptions, limiting their topical applicability. In this work, we introduce AutoQuREO, an Automated framework for full-stack Quantum Resource Estimation and Optimization. AutoQuREO is built around four core novelties: (i) a flexible, user-defined abstraction of the quantum computing stack; (ii) a modular library of reusable stack components enabling rapid full-stack prototyping; (iii) surrogate modeling of layer-wise resources via algorithmic profiling and neuro-symbolic learning; and (iv) integrated multi-objective optimization that embeds QRE directly into deployment pipelines. Together, these design choices enable AutoQuREO to serve as a digital twin for quantum computing stacks, supporting the tractable exploration of complex design spaces. We demonstrate the capabilities of AutoQuREO through representative co-design case studies, including early-fault-tolerant quantum algorithms, small error correction codes, gate decomposition and variational training of parametric quantum circuits. These examples illustrate how AutoQuREO enables systematic discovery of unexploited resource trade-offs that are computationally intractable or abstruse using existing QRE tools. AutoQuREO is positioned as a general-purpose platform for advancing quantum technology readiness.
Read the full abstract

All authors Harshkumar Oza, Aritra Sarkar, Syed Naqi Abbas, Rahul Bhowmick, Aryan Prakash, Prateek P Kulkarni, Krishna Kumar Sabapathy

Wednesday 12 August

Emerging Technologies · Cross-listed from cs.AI

From Extraction to Governed Memory: Multi-Agent Knowledge Graph Construction with Domain-Expert Review

Pranav Bykampadi, Neel Mokaria, Vishesh Narayan et al. (+2 more)

Knowledge graphs used by agentic systems are often treated as flat stores of extracted triples, with little record of who owns a fact, why it was admitted, or how it should be used downstream. We argue that reliable agentic knowledge systems require governance as an essential component of graph construction to bridge this gap. We propose MAGG, a principled multi-agent framework for constructing Governed Knowledge Graphs that introduces explicit governance decisions for reliable and trustworthy knowledge sharing. A domain classifier first induces entity and relation types directly from document content, enabling operation in open-world settings without fixed schemas. Candidate triples are assigned to domain owners, reviewed against supporting evidence, admitted through governance decisions, and stored with audit metadata. The same ownership structure is reused during question answering, where queries are routed to domain-specific graph experts rather than answered through undifferentiated retrieval. Our evaluation demonstrates MAGG's effectiveness: On SciERC, MAGG improves strict triple F1 by 47% and mapped triple F1 by 51% over flat insertion. A blinded review of 120 triples finds governed-only triples more often source-supported than flat-only ones, and revised triples supported in 100% of cases. Finally, on MuSiQue, MAGG outperforms Microsoft GraphRAG by 9.0 exact-match points and 11.2 token-F1 points.
Read the full abstract

All authors Pranav Bykampadi, Neel Mokaria, Vishesh Narayan, Faizan Wajid, Ashok Agrawala

Emerging Technologies · Cross-listed from cond-mat.mes-hall

Spin Vector Control for Heisenberg-Inspired Probabilistic Computing

Yuanqiu Tan, Rahul Tripathi, Saleh Bunaiyan et al. (+5 more)

Probabilistic bits (p-bits) have emerged as a cornerstone of probabilistic computing, enabling energy-efficient hardware implementation for probabilistic inference and combinatorial optimization. A critical challenge in advancing this field beyond binary p-bits lies in realizing and manipulating vector spin information, essential for mapping complex energy-based models such as the Heisenberg Hamiltonian.Here, we demonstrate a spintronic platform capable of real-space vector summation by using dual ferromagnetic spin injections into a monolayer graphene channel. By electrically tuning the spin polarization through independently controlled injection currents, we achieve continuous control over the magnitude and direction of the resulting spin accumulation vector. Experimental observations, supported by theoretical vector summation models and spin-circuit simulations, reveal coherent vector interactions and angular tunability of the spin state. This approach enables direct implementation of vector-based spin logic and lays the groundwork for mapping classical Heisenberg models using stochastic low-barrier magnets. Our results establish a scalable pathway for realizing probabilistic spin circuits based on two-dimensional materials, offering new opportunities for low-power, non-Boolean computing architectures.
Read the full abstract

All authors Yuanqiu Tan, Rahul Tripathi, Saleh Bunaiyan, Ryan Wagner, Neil Dilley, Kerem Camsari, Joerg Appenzeller, Zhihong Chen

Emerging Technologies · Cross-listed from cs.IT

Channel Estimation for OTFS Systems With Overspread Doppler Shifts

Preety Priya, Yi Hong and Emanuele Viterbo

In this paper, we consider an orthogonal time frequency space (OTFS) system in time-varying channels with overspread Doppler shifts, typically found in non-terrestrial multi-satellite links. The overspread Doppler shifts with magnitude greater than half of the subcarrier spacing, result in aliased Doppler shifts in the delay-Doppler (DD) domain due to the OTFS modulo operation. This makes channel estimation very challenging and the traditional channel estimation methods become ineffective. To address this challenge, we propose a DD training frame and a two-stage channel estimation method. The training frame comprises a cosine pilot signal and a pilot symbol. In the first stage of the channel estimation, the pilot symbol in the DD domain is utilized to estimate the delays, aliased Doppler shifts, and channel gains of the propagation paths. In the second stage, the received time domain signal is converted into the frequency domain to detect the peaks of all the Doppler shifts using the cosine pilot signal. Then, we present a threshold-based method to pair the estimated actual Doppler shifts with their corresponding delays and channel gains. The complexity of the proposed channel estimation is also discussed. Finally, the performance of the proposed channel estimation is validated in terms of the normalized mean square error (NMSE) and bit error rate (BER) in various scenarios.
Read the full abstract

Emerging Technologies · Cross-listed from quant-ph

Capability-Adaptive Cryptanalysis with Reduced-Space Quantum Verification

Nivedita Dey, Mrityunjay Ghosh, Pranav Kaushal et al. (+2 more)

Efficient integration of cryptanalytic evidence with quantum verification remains a fundamental challenge in hybrid classical-quantum cryptanalysis. This work presents a capability-adaptive cryptanalytic framework that unifies linear cryptanalysis, differential cryptanalysis, and side-channel leakage analysis within a common candidate-space reduction architecture, followed by reduced-space quantum verification through amplitude amplification. A formal mathematical model is developed for candidate-space construction, adaptive filtering, verification-space reduction, and complexity characterization, supported by the oretical results establishing the relationship between candidate-space contraction and quantum verification effort. A Hamiltonian formulation is further introduced to provide a physically realizable interpretation of the reduced-space verification process. Evaluation using statistically generated cryptanalytic observations demonstrates that the proposed framework reduces an initial candidate-key hypothesis space of 4096 candidates to an effective candidate space of 13 hypotheses, corresponding to an overall reduction of approximately 99.683%. Consequently, the Grover verification requirement decreases from 50 iterations to only 2 iterations, yielding an approximately 25-fold reduction in verification effort, while reduced-space amplitude amplification achieves a target-state success probability of approximately 94.53%. These results demonstrate that adaptive cryptanalytic filtering can substantially reduce quantum verification complexity while preserving cryptanalytic admissibility, providing a practical foundation for capability-aware hybrid cryptanalysis and reduced-space quantum search.
Read the full abstract

All authors Nivedita Dey, Mrityunjay Ghosh, Pranav Kaushal, Abhinab Khare, Amlan Chakrabarti

Emerging Technologies · Cross-listed from cond-mat.dis-nn

Unifying Physical Backpropagation

Cyrill Bösch, Yigithan Gediz, Clara C. Wanjura et al. (+1 more)

Physical computing systems exploit device dynamics for computation, but their gradient-based optimization is challenging: backpropagation through a digital twin suffers from a model-reality gap. On-device gradient computation could resolve this issue, and a handful of theoretical and experimental studies have proposed ways to achieve it. Yet a unifying theory identifying when a physical system can compute the gradient of its own performance has been missing. Here we develop such a unification based on the adjoint method: we identify sufficient conditions under which the adjoint field required for formally exact gradients can be generated on the same hardware that performs the computation. Linear and nonlinear systems obey fundamentally different conditions: for linear systems, damping or gain is admissible provided reciprocity is preserved. For nonlinear trajectory systems, the sufficient conditions are reciprocity of the linearized system and the existence of a time-reversal mirror. Algorithmically, the nonlinear case requires infinitesimal nudging, whereas linear systems admit a finite-amplitude experiment. We recover (quantum) Equilibrium Propagation, Hamiltonian echo backpropagation, fully forward mode training and in situ gradient methods in integrated-photonic and free-space-optical systems. Finally, we show that reciprocity is a special case of more general intertwining conditions. For linear systems, these permit exact on-device gradients in a class of non-Hermitian, non-reciprocal systems. For nonlinear trajectories, they combine with generalized time-reversal mirrors to cover, e.g., PT-symmetric equations. The framework also includes time-dependent parameters and Onsager-reciprocal dynamics, providing a unified basis for formally exact physical learning.
Read the full abstract

All authors Cyrill Bösch, Yigithan Gediz, Clara C. Wanjura, Hakan E. Türeci

Tuesday 11 August

Emerging Technologies · Cross-listed from cs.CV

Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving

Aditya Humnabadkar, Huaizhong Zhang and Ardhendu Behera

Natural-language-based scenario generation offers an intuitive means of describing rare and complex driving interactions, yet it is still uncertain whether training with language-structured data leads to truly adaptive control policies. We propose Language-Structured Relational Q-Learning, instantiated through an Ego-Centric Relational Q-Network (ERQ-Net), which jointly learns inter-vehicle relevance and action values from dynamic traffic graphs. Language descriptions define surrounding-vehicle behaviours during training, while prompts and semantic actor roles are hidden from the policy. ERQ-Net must therefore infer threat relevance solely from observable kinematics and interactions. Across 2,500 safety-critical scenarios, language-structured training improves test success from 49-52% to 55-58% and increases adversary-focused attention from 1.2x to 2.1x, demonstrating emergent threat awareness. However, this representational gain does not consistently translate into adaptive control: trained policies perform similarly to the best constant action, while a portfolio of simple policies solves 76% of scenarios. We formalise this discrepancy as a recognition-control gap and show that reward reweighting and margin shaping do not eliminate the resulting policy collapse. Evaluations of realism, criticality, semantic accuracy, and transfer of state-interface representations to CARLA further highlight both the strengths and the constraints of language-structured relational policy learning in safety-critical driving scenarios.
Read the full abstract

Emerging Technologies · Cross-listed from cs.NI

Media-over-Multipath-QUIC for Realtime Video Applications

Tanya Shreedhar, Zuji Zhou, Nitinder Mohan et al. (+1 more)

Multipath transports place a client's WiFi, cellular, and satellite networks under one connection, yet real-time video gains little from them. The scheduler that assigns packets to paths sees only bytes, so it cannot tell a keyframe that anchors a second of video from an enhancement frame whose loss costs one image. We show that the limiting factor is not a shortage of path diversity but the absence of a channel through which the application can name what the transport cannot see. Media over QUIC Transport (MoQT), already deployed on production CDNs, carries media as named Objects, and relays forward each Object's metadata without interpreting it. The knowledge the scheduler lacks therefore already flows through the subscriber's relay. We present MoMQ, a MoQT extension that turns this metadata into path decisions. Applications and relay operators install declarative rules at the edge relay that match Object metadata and express delivery preferences against labeled paths. The relay evaluates the rules mechanically, so it acts on video semantics while containing no video logic. On a live testbed spanning Starlink and WiFi, four rules cut P99.9 frame completion time from 384.7 ms under the best transport-only scheduler to 114.1 ms and reduce the required playback buffer by 61.5%. MoMQ is the only configuration that meets the 150 ms interactive latency target. Across relays in two countries and subscribers on two continents, the same rules apply unchanged and retain their advantage wherever the paths remain disjoint.
Read the full abstract

All authors Tanya Shreedhar, Zuji Zhou, Nitinder Mohan, Fernando Kuipers

Emerging Technologies · Cross-listed from cs.NI

Conversational Orchestration for Organic 6G

Masoud Shokrnezhad and Tarik Taleb

The Organic 6G vision of a network of networks spanning an edge-cloud continuum complemented by non-terrestrial resources requires, to realize its promise, service provisioning that is simple to operate, scalable across independently administered domains, and agile under domain churn (i.e., domains dynamically joining and leaving). Despite advances in cross-domain orchestration, many proposals rely on heavy integration fabrics, multi-layer coordinators, and deep telemetry pipelines that hinder deployability and amplify coordination overhead. We propose a lightweight, decentralized conversational orchestration framework based on Large Language Model (LLM)-driven domain agents. Each domain remains autonomous: an agent observes local state via tools, reasons in a closed loop, and exchanges summaries with neighboring agents over an Agent-to-Agent (A2A) overlay aligned with data-plane coupling. Fast feasible placement is enabled by periodic, routing-like dissemination of reachability advertisements (latency, bottleneck bandwidth, and compute capacity), while safe re-optimization, scaling, and migration are handled through event-driven requests and negotiation. To meet real-time constraints, we deploy a compact reasoning model trained with verifier-based self-verification and periodically refined online via shadow updates. Simulations show manageable, near-linear control-plane overhead as domains scale and during domain joins, and robust decision quality, including recovery after objective changes. We close by outlining future research directions for principled, secure, and uncertainty-aware agentic orchestration in Organic 6G.
Read the full abstract

Monday 10 August

Emerging Technologies

Unsupervised Detection of Groundwater Storage Anomalies in Ghana Using GRACE Satellite Data

George Yamoah Afrifa, Theophilus Ansah-Narh and Marcellin Atemkeng

Groundwater variability in Ghana remains poorly characterized due to limited long-term in-situ observations. This study investigates groundwater storage anomalies using GRACE-derived data from 2004-2024 combined with statistical analysis and unsupervised machine learning. Groundwater anomalies were standardized using Z-scores, while an ensemble-based Isolation Forest framework was applied for anomaly detection. The results revealed substantial temporal variability, with persistent groundwater deficits during 2004-2009 followed by increasing positive anomalies after 2018. A total of 12 anomalous months were identified, comprising 5 deficit and 7 surplus events, with the strongest anomalies associated with groundwater deficits. Spatial analysis showed more frequent deficit anomalies in northern Ghana and stronger surplus occurrence in southern regions. Comparison with statistical thresholds further indicated that the machine learning framework captured additional subtle deviations beyond conventional threshold-based methods. Overall, the integration of GRACE observations with unsupervised anomaly detection provides a practical framework for groundwater monitoring in data-scarce environments.
Read the full abstract

Emerging Technologies

The Unified Evaluation App for DNA Data Storage Codecs

Aleksandar Anžel, Chisom Anyabolu, Leon Wimbes et al. (+12 more)

Background: Deoxyribonucleic acid (DNA) data storage is a paradigm with great potential for ultra-dense and durable information preservation. However, the rapid proliferation of coding schemes, or codecs, each with their own design constraints and reporting practices, has led to a fragmented landscape that lacks a standardized comparative assessment. Methods: We developed an open-source, modular benchmarking platform that systematically integrates and evaluates state-of-the-art DNA storage encoding and decoding methods (codecs). Our approach uses a curated, diverse set of baseline data and applies multidimensional assessment criteria that are aligned with the consensus standard of the DNA Data Storage Alliance. These criteria include encoding/decoding throughput, computational efficiency, error correction performance across substitutions, insertions, and deletions, and cost efficiency. Results: The developed platform integrates standardized wrapper functions for encoding and decoding, allows for the integration of new methods, and automates reproducible evaluations with comprehensive visual and tabular reporting. Benchmarking both contemporary and classical codecs using their default parameters and multiple metrics demonstrates that no single algorithm is optimal across all evaluated dimensions. The trade-offs between information density, success rate, runtime, and cost are quantified and shown to be critical factors in the design of future-proof formats. Conclusions: Our work establishes a rigorously standardized, open-source evaluation framework that enables reproducible benchmarking, supports evidence-based codec selection, and provides the necessary foundation for translating DNA data storage from experimental research into deployable archival systems.
Read the full abstract

All authors Aleksandar Anžel, Chisom Anyabolu, Leon Wimbes, Luca Staus, Ihsan Tri Heldian, David Sonnabend, Khawla Elhadri, Samuel Becker, Felix Klein, Raffael Schoen, Michael Schwarz, Marius Welzel, Bernd Freisleben, Dominik Heider, Georges Hattab

Emerging Technologies · Cross-listed from quant-ph

Memory-, Circuit-, and Ansatz-Efficient VQLS for CFD on Hybrid Quantum-HPC Systems

Chao Lu, Muralikrishnan Gopalakrishnan Meena, Eduardo Antonio Coello Perez et al. (+2 more)

Fluid dynamics workloads are dominated by repeated solves of large, structured linear systems, motivating the search for quantum acceleration. The Variational Quantum Linear Solver (VQLS) is a leading near-term candidate, but practical deployment on hybrid quantum–high–performance computing (HPC) systems faces three persistent challenges: (i) the linear-combination-of-unitaries (LCU) encoding of the system matrix explodes in memory and runtime as the problem size grows, (ii) ansatz selection is largely empirical, with no clear link between standard circuit metrics and solver convergence, and (iii) end-to-end VQLS pipelines have rarely been exercised on production HPC hardware at non-trivial qubit counts. This work addresses these challenges through three contributions. First, we benchmark four matrix-encoding strategies—naive LCU, PennyLane-integrated, Fast Walsh–Hadamard Transform (FWHT)-based parallel Pauli decomposition, and an singular value decomposition (SVD)-based two-term LCU—and show that the FWHT approach reduces peak memory by up to $1298\times$ on an $11\times 11$ Hele–Shaw grid, while the SVD-based coherent VQLS delivers over $10{,}000\times$ per-iteration speedup over standard Pauli-based VQLS at 8 qubits. Second, we evaluate 11 ansatz families with gradient-free and gradient-based optimizers on canonical Hele–Shaw flow, and find that expressibility and entanglement metrics correlate only weakly with VQLS convergence, motivating problem-aware ansatz design. Third, we deploy the full workflow on the OLCF Frontier supercomputer and successfully simulate a 15-qubit tridiagonal Toeplitz system on a single node. Together, these results establish a practical baseline for VQLS in hybrid quantum–HPC computation fluid dynamic (CFD) workflows and identify the remaining bottlenecks for larger problems.
Read the full abstract

All authors Chao Lu, Muralikrishnan Gopalakrishnan Meena, Eduardo Antonio Coello Perez, Kalyana Chakravarthi Gottiparthi, Seongmin Kim