Papers

Analog in-memory computing performs the matrix-vector multiplications of neural networks directly inside the memory array, which saves time and energy. But each memory cell stores only a few levels and is noisy, so one cell cannot hold a weight precisely. The standard fix is to split each weight across several cells, like digits of a number. The base of this split decides the trade-off: a large base gives fine resolution but amplifies the noise, and a base near one averages the noise but wastes resolution. The best-known prior analysis considers only the noise, and so it always picks a base of one. We propose a principled method that also accounts for the limited number of levels per cell. It gives the optimal base in closed form, from a single measurement of the device noise. On a device-physics simulator, the optimal split is 3.3 dB more accurate than the common base-16 split, and it beats equal splitting in end-application accuracy.

Spatial reception under directed molecule transport requires balancing useful molecule capture against intersymbol interference (ISI). To characterize this balance, we study a fully absorbing spherical receiver that counts absorption events only within a prescribed cap. Using the known joint first-hitting law under aligned drift, we derive exact cap impulse, cumulative, and eventual responses. We perform stable cumulative evaluation. We then assess the derived responses through particle simulations and time-step comparisons, and demonstrate that our conditional-timing analysis explains when opposite drifts of equal magnitude can favor the same wider aperture. We also show that for the studied geometry and a symbol duration of $0.15$ s, the aperture that maximizes the signal-to-interference difference (SID) increases from $29.13^\circ$ at zero drift to $61.86^\circ$ at a drift magnitude of $40$ $\mu$m/s. These results (i) connect joint arrival-time and location statistics to spatial reception design, (ii) characterize the tradeoff between useful molecule capture and ISI, (iii) support SID-based aperture selection for a given drift and symbol duration, and (iv) quantify the SID loss incurred by retaining the zero-drift optimal aperture for the same symbol duration.

Data movement between memory and processing units remains a major bottleneck in data-centric systems. Memristive Stateful Logic (SL) addresses this problem by performing Boolean operations directly on stored resistive states. However, arithmetic efficiency depends jointly on the device-supported logic primitive set, the dependency structure of the resulting operation sequence, and its mapping to datapath architectures. While prior designs typically optimize device, logic, and architecture layers separately, we jointly consider them to co-design a stateful adder. We experimentally demonstrate a mixed MAGIC and FELIX logic primitive set on printed Electrochemical Metallization (ECM) memristors and derive a full adder sequence optimized for inter-bit carry propagation. Mapping this sequence to bit-serial, bit-parallel, and bit-pipelined datapaths reduces the latter two to 2n+6 cycles for an n-bit addition. For 32-bit addition, this requires 60-88% fewer cycles than prior SL adders. Across four representative workloads, we achieve 3.7x-16.4x speedup over prior. These results show how cross-layer co-design can translate into improved workload-level performance.

Many important computational workloads are bound by the performance of the memory system that supplies data to conventional processors. Compute in Memory (CiM) promises to circumvent this bottleneck by performing the processing within the memory. Neural Networks are memory-bound, and a notable example of such is the Hopfield Network. This work explores the design space of non-volatile computing-in-memory (NV-CiM) architectures using an instruction-level simulator to evaluate application mappings and workload-driven architectural adaptations. The exploration examines the resulting design trade-offs and includes theoretical comparisons with representative CPU and GPU architectures.

Benchmarks shape how model capabilities are measured and interpreted, yet their scores can obscure whose conventions, contexts, and judgments define successful performance. We introduce a user-centric audit framework that distinguishes users who contribute benchmark content from those whose needs evaluation results are intended to represent. The framework operationalizes seven dimensions across benchmark content, metadata, documentation, and scoring procedures. Across 16 resources, we find that American-centered conventions account for 62.4-94.4% of matched signals in 11 text benchmarks, while geographic visibility in five image-text resources is concentrated and strongly associated with national GDP share (mean Pearson r = 0.780; Spearman p = 0.778). Documentation reveals a systematic source-target gap: all 16 resources operationalize source-user context, but none operationalize target-user context. Paired scoring experiments further show that meaning-preserving answer variants reduce DROP exact-match accuracy from 100.0% to 33.3%, while TruthfulQA exhibits metric-dependent reductions in reference similarity. Our framework and documentation checklist make user assumptions auditable, clarifying what benchmark scores represent and where evaluation claims are supported.

Electrotactile stimulation, as a haptic feedback method, often struggles to evoke high-quality natural sensations. Consequently, despite its practical advantages, it is generally less preferred in human-machine systems than mechanical approaches. In this paper, we propose a bio-inspired stimulation framework that generates signals modelled on afferent neural activity derived from a touch model. By producing more biologically plausible spiking patterns, the proposed approach aims to enhance the realism of perceived tactile sensations. To evaluate these stimulation patterns in a realistic task context and to quantify the influence of visual and movement cues, we conducted a series of subjective experiments under three conditions. The results indicate that bio-inspired modulation was effective: in particular, bio-inspired co-modulation of amplitude and frequency achieved a favourable pressure-irritation trade-off, increasing the probability of perceiving pressure while maintaining low irritation (pressure: 0.78; irritation: 0.24) relative to the other patterns. Visual cues and hand movement further improved feedback quality by significantly increasing the probability of reporting pressure (p$<$0.001) without a corresponding increase in irritation, and by increasing mean naturalness ratings. Overall, these findings demonstrate the potential of bio-inspired electrotactile patterns to improve haptic feedback quality, and highlight the importance of evaluating such methods under realistic, multimodal interaction scenarios.

Wireless communication remains the most power-consuming operation in embedded systems. Low-power transmitters such as backscatter achieve microwatt-scale operation but produce weak signals susceptible to in-band interference and frequency-selective fading, particularly in non-line-of-sight settings. Multi-band transmission can mitigate these effects through spectral-domain redundancy rather than temporal-domain redundancy, improving reliability without sacrificing bitrate or latency. However, conventional multi-band radio transceivers require duplicated RF chains or power-hungry synthesizers. We introduce HILO, a tunnel diode-based frontend that enables dual-band operation from a single circuit. HILO inverts the conventional paradigm: rather than suppressing the harmonics inherent to tunnel diode nonlinearity, it harnesses them. An external device injection-locks the oscillator to a fundamental frequency, simultaneously stabilizing a higher harmonic and yielding two injection-locked carriers without a second oscillator or frequency multiplier. In transmit mode, HILO exploits self-oscillating mixing to modulate both carriers simultaneously; in receive mode, it uses autodyne downconversion at both frequencies. By turning an inherent nonlinearity into a feature, HILO achieves dual-band links while consuming under 210 $\mu$W.

Quantum computing promises new capabilities beyond classical computation. Mid-circuit measurements (MCMs) enable error correction and outcome-dependent control, but their reliability is limited by noise on superconducting hardware. These operations depend on accurate quantum state discrimination (QSD), which assigns measured signals to state labels. Neural network-based (NN-based) QSD improves this assignment, yet its error remains bounded below by the Bayes risk of the available readout distributions. A complementary challenge is state deterioration as successive measurements accumulate and circuit execution advances, making static calibration less representative of the workload. We present Lifting, a QSD framework that addresses both challenges through repetition encoding, neural decoding, and in situ calibration. Encoding supplies joint in-phase and quadrature (I-Q) readout signals from the target and auxiliary qubits, while contextual calibration trains the decoder for the conditions encountered during execution. Our theoretical analysis shows that retaining additional channels from a fixed joint distribution cannot increase Bayes risk and establishes sufficient conditions for contraction of an overlap-based upper bound. Experiments across three IBM processors evaluate the framework on repeated stabilizer measurement, parity-check, and teleportation workloads.

Equilibrium Propagation (EqProp) updates each synapse by comparing a local term at the free and nudged equilibria. Because these two values occur at different times, a physical implementation requires temporary local memory. We show that this memory can emerge from a two-timescale synaptic model in which neural and volatile synaptic dynamics are jointly derived from a single energy function. A volatile state $w$ relaxes toward the product of the pre- and postsynaptic activities, while a non-volatile state $M$ stores the learned parameter. At equilibrium, $w$ equals the local term entering the EqProp update. Using its net change between phases implements phase-based EqProp, while transferring its successive changes during nudging yields continual EqProp, in which the temporal comparison is carried by the synaptic dynamics themselves. On MNIST with a single-hidden-layer network, the phase-based model reaches $2.01 \pm 0.09\%$ test error versus $2.00 \pm 0.13\%$ for standard EqProp, and the continual model reaches $2.24 \pm 0.11\%$ versus $2.28 \pm 0.16\%$ for continual EqProp. The framework further specifies the dynamical requirements that a volatile–non-volatile synaptic device must satisfy to implement this learning mechanism.

Variational quantum algorithms utilize parameterized circuits many times during training, at new parameter values, over batches of data, and with gradients. Pauli propagation approximates the expectation values of such circuits classically by evolving the observable in the Heisenberg picture as a truncated sum of Pauli strings. We present PADO-Pauli, a GPU engine for Pauli propagation that compiles a circuit and its observables once into sparse matrices in which each rotation angle contributes only a cosine and a sine factor, so every later evaluation reuses the compiled program. It evaluates batches of parameters and data inputs embedded as rotation angles. By default its gradients come from a vector-Jacobian product registered with PyTorch's automatic differentiation, so they flow through the angles to any classical layer that produces them. PADO-Pauli also provides truncation by Pauli weight and coefficient size, and noise channels. Zero-filtering removes terms that do not contribute to an expectation value in the all-zero reference state and makes the batched forward pass about 1.5 times faster. Compared to cuPauliProp on an NVIDIA A100, PADO-Pauli evaluates up to 10 times faster on 4 to 12 qubits and uses up to 3.1 times less memory on 16 qubits. We check its accuracy against exact statevector simulation up to 28 qubits. These features make PADO-Pauli a practical toolkit for training variational quantum algorithms, including quantum machine learning models.

Compute-in-Memory (CiM) is an alternative computing paradigm that circumvents the memory bandwidth bottleneck. Variants utilizing non-volatile memory cells (NVCiM) to perform Boolean operations directly within memory arrays offer significant density and energy-efficiency advantages. Fully realizing this potential, however, requires NVCiM-specific compiler optimizations. In conventional digital NVCiM systems, an output cell must be written – or preset – to an operation-specific value before any Boolean operation can occur. Preset-induced extra writes can degrade energy efficiency and cell endurance, often to the point of negating CiM's efficiency gains. In this paper, we introduce PRIM (Preset Reduction In Memory), a compiler optimization framework tailored for digital NVCiM that effectively minimizes preset overhead without compromising computational accuracy or architectural complexity. Unlike existing optimization approaches, PRIM is not restricted to specific types of Boolean operations. Experimental results show that PRIM can reduce execution time and energy consumption by up to 50/% and 44/%, respectively, while boosting cell endurance by up to 50/%.

Decoding visual stimuli from the spiking activity of retinal ganglion cells (RGCs) informs both our understanding of the retinal code and the design of visual prostheses. Natural vision is dynamic, yet state-of-the-art decoders mostly reconstruct single frames with artificial neural networks that process dense, continuous-valued signals rather than the retina's sparse, asynchronous spikes. We asked whether dynamic scenes can be reconstructed and tracked entirely within the spike domain, and at what computational cost. We built an end-to-end spiking neural network (SNN) whose neurons integrate the recorded RGC spikes over time. Populations of up to 1836 RGCs were recorded ex vivo from mouse retina on high-density microelectrode arrays while flashed images, moving bars, drifting gratings and randomly moving handwritten digits were presented. For moving digits, one shared spiking encoder fed two readouts, one reconstructing the image and one decoding the digit's position. Compute energy was estimated from counted synaptic operations. The SNN decoded what and where at once. It reconstructed the stimuli with a structural similarity (SSIM) of up to 0.93, refining the reconstruction as spikes accumulated, and tracked a randomly moving target with a mean error of 1.33 pixels on a $32\times32$ grid. Its fidelity nearly matched that of the best of 17 larger spiking and artificial decoders (5.9 million to 1.7 billion parameters, versus 5.4 million), and at a mean firing rate of 14.2% it needed an estimated $\sim$70-fold less compute energy than a dense artificial network of the same architecture. Dynamic visual scenes can thus be decoded from retinal population activity in the spike domain, with a compact network and at a compute cost compatible with implantable and wearable neural interfaces, supporting spike-native decoders for visual prostheses.

Scaling fault-tolerant quantum computing (FTQC) requires orchestrating millions of operations continuously across a tightly coupled classical-quantum control stack. A critical challenge in this orchestration is determining the optimal hardware-software boundary, i.e., the Quantum Instruction Set Architecture (QISA). We focus on cryogenic, fixed-connectivity qubit platforms, such as superconducting and topological Majorana qubits. For these, existing QISA choices either operate at too low a level, violating cryogenic thermal budgets at scale, or at too high a level, imposing unrealizable complexity on the control engine. To reason about these tradeoffs, we introduce the ARIES instruction set hierarchy, which categorizes QISAs into distinct abstraction levels. To systematically evaluate these levels, we develop an end-to-end evaluation toolflow featuring a cycle-accurate classical driver and hardware engine simulator. Using eight quantum applications across superconducting and topological qubit platforms, we measure instruction bandwidth, transceiver power dissipation, and measurement processing overheads. We demonstrate that while physical-level abstractions exceed the thermal limits of dilution refrigerators by orders of magnitude, canonical-level abstractions significantly underestimate hardware routing congestion and execution runtimes. Our Level 4 QISA (local logical operations) offers an optimal architectural balance. As quantum hardware platforms scale toward FTQC in the coming years, our findings provide concrete architectural guidance for the design of their classical control stacks and instruction interfaces.

Tracing money flows in cryptocurrency transaction networks is challenging because funds can move across accounts, transactions, and token layers. We propose a transaction-aware graph in which nodes represent account–asset states within transactions, while edges encode observed transfers, inferred asset transformations, and temporal inter-transaction sequences. Applied to Aave borrow–repay flows, we compare graph construction and edge-pruning strategies and extract shortest paths using a heuristic weighting. Our results reveal a trade-off between reachability and interpretability and uncover frequent token sequences underlying how borrowed assets are transformed before repayment. This work lays a foundation for general, interpretable multi-token flow tracing.

Four-level indium-gallium-zinc oxide (IGZO) 2T0C memory needs reliable state formation, sensing references, and a path from cell errors to digital weights. We analyze 107 measured DC transfer curves from 37 isolated IGZO transistors (W/L = 3/0.5 micrometers). Four gate-bias operating points inferred from the first sweep are evaluated on later sweeps of 35 devices. Device-resolved operating points reduce second-sweep across-device current standard deviation by 49.4-52.4%, yet retaining initial thresholds increases misclassified points from 37/140 to 55/140. Leave-one-device-out reference estimation gives 0/140 on the second sweep; freezing second-sweep thresholds yields 6/140 on the third. Sampling eight reference transistors on the third sweep gives a mean held-out error fraction of 0.15% over 2,000 selections, versus 4.33% with frozen thresholds on matched held-out devices. We present conditional confusion matrices, a counterfactual common-gain sensitivity, and an INT4 error-propagation proxy. A simultaneous four-state bank with eight reference cells per state requires at least 32 physical reference cells, 12.5% of a proposed 256-cell data bank before peripheral overhead. A charge-conservation model and a partitioned hierarchical error model specify the pulse, retention, and array measurements needed to test transfer from DC device statistics to pulsed 2T0C operation.

Thermodynamic computing lets stochastic physical dynamics carry out a computation and reads out the result as a time average. Recently, thermodynamic computing has been demonstrated on several hardware platforms, including digital CMOS circuits based on stochastic computing. Superconducting single-flux-quantum (SFQ) logic offers very high clock rates and very low switching energy, but it heavily penalizes state, memory, fan-out, and feedback, i.e., exactly the per-neuron accumulators on which such designs rely. We therefore realize thermodynamic computing in SFQ logic through a co-design of a generative model and its circuit in which the datapath holds no state at all. The model is a class-conditional flow-matching image generator, which transforms Gaussian noise into an image of the requested class along a short, deterministic path of four Euler steps; in hardware, these steps are unrolled into a feed-forward pipeline of stochastic bitstream operators, whose output is read out as a time average, as in thermodynamic computing, without any relaxation phase. Each layer of the model is a butterfly-factorized linear map built exclusively from multiplexers that operate on split-unipolar bitstreams. The ReLU activation, which in stochastic computing requires a per-neuron up/down counter (accounting for 60-70% of the JJs in a counter-based design), is replaced by a stateless AND-NOT gate whose one-clock input skew is provided at no cost by SFQ gate-level pipelining. The models are trained directly on the stream representation of the hardware, with a regularizer that bounds the stream gains. Synthesis with an SFQ design flow shows that stochastic streams tolerate relaxed path balancing, which reduces the Josephson-junction (JJ) count of a hard-wired stage by 40-47%.

Use of carbon electrodes integrated with CMOS technology has great promise for lab-on-chip, biomedical, environmental, and industrial applications. One method of developing carbon electrodes is to 3D print polymer nanostructures on a CMOS chip, and use a thermal annealing process to pyrolyze them into carbon at high temperature, approximately 950 {\deg}C. Such high temperature is detrimental to the CMOS sensor circuits, causing severe degradation. We present a novel solution of localized Joule heating of these polymer nanostructures to achieve complete carbonization of the electrodes. Both analytical and COMSOL simulations guided the experiments to characterize the carbonization process conditions. Partial carbonization was achieved at 500-600 {\deg}C of global temperature, and complete carbonization was achieved by localized Joule heating. Pyrolized nanostructures monolithic samples on a silica wafer were fabricated using standard material processing, such as 2-Photon Polymerization. Pyrolysis was achieved by Rapid Thermal Processing (RTP). Observations using optical microscopy, scanning electron microscopy, and Raman spectroscopy demonstrated the feasibility of polymer carbonization within a CMOS compatible temperature window.

Cancer detection using AI methods and image databases faces several challenges, including limited and imbalanced image datasets, long algorithm training times, and high-complexity feature generation. In particular, many cancer image datasets contain very few malignant examples compared to benign ones, limiting the amount of minority-class data available for training. Although classical AI and especially deep learning methods have had some success in providing cancer diagnostics, Quantum Machine Learning (QML) methods may offer advantages through high-dimensional feature representations, generative data augmentation, and the exploitation of feature correlations through quantum properties such as entanglement. In this paper, we propose using a generative QML method on the compressed breast ultrasound image dataset (BUSI) to generate synthetic images and hence augmented datasets for classifier training. In particular, we train a convolutional neural network with real, synthetic, and a combination of real and synthetic images. We test the algorithm on real images and provide comparative results for all three training cases. Our results show that the classifier trained entirely on synthetic images achieved an AUC-ROC of 0.802 (80.2%), compared with 0.760 (76.0%) for the real-data baseline.

AI coding agents operate in iterative loops: they modify code, run tools, inspect the results, and decide what to do next. We call the design of this process loop engineering, which includes context management, model selection, compaction, and termination. Existing efficiency work mainly compresses tool observations or routes selected turns to cheaper models, but the practical opportunity for these optimizations remains unclear. We analyze 600 publicly released coding-agent trajectories spanning two agent scaffolds and two language models. Median observations contain only 118-286 tokens, while the large build logs commonly used to motivate compression occur near the ninety-ninth percentile. Moreover, 98.6% of input tokens are served from a prefix cache, so flat input pricing overestimates loop cost by roughly 7x on average under near-ideal caching. We derive and empirically validate a closed-form break-even condition for model delegation. Delegation increases cost in half of the evaluated settings, near the predicted boundary. More broadly, turn count explains 84% of the variance in log trajectory cost, compared with 4% for mean observation size; the most expensive trajectories have the most turns despite having the smallest observations. These associations suggest that observation-focused methods target a secondary cost factor, and identify unnecessary turns as the larger and largely unexamined opportunity, though we do not establish that turns can be removed while holding task success fixed. We release TurnCost, a tool for re-scoring trajectory corpora under alternative caching, pricing, compression, and routing policies.

In integrated sensing and communication (ISAC) systems, strict sensing requirements can substantially constrain the transmit power available for communication. A hybrid reconfigurable intelligent surface (HRIS) with passive reflection and active amplification capabilities mounted on an unmanned aerial vehicle (UAV) can enhance coverage and strengthen communication links in such power-limited regimes. Meanwhile, rate-splitting multiple access (RSMA) provides an effective transmission strategy for managing inter-user interference, particularly in dense multiuser wireless scenarios. Motivated by this, we analyze the performance of a UAV-mounted HRIS-assisted monostatic ISAC system employing RSMA scheme. We consider the imperfections in the channel state information and successive interference cancellation in the RSMA, which are inevitable in practical system implementations. We then derive highly accurate expressions for outage probability, ergodic rate, and throughput to evaluate communication performance over Nakagami-$m$ fading channels. These expressions reveal key insights into the diversity order and coding gain of the system in the high signal-to-noise ratio regime. To characterize the sensing performance, we derive expressions for the probability of detection, the probability of false alarm, and the Cramér-Rao lower bound. Numerical results validate the theoretical findings and highlight the performance gains of the proposed system over conventional multiple-access schemes and RIS-based systems.

This paper presents wideband indoor channel measurements and measurement-based statistical modeling of channel parameters in the upper 6 GHz (U6G) band using directional and quasi-omnidirectional antenna pairs. Measurements span 6.425-7.125 GHz at 32 matched positions per configuration in an open hall and an L-shaped corridor. System calibration and frequency-dependent peak-gain normalization retain antenna radiation-pattern weighting. Close-in and floating-intercept models are fitted to line-of-sight (LOS) and non-LOS (NLOS) path loss, respectively. Shadow-fading residuals, RMS delay spread (DS), and equivalent K factors derived from power delay profiles are characterized. LOS exponents range from 1.98 to 2.14. Despite similar attenuation slopes, directional links exhibit smaller LOS residuals and DS and higher equivalent K factors. Hall DS medians are 1.78 and 36.52 ns for directional and quasi-omnidirectional links. Corridor NLOS slopes are -1.12 and 2.54, respectively, while DS ordering varies by position. These results characterize antenna-dependent parameters for U6G indoor channels.

Generating an image with a diffusion or flow-matching model typically involves integrating a velocity field for hundreds of sequential steps. That computation suits a GPU poorly and an analog neuromorphic substrate well, where one fast, low-power physical relaxation could replace the whole sequence. Two things are needed: a generative algorithm whose computation is a relaxation, and a way to train it without backpropagation, whose global gradient transport is hard to realise in analog. Energy Matching supplies the first, sampling by gradient-descent relaxation on a time-independent potential - but obtaining the gradient that descent follows is itself a backward pass, once per integration step. We prove that a predictive coding network with a single linear injection term added to its energy relaxes to a state whose visible layer carries that gradient inherently, with an error that vanishes for small injection strengths. An Energy Matching model trained by backpropagation can therefore be loaded into this network unchanged and sampled by relaxation alone, matching the original model's FID to within 0.04 at CIFAR-10 and ImageNet-32 scale. The network can also be trained without backpropagation: Equilibrium Propagation reproduces backpropagation's parameter updates to a cosine similarity of 0.9998, and training end to end with those updates reaches an FID of 3.64 on unconditional CIFAR-10 against 3.54 for backpropagation. To our knowledge this is the first CIFAR-10 generative model trained with Equilibrium Propagation, and the first at competitive quality by any backpropagation-free rule. This quality does not require group normalisation or attention: an architecture of only convolutions and pointwise nonlinearities, both native to an analog crossbar, reaches an FID of 3.51 when trained by backpropagation.

Clinical sleep medicine repeatedly documents profound divergences between objective sleep duration and subjective daytime impairment, notably exemplified by sleep state misperception in paradoxical insomnia and boundary instability in narcolepsy type 1. Here, we present DyssomniaNN: a multi-scale biophysical computational framework coupling Borbély's two-process regulation of sleep homeostasis and circadian drive with Saper's hypothalamic reciprocal flip-flop circuit and a recurrent corticothalamic leaky integrate-and-fire spiking neural network. We formalize five clinical cohorts across an identical 72-hour photoperiod: healthy control, circadian rhythm sleep-wake disorder (delayed-phase), chronic insomnia, paradoxical insomnia, and narcolepsy type 1. The synthetic local field potential converges closely with empirical human electroencephalography spectra from the PhysioNet Sleep-EDF database across all canonical frequency bands (spectral mean absolute error of 0.33–0.35). Furthermore, we introduce the Well-Rested Index to quantify restorative efficacy. Simulations reveal that sleep duration fails to predict restorative recovery: paradoxical insomnia achieves normal total sleep time (21.15 h) yet yields low restoration (37.6%) from unsuppressed cortical beta-band intrusion, whereas hypocretin deficiency in narcolepsy produces severe nocturnal fragmentation (781 min of wake after sleep onset) and 35 sleep-onset rapid eye movement periods. DyssomniaNN provides an in silico platform for mechanistic sleep research and virtual neuropharmacology.

Scaling trapped-ion processors by adding ions remains challenging. A complementary route exploits the multilevel internal structure of each ion to increase the computational Hilbert space per ion. We introduce a hardware-native hybrid qudit-oscillator instruction set for a general d-level internal register coupled to bosonic modes and prove that three of its native operation families suffice for universality of one qudit coupled to several modes. Adding an entangling operation between ions extends universality to a connected register of several qudits. The architecture natively supports both phase-space and sideband instruction sets and is implemented in the open-source framework HybridLane. As a representative application, we reproduce the dissipative vibronic dynamics of a three-chromophore complex previously simulated on a hybrid qubit-oscillator superconducting platform. Moving from a one-qubit-per-state register to a single qudit stores the entire electronic manifold in one ion, so every electronic coupling becomes a local intra-ion transition and the lab-frame Hamiltonian compiles directly into the ISA. Under the stated sequential timing model from a 40Ca+ qudit processor, one Trotter step is projected to run about ten times faster on the qudit, with an average RMSE of approximately 1.1 percent against the published reference.

The fast pace in which protocols, frameworks, and technologies are being proposed for LLM-based agents results in a complex scenario for anyone aiming to create state-of-the-art AI solutions. Without an assessment on how different protocols, frameworks, and libraries work together to compose an Agentic Web solution, developers and organizations face different challenges (e.g., skilling, governance, infrastructure) even before becoming part of the Agentic Web ecosystem. In this context, we propose a system for performing Agentic Web evaluations called MASTIF (Multi-Agent System TestIng Framework) and present a baseline assessment of Agentic Web technologies, including 3 LLMs, 3 protocols, and 4 agent implementation frameworks, against 300 Mind2Web benchmark tasks. Results suggest that selecting a task-specific technology stack may result in gains of task completion rate, efficiency score, and social value stability scores; and reductions in verbosity and time to complete tasks. With the presented results, we expect to present a baseline in terms of metrics and results for this complex and fast evolving scenario for those aiming at designing, assessing, and developing multi-agent systems.

Proteinoid microspheres are electrically active assemblies of thermally polymerised amino acids that exhibit spontaneous oscillations, spike-like events, stimulus-dependent responses, adaptation and collective dynamics. We formulate these observations as an applied-mathematical problem and develop a unified dynamical framework for information processing in proteinoid networks. Individual microspheres are represented by nonlinear excitable or oscillatory systems, whereas interactions are described by weighted adaptive graphs and, in the spatial limit, reaction–diffusion dynamics. The framework combines signal propagation, attenuation, fading memory, adaptive coupling, synchronisation, information transfer, reservoir readout, robustness and energetic constraints. We define a task-relative proteinoid intelligence functional from normalised measures of stimulus information, memory, adaptation, directed information transfer, task performance and robustness, penalised by energetic and structural costs. Analytical results establish a joint-response information bound, an upper bound on linear memory for stable linear networks, and existence of an optimal admissible coupling matrix under compact constraints. Dimensionless groups identify the relative time scales and coupling regimes governing the problem in nonlinear and adaptive network dynamics.

Counterfactual text editing is the ability to revise a given text in light of a hypothetical change while preserving facts not causally affected by that change. When asked what would have happened if a doctor had not recommended surgery, the model output should retain the patient's medical history and modify only the consequences of the decision. This requires causal understanding: a model must determine, without being explicitly instructed to, what events are affected by the hypothetical change and what should remain fixed. This skill is central to human reasoning, especially in safety-critical domains like healthcare or law. By contrast, the task of associational text generation merely generates text statistically associated with the altered condition. In the medical example, an associational response might make the patient's symptoms less severe because milder cases correlate with no surgery. Such responses produce a plausible scenario, but not the counterfactual version of the given one. Prior counterfactual-text datasets do not clearly separate these modes of reasoning. We introduce the Counterfactual Benchmark (CoBe), a testbed for models' zero-shot ability to counterfactually edit a given textual scenario when prompted to do so in a conversationally natural way. CoBe contains 2000 questions spanning healthcare, science and engineering, business, and everyday scenarios. We test a suite of frontier and smaller models, with frontier systems achieving an average score of $58.05\%$. Our findings indicate that models frequently default to associational generation rather than preserving the causal structure of the original scenario. Moreover, performance does not scale consistently with either model size or output token length, suggesting that the failure may reflect model training objectives rather than insufficient model capacity.

Deep neural networks (DNNs) deployed on heterogeneous edge accelerators are increasingly exposed to runtime hardware faults. Tight power and resource budgets push these platforms toward aggressive voltage scaling and limit the use of hardware fault mitigation, while process variation, thermal stress, and device aging raise fault rates further. The resulting transient bit level corruptions propagate through inference and can sharply reduce prediction accuracy. Existing DNN partitioning frameworks optimize latency and energy under fault free assumptions, so their deployment strategies often fail to sustain reliable inference in realistic operating conditions. This paper presents RAPID DNN, a reliability aware partitioning framework that adds runtime reliability as a third optimization objective alongside latency and energy. RAPID DNN first characterizes layer wise reliability through systematic fault injection in the weight and activation domains to identify vulnerability critical layers. The resulting sensitivity profile guides a three objective NSGA II optimizer that jointly minimizes inference latency, energy consumption, and expected fault induced accuracy degradation. We evaluate RAPID DNN on AlexNet, SqueezeNet, ResNet18, VGG16, and MobileNetV2 using heterogeneous Eyeriss and SIMBA accelerator profiles. Across fault models and injection probabilities, it consistently improves inference robustness over the fault agnostic baseline. Under the representative mixed random fault configuration, RAPID DNN raises average Top 1 accuracy by 9.81 percentage points and reduces average expected fault induced accuracy degradation by 36.86%, at a cost of 2.38% average latency overhead and 3.34% average energy overhead. These results show that treating reliability as a partitioning objective enables robust DNN deployment on heterogeneous edge accelerators under runtime hardware faults.

Energy harvesting (EH) technology has emerged as a key approach for extending the operational autonomy of Internet of Things (IoT) devices. In this context, medium access control (MAC) protocols for EH-IoT make use of the harvested energy efficiently to enhance the application performance. However, many existing MAC protocols rely on stored energy the battery in devising energy-aware strategies, which can interrupt the device operation and hinder the execution of ongoing tasks in dynamic and low EH conditions. To address this, this paper presents an energy-aware receiver-initiated MAC protocol for EH-IoT devices, called EARI-MAC, that adjusts the duty cycle of the receiver device based on stored energy and current harvesting power using the mathematical formulation. This approach supports sustained receiver operation under low EH conditions and improved application performance when harvested energy is abundant. The performance of the EARI-MAC has been evaluated in simulation using the real solar data gathered in Novi Sad, for three consecutive days. The simulation results demonstrate that the EARI-MAC utilizes the available energy resources efficiently and improves the packet delivery ratio and network throughput by more than 10%, reduces the receiver energy consumption by 5.6% and 8.2% and energy consumption per bit by 9.6% and 13.9% compared with QPPD-MAC and energy-unaware MAC, respectively.

The Minimum Birkhoff Decomposition (MBD) seeks a sparse weighted sum of matchings and is a challenging optimization problem with applications in network scheduling and energy trading. We scale a quantum-assisted decomposition method by combining single-layer QAOA sampling with Extended Fully-Corrective Frank-Wolfe (E-FCFW) optimization, spectral graph partitioning, and greedy feasibility repair. We demonstrate the pipeline on the 1,354-node PEGASE bus test case (representing a 1,354-bus transmission grid with 1,710 transmission lines), whose 1,710 edges define a native 1,710-variable matching optimization problem requiring 1,710 qubits in the unpartitioned encoding. Partitioning enables distributed execution on IBM superconducting quantum processors. The best reported hardware result uses a 50-qubit partition bound, while reducing partitions to 20 qubits degrades convergence in the partition-size comparison. Larger partitions are more demanding for matrix product state (MPS) simulation, and bond-dimension tests show that truncating quantum correlations reduces candidate quality. The results therefore motivate retaining a substantive quantum sampling task within each partition as the overall problem scales. On the PEGASE-1354 benchmark, repaired QAOA samples achieve lower residual decomposition error than both simulated annealing and uniform random sampling baselines, while spatial circuit packing reduces hardware execution time by approximately a factor of three. These results demonstrate that combining spectral partitioning, spatial circuit packing, and classical feasibility repair provides an executable path for deploying gate-based quantum sampling on thousand-variable constrained optimization problems on current hardware.