Computer Science & Mathematics

A long reflecting wall gives every wireless transmitter a mirror image, pairing its direct and reflected paths. Motivated by intelligent surfaces, we study the resulting signal point processes under two treatments: keeping the powers apart and superposing their point processes, or adding the powers transmitter by transmitter to form a recombined process. A threshold on inverse received power selects a region of transmitter locations, so Campbell's theorem reduces the mean measures to area calculations without assuming Poisson transmitters. For power-law path loss and paired fading marks, we derive the ratio of the recombined and superposed mean measures at large thresholds. The limit is independent of transmitter density and wall distance, but generally depends on the joint fading distribution. Under common fading, that dependence disappears and the limit depends only on the reflection coefficient and path-loss exponent. For Poisson transmitters, we derive paired count fluctuations and convergence of the count ratio. We also calculate the probability that the strongest individual path is direct. When the wall passes through the receiver, this probability has a closed form and the strongest received power has an exact Fréchet distribution. These results provide a reference model for wireless networks with reflecting surfaces.

Energy-efficient virtual machine (VM) consolidation matters as data centre electricity demand rises. Bio-inspired metaheuristics, including ant colony optimisation (ACO), particle swarm optimisation (PSO), and genetic algorithms, have reported substantial energy savings. However, these improvements are commonly evaluated against conventional allocation policies that differ in both initial VM placement and subsequent migration optimisation, making it difficult to determine the contribution of metaheuristic search. This study reproduces an existing consolidation implementation in CloudSim Plus and introduces a matched control that retains the same initial packing and empty-host deactivation mechanisms but excludes migration search. ACO, PSO, and a modified genetic algorithm (MGA) are evaluated across two fleet configurations and four workload regimes, with ten stochastic replications per condition. The matched control achieves energy savings of 0.8-81.4% relative to the spreading baseline, the amount available falling sharply as load rises, without migrations or incomplete workload execution. Across 24 comparisons, metaheuristic search increases energy consumption in 11 cases, produces identical outcomes in seven, and reduces consumption in six. Of these, ten increases and five reductions remain statistically significant after Holm correction. Five of the six apparent reductions coincide with unfinished tasks and disappear after normalisation by delivered computational work. Only ACO under light heterogeneous load retains a 3.22-percentage-point improvement (95% CI 2.96-3.47). These findings demonstrate that initial packing and host deactivation account for most of the measured energy savings in the examined implementations, highlighting the importance of matched control conditions and workload-completion measures for reliable evaluation of VM consolidation algorithms.

Speculative decoding significantly accelerates autoregressive text generation in Large Language Models (LLMs), but its efficacy depends largely on two key factors: the distributional alignment between the draft and the target model, and the draft model's latency during inference. However, existing approaches typically utilize independently pretrained small draft models or seek to prune the target model to a manually chosen architecture, thereby failing to explicitly balance the trade-off between draft model alignment and token generation efficiency. To address this gap, we formulate draft model design as an optimization problem and propose T2D, a framework that constructs draft models directly from the target model using latency-aware structured pruning and knowledge distillation. In this manner, T2D obtains draft models that are jointly optimized for both measured inference latency and cross-entropy loss on the target model's responses, resulting in superlative speculative decoding performance. Through extensive experiments on multi-domain speculative decoding benchmarks and across several target model families of differing scale, we demonstrate how T2D prunes up to 92\% of the target model to obtain highly performant draft models compared to competitive baselines. Moreover, draft models obtained via T2D can also be utilized by other speculative decoding token-alignment and knowledge distillation methods in a plug-and-play fashion, leading to further gains. Our findings underscore the importance of jointly navigating the draft model architecture-alignment frontier to unlock the full potential of speculative decoding for LLMs.

Video diffusion models provide a source of interaction representations for robot control, but transferring these representations requires reconciling variable camera inputs with heterogeneous action interfaces and closed-loop execution. We present X-World, a framework that transfers a video prior through a fixed-length token sequence to a shared action expert. A residual cross-view attention branch extends a pretrained video backbone to one, two, or three views while retaining its original self-attention path. A layer-aligned Q-Former aggregates backbone features, and dataset-specific modules map between native state and action interfaces and the shared action expert. Training separates video adaptation from action-only pretraining by freezing the video backbone in the latter stage, followed by joint target-domain post-training. The policy extracts video features with a single backbone pass and uses asynchronous action chunking for execution. On LIBERO, X-World reaches 98.5\% mean success; its LIBERO-trained checkpoint obtains 76.5\% on LIBERO-Plus without further adaptation. The seven-shift breakdown reveals gains in robot, language, lighting, and layout perturbations, alongside weaknesses under camera, background, and noise shifts relative to OpenVLA-OFT. On six dual-arm real-world tasks, it achieves 81.7\% mean success over 20 trials per task. Removing video adaptation or cross-view fusion produces the largest observed success drops among the tested removals.

Large language models increasingly collaborate with humans and peer AI agents whose contributions may be incomplete, inconsistent, or incorrect. Effective collaboration requires calibrated trust, whereby decisions to rely on, verify, or assist a partner adapt rationally as empirical evidence accumulates. We investigate whether models translate partner evidence into appropriate reliance policies. We introduce a benchmark of 324 scenarios across 23 collaboration categories. Each scenario involves three sequential decisions with explicit transition dynamics, feedback emissions, and cost schedules, permitting exact analytical evaluation of choices against their expected consequences over the remaining interaction. Evaluating seven models alongside 36 controlled-history probes, we dissociate evidence sensitivity from normatively appropriate action. While the strongest frontier model approaches near-optimal decisions, other models exhibit recurring pathologies: procuring excessive safeguards, purchasing verification tests with zero expected value of information, prioritizing immediate savings over cooperative learning, and misallocating assistance to suboptimal channels. Supplying explicit posterior distributions resolves certain errors while introducing new failure modes. Calibrated trust in autonomous agents critically demands coupling empirical evidence with forward-looking decision utility.

Vision-Language-Action (VLA) models map a language instruction and the current observation directly to actions, without checking whether the instruction has been completed. For long-horizon tasks, a single failed step therefore goes unnoticed and leaves the whole task unfinished. Such failed episodes, however, often end with the task partially complete. Moreover, the unfinished steps are reusable atomic skills that recur across demonstrations. A failed attempt can thus be recovered by composing with atomic skills. We propose RoboDuo, a closed-loop dual-system framework that uses code as the interface between a coding agent and two VLAs. The agent writes a program that lets a full-task VLA attempt the whole task and checks whether it is complete; on failure, an atomic VLA finishes the remaining steps. Since the agent emits no actions and only decides which VLA for execution, RoboDuo wraps an existing full-task VLA without updating its weights. To train the atomic VLA and the coding agent with the completion-checking ability, we build LIBERO-Atomic, LIBERO-Code, and LIBERO-Judge from a single set of LIBERO demonstrations. RoboDuo consistently improves X-VLA and $π_{0.5}$ on LIBERO and LIBERO-PRO, with the largest gains on long-horizon tasks, and recovers failed rollouts of a frozen full-task VLA on a real robot.

Geo-filtered approximate nearest-neighbor (GFANN) search retrieves the objects inside a query-specified geographic disk whose feature vectors are closest to a query vector, a core operation in location-aware Web search. A disk filter on a standard proximity graph invalidates out-of-disk routing vertices; on selective disks, these removals break greedy paths and recall drops sharply. Existing methods scan all in-disk objects, over-fetch unfiltered candidates, or maintain separate sub-indexes across spatial partitions, trading latency for index overhead. We propose REACH, a single-graph index based on row-local active-order invariance: if every vertex stores its outgoing neighbors in a fixed-width row sorted by vector distance, any disk filter masks out-of-disk entries while surviving in-disk (active) neighbors remain vector-sorted. Offline, each vertex merges globally closest vector candidates with a nearest-first geographic stream into one row; online, beam search evaluates vector distances only for the first l active entries of each visited row. Because co-located objects share the same distance to every query center, every disk admits a coordinate group in full or not at all, decomposing active connectivity into intra-group and inter-coordinate conditions. Under a spatial marked-site model with repeated coordinates, we prove that polylogarithmic out-degree guarantees strong connectivity across all disks and constant-factor distance reduction per hop on compact disks, while a cut-rank threshold characterizes the minimum scan budget l. Across four datasets up to 10^7 objects, one REACH index per dataset outperforms SOTA methods, achieving 2.49x higher geometric-mean QPS than the strongest baseline at 0.95 Recall@10 on selective queries, while building 3.8x-11.9x faster and using 37%-45% less space than a SOTA spatial-graph index.

Every explicit non-cyclic crossed product space--time code for more than four antennas has relied on transcendental structure constants, and its coding gain is therefore not bounded away from zero. We explain this phenomenon and show how to avoid it. Over a number field, the commuting-generator (tensor product) construction yields a division algebra only in the cyclic case, and a division crossed product of prime-power degree requires two places with full decomposition group; in particular, no $(\Z/2)^r$, $r\ge3$, crossed product division algebra exists over $\Q(i)$. We then introduce Abelian crossed products with a commutator twist, give the general parametric form of their codewords as a $2\times2$ block matrix of cyclic-algebra codewords, and prove the division property with a centralizer argument and a tame norm-residue symbol. This yields algebraic codes over $\Q(i)$ with non-vanishing determinants and closed-form minimum determinants: an information-lossless $4\times4$ $(\Z/2)^2$ code with $δ_{\min}=1/2000$, and an infinite family of DMT-optimal $8\times8$ $\Z/4\times\Z/2$ codes indexed by primes $p\equiv5\pmod8$, whose commutator and cyclic constants are roots of unity. The member $p=5$, defined over $\Q(ζ_{40})$, has minimum determinant exactly $1/5$ before normalization, at least $34.7$ times that of the $8\times8$ perfect code at equal energy, and its rate-2 subcode for $8\times2$ systems has at least $5$ times the minimum determinant of the punctured perfect code. A determinant-spectrum analysis shows that at practical error rates the multiplicity of small determinants, rather than their minimum, governs performance, and it explains all simulated results.

Physical wave processors are constrained by diffraction, but the bandwidth of the mathematical functions they implement need not be set by it. Here, we demonstrate continuous and multiplexed optical processing of superoscillatory functions, in which a scalar argument is encoded onto a wavefront and a fixed diffractive wave processor synthesizes optical responses that approximate the corresponding function outputs in parallel. Unlike conventional superoscillatory optics that generates predetermined fields, superoscillation here is expressed in the input-output dependence on the encoded argument. Its frequencies are therefore distinct from the transverse spatial frequencies of propagating light and are not subject to the same diffraction-imposed cutoff. We demonstrate complex-valued superoscillatory function synthesis and introduce a multiplexed architecture that simultaneously evaluates distinct target superoscillatory functions whose harmonic content exceeds that of the optical input encoding. Visible-light experiments further validate this beyond-encoding superoscillatory regime. Together, these results establish functional superoscillation as a design principle for wave-based computation and show how interference, diffractive depth and spatial channel allocation extend the functional reach of passive wave processors. More broadly, these analyses establish a separation between functional bandwidth and physical wave bandwidth in analog optical computation.

Functional data are costly to collect; samples of tens to hundreds of curves are common. Synthetic data augmentation can help, but its theory is developed for finite-dimensional data and relies on total variation, which is uninformative for functions. We study augmentation with a class-conditional functional diffusion generator and state guarantees in the Wasserstein-1 distance. For stable downstream estimators, we bound the risk of training on synthetic curves, from above and below, by the risk of training on as many real curves plus a generation penalty, and derive the best number of synthetic curves and a condition under which synthetic data outperform real data. For allocating synthetic curves under class imbalance, we bound the excess risk of training on real and synthetic curves together, without sample splitting. In applications to temperature, hyperspectral and gait curves, augmentation improved classification, function-to-scalar regression and function-to-function regression, and directing synthetic curves to the minority class gave the largest gain under class imbalance.

Multi-agent reinforcement learning supports cooperative decision-making in Internet of Things systems, but strategic deviations can disrupt coordination and undermine confidence in the training process. This paper proposes Blockchain-Enabled Centralized Training and Decentralized Execution (BE-CTDE), which uses blockchain as an active training-management layer. A behavioral verifier compares executed trajectories with shared-policy recommendations; its flags drive a token mechanism combining slashing, burning, rewards, and compensation. Retained balances determine each agent's contribution to the global policy update, connecting auditable behavior to learning influence. We derive a one-round incentive condition that accounts for action-dependent compensation and the payoff from deviation, and a finite expected exclusion-time bound under explicit conditional detection and accounting assumptions, showing perfect detection is unnecessary. An exact finite-population capacity bound characterizes the limits of the population-relative detector. In multi-agent path finding experiments, BE-CTDE outperforms a state-of-the-art learning-based path planner with substantially higher success rates under attack, even when adversaries form the majority. It also outperforms partially defended baselines in all configurations, with collision rates up to 35% lower than under detection without economic consequences and up to 43% lower than under conventional stake slashing. Together, these results characterize how behavioral verification and token feedback can support accountable cooperative learning.

Type 2 diabetes is a widespread chronic disease that requires early and accurate diagnosis to prevent serious complications. Traditional diagnostic methods often lack sufficient accuracy, which highlights the need for more reliable computational solutions. In this study, we propose a model that integrates the Random Forest classifier with the Bat Optimization Algorithm for simultaneous feature selection and hyperparameter tuning. The SMOTE-ENN method was first applied to the Pima Indians Diabetes Database to correct class imbalance and remove noisy or ambiguous samples, producing a more balanced and cleaner dataset. The optimized model trained on this refined dataset achieved 89% accuracy, 88% precision, 90% recall, and an F1-score of 89%, clearly outperforming the baseline Random Forest and other existing approaches. These results demonstrate the potential of combining evolutionary algorithms with ensemble learning to provide a practical and cost-effective tool for early detection of type 2 diabetes in clinical practice.

Large language models (LLMs) bring world knowledge to user preference modeling in sequential recommendation, but generation-based recommenders express next-item predictions indirectly as text. Generated outputs may fail to identify a unique candidate item, depend on candidate presentation order in the prompt, and lack explicit confidence in the recommendation. Recent studies explore Jev, a model for fast option selection with explicit confidence. Inspired by Jev, we introduce decision-native recommendation, a new paradigm that jointly models item choice and recommendation confidence using LLM representations. It requires valid candidate selection, consistency under candidate reordering, and confidence that reflects recommendation accuracy. We propose Direct Item Choice Estimation (DICE), which learns a personalized next-item distribution over supplied candidates from user interaction history and recommends the highest-scoring item with calibrated confidence. Tree-structured attention shares history computation across isolated candidate branches for order-consistent candidate scoring in a single forward pass. On three recommendation datasets, DICE achieves higher top-one accuracy than all evaluated baselines. Separate evaluations show unchanged item choices under tested candidate reorderings and end-to-end speedups of up to approximately $6\times$ over the evaluated generation-based methods. Its confidence estimates support selective recommendation by identifying more reliable predictions and guide additional computation for low-confidence examples in offline routing.

Humanoid robots have recently achieved remarkable success in motion tracking. However, during real-world deployment, robots inevitably encounter dynamics gaps arising from either extra physical disturbances or inaccurate simulation modeling, resulting in a significant training-deployment gap. Therefore, effectively leveraging real-world data to fine-tune policies represents the crucial last mile for robust deployment. To bridge this gap, we propose HumanoidNDM, an online fine-tuning framework for humanoid motion tracking driven by a real-world-trained neural dynamics model. First, in the data collection stage, we introduce a low-frequency reference perturbation method with an adaptive intensity mechanism, balancing deployment feasibility and exploration efficiency. Second, for the model training stage, we train a robust history-based, proprioceptive neural dynamics model in Euclidean space by incorporating differentiable forward kinematics, eliminating the reliance on costly motion capture systems. Finally, in the policy fine-tuning stage, we directly fine-tune the policy via backpropagated model gradients, achieving high data efficiency. Through physical validation on the Unitree G1 robot, we demonstrate that HumanoidNDM enables rapid real-world adaptation to various perturbations, representing the first online, MoCap-free fine-tuning framework for humanoid tracking policies.

Open large language models (LLMs) small enough to run on a laptop are an obvious candidate for automatic bug severity prediction. We measure what they add over cheaper classifiers on one task: assigning one of four Bugzilla severity levels (Critical, Major, Minor, Trivial) to Eclipse bug reports. From a public dump of 361,006 unique Eclipse reports we draw a class-balanced sample of 20,000 rows and compare eleven configurations of nine model types: six TF-IDF classifiers, a fine-tuned CodeBERT encoder, and Llama 3 8B and Qwen 2.5 7B, each run zero-shot and after QLoRA fine-tuning. All eleven are scored on the same 2,000 held-out reports, with bootstrap intervals and McNemar tests. The zero-shot runs reach macro-F1 0.305 and 0.321, below every TF-IDF model (0.430-0.489). The fine-tuned runs reach 0.526 and 0.485, but the zero-shot and fine-tuned setups also differ in checkpoint, serving runtime and decoding, so this gap cannot be credited to fine-tuning alone. Fine-tuned Llama 3 shows no detectable difference from CodeBERT (0.518; difference +0.007, 95% CI -0.017 to +0.032), and fine-tuned Qwen 2.5 is below it on macro-F1. The models differ more by class than in aggregate: fine-tuned Llama 3 finds 36% of Critical reports, Qwen 2.5 finds 69% but only 21% of Minor ones. The dump repeats 60,000 rows, and 8% of the test reports duplicate a training report; removing them lowers TF-IDF scores by up to 0.031 and leaves the LLMs almost unchanged. We release split identifiers, per-report predictions and the analysis script at https://github.com/tbayan/bug_finetuning_llm_artifacts, together with CyberTriage, a tool that serves the fine-tuned classifier locally.