arXiv:2601.08030v2 Announce Type: replace Abstract: Extracting higher-order structures from multivariate data has become an area of intensive study in complex systems science, as these multipartite interactions can reveal insights into fundamental features of complex systems like emergent phenomena. Information theory provides a natural language for exploring these interactions, as it elegantly formalizes the problem of comparing "wholes" and "parts" using joint, conditional, and marginal entropies. A large number of distinct statistics have been developed over the years, all aiming to capture different aspects of "higher-order" information sharing. Here, we show that these functions are special cases of a more general function, $\Delta^{k}$ which is parameterized by a free parameter $k$. Generally, the $\Delta^{k}$ function is arranged into a hierarchy of increasingly high-order synergies; for a given value of $k$, if $\Delta^{k}(\mathbf{X})>0$, then $\mathbf{X}$ is dominated by interactions with order greater than $k$, while if $\Delta^{k}(\mathbf{X})<0$, then $\mathbf{X}$ is dominated by interactions with order lower than $k$. Using the entropic conjugation framework, we also find that the conjugate of $\Delta^{k}$, which we term $\Gamma^{k}$ is arranged into a similar hierarchy of increasingly high-order redundancies. Finally, we show that the interpretation of $\Delta^{k}$ as a measure of synergy is combinatorial, rather than specific to any particular information-theoretic measure, allowing us to generalize the whole framework and define measures of synergy on any set function that meets certain criteria. Using the graph cyclomatic number as a case study, we derive topological analogues of the dual total correlation, O-information, and S-information that describe the cyclic structure of simple graphs.
Science Journals
arXiv:2602.06513v2 Announce Type: replace Abstract: We demonstrate that the one-dimensional shallow water moment equations satisfy an auxiliary entropy conservation law, where the entropy function corresponds to the total energy. Additionally, we show that the classical Newtonian slip friction and Manning friction terms are entropy dissipative with respect to the developed entropy variables. The results from the continuous entropy analysis are used to construct an entropy stable and well-balanced nodal discontinuous Galerkin spectral element method for the spatial approximation. Key to ensure the entropy stability of the scheme is the derivation of entropy conservative numerical fluxes that satisfy a discrete version of the entropy flux compatibility condition. Finally, numerical examples demonstrate the performance of the scheme and validate the theoretical results.
arXiv:2606.23752v1 Announce Type: new Abstract: Software developers increasingly work with multiple LLM coding agents, switching among tools such as Codex, Grok, Claude Code, and other assistants as context windows fill, sessions end, or a particular agent becomes better suited to a subtask. Each agent, however, persists its conversation in a private and vendor-specific log. The result is conversational state drift: goals, decisions, open tasks, and rationales established with one agent are not reliably available when another agent takes over. This paper presents \emph{ESAA-Conversational}, a domain specialization of Event-Sourcing Agent Architecture (ESAA)~\cite{esaa} for shared conversational memory across heterogeneous agents. The method treats the visible conversation as a local event store: hooks and watchers capture visible turns, normalize them into an append-only \texttt{activity.jsonl}, and deterministically project read models such as \texttt{handoff.md}, \texttt{state.md}, \texttt{decisions.md}, and \texttt{tasks.json}. Mechanical capture does not require LLM inference; agents use judgment only for explicit curation, recording durable decisions and conversational tasks through domain commands. The public v1.1.0 release implements a PowerShell CLI with \texttt{init}, \texttt{enable-hooks}, \texttt{sync}, \texttt{project}, \texttt{verify}, \texttt{context}, \texttt{decide}, and \texttt{task}; includes \texttt{workspace\_root} isolation and a write-path lockfile; and is distributed as a greenfield package with an empty public log. A self-referential case study with 570 development-lab events shows that heterogeneous agents can collaborate through a shared log without a direct agent-to-agent channel, while the public distribution preserves privacy by excluding the private conversational history.
arXiv:2406.07884v3 Announce Type: replace-cross Abstract: Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantum interactive dynamics with the potential to address outstanding challenges in quantum state preparation and compression, quantum control, and quantum complexity. We present a deep reinforcement learning (RL) approach using an actor-critic algorithm for constructing short disentangling circuits for states with up to 16 qubits. With access to only two-qubit reduced density matrices, our agent decides which pairs of qubits to apply two-qubit gates on; requiring only local information makes it directly applicable on modern NISQ devices, as we demonstrated experimentally on a trapped-ion quantum computer. Utilizing a permutation-equivariant transformer architecture, the agent can autonomously identify qubit permutations within the state, and adjusts the disentangling protocol accordingly. Once trained, it provides circuits from different initial states without further optimization. We demonstrate the agent's ability to identify and exploit the entanglement structure of multi-qubit states. We analyze the disentangling circuits constructed by the agent for 4- and 5-qubit Haar-random states, and observe strong correlations between consecutive gates and among the qubits involved. Through extensive benchmarking, we show the efficacy of the RL approach to find disentangling protocols with minimal gate resources. We explore the resilience of our trained agents to noise, highlighting their potential for real-world quantum computing applications. Analyzing optimal disentangling protocols, we report a general circuit to prepare an arbitrary 4-qubit state using at most 5 two-qubit (10 CNOT) gates.
arXiv:2604.00725v2 Announce Type: replace Abstract: End-to-end OCR for historical newspapers remains challenging, as models must handle long text sequences, degraded print quality, and complex layouts. While Transformer-based recognizers dominate current research, their quadratic complexity limits efficient paragraph-level transcription and large-scale deployment. We investigate linear-time State-Space Models (SSMs), specifically Mamba, as a scalable alternative to Transformer-based sequence modeling for OCR. We present to our knowledge, the first OCR architecture based on SSMs, combining a CNN visual encoder with bi-directional and autoregressive Mamba sequence modeling, and conduct a large-scale benchmark comparing SSMs with Transformer- and BiLSTM-based recognizers. Multiple decoding strategies (CTC, autoregressive, and non-autoregressive) are evaluated under identical training conditions alongside strong neural baselines (VAN, DAN, DANIEL) and widely used off-the-shelf OCR engines (PERO-OCR, Tesseract OCR, TrOCR, Gemini). Experiments on historical newspapers from the Bibliotheque nationale du Luxembourg, with newly released >99% verified gold-standard annotations, and cross-dataset tests on Fraktur and Antiqua lines, show that all neural models achieve low error rates (~2% CER), making computational efficiency the main differentiator. Mamba-based models maintain competitive accuracy while halving inference time and exhibiting superior memory scaling (1.26x vs 2.30x growth at 1000 chars), reaching 6.07% CER at the severely degraded paragraph level compared to 5.24% for DAN, while remaining 2.05x faster. We release code, trained models, and standardized evaluation protocols to enable reproducible research and guide practitioners in large-scale cultural heritage OCR available at https://github.com/MarcoPerson/ssm-ocr-benchmark.
arXiv:2407.18957v5 Announce Type: replace-cross Abstract: Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fundamentals, and global events)? These factors, which frequently influence trading behaviors, are critical elements in the quest for maximizing investors' profits. Our work attempts to solve this problem through large language model based agents. We have developed a multi-agent AI system called StockAgent, driven by LLMs, designed to simulate investors' trading behaviors in response to the real stock market. The StockAgent allows users to evaluate the impact of different external factors on investor trading and to analyze trading behavior and profitability effects. Additionally, StockAgent avoids the test set leakage issue present in existing trading simulation systems based on AI Agents. Specifically, it prevents the model from leveraging prior knowledge it may have acquired related to the test data. We evaluate different LLMs under the framework of StockAgent in a stock trading environment that closely resembles real-world conditions. The experimental results demonstrate the impact of key external factors on stock market trading, including trading behavior and stock price fluctuation rules. This research explores the study of agents' free trading gaps in the context of no prior knowledge related to market data. The patterns identified through StockAgent simulations provide valuable insights for LLM-based investment advice and stock recommendation. The code is available at https://github.com/MingyuJ666/Stockagent.
arXiv:2606.24726v1 Announce Type: new Abstract: Video MLLMs often struggle with fine-grained spatio-temporal reasoning, sometimes generating correct answers based on irrelevant frames or objects. Although outputting spatio-temporal evidence during reasoning is a promising direction, existing RL frameworks typically rely on geometry-only (IoU) rewards, which can be sensitive to boundary perturbations and overlook semantic alignment. To address this, we propose Semantic Evidence Reward (SER), which reformulates spatio-temporal evidence grounding as a constrained verification task. Instead of computing pixel-level overlap, SER uses a referee VLM as a local checker to evaluate model-generated evidence claims across two dimensions: relevance and localization quality, combined with a temporal penalty. This design reduces the reliance on dense box annotations and enables training directly on standard video QA data. On the V-STAR benchmark, SER achieves 49.6% mLGM, improving by 3.0 points over the strong evidence-grounded baseline Open-o3-Video, demonstrating its potential in enhancing both answer accuracy and evidence grounding.
arXiv:2606.24727v1 Announce Type: new Abstract: Accurate massive MIMO channel state information (CSI) acquisition with low pilot overhead is critical in dynamic propagation environments. Exploiting temporal correlation is key to reducing pilot overhead, yet most existing methods often rely on impractical assumptions. The approximate message passing with side information (AMP-SI) algorithm, built upon a birth-death-drift (BDD) model, represents a significant step in this direction. However, its practical deployment is hindered by three major limitations: reliance on i.i.d. Gaussian sensing matrices, need for perfect BDD parameter knowledge, and a statistically approximate treatment of temporal information. To address these limitations, we introduce BDD-VAMP-EM, a fully automated algorithm that relies on the BDD model, vector AMP (VAMP), and expectation-maximization (EM) in a unified framework. Simulations show that BDD-VAMP-EM consistently outperforms existing benchmarks, particularly under model parameter mismatch, confirming its practical viability.
arXiv:2606.24731v1 Announce Type: new Abstract: We propose a hybrid half-day workshop at IEEE VIS 2026, calling for participation from visualization researchers and science fiction creators in order to develop a systematic understanding of the two-way relationship these communities have long shared. We invite submissions of creative formats showcasing connections and inspiring future research. Our workshop plan includes a keynote, lightning talks, brainstorming, cross-community critique, affinity mapping, and discussion around identified themes.
arXiv:2606.23942v1 Announce Type: new Abstract: We present a large-scale empirical study isolating the contributions of the Derivative Regularization penalty (DREG). Across a fully-crossed factorial sweep of 960 experiments spanning 4 activations, 6 regularizers, 8 datasets, and 5 random seeds, we ask: when, where, and why does DREG work? Our results establish three principal findings. First, DREG achieves the highest overall and clean-regime accuracy among all regularizers evaluated (significantly so against the unregularized baseline, Weight Decay, and IGPen; Wilcoxon $p \leq 0.031$). It ranks second in noise robustness behind Spectral Normalization (SN) - the only two layer-wise regularizers in the study. Second, DREG is globally the best-performing regularizer under GELU, the default activation in modern transformer architectures, particularly on both messy vision and messy NLP benchmarks, suggesting direct applicability to frontier deep learning settings. Third, DREG's advantage over competing regularizers is most pronounced under data scarcity, consistent with its role as a geometric inductive bias that substitutes for the regularizing effect of data volume. Throughout, DREG is applied with a single fixed hyperparameter $\lambda = 10^{-2.5}$ and no per-dataset tuning, supporting its characterization as a plug-and-play regularizer for neural networks with nontrivial Jacobian structure. These findings are consistent with DREG's design: concentrating regularization pressure on layers where the activation derivative is largest, rather than constraining the network uniformly.
arXiv:2606.23943v1 Announce Type: new Abstract: Tokenization is a foundational step in NLP pipelines, yet standard evaluation metrics such as fertility rate fail to capture morphological correctness for agglutinative languages. We present QuechuaTok, a systematic benchmark comparing four tokenization strategies - BPE, Unigram LM, WordPiece, and a morphology-aware PRPE tokenizer - for Southern Quechua (quz), a low-resource agglutinative language spoken by 8-10 million people in South America. Using a 200k-sentence corpus and the SQUOIA finite-state morphological analyzer (Rios, 2016) as silver standard, we evaluate three metrics: fertility rate, OOV rate, and morphological boundary accuracy (MorphAcc). Our results show that BPE achieves the lowest fertility rate (1.636 at 16k vocab) by memorizing surface word forms, while achieving only 6.67% MorphAcc. PRPE achieves 83.33% MorphAcc - the highest of all systems - demonstrating that fertility rate alone is insufficient to evaluate tokenizers for agglutinative languages. All code and models are publicly available at kaggle.com/code/macmaky/quechuatok
arXiv:2606.20989v2 Announce Type: replace Abstract: Make-lineage workflow runners decide whether a job must re-run from file-modification time (mtime, a timestamp) -- a broken proxy for the question that matters: did the content change? A git checkout, a tree copy, or a backup restore rewrites mtimes without touching content, forcing spurious re-execution; and in the reverse case -- when an output looks newer than its inputs but its content is stale -- the stale output is silently reused. (Snakemake 7's per-output provenance survives this churn, as local bookkeeping; GNU Make and pure-mtime fast paths are where it bites.) OxyMake, a single-binary Rust workflow engine, replaces the proxy with a content-addressed cache key: a BLAKE3 hash of rule source, input content, parameters, environment, and platform. Because the key is a pure function of these declared inputs, the caching decision survives mtime churn and travels across same-platform machines and shared caches. Phantom re-runs vanish for declared inputs (no sandbox: an undeclared input is invisible to the key). The spec stays declarative and statically parseable, keeping the Make rule model so Snakemake pipelines port directly. DAG resolution is an order of magnitude faster than Snakemake's on large graphs, but a cold end-to-end run is slower -- the price of content-addressed bookkeeping -- repaid several-fold on the warm re-run that caching exists to serve (exact figures, hardware, and a bundled reproducer are in the evaluation). Execution is daemon-free via a cooperative claim/reclaim protocol (sessions claim jobs, reclaiming stalled ones); today two sessions duplicate work safely rather than coordinate, and wiring the protocol as a hard execution gate is staged, not yet done. Cross-session safety is specified in TLA+ and model-checked over all interleavings for 2-3 sessions, assuming atomic state commits. A plan-of-record lockfile and NDJSON event stream record exactly what ran.
arXiv:2606.20856v2 Announce Type: replace Abstract: Multi-view surface reconstruction is a core problem in computer vision. One prominent line of work represents the surface implicitly as a signed distance field (SDF), optimizing it based on the photometric loss between rendered and observed pixel colors. These approaches typically employ SDF-based volume rendering to obtain a differentiable relaxation of discontinuous visibility along rays, thereby reducing reliance on silhouette supervision. In this paper, we reformulate SDF-based volume rendering as probabilistic surface rendering, where each pixel color is modeled as a mixture distribution induced by the random first ray-surface intersection. To this end, we introduce Stochastic Signed Distance Processes (SSDP), which model the SDF along each ray as a stochastic process, inducing a first-passage-time distribution for each ray. We then derive the first-passage probability for each sampling interval based on Bayesian filtering, together with its practical approximation for parallel rendering. We further show that NeuS, an existing SDF-based volume rendering method, arises as a special case of our formulation. Experiments on the DTU and MobileBrick datasets demonstrate that our method outperforms baselines in both surface reconstruction and uncertainty quantification, supporting the effectiveness of our first-passage formulation. Our code is available at https://github.com/skmhrk1209/SSDP.
arXiv:2606.21118v2 Announce Type: replace Abstract: Optical frequency combs on photonic integrated platforms are revolutionizing precision metrology, bio-imaging, atomic and molecular sensing, and ultrafast photonics, yet most remain confined to the near-infrared. This restriction prevents access to the ultraviolet, visible, and mid-infrared bands critical for a vast array of quantum, atomic, and molecular systems. The fundamental obstacle has been the lack of a nanophotonic waveguide that simultaneously provides an ultra-broad transparency window, engineered dispersion, ultra-low propagation loss, and a strong Kerr nonlinearity, all while suppressing detrimental two-photon absorption at short wavelengths. Here, we overcome this challenge by exploring tantalum pentoxide for ultra-broadband supercontinuum spanning continuously from the ultraviolet to the mid-infrared, leveraging its broad transparency window (300-8000 nm), a high nonlinear refractive index three times larger than that of silicon nitride, and a wide bandgap that suppresses two-photon absorption. Critically, by using a photolithography assisted chemo-mechanical etching process that avoids a lossy SiO2 upper cladding, we achieve dispersion engineered waveguides with record-low propagation losses of 0.066 dB/cm at telecom wavelengths and 0.43 dB/cm at 780 nm, significantly facilitating the supercontinuum spectral extension into the ultraviolet and the mid-infrared. Pumping these anomalous-dispersion waveguides with femtosecond pulses at 1550 nm yields a gap-free, 3.2-octave supercontinuum spanning from 350 to 3200 nm via a soliton-based dynamics at only 54 pJ pulse energy, representing the broadest comb spectrum on this platform. We further demonstrate a relatively flat spectrum with a -30 dB bandwidth of 1182 nm by engineering normal dispersion, validate the comb coherence via heterodyne detection, and achieve soliton-effect pulse self-compression from 126.7 fs to 19.2 fs.
arXiv:2606.22136v2 Announce Type: replace Abstract: Scaling dexterous manipulation requires generalization across objects, scenes, and tasks, yet existing data sources face a trade-off between scale and scene/embodiment alignment: teleoperation data is well aligned with robot deployment but expensive to collect; simulation is scalable but limited by the sim-to-real gap; and real egocentric videos scale effectively but remain misaligned with robot deployment. We propose Wh0, a framework that uses generative video world models as scalable and controllable sources of egocentric human-hand manipulation data to unlock the manipulation capabilities of pretrained dexterous VLA models. Conditioned on language, objects, and scenes, Wh0 uses a generative world model to produce WM-H, a 50k-episode dataset of egocentric human-object interaction videos. Wh0 then converts the generated videos into robot-trainable supervision through hand motion reconstruction and visual editing. Co-trained with a limited amount of real robot data, WM-H adapts pretrained VLA models to dexterous manipulation deployment. Across 18 real-world dexterous manipulation tasks, compared with a model post-trained only on robot data, Wh0 improves zero-shot success on unseen tasks from 8.3% to 38.9%. Ablation studies further show that scalable generation and scene/embodiment alignment are key drivers of performance gains. Videos and open-source code can be found on our project website: https://chenyt31.github.io/wh0.github.io/.
arXiv:2606.24255v1 Announce Type: new Abstract: Although text-to-motion generation has achieved strong progress in synthesizing realistic single-person motions from language, extending it to text-driven 3D human-human interaction (HHI) remains non-trivial, as HHI requires modeling the underlying \textbf{social structure} that governs phase progression, actor roles, and inter-actor coordination. In this paper, we formulate HHI generation as a social structure modeling and grounding problem: the model must first infer how an interaction unfolds and how the two actors coordinate their roles, and then realize this structure as continuous, physically plausible, and partner-aware 3D motion. To study how such structure should be modeled, we first examine the capability boundary of large language models (LLMs) for HHI generation. Our analysis shows that LLMs can \textit{think} by recovering phase decompositions and partner-aware roles, but cannot directly \textit{move}, as they fail to generate dynamic, physically plausible, and interaction-aware motion. This motivates our planner-executor paradigm, \textbf{Think with LLM, Move with Motion Skill}. The LLM planner converts implicit interaction semantics into motion-aligned social supervision by decomposing interactions into phases, assigning partner-aware actor roles, and aligning them with motion sequence. The motion executor then grounds the planned social structure into coordinated two-person motion by adapting a pretrained solo motion model with LoRA, previous-phase self-conditioning, and ego-relative partner conditioning. Together, our Solo-to-Social framework bridges social organization and motion realization, producing 3D HHI with improved phase consistency, role alignment, and partner-aware coordination.
arXiv:2606.24260v1 Announce Type: new Abstract: Addressing problems beyond classical computing limits is sparking an increasing interest in Quantum Computing. However, despite their adequacy to address specific problems, quantum algorithms cover a limited subset of the functionality required in real-world computing systems. Additionally, they require expensive specialized hardware. To overcome this issue, hybrid (quantum-classical) software systems are emerging as a promising way to integrate both computing paradigms by applying the principles of Service-Oriented Architectures (SOA). Still, the design and deployment of hybrid service-based systems faces unique challenges like the idiosyncrasies and constraints of NISQ computers (e.g., algorithms that can only run in specific machines, disparate quality attribute metrics), and the management of structural and behavioural properties of service-based applications. From the SOA perspective, architectural decisions need to be made by performing a trade-off analysis and providing quantitative guarantees of system configurations under prescribed levels of uncertainty. In this paper, a method to explore the design space of quantum-classical applications is provided by a formalization of an architectural style of hybrid applications. The obtained results demonstrate that the proposed method successfully identifies decision boundaries. It enables the dynamic selection of the most suitable hybrid or classical configuration based on the user's QoS criteria.
arXiv:2606.22998v2 Announce Type: replace Abstract: Text-conditioned motion generation is a promising interface for programming humanoid robots, yet current generators are often trained on human motion datasets retargeted to robot morphologies. Although such data provides rich semantic and kinematic priors, it fails to capture the nuances of whole-body tracking controllers, including balance, contact dynamics, actuation limits, and controller-specific failure modes. As a result, generated motions can be semantically plausible but difficult or impossible for the robot to execute. We introduce TEXEDO, a test-time scaling framework for humanoid motion generation that improves motion quality without requiring a stronger underlying generator. Given a text prompt, TEXEDO samples multiple candidate motions from a pretrained text-conditioned generator and selects the best motion that is both executable and task-aligned. The reward model combines a dynamic feasibility verifier, distilled from whole-body tracking rollouts to predict physical executability, with a semantic alignment verifier that measures text-motion alignment in a learned co-embedding space. Our pipeline treats dynamic feasibility as a hard constraint and semantic alignment as the selection objective within the feasible set. Through large-scale simulation studies and real-world deployment on a Unitree G1 humanoid robot, we show that TEXEDO consistently improves both tracking fidelity and text alignment. These results demonstrate that grounded verification is an effective path toward deployable language-guided humanoid motion generation. Project website: https://jianuocao.github.io/TEXEDO/
arXiv:2606.24327v1 Announce Type: new Abstract: The Triangular mesh-based gyrokinetic code TRIMEG-C1 solves the gyrokinetic equations using the particle-in-cell scheme to simulate electromagnetic instabilities in tokamak plasmas. TRIMEG-C1 utilizes a high-order C1 finite element method, which captures the accurate physics with lower grid resolution than the C0 method. In this work, we focus on achieving a portable implementation on multiple graphics processing unit (GPU) architectures to accelerate the TRIMEG-C1 code for future physics studies. The OpenMP framework is chosen as the acceleration framework for GPU offloading on different hardware platforms, specifically, NVIDIA and AMD GPUs. The particle pushing procedure, as well as particle-to-grid operations have been adapted for GPU execution. A speedup of $\approx9$ for the particle pusher kernel is achieved on 2 AMD MI300A APUs (Accelerated Processing Unit) compared with 2 AMD 9754 CPUs. In addition, the efficiency of hybrid MPI-OpenMP offloading parallelization was assessed by oversubscribing GPU resources. The Ion Temperature Gradient (ITG) mode was simulated using the GPU implementation, and its correctness was verified by comparing the physics results in terms of the energy growth rate and the two-dimensional mode structures.
arXiv:2606.24472v1 Announce Type: new Abstract: Vision-language-action (VLA) models have made rapid progress in generalist robot manipulation by harnessing semantic knowledge from pretrained vision-language backbones, but their visual tokens remain grounded in 2D image coordinates rather than the calibrated geometry of the robot's cameras -- a mismatch especially pronounced in multi-camera setups, where views are coupled by known intrinsics and extrinsics yet processed as independent images. We propose G$^3$VLA, a camera-aware geometric module that injects calibrated structure into the visual-token stream of a pretrained VLA without altering its action space or imitation objective, combining intrinsic-conditioned ray embeddings, projective positional encoding (PRoPE), and bidirectional cross-view fusion. Geometric supervision is provided either from ground-truth point maps when available, or from confidence-gated $\pi^3$X teacher predictions, requiring no depth sensors or manual annotations. Instantiated on $\pi_0$, G$^3$VLA yields consistent gains across the LIBERO suites, RoboCasa24, RoboTwin2.0, and real-robot settings, with the largest improvements on spatially and object-sensitive tasks. We further validate on $\pi_{0.5}$ and GR00T 1.5, with results suggesting that geometric transfer is most effective when geometry-aware tokens have direct access to the action generation pathway. Our project page is at https://sites.google.com/view/g3vla
arXiv:2601.18747v2 Announce Type: replace Abstract: Modern AI agents increasingly rely on search infrastructure to execute complex, neuro-symbolic reasoning workflows. These workflows often compile into deeply nested, non-monotonic Boolean queries over text fields. However, standard query evaluation strategies over inverted indices face severe theoretical limits when handling these structures. Stateful iterator models (Document-at-a-Time) are structurally bounded by $\text{NC}^1$ formula evaluation, suffering a worst-case $O(2^{|Q|})$ exponential blowup in query complexity when unrolling re-convergent logic. Conversely, recursive materialization models (Term-at-a-Time) incur an $\Omega(|U|)$ space complexity penalty (the Universal Scan) when evaluating logical negation over the document universe. In this paper, we establish the theoretical boundaries of executing complex logic natively over an inverted index. We formalize a retrieval language ($\mathcal{L}_R$) based on Directed Acyclic Graphs (DAGs) and prove that its evaluation problem is strictly \textbf{$\mathbf{P}$-Complete}. To make evaluation tractable, we introduce \texttt{ComputePN}, a deterministic, sparsity-aware evaluation algorithm. By decoupling logical negation from universe-scale materialization via a novel Positive-Negative dual representation, and utilizing native DAG memoization, \texttt{ComputePN} strictly bounds evaluation time to $O(|Q| \cdot |U_{\mathit{active}}|)$. This approach successfully evaluates $\mathbf{P}$-Complete queries natively over the index, avoiding both the combinatorial tree-expansion bottleneck and the universal scan penalty, laying the formal foundation for computational retrieval.
arXiv:2606.23984v1 Announce Type: new Abstract: Domain-Driven Design (DDD) is an influential software development methodology that structures software around business domain complexity through tactical building blocks such as Entities, Value Objects, Aggregates, and Repositories. Despite its prominence in software engineering, large-scale empirical evidence on how DDD is practiced, how it evolves, and how it relates to software maintenance quality in open-source projects remains scarce. This study presents a pre-registered empirical investigation of the distribution, evolution, and maintenance implications of DDD tactical building blocks in open-source GitHub repositories, addressing the call for large-scale empirical evaluations identified in a recent systematic literature review. We will collect DDD-related repositories from GitHub using the Search API, apply automated keyword filtering and manual relevance assessment, and analyze the resulting dataset through four research questions covering: (RQ1) the static distribution and co-usage of DDD building blocks across repository types, (RQ2) their longitudinal evolution over commit history, (RQ3) the extent and maintenance implications of Bounded Context boundary violations (the primary technical challenge in DDD adoption, unquantified at scale), and (RQ4) the temporal association between maintenance activities and building block churn or Bounded Context violations in DDD repositories. Together, these RQs trace the full maintenance and evolution lifecycle of open-source DDD projects and establish an empirical foundation for future DDD tool support and methodology refinement.
arXiv:2606.23986v1 Announce Type: new Abstract: A microwave linear analog computer (MiLAC) is a tunable microwave network that performs linear operations directly on radio-frequency signals through wave propagation. Used as an antenna-array front end, it can map many antenna signals to a small number of active RF chains. While lossless reciprocal MiLACs have been shown to provide flexible or capacity-achieving beamforming for wireless communications, their sensing performance remains largely unexplored. We analyze direction-of-arrival estimation for $K$ far-field targets using a tunable receive-side lossless reciprocal MiLAC combiner. We show that the Fisher information matrix depends on the combiner only through the orthogonal projector onto its row space and never exceeds that of a fully digital receiver. Equality holds when the row space contains the $2K$-dimensional joint steering--derivative subspace, establishing a zero-gap threshold of two RF chains per target. A dimension-counting argument lower-bounds the number of tunable components required to achieve the digital Cram\'er--Rao bound for every target configuration. The stem-connected MiLAC attains this bound asymptotically, up to an antenna-count-independent additive overhead, while scaling linearly with the antenna and target counts. Unlike a phase-shifter front end with the same number of RF chains, MiLAC can exactly attain the fully digital bound. Numerical results validate the analysis.
arXiv:2606.24533v1 Announce Type: new Abstract: Cryogenic phase-change phenomena play an important role in a wide range of engineering applications, including cryogenic cooling systems, superconducting technologies, and space propulsion systems. In particular, the three-phase contact line is recognized as a key region governing evaporation and heat transfer. However, direct measurements of temperature distributions near cryogenic three-phase contact lines remain limited because conventional infrared thermography becomes increasingly difficult at extremely low temperatures. In this study, a two-color temperature-sensitive paint (2C-TSP) technique was applied to visualize the temperature field around a liquid-nitrogen three-phase contact line. A temperature-sensitive dye and a temperature-insensitive reference dye were incorporated into a single coating, enabling robust temperature measurements based on luminescence intensity ratios by compensating for changes in optical intensity caused by refraction and reflection at the liquid-gas interface. Temperature distributions were measured under three heating conditions with heat fluxes of 110, 430, and 900 W/m2. The measured temperature fields revealed a localized temperature minimum at the observed three-phase contact line, suggesting localized cooling associated with phase change. Quantitative analysis showed that the average temperature in the liquid region remained nearly constant, whereas the temperature in the gas region increased with increasing heat flux. These observations reveal a non-uniform thermal structure around the cryogenic three-phase contact line. The present results demonstrate that 2C-TSP is a promising technique for direct visualization of temperature fields around cryogenic three-phase contact lines and provides new insights into phase-change phenomena in liquid nitrogen.
arXiv:2606.24542v1 Announce Type: new Abstract: We consider a class of pursuit-evasion games in which multiple defenders and attackers move in the plane with bounded speeds, while each defender observes the states of other agents with a constant time delay. For the one-attacker-one-defender case, we derive an explicit analytical characterization of the attacker's delayed attack region and prove its convexity under mild assumptions. When the defender can guarantee capture, we formulate a convex optimization problem to compute the capture point and derive optimal strategies for both players. These strategies are shown to constitute a subgame-perfect Nash equilibrium by exploiting the sequential structure induced by the information delay. The analysis is further extended to the one-attacker-multiple-defender scenario and to the general multiplayer setting. In the latter case, delay-aware pairwise winning relations are incorporated into a maximum matching formulation to address the defender-attacker assignment. Numerical simulations for one-on-one, one-vs-multiple, and multi-agent cases validate the theoretical results and illustrate the impact of information delay on game outcomes and optimal strategies.