arXiv:2604.17612v3 Announce Type: replace
Abstract: Multi-agent systems built on large language models (LLMs) are difficult to reason about. Coordination errors such as deadlocks or type-mismatched messages are often hard to detect through testing. We introduce a domain-specific language for specifying agent coordination based on message sequence charts (MSCs). The language separates message-passing structure from LLM calls, tool calls, and human control points, whose outcomes remain unpredictable. We define the syntax and semantics of the language and present a syntax-directed projection that generates deadlock-free local agent programs from global coordination specifications. We illustrate the approach with a diagnosis consensus protocol and show how coordination properties can be established independently of LLM nondeterminism. We also describe a runtime planning extension in which an LLM dynamically generates a coordination workflow for which the same structural guarantees apply. An open-source Python implementation of our framework is available as ZipperGen.
Science Journals
arXiv:2604.17899v2 Announce Type: replace
Abstract: Unlike macro-expression, micro-expression does not follow a strictly consistent mapping rule between emotions and Action Units (AUs). As a result, some micro-expressions share identical AUs yet represent completely opposite emotional categories, making them highly visually similar. Existing microexpression recognition (MER) methods mostly rely on explicit facial motion cues (e.g., optical flow, frame differences, AU features) while ignoring implicit emotion information. To tackle this issue, this paper presents a Motion Emotion Feature Decoupling Network (MEDN) for MER. We design a dual-branch framework to separately extract motion and emotion features. In the motion branch, an AU-detection task restricts features to the explicit motion domain, and orthogonal loss is adopted to reduce motion emotion feature coupling. For implicit emotion modeling, we propose a Sparse Emotion Vision Transformer (SEVit) that sparsifies spatial tokens to highlight local temporal variations with multi-scale sparsity rates. A Collaborative Fusion Module (CoFM) is further developed to fuse disentangled motion and emotion features adaptively. Extensive experiments on three benchmark datasets validate that MEDN effectively decouples motion and emotion features and achieves superior recognition performance, offering a new perspective for enhancing recognition accuracy and generalization.
arXiv:2604.18155v2 Announce Type: replace
Abstract: Scaling the photon-detection area of superconducting nanowire single-photon detectors (SNSPDs) has traditionally been achieved by nanowire meandering. However, material inhomogeneities and fabrication-induced defects, such as line-edge roughness, increase with nanowire length, leading to reduced internal photon-detection efficiency and elevated dark-count rates. This trade-off becomes increasingly pronounced as nanowires are scaled to sub-100 nm widths and sub-5 nm thicknesses required for mid- to far-infrared sensitivity. Here, we demonstrate an antenna-coupled SNSPD architecture that enhances the effective photon-detection area without increasing nanowire length. A crossed bowtie antenna integrated with an 80 nm-wide, 3 nm-thick WSi nanowire yields 15.7$\times$ increase in effective detection area at 7.4 $\mu$m compared to a bare nanowire of identical geometric footprint, while maintaining the same internal detection efficiency and dark-count rate. Antenna coupling provides a scalable approach to increasing photon-detection area while reducing the noise-equivalent power, offering performance benefits for applications in astronomy, biological imaging, and molecular spectroscopy.
arXiv:2604.19982v2 Announce Type: replace
Abstract: Spatial join is a fundamental operation in spatial databases. With the rapid growth of 3D data in applications such as LiDAR-based object detection and 3D digital pathology, there is an increasing need to support spatial join over 3D datasets. However, existing techniques are largely designed for 2D data, leaving 3D spatial join underexplored and computationally expensive. We present 3DPipe, a pipelined GPU framework for scalable spatial join over polyhedral objects. 3DPipe exploits GPU parallelism across both filtering and refinement stages, incorporates a multi-level pruning strategy for efficient candidate reduction, and employs chunked streaming to handle datasets exceeding GPU memory. Its pipelined execution overlaps CPU data preparation, host-device data transfer, and GPU computation to improve throughput. Experiments show that 3DPipe achieves up to 9.0$\times$ speedup over the state-of-the-art GPU solution, TDBase, while maintaining excellent scalability. 3DPipe is open-sourced at https://github.com/lyuheng/3dpipe.
arXiv:2604.20032v2 Announce Type: replace
Abstract: More than half of the Top 500 supercomputers employ GPUs as accelerators. On GPU-accelerated platforms, developers face a key diagnostic gap: profilers show source lines where stalls occur, but not why they occur. Furthermore, the same kernel may have different stalls and underlying causes on different GPUs. This paper presents LEO, a root-cause analyzer for NVIDIA, AMD, and Intel GPUs that performs backward slicing from stalled instructions, considering dependencies arising from registers as well as vendor-specific synchronization mechanisms. LEO attributes GPU stalls to source instructions with the goal of explaining root causes of these inefficiencies. Across 21 workloads on three GPU platforms, LEO-guided optimizations deliver geometric-mean speedups of 1.73$\times$--1.82$\times$. Our case studies show that (1) the same kernel may require different optimizations for different GPU architectures, and (2) LEO's structured diagnostics improve code optimization with large language models relative to code-only and raw-stall-count baselines.
arXiv:2604.22280v3 Announce Type: replace
Abstract: Multimodal Large Language Models (MLLMs) have emerged as a promising foundation for universal multimodal embeddings. Recent studies have shown that reasoning-driven generative multimodal embeddings can outperform discriminative embeddings on several embedding tasks. However, Chain-of-Thought (CoT) reasoning tends to generate redundant thinking steps and introduce semantic ambiguity in the summarized answers in broader retrieval scenarios. To address this limitation, we propose Rewrite-driven Multimodal Embedding (RIME), a unified framework that jointly optimizes generation and embedding through a retrieval-friendly rewrite. Meanwhile, we present the Cross-Mode Alignment (CMA) to bridge the generative and discriminative embedding spaces, enabling flexible mutual retrieval to trade off efficiency and accuracy. Based on this, we also introduce Refine Reinforcement Learning (Refine-RL) that treats discriminative embeddings as stable semantic anchors to guide the rewrite optimization. Extensive experiments on MMEB-V2, MRMR and UVRB demonstrate that RIME substantially outperforms prior generative embedding models while significantly reducing the length of thinking. Code is available at https://github.com/PeppaWu/RIME.
arXiv:2604.22433v2 Announce Type: replace
Abstract: Heat exposure connects the built environment and public health, directly shaping the livability and sustainability of urban areas. Understanding the spatial heterogeneity of heat exposure and its drivers is vital for climate-adaptive urban planning. However, most planning-oriented studies rely on land surface temperature (LST), and whether LST adequately represents human heat exposure and how it differs from physiologically relevant heat stress remains insufficiently examined. Here, using Landsat-retrieved 30-m LST and GPU-accelerated 1-m universal thermal climate index (UTCI) in Singapore, this study establishes a comprehensive "Modeling-Comparing-Assessing" framework to systematically evaluate the spatial and mechanistic differences between these two metrics. We further investigate their pronounced non-stationary and threshold-based relationships with urban factors using a novel geographically weighted XGBoost (GW-XGBoost) and generalized additive model (GAM) workflow. Our results reveal substantial differences in the spatial patterns of LST and UTCI, along with marked spatial heterogeneity in how 2D and 3D urban factors impact these thermal metrics, as demonstrated by explainable GW-XGBoost models (test R2 = 0.855 for LST and 0.905 for UTCI). Crucially, spatially explicit SHAP shows that sky view factor plays a central role in explaining UTCI variability but exhibits a comparatively marginal independent contribution to LST, indicating that LST inadequately captures shading-driven and radiative processes governing actual human heat stress. Moreover, SHAP-GAM analysis indicates that higher albedo is associated with increased UTCI. These findings provide model-informed planning implications for integrating physiologically relevant thermal indices to support targeted heat risk management and human-centric urban planning.
arXiv:2604.26258v3 Announce Type: replace
Abstract: LLM workflows, which coordinate structured calls to individual LLMs/agents to achieve a particular goal, offer a promising path towards building powerful AI systems that can tackle diverse tasks. However, existing approaches for building such workflows generally rely on human-crafted pipelines and prompts, which presents a substantial bottleneck in real world deployment. How can we automatically induce LLM-based agents and workflows in a data-driven way? This paper describes a simple data-driven approach for automatically inducing agents and LLM workflows. We formulate workflow induction as a bilevel optimization problem: an outer loop which optimizes a high-level sketch of the workflow (in particular how the LLM calls should be structured), and an inner loop which optimizes each individual LLM call one-by one. Both loops are optimized with ``textual gradients'' where for the inner loop we optimize each component in a modular way through ``backpropagating'' textual gradients layer-by-layer. We find that LLM workflows discovered through our \textsc{FlowBot} (work\textbf{flow} induction through \textbf{b}ilevel \textbf{o}ptimization and \textbf{t}extual gradients) approach performs competitively against strong baselines that make use of human-crafted or generated workflows.
arXiv:2604.26979v2 Announce Type: replace
Abstract: In-memory computing (IMC) is a paradigm that enables neural network inference by computing analog matrix-vector multiplications (MVM) directly in memory crossbar arrays, with the potential for energy efficiency gains over conventional von Neumann architectures. In this work we present a simulation framework for N-ary crossbar architectures that retrieves MVM results with minimal implementation assumptions. The XOR and MNIST classification tasks were successfully inferred using a simulated crossbar array of (4x4) 4-states magnetic tunnel junctions (MTJ). MNIST accuracy reached 93.56% (vs. 97.56% software baseline). PCA dimensionality reduction was shown to drastically lower the number of required operations and improve the software baseline, for only a modest reduction in crossbar inference accuracy. We identified weight quantization as the primary error source, and studied its impact alongside systematic non-idealities and random noise. We find that cell-specific random noise is less detrimental than systematic errors due to averaging across the array. Finally, we demonstrate an optimal number of states per cell that balances quantization error against resistance state resolution to minimize total MVM error.
arXiv:2604.27031v2 Announce Type: replace
Abstract: In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capabilities. We argue that this dilemma has an architectural root. A finite network has limited representational and plastic resources, yet the required capacity depends on properties of the future task stream that are unknown: how many tasks will be encountered, and how much they overlap in feature space. Regularization-based methods preserve past knowledge within fixed-capacity architectures and therefore implicitly rely on an oracle architecture sized for this unknown future. When tasks are only weakly related, fixed architectures progressively run out of plastic resources; when tasks are few or strongly overlapping, models are often over-provisioned. Inspired by neurogenesis in biology, we propose NORACL to address the stability-plasticity dilemma by tackling the oracle architecture problem through neuronal growth. Starting from a compact network, NORACL grows only when needed by monitoring two complementary signals for representational and plasticity saturation. We evaluate NORACL against oracle-sized static baselines across varying task counts and geometries. Across all settings, NORACL achieves final average accuracies that are better than or on par with oracle-provisioned static baselines while using fewer parameters. Additionally, NORACL yields architectures with interpretable growth, i.e. dissimilar tasks predominantly expand feature-extraction layers, whereas tasks which rely on common features shift growth toward later feature-combination layers. Our analysis further explains why fixed-capacity networks lose plasticity as tasks accumulate, whereas NORACL creates fresh capacity for new tasks through growth. Together, these results show that adaptive neurogenesis pushes the stability-plasticity Pareto frontier of continual learning.
arXiv:2604.27088v2 Announce Type: replace
Abstract: Fluid equations are nonlinear, dissipative, and non-Hamiltonian, which makes their relation to Schr\"odinger evolution and quantum algorithms nontrivial. We derive an exact Eulerian Cole-Hopf-type reformulation of isothermal compressible Navier-Stokes (NS) flow in Schr\"odinger-type amplitude variables. To our knowledge, this gives the first exact Cole-Hopf-type Schr\"odinger-variable reformulation of compressible NS flow. In two dimensions, a Helmholtz decomposition separates the velocity into compressive and vortical potentials, whose logarithmic transforms yield two scalar imaginary-time Schr\"odinger-type equations with nonlinear self-consistent potentials. We show that the mixed density-compressive amplitude $\Psi_\alpha=\rho^\alpha\Theta^{1-2\alpha}$, where $\rho$ is the density, $\Theta$ is the compressive amplitude, and $\alpha\neq 0,\,1/2$, satisfies a nonlinear Schr\"odinger-type equation with a vector-potential-coupled Laplacian. The transformed system is exactly equivalent to compressible NS and is nonlocal only through Helmholtz and Poisson projections. In three dimensions, the density-carrying equation retains the same vector-potential-coupled structure, while the solenoidal sector admits a compressible analogue of Ohkitani's incompressible NS Cole-Hopf formulation. Unlike unitary hydrodynamic Schr\"odinger-flow representations, the present equations are imaginary-time heat or drift-diffusion equations with self-consistent potentials, but they remain an exact change of variables for compressible NS. A two-dimensional Kelvin-Helmholtz unstable shear-layer calculation verifies the transformed equations against a direct compressible NS simulation. The formulation exposes operator structures that may be useful for reduced flow descriptions, quantum algorithms for operator evolution, and quantum partial differential equation solvers.
arXiv:2604.27787v2 Announce Type: replace
Abstract: We study when a sound arithmetic theory $\mathcal S\supseteq S^1_2$ with polynomial-time decidable axioms efficiently proves the bounded consistency statements $Con_{\mathcal S+\phi}(n)$ for a true sentence $\phi$. Equivalently, we ask when $\mathcal S$, viewed as a proof system, simulates $\mathcal S+\phi$. The paper gives two unconditional constraints on possible characterizations. First, for finitely axiomatized sequential $\mathcal S$, if $EA\vdash Con_{\mathcal S}\rightarrow Con_{\mathcal S+\phi}$, then $\mathcal S$ interprets $\mathcal S+\phi$, implying $\mathcal S\vdash^{n^{O(1)}}Con_{\mathcal S}(p(n))\rightarrow Con_{\mathcal S+\phi}(n)$ for some polynomial $p$, and hence $\mathcal S\vdash^{n^{O(1)}}Con_{\mathcal S+\phi}(n)$. Second, if $\mathcal S$ fails to simulate $\mathcal S+\phi$ for some true $\phi$, then for all sufficiently large $k$ it also fails to simulate $S^1_2+\phi_{BB}(k)$, where $\phi_{BB}(k)$ asserts the exact value of the $k$-state Busy Beaver function. Thus any hard true extension yields a canonical Busy Beaver witness to nonsimulation.
The paper's central conjectural proposal is: for sound, finitely axiomatized sequential $\mathcal S$, if $EA\not\vdash Con_{\mathcal S}\rightarrow Con_{\mathcal S+\phi}$, then for every constant $c>0$, $\mathcal S\not\vdash^{n^c}Con_{\mathcal S+\phi}(n)$. Under this proposal, hardness follows when $\phi$ is $Con_{\mathcal S}$ or a Kolmogorov-randomness axiom. The latter yields further conjectural consequences and extensions.
arXiv:2605.03723v2 Announce Type: replace
Abstract: The rise of large language models (LLMs) has created an urgent need to distinguish between human-written and LLM-generated text to ensure authenticity and societal trust. Existing detectors typically provide a binary classification for an entire passage; however, this is insufficient for human--LLM co-authored text, where the objective is to localize specific segments authored by humans or LLMs. To bridge this gap, we propose algorithms to segment text into human- and LLM-authored pieces. Our key observation is that such a segmentation task is conceptually similar to classical change point detection in time-series analysis. Leveraging this analogy, we adapt change point detection to LLM-generated text detection, develop a weighted algorithm and a generalized algorithm to accommodate heterogeneous detection score variability, and establish the minimax optimality of our procedure. Empirically, we demonstrate the strong performance of our approach against a wide range of existing baselines. The python implementation of our proposal is available at https://github.com/Mamba413/DetectLLMSegmentation.
arXiv:2605.05000v2 Announce Type: replace
Abstract: LLM agents have been increasingly adopted for solving security tasks. However, existing evaluations usually require source code access, while commercial off-the-shelf (COTS) binaries dominate deployed software and require reasoning from stripped, optimized machine code. This discrepancy raises an important question: can modern LLM agents reason about vulnerabilities in critical COTS binaries? Motivated by this question, we build SLYP, a REACT-style pipeline for end-to-end vulnerability discovery and validation of COTS binaries. SLYP combines extensible MCP servers for binary exploration and dynamic debugging, and validates candidate vulnerabilities by synthesizing debugger-verified proof-of-concept (PoC) crashes. We evaluate SLYP and production coding agents, including Claude Code and Codex, on COTS Windows binaries centered on a 20-object COM benchmark. SLYP uncovers all 64 vulnerable entry functions while default production agents miss up to 15; SLYP also surfaces more true vulnerabilities than the state-of-the-art static analyzer, which discovers at most 35 with a large number of false positives. For validation, SLYP generates debugger-verified PoCs for 67.5% of cases, while default production agents generate none. Further ablations show that tool sets and model choice materially affect COTS binary reasoning. Our additional evaluation also demonstrates the generalizability of SLYP on Windows kernel targets. To date, SLYP has uncovered 39 zero-day vulnerabilities, 31 in COM/RPC services and 8 in kernel drivers, all disclosed to the Microsoft Security Response Center (MSRC), with 23 assigned CVEs and $203,000 bounty awards.
arXiv:2605.07742v2 Announce Type: replace
Abstract: Inter-manufacturer plug-and-play communication in agricultural machinery is currently based on the ISO 11783 standard series, which specifies a 250 kbit/s CAN bus communication layer. To support higher-bandwidth use cases, the ISO~23870 series is being developed for next-generation Ethernet-based agricultural machine-to-machine communication. Modern Ethernet/IP-based architectures often make use of a middleware for discovery, data exchange, quality of service configuration, and security. This paper evaluates the Data Distribution Service (DDS) as a candidate middleware for secure, plug-and-play agricultural machinery networking. DDS-based proof-of-concept communication design is presented for a representative Task Controller (TC) and implement scenario, including implement-description topics and separate best-effort and reliable topics for runtime process data. Design was implemented in C++ using the FastDDS library and benchmarked on embedded hardware representative of agricultural machinery. Runtime throughput was evaluated for one-to-one and one-to-two TC-implement scenarios under four DDS security configurations. The results show that DDS security mechanisms substantially reduce maximum throughput on embedded hardware. In the tested best-effort scenarios, signing and encryption reduced mean throughput by approximately 70-84% compared with the unsecured configuration. Encrypted one-to-one best-effort case achieved approximately 4980 received process data updates per second on both the TC and implement, corresponding to about 50 process data updates per second per simulated section for 100 rate-controllable sections. These results indicate that DDS is a technically plausible middleware candidate for secure Ethernet-based agricultural machinery interoperability, while further work is required to evaluate latency, scalability, vendor interoperability, and lower-power devices.
arXiv:2605.09028v3 Announce Type: replace
Abstract: Machine learning-based Android malware detectors often fail in real-world deployment due to domain shift, where models trained on one data source perform poorly on applications from another. This paper presents a comprehensive study on the generalizability and interpretability of permission-based detectors under cross-domain conditions. Using two complementary datasets (PerMalDroid and NATICUSdroid) and five ensemble classifiers, we first establish an intra-domain baseline, where models achieve over 92% accuracy, and then quantify a severe asymmetric performance drop. While models trained on PerMalDroid generalize well to NATICUSdroid (86% accuracy), the reverse direction sees a drastic drop to 73% accuracy. Explainable AI analysis reveals bimodal feature distributions and shows that feature importance is highly unstable, with key permissions losing or gaining influence across domains. The predictive feature sets for different domains are fundamentally mismatched, as models rely on different, dataset-specific permissions. Most importantly, an ablation study demonstrates that for most models, training on a noisy feature set leads to poor generalization, confirming that domain-specific artifacts are a greater obstacle than missing features. To mitigate this, we validate a hybrid training strategy based on the intersection of common features and successfully recover cross-domain performance, achieving 88% accuracy on PerMalDroid and maintaining 97% on NATICUSdroid. These findings highlight the importance of explainable, cross-domain-robust malware detection systems and provide a practical pathway toward improving real-world deployment of permission-based Android malware detectors.
arXiv:2605.09835v3 Announce Type: replace
Abstract: We report a comprehensive measurement of the environmental $\gamma$-ray flux in Hall C of the Gran Sasso National Laboratory. A spatial mapping of the radiation was carried out using a high-purity germanium detector mounted on a movable cart and deployed at eight locations within the hall. The detector response function and full-energy-peak efficiencies were determined through Geant4 simulations validated with calibrated $\gamma$-ray sources, with particular attention devoted to the efficiency modeling and associated systematic uncertainties. In the energy range of 57-2800 keV, the average $\gamma$-ray flux is measured to be $(\mathrm{0.46} \pm \mathrm{0.06}_{stat} \pm \mathrm{0.03}_{syst})$ $\mathrm{cm}^{-2}$ $\mathrm{s}^{-1}$. The radon level was monitored for about a month using a radon detector mounted on the same cart, and a clear correlation is observed between the environmental $\gamma$-ray rate and the ambient radon concentration, consistent with the short-lived daughters of $^{222}\mathrm{Rn}$. This result represents the first high-precision and efficiency-corrected mapping of the $\gamma$-ray flux in Hall C, substantially improving its radiological characterization and providing key input for future rare-event experiments operating in this hall.
arXiv:2605.10669v2 Announce Type: replace
Abstract: The one-centre Coulomb-Sturmian convergent close-coupling method is applied
to proton collisions with the boron atom and singly charged carbon ion.
Here we report an update to our target-structure implementation, in which
configuration state functions are constructed using the method of
coefficients of fractional parentage. To assess the quality of the
structure models for the two targets, we present the excitation energies,
oscillator strengths, and dipole polarisabilities obtained from the present
configuration interaction calculations. Cross sections for total and state-selective target
excitation and electron loss are calculated from 10 keV to 1 MeV. For both
systems, the total excitation cross section is found to be dominated by
excitation of the $2s$ subshell. This emphasises the importance of a
multi-electron description of the target in such scattering calculations.
Comparisons with previous theoretical and experimental data are presented and
discussed. In particular, we find that the present calculation for the
electron-loss cross section in $p$ + C$^{+}$ collisions is in good agreement
with the available measurements across the entire overlapping incident-energy
range.
arXiv:2605.10821v2 Announce Type: replace
Abstract: Diffusion-based vision-language-action (VLA) models have emerged as strong priors for robotic manipulation, yet adapting them to real-world distributions remains challenging. In particular, on-robot reinforcement learning (RL) is expensive and time-consuming, so effective adaptation depends on efficient policy improvement within a limited budget of real-world interactions. Noise-space RL lowers the cost by keeping the pretrained VLA fixed as a denoising generator while updating only a lightweight actor that predicts the noise. However, its performance is still limited due to inefficient autonomous exploration. Human corrective interventions can reduce this exploration burden, but they are naturally provided in action space, whereas noise-space finetuning requires supervision over noise variables. To address these challenges, we propose UniSteer, a Unified Noise Steering framework that combines human corrective guidance with noise-space RL through approximate action-to-noise inversion. Given a human corrective action, UniSteer inverts the frozen flow-matching decoder to recover a noise target, which provides supervised guidance for the same noise actor that is simultaneously optimized via reinforcement learning. Real-world experiments on diverse manipulation tasks show that UniSteer adapts more efficiently than strong noise-space RL and action-space human-in-the-loop baselines, improving the success rate from 20% to 90% in 66 minutes on average across four real-world adaptation tasks.
arXiv:2605.13348v3 Announce Type: replace
Abstract: As neural components are increasingly embedded in existing symbolic software -- including safety-critical systems -- the question arises of how to specify and enforce the safety of the newly introduced neural parts. Unlike traditional logical specifications, these must be amenable not only to the standard Boolean interpretation, but also to training and optimisation. The latter calls for a quantitative interpretation of the logical syntax, subject to further requirements such as smoothness and differentiability. Moreover, the qualitative and quantitative sides of the logic must share a unifying proof-theoretic and categorical semantics. Finally, the new logic should link cleanly to the substructural and program logics that underpin the verification of existing symbolic programs. In this paper, we present a logic that ticks all of these boxes. We introduce a family of calculi, pQLL, indexed by a hardness degree $p$, prove a cut-elimination theorem for them, and establish completeness with respect to enriched residuated `soft' lattices. At $p = \infty$, \pQLL reduces to multiplicative additive linear logic (MALL), and provability in pQLL converges to provability in MALL as $p \to \infty$. We express optimisation objectives in the syntax of this logic and prove the quantitative adequacy of neuro-symbolic loss functions -- a result that has eluded the neuro-symbolic machine learning community for nearly a decade.
arXiv:2605.17644v2 Announce Type: replace
Abstract: The particle representation model (PRM) and interacting particle representation model (IPRM) describe homogeneous turbulence through orientation-conditioned structural states. In their original form, the conditional state is organized by the unit spectral direction, while the radial spectral coordinate is integrated out. We introduce a scale-conditioned Ray-Column extension in which the spectral vector is decomposed into orientation and radial wavenumber, and the conditional structure state is projected onto finite radial bands.
The formulation starts from the continuum spectral tensor and is then reduced to the ray-packet ensemble sums used in the implementation. The bands are projections of an orientation-wavenumber tensor density and retain scale-conditioned structural populations for closure evaluation. The rapid dynamics remain ray-packet resolved, while the nonlinear slow and terminal closure coefficients are evaluated from band-aggregate structure tensors formed by integrating over orientation and wavenumber within each band. The present reference closure omits conservative cascade modeling among bands.
A reference closure is built from PRM rapid kinematics, band-local effective-gradient response, slow rotational randomization, and an active large-scale enstrophy (LSE) terminal-drain map. In the active-LSE closure, the misalignment-sensing factor Psi_fd regularizes the LSE structure-to-dissipation map; the Ray-Column formulation evaluates this map on band-aggregate structural populations. The model is assessed in irrotational strain, homogeneous shear, elliptic-streamline, and rotating-shear configurations. The rotating-shear comparison with filtered LES data illustrates the payoff of retaining band information: filtered or low-pass observables can be formed before scale information is lost in the one-point reconstruction.
arXiv:2605.19302v3 Announce Type: replace
Abstract: We introduce Coherent Utility Measure Games (CUMGs) in which players' uncertainty about the distribution of payoffs is modeled using coherent utility (risk) measures. Such measures, including mean semideviation risk and conditional value-at-risk, allow for interpretable notions of players' risk aversion while retaining formal equivalence to distributionally robust games. While CUMGs, which are a subclass of distributionally robust games, are continuous games in general, they can be viewed as finite games ``lifted'' to the mixed strategy space, which illustrates computational challenges. Prior results extend to guarantee equilibrium existence in data-driven CUMGs. We show that the computation of approximate equilibria for CUMGs parameterized by several risk measures lies in PPAD. Consequently, we obtain finite multilinear complementarity programs for the computation of equilibrium for these games, which grow with $K$, the number of data samples. Unlike standard games, these programs are not linear in a two-player setting. Next, we establish the existence of approximate equilibria in finite data-driven CUMGs with small supports in the pure actions for the players, together with sparse data subsamples that guide the search for such equilibria. We also develop a stochastic first-order approach for smoothed CUMGs using data mini-batches, with bounds linking first-order error to approximate equilibrium. We include numerical experiments comparing the sparse-support search algorithm with complementarity-program solvers.
arXiv:2605.19844v5 Announce Type: replace
Abstract: Many decision processes run for a long and unknown duration: in each round new requests arrive, an irrevocable choice must be made immediately, and the system is judged by ongoing fairness requirements. Examples include food banks allocating donations, computing systems repeatedly scheduling scarce resources across users, and institutions making repeated decisions while remaining fair over time. We propose a general approach based on \emph{deficits}, which measure how far the current outcome is from satisfying each fairness requirement. The goal is to keep all deficits small at each time step, without knowing the horizon or future agent valuations. This viewpoint also highlights a natural modeling question for long-running systems: how much of the past should be counted when fairness is evaluated? We first study the full-history model, where all past rounds count equally. We propose an efficient fully-online rule. For $n$ agents, we prove anytime guarantees: after any $t$ rounds, all requirements remain satisfied up to a slack of order $\tilde O(\sqrt{t/n})$. We instantiate the rule for online allocation of indivisible goods, yielding natural relaxations of proportionality and envy-free, and for online public decision-making. We show that this slack is tight even for weak proportionality. For unrestricted classical $\mathrm{EF}c$, the exact worst-case parameter at horizon $T$ is $\lceil T/n\rceil$. We then study discounted-memory fairness, where older deficits carry smaller weight. The same fully-online rule applies to these discounted deficits, and the resulting threshold is controlled by the discount function. In particular, the time dependence is never worse than the full-history $\sqrt t$ dependence. Overall, our results show that memory is a central part of perpetual fairness. The question is not only which requirement to impose, but also how the system should count past unfairness.
arXiv:2605.21751v2 Announce Type: replace
Abstract: Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coefficient, index, and parameter in the concrete problem data. We study this via Text2Opt-Bench, a scalable benchmark of solver-verified optimization problems spanning 12 categories, from textbook linear programs to stochastic and multi-objective formulations with up to thousands of variables. Across 10+ models, we find that accuracy collapses as instance data grows, even when the formulation itself is simple. We call this the effective binding limit. We study it with a family of techniques, BIND, that externalize numeric data to structured files so the model binds data programmatically rather than transcribing from the prompt. When using an oracle for externalizing data, we recover between 12 and 27 accuracy points, confirming binding as a key -- but recoverable -- failure mode. In a deployable setting without oracle access, we validate our hypothesis by finetuning a model exclusively on binding and show that it outperforms end-to-end SFT and RL across three structurally distinct optimization categories, with a 1.5B binding specialist alone matching a 7B end-to-end baseline.
arXiv:2605.25170v2 Announce Type: replace
Abstract: Training data for olfaction is scattered through disparate, non-standardized datasets that limit the ability to build representative world models. Olfactory navigation is a highly dynamic and non-stationary task that benefits from real-time continual learning. We introduce an adaptive framework called Grow-Prune-Freeze (GPF) networks that enable an agent to continually learn through growing, pruning, and freezing early layers of its policy in response to world complexity. Grounding GPFs in non-linear random matrix theory, we show that the work of Pennington & Worth (2017) can be extended from single hidden layers to n-layer continual-learning models, and that eigenvalue composition of network weights is preserved as successive layers are added. We show that GPFs based on Expected SARSA achieve a 94% success rate on turbulent plume navigation - a partially observable, non-stationary task representative of the "big world" challenges that motivate adaptive learning in robotics - and provide supporting methodology for applying GPFs in other world models. Further experiments amount evidence that GPFs may generalize well to other machine learning tasks such as reinforcement learning in Atari, image classification, and autoregressive language models. We open source all code and data to encourage improvements on and more research in olfactory robotics.