arXiv:2607.17817v1 Announce Type: new
Abstract: Photonic memory underpins optical information processing, neuromorphic photonics, and photonic computing. Existing studies typically treat dispersive, nonlinear, and driven-dissipative memory as distinct physical phenomena, despite all being governed by the evolution of the optical field. This work proposes a unified phase-based framework in which photonic memory is interpreted as a dynamical phase, with transitions between memory regimes governed by optical phase evolution, Kerr nonlinearity, and the balance between delayed feedback and dissipation. Within this framework, dispersive memory arises from frequency-dependent phase accumulation, nonlinear memory emerges through intensity-dependent phase evolution leading to bistability and hysteresis, and driven-dissipative memory is established through attractor convergence and memory stabilization. The proposed framework is validated through theoretical analysis and numerical simulations, demonstrating a continuous progression from linear dispersive memory to nonlinear and ultimately driven-dissipative memory. Experimental validation is performed using a silicon photonic waveguide incorporating chirped Bragg gratings. Group delay measurements reveal distinct linear and nonlinear memory responses, while the reconstructed memory distribution demonstrates the coexistence of dispersive, nonlinear, and driven-dissipative memory within a single integrated photonic device. To the best of the author's knowledge, this constitutes the first experimental demonstration supporting the coexistence of all three photonic memory regimes in a single photonic platform. These results establish optical phase as the unifying physical quantity underlying photonic memory and provide a common framework for designing future neuromorphic photonic systems, reservoir computers, and integrated photonic processors.
Science Journals
arXiv:2607.17818v1 Announce Type: new
Abstract: Efficient, collision-free, and time-optimal motion planning is a fundamental requirement for autonomous forestry cranes operating under hydraulic pump-flow constraints. The Via-Point-based Stochastic Trajectory Optimization (VP-STO) algorithm has demonstrated near-time-optimal hybrid motion planning in this domain, but requires a fixed terminal joint configuration specified prior to optimization. For kinematically redundant manipulators such as forestry cranes, this pre-commitment to a single inverse kinematics solution restricts the planner's ability to exploit redundancy, particularly under the nonlinear, globally coupled pump-flow constraint where admissible joint velocities depend on their combined hydraulic demand. This paper presents TSC-VP-STO, a task-space-constrained extension of VP-STO that replaces the strict terminal joint-space constraint with a task-space constraint, jointly optimizing the trajectory and the redundant degrees of freedom of the terminal configuration. This enables the planner to adapt end configurations to the environment-dependent motion and hydraulic flow allocation, yielding more balanced pump utilization and shorter trajectory durations. We formalize the approach through a configuration space decomposition and derive a concrete reachability constraint for the forestry crane kinematics. Experimental evaluations across multiple planning targets and via-point configurations demonstrates a reduction on trajectory durations by 12-15% on average and improved pump-flow utilization compared to the baseline VP-STO. The practical applicability of TSC-VP-STO is validated through real-world deployment on a forestry crane, including a full log-loading cycle.
arXiv:2607.16811v1 Announce Type: new
Abstract: We revisit Gaussian Mixture Models (GMMs) as a lightweight, interpretable tool for anomaly detection and, in particular, for detecting distributional drift in data streams. We make three practical choices explicit and evaluate them on seven public benchmarks. First, the number of mixture components is selected automatically by the Bayesian Information Criterion, initialised by k-means, removing the need to fix it in advance. Second, individual observations are scored by their negative log-likelihood under a GMM fitted to normal data, with thresholds set at a target false-alarm rate using Extreme Value Theory. Third, the same interpretable model extends to distributional drift: each Gaussian component is a named "regime," and the fraction of a stream window that matches no regime -- its unexplained mass -- is a drift signal that is itself the explanation. We benchmark this against a model-free kernel two-sample test (Maximum Mean Discrepancy, MMD) and against two GMM-to-GMM divergences (a closed-form Cauchy-Schwarz divergence and a matching-based KL surrogate). Across seven benchmarks ranging from 3 to 64 dimensions and five random splits, the GMM point detector is competitive with -- though rarely more accurate than -- Isolation Forest, Local Outlier Factor, one-class SVM, ECOD, COPOD and an autoencoder, while uniquely yielding an interpretable model. For drift, MMD is the strongest pure detector, but the interpretable unexplained-mass statistic matches it when anomalies form novel regimes (and honestly fails, as MMD does not, when drift is a pure re-weighting of existing regimes). Every alarm is explainable: anomalies lie a median of 3-10 sigma outside their nearest regime vs. about 1 sigma for normal points, and a drift alarm reports the fraction of the window matching no known regime. All code and experiments are released.
arXiv:2607.17820v1 Announce Type: new
Abstract: Training-free few-shot adaptation methods have gained significant attention recently in the context of Vision-language Models (VLMs). Yet, current benchmarks rely on strong assumptions about the statistics of the adaptation data, e.g., class balance. We question these simplifying assumptions and introduce a more realistic benchmark that varies both the levels of class balance and the effective number of classes in few-shot tasks via Dirichlet sampling. Surprisingly, under our setting, we observe substantial drops in the performances of state-of-the-art methods, more so when the number of labeled samples increases. To mitigate this, we introduce PRiSM, a class-prototype regularization that can be deployed as a plug and play module on top of any existing baseline method, significantly improving performances. Our method optimizes a novel multi-term loss, which includes a regularizer maximizing inter-class pairwise distances, along with additional terms promoting support-feature alignment and fidelity to the baseline prototypes. Furthermore, we introduce an effective and computationally efficient block Majorize-Minimize optimizer for our objective. More specifically, we derive a valid blockwise Lipschitz constant (i.e., a bound on the Hessian's spectral norm), which can be computed efficiently via the Gershgorin circle theorem. Extensive experiments show that PRiSM improves several training-free baselines, with large gains when dealing with severe class imbalance and high numbers of classes.
arXiv:2607.16537v1 Announce Type: new
Abstract: While Mars rover operators plan drives across hazardous Martian terrain and diagnose unexpected faults, the necessary information is distributed across separate systems and often reconstructed through manual correlation and memory. To address this challenge, we partnered with Mars rover operators at the NASA Jet Propulsion Laboratory to introduce Hindsight, a visual analytics system that unifies previously disparate rover drive data into a single workspace for search, comparison, and investigation. This paper presents a design study of the Hindsight application. The partnership revealed that operators reason about drives as holistic spatiotemporal episodes rather than discrete parameters. By externalizing operator intuition into an explicit visual query process, we argue that Hindsight transforms analysis into a structured, shareable workflow. Preliminary feedback from operators suggests Hindsight supports their ability to correlate terrain, telemetry, and fault events within a singleworkspace.
arXiv:2607.16813v1 Announce Type: new
Abstract: Sparse-support uncertainty is usually quantified by treating the dictionary as known, an assumption that can produce overconfident, label-dependent conclusions when the dictionary is learned from latent sparse mixtures. Near collisions of coherent atoms, a test signal may identify the active physical group even though the training data cannot distinguish the physical rays within it.
We develop inference for active physical rays, unit atoms modulo sign, after latent dictionary learning. In a fixed-dimensional Gaussian train-test experiment, we retain all dictionaries compatible with a robust training-moment region, profile the test representation over them, and project surviving configurations onto a permutation-invariant support space. The resulting confidence correspondence can report cross-sheet inconclusiveness, group resolution with child ambiguity, or fine-support resolution.
We characterize both its statistical cost and decision-theoretic benefit. Residual block orientation first affects the latent training density at cubic order, yielding information of order $s^6$, where $s$ is the within-block collision scale. The correspondence provides high-probability-over-training conditional test coverage, with resolution governed separately by parent detectability, test-time support separation, and learned-dictionary orientation. In the resolved fixed-shell regime, its projective Hausdorff diameter contracts at the minimax-optimal rate $s \wedge (\sqrt{N}s^2)^{-1}$, up to constants. A restricted-task theorem further determines when coefficient asymmetry allows test replication to supplement training information and when calibration uncertainty remains irreducible. The framework thus yields honest, resolution-adaptive support statements and guides the allocation of training versus test measurements.
arXiv:2607.17826v1 Announce Type: new
Abstract: Despite the growing availability of customizable social artificial intelligence (AI), such as ChatGPT, Grok, and Character.ai, we know little about how users actively shape social AI to reflect their personal preferences. This study examines why and how users (N = 169) customize social AI through the lens of the newly developed concept of AI individualism. Through reflexive thematic analysis of open-ended responses, we identified several motivations for customization, including (1) enhanced pragmatic support, (2) emotional support or companionship, (3) trust and reliability, (4) pushback, (5) a tailored degree of human likeness, (6) creativity or playfulness, and (7) having the AI function as an extension of the self. In line with the concept of AI individualism, our findings show that, for many users, customization is a co-creative process between the human and the AI that is perceived as strengthening support, autonomy, ownership, and engagement, potentially contributing to a closer and more personal relationship. Through customization users may come to view social AI as a personalized social resource that increases their sense of individualism, freedom, and control. We discuss how these perceptions may foster pseudo-autonomy, whereby customization creates an illusion of individual control over powerful social AI systems.
arXiv:2607.17828v1 Announce Type: new
Abstract: Many Bangla words are at once personal names and culturally loaded common nouns, "Maya" is both a girl's name and a word for affectionate compassion. Choosing the right reading demands cultural knowledge that is scarce in the pretraining data of modern language models. We introduce Culturally Entangled Homograph (CEH) disambiguation and build a Bangla benchmark of 1,516 expert-verified sentences (3,032 labelled occurrences) in which one word appears twice with two distinct readings, each labelled with a culturally grounded category and an explanation of the reasoning behind it. Across open- and closed-source models, we find a systematic dominant-meaning bias: models default to the common-noun sense and overlook the name. A Bangla-specific model fails under every prompting regime we test, showing that language-specific pretraining alone does not confer cultural grounding. We further show that contrastive chain-of-thought prompting can sharply reduce this bias without training, and that distilling cultural explanations teaches small (1-3B) models to reason toward the correct reading rather than memorise labels, cutting dominant-meaning bias from as high as 100% to under 5% and turning the failed Bangla-specific model into our strongest system. Dataset and code are available at https://github.com/ashuvo25/BanglaCEH.
arXiv:2607.16322v1 Announce Type: new
Abstract: Micro-gesture recognition demands the detection of fleeting, spatially localized movements that are frequently overwhelmed by dominant static appearances and background noise. While Multimodal Large Language Models (MLLMs) excel at general video understanding, they inherently struggle with subtle kinematics and often rely on static posture priors. To this end, we propose GMoT, a Gated Motion-Aware Tokenization module that explicitly distills sparse kinematic evidence into a compact sequence prior to temporal modeling. GMoT dynamically spotlights action-relevant regions via spatially weighted pooling, extracts adjacent-frame temporal differencing to capture precise motion energy, and adaptively fuses these cues into the visual stream using a conservatively initialized semantic gate. To transition from simple classification to evidence-grounded reasoning, we further introduce a progressive reward-guided policy refinement paradigm, supported by a semi-supervised annotation pipeline that generates anatomically focused captions. Beyond achieving the best Top-1 accuracy among the compared methods on iMiGUE (67.32\%) and SMG (73.11\%), improving the Qwen3-VL-8B baseline by +6.80 and +3.11 points, our framework introduces Body-Region Grounding (BRG) Recall as an anatomical-grounding proxy conditioned on correct predictions, together with an overlapping-label cross-domain transfer protocol between iMiGUE and SMG. Extensive evaluations demonstrate that our GMoT-augmented model improves in-domain accuracy, retains clear gains under label-preserving corruptions, and improves accuracy-oriented cross-domain transfer under explicit small-split caveats while maintaining high anatomical grounding in its generated rationales.
arXiv:2607.16584v1 Announce Type: new
Abstract: As a fundamental arena for the development of his dynamics, Newton postulated the existence of absolute space, in which bodies innately possess absolute velocity. Despite this, Newton argued that, although real, absolute properties cannot be detected. Since then, the claim that absolute velocity would be undetectable in such a Newtonian universe has been generally accepted. Here, we show that standard arguments for such a claim, beginning with the one offered by Newton himself, beg the question. We conclude that there are no formal reasons to believe that absolute velocity would be undetectable in a Newtonian universe.
arXiv:2607.17525v1 Announce Type: new
Abstract: Multi-provider LLM gateways reverse proxies that route, load-balance, and rate-limit requests across foundation-model APIs have become critical production infrastructure. Yet the failure modes specific to this architectural layer remain undocumented, scattered across issue trackers and post-mortems with no unifying framework. We introduce \fa{}, a two-axis taxonomy that classifies failures by their \emph{origin layer} (Network/Transport, Streaming/Protocol, State/Session, Model~Behavior, Governance/Cost) and their \emph{detectability} (Loud vs.\ Silent). We populate this taxonomy with five verified catalog entries sourced from public bug reports and first-hand stress testing, each accompanied by a mechanistic root-cause analysis. Three entries include standalone reproduction scripts. Our principal finding is that the most operationally severe failures are \emph{silent}: they return HTTP~200, pass every standard health check, and corrupt application state in ways that require semantic-level observability to detect. Two such silent failures a concurrency race condition causing history loss and a streaming index collision corrupting tool-call payloads were discovered first-hand during \cb{} evaluation campaigns.
arXiv:2607.17005v1 Announce Type: new
Abstract: Large language models may make precise but dormant algorithmic problems practical to revisit, and may expose new paths toward fundamental ones. We demonstrate this possibility through Prim-Dijkstra routing, a classic VLSI problem whose terminal-only Manhattan complexity remained open despite decades of practical work. We prove weak NP-completeness, derive a continuous cost-radius tradeoff with a balanced (2,2) guarantee, and build HP-RCRST, a height-partition-based multi-mode solver. On 28 development instances, its stronger modes Pareto-dominate the published-method union on 23 and tie on five. The case shows how conflicting conjectures, counterexamples, formal checks, and implementation can reopen neglected questions. Code and reproducibility materials are available at https://github.com/CODA-Team/hp-rcrst.
arXiv:2607.17489v1 Announce Type: new
Abstract: The rapid scaling of advanced air mobility (AAM) makes corridor-based structured airspace a promising infrastructure for high-density unmanned aerial vehicle (UAV) traffic. Formation flight can improve corridor capacity by suppressing shockwave propagation, but rigid formations become inefficient or unsafe during ramp branching, merging, and congestion. To address this problem, this paper proposes a task-driven diverge-merge control framework for UAV formations in structured airspace. At the beginning, a corridor-ramp branching structured airspace model is established to characterize the traffic dynamics and spatial constraints. Building upon this, a fast task-driven clustering mechanism integrates spatial connectivity, flight intent, and aerial task interactions to enable real-time diverge and merge for ramp branching and traffic reshaping. To make the diverge-merge reconfigurations executable at the media access control (MAC) layer of the formation, a cluster-aware distributed time division multiple access (CAD-TDMA) protocol is further designed. It protects intra-cluster control synchronization while conservatively reusing low-risk inter-cluster slots. Simulation results show that the proposed diverge-merge algorithm maintains near-zero geometrical misclassification under severe physical overlapping and congestion. With the formation diverge-merge traces, CAD-TDMA achieves the best delay--loss--throughput tradeoff over fixed TDMA and WiFi MAC. It shows that the proposed formation control framework can jointly support real-time formation reconfiguration and reliable communication in corridor-ramp structured airspace.
arXiv:2607.17832v1 Announce Type: new
Abstract: Electrochemical impedance spectroscopy (EIS) is a valuable tool for non-invasive battery characterisation as it provides a compact data representation of physical processes over a wide range of time scales. Commonly, sinusoids at different frequencies are injected sequentially (single-sines). Alternatively, a multisine excitation (a sum of sines) is advantageous for reducing experiment time and allowing impedance to be measured during operational conditions (e.g.\ charging, discharging, relaxation, and during temperature changes). In this work, we demonstrate high-fidelity multisine EIS measurements on Li-ion cells over a wide frequency range (20 mHz to 1 kHz) taken with a commercial potentiostat, and compare these to single-sine EIS, discussing the advantages of both techniques and how to verify the conditions of linearity and stationarity. We also measure broadband multisine impedance at different operating conditions (during charging/discharging, relaxation, and temperature changes), showing how this tool gives new insights into battery dynamics, material properties, charge transfer processes, and thermal performance.
arXiv:2607.17833v1 Announce Type: new
Abstract: Image relighting modifies illumination while preserving non-lighting content such as identity and geometry. Existing diffusion-based methods often suffer from unstable illumination changes or inconsistent content preservation under complex lighting, as they lack an explicit mechanism to learn feature transformations between images. We reformulate relighting as an illumination feature transport problem and introduce Consistent Feature Transport (CFT), a training principle that explicitly enforces illumination-consistent transport between source and target image distributions. Built upon rectified flow, CFT jointly models noise-to-image generation and illumination-consistent source-to-target transport through trajectory-level supervision. This dual-transport formulation encourages isolation of illumination-specific variations while preserving content-aligned features. To support complex lighting scenarios, we construct a large-scale portrait relighting dataset with diverse relighting effects. Experiments show consistent improvements over existing state-of-the-art relighting approaches and demonstrate that CFT can generalize to other editing tasks, including style transfer. Code is available at https://github.com/Dixin-Lab/CFT.
arXiv:2607.16346v1 Announce Type: new
Abstract: We introduce the notion of fuzzy directed simulation between fuzzy Kripke models over any linear and complete residuated lattice and investigate its fundamental properties. In particular, we prove that all positive formulas of the fuzzy modal logic $\mathit{fPDL}$ are preserved under fuzzy directed simulations and establish a Hennessy-Milner theorem for this notion. Furthermore, we present a method for computing the greatest fuzzy directed simulation between two finite fuzzy Kripke models and implement it for the case where the underlying residuated lattice is the G\"odel, product, or Lukasiewicz structure. Finally, we experimentally evaluate the performance of the implementation and present the obtained results.
arXiv:2607.16562v1 Announce Type: new
Abstract: Mixture of Experts (MoE) are increasingly deployed over wireless cloud-edge networks, as a single edge device lacks sufficient resources to host large-scale models locally. In this distributed architecture, a cloud-hosted pretrained Large Model (LM) acts as a shared backbone for latent feature extraction, while heterogeneous experts deployed across distributed, wirelessly-connected clients collaboratively form the task head. However, deploying MoE over wireless links exposes two coupled bottlenecks. On the one hand, routing which clients to activate generally overloads bandwidth-limited uplinks due to required raw feature transmission. On the other hand, aggregating the activated experts' outputs over wireless links is hindered by channel noise and poor scalability. To break these bottlenecks, we propose a statistic-augmented over-the-air MoE (AirMoE) paradigm. Specifically, on the routing side, each client queries its local Feature Retrieval Library (FRL) with a cloud-broadcast compact query, retrieves a prototype-induced statistic, and reports it digitally to the cloud, drastically reducing uplink traffic; the cloud then selects the most relevant clients by aligning these statistics with the LM-extracted features via Jensen--Shannon (JS) divergence. On the aggregating side, selected experts simultaneously transmit their outputs over the multiple-access channel, which physically computes the reweighted sum via waveform superposition, with reweighting coefficients realized through channel-aware power control. The two mechanisms are thus decoupled both algorithmically and physically. We further provide theoretical analyses on convergence and iteration complexity. Taking semantic segmentation task as an example, extensive experiments demonstrate that AirMoE outperforms MoE baselines and single-model competitors. Ablations further confirm the effectiveness of each incorporated component.
arXiv:2607.17709v1 Announce Type: new
Abstract: In this work, we propose a novel framework for the functional controllability of the ant swarm model, a well-known and relevant model of collective behaviour. Our approach introduces a population of controlling stigmergic agents, trained via Reinforcement Learning (RL), that act on the environment to influence the system dynamics and promote the emergence of ordered behaviour. Stigmergic agents are optimized in a centralized-training decentralized-execution setting, interacting with ants only through the shared pheromone field. The reward design promotes trail pheromone structures and alignment of ant positions with high-pheromone paths, without requiring control of specific microscopic configurations. Our results demonstrate that the learned policies effectively shift the phase transition line that characterizes the global behaviour of the system, enabling the emergence of trails scenarios in regimes that are typically dominated by randomness. This study provides insights into the potential of RL based control strategies for complex systems, contributing to the general understanding of functional controllability in this field.
arXiv:2607.17834v1 Announce Type: new
Abstract: Endoscopic visual question answering (VQA) increasingly asks complex questions that combine several endoscopic answer components rather than isolated factual queries. Such complex answers may be scored as correct even when the same model fails on associated atomic questions. We introduce EndoCA, a paired complex-atomic answer consistency benchmark for evaluating whether complex answers remain consistent with same-image atomic answers. EndoCA contains two suites: EndoCA-Core evaluates compact question-complexity patterns commonly seen in practical endoscopic VQA, and EndoCA-Diagnostic supports controlled analysis across increasing question complexity. We evaluate 11 VLMs spanning open, medical, endoscopy-adapted, and closed-source models on EndoCA. Some VLMs achieve high complex-answer accuracy, yet their atomic-answer accuracy and complex-atomic answer consistency remain substantially lower. To reduce this complex-atomic inconsistency, we introduce Atomic-Support Reconciliation (ASR), a training-free mechanism that uses model-generated atomic answers as contextual premises for answer revision and consistency-guided selective answering. On four selected publicly available models, ASR-Revise improves paired complex-atomic correctness with modest changes in complex-answer accuracy, while ASR-Selective improves accuracy on answered cases by allowing the model to abstain from less reliable cases. Together, EndoCA and ASR provide a consistency-aware benchmark and a training-free mechanism for answer reconciliation and selective answering in endoscopic VQA.
arXiv:2607.16499v1 Announce Type: new
Abstract: We present a case study on proof-driven software understanding of mature, security-critical infrastructure. While formal methods are traditionally applied during the design phase, we present our experience applying formal reasoning onto a mature industrial C++ codebase. We focus on a formal analysis of the core algorithm that implements the Stellar blockchain's SDEX order book. By combining large language models (LLMs), Prototype Verification System (PVS), and SeaHorn, we are able to prove core properties of the production codebase. Our approach also identified an inconsistency in documentation related to the reachability of an exception location. Most importantly, however, we produce artifacts that make it easy for code changes to be checked against established invariants. This work demonstrates how the strategic combination of theorem proving and model checking provides a path for delivering robust assurance to legacy systems.
arXiv:2607.17518v1 Announce Type: new
Abstract: Modern cloud platforms increasingly combine strong software isolation mechanisms with shared hardware resources to improve performance and resource efficiency. Conventional containers do this by sharing the host kernel directly, whereas sandboxed runtimes (e.g., gVisor) and VM-based runtimes (e.g., Kata, QEMU/KVM) provide progressively stronger isolation. In all cases, when tenants access host-backed filesystem state, the host page cache can remain shared and observable. Although OS-managed, this page-cache channel forms an OS-mediated microarchitectural timing side channel whose signal is shaped by the processor microarchitecture, memory and storage hierarchies, and virtualization mechanisms. We thus investigate whether unprivileged timing measurements can reveal page-cache residency across these isolation boundaries. Our evaluation covers Docker; gVisor with systrap and KVM; Kata Containers using QEMU and Cloud Hypervisor with shared host filesystems; Kata using QEMU, Cloud Hypervisor, and Firecracker with block-device-backed storage; and QEMU/KVM virtual machines under multiple host cache policies. Our results show that the timing signal persists whenever the I/O path exposes shared, host-cacheable file-backed objects, including under OverlayFS layers, virtio-fs exports, and loop-backed block devices. However, direct I/O and dedicated block devices substantially attenuate or eliminate the signal. Virtualization therefore reshapes leakage through added latency and algorithmic noise but does not remove the underlying dependence on shared hardware and cache state. We showcase this through a case study in which we recover coarse-grained activity from a WordPress deployment backed by MySQL. These results place page-cache attacks within the broader class of OS-mediated microarchitectural timing channels and motivate coordinated hardware, virtualization, and OS support for timing isolation.
arXiv:2607.16508v1 Announce Type: new
Abstract: Fish-like swimming has inspired the design of several dozens if not hundreds of bioinspired robots in the last few decades. But the control and motion planning of such robots has been challenging due to the poorly modeled fluid-structure interaction and the nonlinear underactuated dynamics of such robots. While reinforcement learning has allowed significant advances in the context of ground and aerial robots, the lack of a suitable simulation environment with appropriate computational speed and accuracy have prevented similar progress for fish-like robots. We address this two-fold problem by developing a simulation platform that approximates the motion of our fish-like robot with computational efficiency. Then the motion control and path tracking by the robot is performed using PID control where the (variable) gains are learned using back propagation through time and training on a curriculum. The policy learned in the simulation is then applied on the physical platform, demonstrating an excellent match.
arXiv:2607.16897v1 Announce Type: new
Abstract: With the growing demand for robotics, autonomous drones, and wearable extended reality systems, the deployment of Visual SLAM on embedded devices remains challenging. Tracking must sustain high frame rates while preserving compute resources for map extension and maintenance. This paper presents GLidE-SLAM, a monocular hybrid indirect-direct framework that addresses this by architectural separation: the system performs GPU-accelerated direct tracking on intermediate frames, while reserving the full indirect pipeline for map extension and global consistency. We leverage highly parallel image-alignment operations for pose-only estimation without depth optimization or map point creation, making the workload suitable for GPU offloading and freeing CPU resources for backend tasks. We implement the direct tracker using vendor-agnostic OpenGL ES~3.1 compute shaders, enabling deployment across a broader range of commodity embedded platforms without requiring CUDA support. To our knowledge, this is the first complete direct photometric pose estimator realized via compute shaders for embedded-class devices. Experiments on target platforms demonstrate up to 9$\times$ higher frame rates than the CPU-only baseline while maintaining trajectory accuracy and improving practical deployment across commodity resource-constrained hardware.
arXiv:2607.16586v1 Announce Type: new
Abstract: Although standard auction mechanisms help truthfully reveal preferences of bidders, they can inadvertently result in unbounded harms when they fail to account for externalities caused by bid allocations affecting non-bidders. Ad markets, that buy and sell user attention represent such auctions. This research explores a welfare improving auctioneer's audit-and-penalty mechanism that helps screen the worst externalities. We prove this mechanism can internalize externalities formally, then explore social welfare gains empirically.
arXiv:2607.17521v1 Announce Type: new
Abstract: Autonomous driving requires both safe and efficient planning decisions in dynamic 3D environments. Although recent Vision/Video-Action models learn policies directly from visual observations and scale well with advances in vision transformers and large-scale training data, they often lack explicit geometric grounding and future-aware spatial guidance, limiting their ability to balance collision avoidance and driving progress. In this work, we propose GeoWorldAD, a geometry world action model that grounds trajectory planning in ego-aligned 3D space and anticipates short-horizon scene evolution with latent future geometry tokens. Present geometry provides essential spatial constraints for safe planning, while future geometry reveals how surrounding agents and ego-centric free space may evolve, reducing overly conservative decisions without sacrificing safety. To efficiently exploit these geometric cues, GeoWorldAD progressively aggregates multi-scale present geometry and latent future geometry through iterative trajectory refinement. Experiments on NAVSIM v1 and v2 demonstrate state-of-the-art performance, highlighting the effectiveness of explicit 3D geometry grounding and future geometry world modeling for safe and efficient autonomous driving.