arXiv:2607.09142v3 Announce Type: replace
Abstract: Large language models (LLMs) are increasingly deployed in online medical consultation, yet existing benchmarks remain poorly aligned with real clinical practice. Many rely on synthetic conversations or patient simulators, omit patient-uploaded medical images, or evaluate open-ended clinical responses using multiple-choice or lexical-overlap metrics that poorly reflect clinical quality. We introduce \textbf{MedRealMM}, a large-scale benchmark for multimodal online medical consultation built from de-identified patient-doctor interactions collected from a nationwide Chinese internet hospital. MedRealMM uses a Multimodal Clinical Challenge Point (MCCP) extraction framework to identify clinically demanding moments in authentic consultation trajectories and converts each into a standardized next-response generation task while preserving the preceding text-image context. Each instance is paired with a case-specific rubric refined by physicians that rewards clinically desirable behaviors and penalizes unsafe, unsupported, or contradictory responses. The current release contains 5,620 real-world multimodal cases spanning 64 clinical departments. We evaluate 19 general-purpose and medical-specialized LLMs, including text-only and multimodal systems. Our results show that image information is critical for reliable clinical performance and that current frontier models remain below the online physician response. Although some frontier models satisfy as many or more positive clinical criteria than physicians, they trigger more negative criteria, indicating that safety-sensitive error avoidance remains a central bottleneck. MedRealMM offers a realistic and reproducible benchmark for evaluating multimodal medical reasoning in real-world online consultation. The dataset will be publicly available on Hugging Face at https://huggingface.co/datasets/jdh-algo/MedRealMM.
Science Journals
arXiv:2607.09449v2 Announce Type: replace
Abstract: Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference. However, its behaviour under latent confounding remains poorly understood, as existing work typically notes that confounding breaks identifiability without characterising how the posterior distribution over DAGs responds. In this work, we analyse posterior behaviour under latent confounding in linear Gaussian causal models, focusing on additive latent confounding between exactly two observed variables. We derive a critical correlation threshold above which the score function favours graphs with a spurious edge between the confounded variables, and show that this threshold decreases with sample size -- more data lowers the correlation required for the spurious edge to be favoured. Beyond this threshold, we characterize two distinct posterior failure regimes determined by the local structure around the confounded variables. Our findings are supported by exact posterior computations on multiple graph structures, demonstrating both the predicted failure regimes.
arXiv:2607.09815v2 Announce Type: replace
Abstract: A central challenge in image-goal UAV navigation under Global Navigation Satellite System (GNSS) denial is estimating metric distance and heading between current and goal views. Dense pairwise geometry models capture relative scene structure, but without a calibrated metric scale, they cannot directly provide reliable distance estimates for navigation. Although global scale calibration corrects the dominant scale bias, the remaining errors vary systematically with distance. In this paper, Range-Aware Scale Recovery (RASR) is proposed, which complements global scale calibration with range-aware residual correction. RASR encodes pairwise geometry extracted by a frozen Matching And Stereo 3D Reconstruction (MASt3R) backbone as a compact descriptor and separates the scale-recovery core from task-specific command calibration. On the official online evaluation of the UAVs in Multimedia 2026 PairUAV challenge, RASR achieved a total error of 0.003189, achieving a lower total error than global scale calibration alone. The results demonstrate that range-aware residual correction improves metric distance estimation beyond global scale calibration. Code and materials are available at https://github.com/lht-research/rasr-pairuav.
arXiv:2607.10020v2 Announce Type: replace
Abstract: We present FindMyText, an open-source Python package designed to efficiently assess whether a given text appears, in part or in full, within a text corpus. The tool builds on prior techniques for document fingerprinting, but extends them with a novel mechanism to explicitly capture sequences of matching fingerprints. By identifying such chains, the tool can more reliably detect near-verbatim copies of a given text rather than mere textual similarities. This makes FindMyText particularly suited for verifying the presence of copyrighted material in a corpus. Leveraging a distributed, disk-based indexing framework, the system scales to large web-crawled datasets. Using a new benchmark for evaluating text containment methods, we show that FindMyText outperforms alternative approaches across three datasets (ArXiv papers, Wikipedia, and generic web content).
arXiv:2607.14666v1 Announce Type: new
Abstract: Identification (ID) coding, introduced by Ahlswede and Dueck, extends Shannon's classical communication paradigm by replacing message reconstruction with hypothesis testing. Instead of decoding the transmitted message, the receiver only decides whether a particular message was sent. A fundamental result of ID theory is the double-exponential growth in the number of identifiable messages with respect to (w.r.t.) the blocklength. This scaling behavior enables fundamentally new communication architectures for large-scale distributed systems and forms a key building block of post-Shannon communication.
While ID cannot replace classical communication in general, it is particularly well-suited for scenarios in which full message reconstruction is unnecessary, such as monitoring, alarming, and control systems.
In this survey, we review the theoretical foundations of ID coding and discuss emerging communication architectures and application domains based on this paradigm. Particular emphasis is placed on practical use cases, including monitoring systems, special-purpose data storage, joint identification and sensing (JIDAS), semantic communications, mobile-network control systems and networked consensus testing systems. We further highlight recent system concepts, industrial perspectives, and implementation examples that illustrate how ID-based principles can be realized in practical communication systems.
arXiv:2607.14600v1 Announce Type: new
Abstract: This paper discusses some aspects related to gradient-based optimization algorithms with special focus on the requirements associated to their use in the implementation of Nonlinear Model Predictive Control. Based on a dedicated discussion, a new algorithm, termed Search and Accelerate (SaA) is proposed that mixes together a novel line search, a trust region mechanism together with an adaptation of the gradient acceleration scheme. A dedicated benchmark involving a set of 600 instances of box constrained optimization problems is designed and used in order to show the algorithm performances which make it a highly competitive general purpose gradient-based alternative for box-constrained optimization problems. An appealing feature of the algorithm is its robustness to the choice of the few parameters involved in its definition making the default values a valid option for any problem without a priori knowledge of the related Lipchitz constant. Moreover, an example of use of the proposed algorithm in NMPC implementation is proposed showing the possibility to reduce the control updating period which might be mandatory in some circumstances.
arXiv:2607.14293v1 Announce Type: cross
Abstract: Ionizing radiation from cosmic rays and gammas can induce discontinuous jumps in the environmental charge of superconducting qubits (charge jumps), causing correlated errors that challenge fault-tolerant quantum computing while simultaneously providing a detection signature for quantum sensing applications. Current detection methods operate offline, introducing latency incompatible with in-the-loop qubit control. In this paper, an online detector of charge jumps for superconducting qubits, based on a dilated causal convolutional neural network (DCCNN) designed for in-the-loop deployment on the Quantum Instrumentation Control Kit (QICK) platform, is presented. The network is trained on synthetic Ramsey tomography scans generated from qubit templates measured at the Northwestern Experimental Underground Site (NEXUS) at Fermilab, and translated to FPGA firmware via hls4ml with ap_fixed$\langle 16,6 \rangle$ quantization, reaching a per-inference latency of $6.19 \mu$s on the Zynq UltraScale+ RFSoC ZCU216. At this operating point the DCCNN matches the detection efficiency of the established offline $\chi^2$ algorithm ($0.843 \pm 0.022$ vs. $0.866 \pm 0.020$ on $|\Delta q| \in [0.1, 0.5] e$ at matched false-positive rate), while requiring no per-qubit hyperparameter tuning. This shifts charge-jump detection from a post-hoc diagnostic to a control-loop primitive, enabling adaptive protocols that respond to radiation-induced events in situ, with applications to quantum-computing error mitigation and to the use of superconducting qubits as particle detectors.
arXiv:2607.14673v1 Announce Type: new
Abstract: Evaluations (Evals) are a deployment bottleneck for real-world AI applications: public benchmarks rarely match a team's users, context, or policies, and human review is often tedious to scale. Motivated by our work with AI applications in the public sector, this project addresses recurring evaluation challenges encountered when applications must satisfy local policy and governance requirements. We present Kaleidoscope, an integrated workflow for contextual functional evaluation that links persona-based test generation, contextualized rubrics, and human review for reliability-gated automated scoring. Generated test cases are scored against application-specific rubrics; human annotations provide reviewable labels; and LLM judges automate scoring only when their agreement with those labels meets a configured threshold. Kaleidoscope is therefore a practical, inspectable, iterative workflow for product teams. We report early evidence from a three-week pilot across four organizational use cases and custom-rubric judge experiments on 108 annotated Q\&A pairs spanning four domains and 14 evaluation dimensions. The results highlight useful features for end-to-end reliable, automated scoring.
arXiv:2606.28149v2 Announce Type: replace
Abstract: In-context segmentation (ICS) requires a model to segment target regions in a query image using only a few reference images and their corresponding masks, without updating any parameters. Despite recent progress, prior ICS studies have largely overlooked a critical aspect: system robustness, ie, whether the model can produce stable segmentation results for the same query under different references. In this work, we revisit ICS from the robustness perspective and introduce a novel paradigm, Concept-Guided In-Context Segmentation (CG-ICS), which performs segmentation by extracting high-level semantic concepts from references rather than relying solely on low-level visual matching. Specifically, CG-ICS introduces a concept reasoning module that uses an MLLM to propose candidates and a SAM3-driven scoring function with tree-search refinement to select reliable textual concepts, together with a parallel visual exemplar route that provides query-side spatial grounding via a simple context construction. Both the textual concept and the visual exemplar are then used to activate the segmentation capability of a frozen SAM3 backbone. Extensive experiments on standard ICS benchmarks demonstrate that CG-ICS not only achieves state-of-the-art accuracy but also substantially improves robustness, yielding a more reliable ICS system with significantly reduced variance across diverse reference choices. Code is available at https://github.com/Kakarot1103/CG-ICS.
arXiv:2607.10132v2 Announce Type: replace
Abstract: Dynamic loco-manipulation requires legged robots to coordinate whole-body motion while maintaining stable physical interaction with grasped objects under uncertain external forces. While tactile sensing has been widely studied for robotic manipulation, its role in dynamic whole-body control remains largely unexplored. Existing works without tactile feedback commonly grasp firmly rather than regulate the grasp according to the interaction. We propose TAC-LOCO, a tactile-augmented unified reinforcement learning framework that encodes tactile array observations from compliant grippers into a compact latent representation and joins it with proprioception for unified control of the legs, arm, and gripper. With effective grasp stability reward design, the policy learns to simultaneously track body velocity and end-effector trajectories, moderate grasp force, and prevent object slip under both gradual load changes and sudden release events. We deploy the policy zero-shot on a Unitree Go2 with an Interbotix WidowX 250 arm and tactile gripper, demonstrating dynamic tactile-informed loco-manipulation under varying external interactions, achieving a 47% reduction in grasping force and an object drop rate of less than 1%.
arXiv:2607.10255v2 Announce Type: replace
Abstract: Min-wise hashing and its $k$-min-wise variant are standard tools in similarity estimation, sampling, sketching, and streaming. A $k$-min-wise family requires every prescribed $r$-subset of a fixed set, for $r\le k$, to appear as the $r$ smallest hash values with approximately the fully random probability, up to multiplicative error $\delta$. Previous analyses show that $O(\log(1/\delta)+k\log\log(1/\delta))$-wise independence suffices. Consequently, for $k=\Theta(\log N)$ and $\delta=N^{-c}$, the standard polynomial construction uses $O(k\log N\log\log N)$ seed bits. Recent work of Chen, Huang, and Li achieves the optimal $O(k\log N)$ seed length for $k=\log^{O(1)}N$, but only with almost-polynomial error $2^{-O(\log N/\log\log N)}$, leaving open whether polynomially small error is possible with the same seed length. We prove that the standard $s$-wise independent polynomial hash family is $k$-min-wise with multiplicative error $\delta$ for $s=O(k+\log(1/\delta)).$ Thus, when $k=\Omega(\log(1/\delta))$, only $O(k)$-wise independence is required. In particular, for $k=\Theta(\log N)$ and $\delta=N^{-c}$, this gives an explicit family with seed length $O(k\log N)$, matching the support-size lower bound up to constant factors. The proof conditions on the prescribed bottom set and bounds the error only after averaging over the random threshold given by its largest hash value, rather than controlling every threshold separately.
arXiv:2607.14996v1 Announce Type: new
Abstract: Chiral optical modes provide a fundamental platform for spin selective light matter interactions and underpin emerging applications ranging from chiral emission to polarization-controlled photonic devices. They are typically achieved by tailoring specific structural asymmetries to directly induce circularly polarized radiation. Here, we introduce hybrid symmetry breaking as a general route for generating and controlling chiral quasi-bound states in the continuum (qBICs). By combining multiple orthogonal symmetry perturbations, optical chirality emerges in otherwise achiral photonic structures and evolves continuously from linear to circular polarization. A parity based analysis reveals that the chiral qBICs originate from the symmetry-controlled evolution of parity allowed radiative channels. We demonstrate the universality of the approach across multiple BIC platforms, achieve deterministic control of the polarization state across nearly the entire Poincare sphere, and further establish dynamic chirality reconfiguration in a fixed structure through a phase-change material. More broadly, our results demonstrate that optical chirality can emerge from symmetry engineering, providing a general framework for the design of chiral photonic states.
arXiv:2607.14664v1 Announce Type: new
Abstract: Epigenetic changes -- heritable, long-lived, yet actively reversible modifications of the chromatin state -- display memory, threshold activation and hysteresis, features that are the hallmark of rate-independent dissipative evolution. We propose a mathematical framework, Rate-Independent Epigenetics, that models epigenetic change within the theory of rate-independent systems and is consistent with two fundamental principles identified with the laws of thermodynamics. In this framework, a model is specified by a state space of epigenetic configurations, a stored-energy functional depending on the state and on an external loading, and a 1-homogeneous dissipation potential encoding the resistance of the epigenetic machinery to change. Assuming an energetic evolution principle, the governing equations follow, with no further modelling hypotheses. The energy balance is exact energy conservation, and the 1-homogeneity of the dissipation potential forces a non-negative, minimal (economical) dissipation. Under natural coercivity and continuity assumptions we establish existence of energetic solutions and, via vanishing viscosity, of balanced-viscosity solutions that resolve the ambiguity of the energetic formulation at epigenetic switches; uniqueness holds under convexity and, in the scalar case, under a mild finite-multiplicity condition. We then build a variational time integrator, prove its convergence to energetic solutions and a global energy-consistency estimate. The framework is illustrated on the scalar linear play operator, on an example with a double-well energetic potential, showing the ability of the framework to study multi-stability scenarios and catastrophic switches and on a nonlinear problem, proving that the theoretical results hold. The presented framework can be seen as a skeleton for a richer thermodynamically-consistent theory incorporating viscous dissipation features.
arXiv:2607.14683v1 Announce Type: new
Abstract: Understanding driver emotion and state is critical for the next generation of intelligent in-cabin systems that ensure safety and enhance human-vehicle interaction. However, existing public datasets for in-cabin affective computing are largely limited to visual modalities and rarely include conversational information, making it difficult to capture the linguistic and interactive cues underlying driver emotion. To address these gaps, we introduce InCarEmo, a multimodal dataset for in-cabin emotion recognition and driver state monitoring. InCarEmo integrates RGB and infrared video, in-cabin audio, and dialogue text collected from scripted in-cabin scenarios designed to simulate realistic driver behaviors, covering diverse lighting conditions and driving contexts. The dataset supports three primary tasks: 1) multimodal emotion recognition, 2) fatigue detection, and 3) distraction monitoring. In addition to the original Chinese data, we construct an auxiliary English benchmark to support preliminary cross-lingual evaluation. We provide a unified benchmark with extensive baseline results across unimodal and multimodal methods, including analyses under modality-missing and noise conditions. Experimental results demonstrate the benefits of multimodal fusion and reveal remaining challenges under real-world noise and low-light conditions. By releasing InCarEmo, we aim to establish a comprehensive foundation for robust, interpretable, and human-centric in-cabin affective understanding, promoting safer and more empathetic driver-vehicle interaction.
arXiv:2607.15103v1 Announce Type: new
Abstract: The DFT-D3 dispersion correction is routinely added to machine learning force fields (MLFFs) trained on dispersion-deficient functionals such as PBE. Its environment-dependent pair coefficients, however, break the atom-centered separability that fast summation methods require, forcing practitioners either to truncate D3 or to accept a substantial slowdown. We introduce FourierD3, a method that uses a functional low-rank decomposition to restore this separability and enable particle-mesh evaluation in $O(N\log N)$ time without a real-space cutoff on the dispersion sum.
arXiv:2607.14999v1 Announce Type: new
Abstract: We present a formal verification of an abstract transition-system presentation of the Davis-Putnam-Logemann-Loveland (DPLL) procedure in the Rocq proof assistant. Following Nieuwenhuis et al., SAT solving is modeled as a set of rule-based transitions between states rather than as a concrete algorithm. We formalize the syntax and semantics of propositional formulas, define the classical and base DPLL transition systems, and prove their key metatheoretic properties. In particular, we establish correctness and completeness with respect to satisfiability, and we prove termination by showing that the transition relation is well-founded. The formalization extends the original abstract system by also including the pure literal rule. Building on the verified transition system, we introduce an abstract notion of strategy and derive a terminating solver from any strategy satisfying suitable conditions. We then implement a concrete strategy in Rocq and show that it satisfies the strategy specification.
arXiv:2607.14899v1 Announce Type: new
Abstract: Map representations which are consistent across repeated visits to a real-world semi-static environment are very useful for long-term robotic inspection. In such settings, the scene may evolve while the robot is absent, with objects appearing, disappearing, moving, or being replaced, quickly making a static map outdated. Existing change-detection methods reason through geometry, category-level semantics, or object persistence. However, achieving reliable object association across revisits remains a key challenge, especially under partial views, occlusion, and imperfect segmentation. In this work, we propose OASIS-Map, a multi-session mapping system that maintains a spatio-temporally consistent object-level map by establishing dense patch-level semantic correspondences between temporal observations. These correspondences detect where the scene has changed and incrementally associate objects across revisits as the robot re-observes the environment. We demonstrate OASIS-Map on three challenging real-world scenarios: object rearrangements in 3RScan, visually similar car replacements in a car park, and large-scale scene changes in an outdoor market. We achieve 0.783 F1 on change detection in a car replacement scenario in a car park and 0.667 F1 on moved object association in 3RScan. https://dynamic.robots.ox.ac.uk/projects/oasis-map/
arXiv:2607.14722v1 Announce Type: new
Abstract: We present magnetic configurations in the Helically Symmetric eXperiment (HSX) and Wendelstein 7-X (W7-X), in which the edge magnetic structure is dominated by island chains which are spatially larger than the previously reported configurations. These ``giant" island chains (with rotational transform $\iota=4/3$ or $4/4$ for HSX and $\iota=5/6$, $5/5$ or $5/4$ for W7-X) are obtained by reducing the coil current in main coil 6 for HSX and non-planar coil 5 for W7-X (i.e. the coil nearest the up-down symmetric cross-section $\phi=36^\circ$ for W7-X and $\phi=45^\circ$ for HSX); this appears a sufficient (but not necessary) condition for giant islands. The giant islands create relatively straight X-point legs which transport plasma to the plasma-facing components (PFCs). In the most extreme cases, the island O-points leave the domain of the field line map and the divertor legs of the remaining ``unpaired" X-points do not close around the island. We use the anisotropic heat diffusion code EMC3-Lite to find ``giant island" W7-X configurations which are promising for PFC heat loads. Coil forces analysis (in addition to other effects such as neoclassical transport and magnetohydrodynamic stability) would also be required but are not explored here. It is not known whether giant islands are intrinsically favourable for divertor performance but we demonstrate that such regimes, which are far from the ordinary island divertor, are obtainable and can in principle be studied experimentally. This also reveals the flexibility of existing machines for edge studies beyond their original design space.
arXiv:2607.14686v1 Announce Type: new
Abstract: Credit-card fraud losses are monetary, but papers often judge models with transaction-level scores. We ask whether transaction amount should shape training weights or be used later to order alerts. To separate this question from ordinary class imbalance handling, we keep total fraud-case weight fixed and vary only its allocation across fraud cases. The experiments test two chronological card-fraud datasets with XGBoost under unweighted training, standard class weighting, matched log-amount weighting, stronger amount-weighted variants, and score times amount reranking. Metrics are average precision, dollar recall, and dollar precision at fixed alert budgets over five seeds, with 95 percent day-block bootstrap intervals for the main contrasts. Results are narrower than expected. Amount-derived ratio and velocity features carry much of the signal, while raw amount fields add little once those features are in the model. In the matched setting, amount-conditioned training gives only small gains over class weighting and does not consistently beat the plain unweighted model. Stronger amount weights recover more fraudulent dollars, but at lower ranking quality and dollar precision. Reranking alerts by score times amount after training gives the largest dollar-recall shift. A small SHAP audit finds larger month-to-month attribution movement for fraud cases than for aggregate traffic. In these tests, amount is useful as a feature and as an alert-ordering variable, not by itself as a better sample-weighting rule.
arXiv:2607.14760v1 Announce Type: new
Abstract: A surveillance camera is an image sensor whose silent physical degradation invalidates every downstream consumer of its data. In-situ integrity alarms for such vision sensors require low false-alarm rates, bounded computation, and diagnosable behavior under nuisance illumination changes. This paper studies a deliberately narrow streaming integrity monitor for two low-cost sensor-fault signatures: texture-collapsing lens occlusion and abrupt photometric scene transition. The detector compares sampled luminance and local-gradient statistics with a clean-only sliding reference, applies coarse-grid structured-light rejection and mode/rapid-brightness suppression, and emits at most one notification per tamper episode. We formalize the decision predicates and derive a consistency rule for when rapid-brightness suppression makes the scene-transition path unreachable. On 320 in-scope controlled sequences, the default state machine attains 0.800 F1 and 0.822 balanced accuracy (significantly better paired correctness than the strongest baseline, though the F1 margin is not statistically resolved); on a magnitude-swept public audit it attains the highest partial AUC under a 5\% false-alarm budget, and a separate extended-stress FPR-constrained sweep reaches 0.925 recall at 0.025 false-positive rate. Public Xiph, Bremen IoT, and UHCTD diagnostics show the fixed predicates preserve low false alarms while recall concentrates inside the declared envelope (UHCTD in-scope covered recall 0.667 versus 0.016 out of scope), and a 9.09-camera-hour verified-negative public audit records zero false alarms. The method is best interpreted as an auditable sensor-health subsystem rather than a universal camera-tamper classifier.
arXiv:2607.14698v1 Announce Type: new
Abstract: Vision-Language-Action (VLA) models have emerged as a powerful paradigm for general-purpose robot manipulation; however, their transition to real-world environments reveals vulnerabilities to minor environmental perturbations. We propose FLARE, an optimized physical spotlight attack framework that exploits these vulnerabilities via targeted illuminations, dropping baseline task success rates to zero without any access to model internals. While adversarial training is the standard countermeasure, we identify a critical and previously underestimated defensive pitfall: naive data augmentations incorrectly condition VLA models to discard color as noise, collapsing their visual perception into a purely shape-biased processor. We expose this degradation through a diagnostic grayscale evaluation, in which the defended model maintains high success rates on grayscale inputs, while its success rate on benign, color-dependent real-world tasks drops to at most 47.5%, well below the undefended baseline. To address this, we propose ChromaGuard, a chroma-preserving adversarial training method. On a physical 6-DoF robotic platform, we demonstrate that ChromaGuard achieves 97.5% and 92.5% success rates in benign and attacked color-dependent tasks, respectively.
arXiv:2607.15088v1 Announce Type: new
Abstract: Large Faraday rotations can be generated by circular birefringence of atomic samples in an axial magnetic field in the vicinity of atomic resonance lines. The Faraday angle is a function of the magnetic field strength, the optical density of the atomic sample which may be varied by changing the temperature of the atomic gas, and of course the optical detuning from the transition frequencies. More generally, magneto-optical effects in atomic samples include circular dichroism in addition to birefringence, resulting in a modification of the ellipticity as well as the polarisation alignment. Usually such effects are investigated for homogeneous linear polarisations, but the mechanisms apply also to polarisation structures such as vector vortices. We investigate the effect of optical activity of a rubidium vapour in the Hyperfine Paschen-Back regime, for the example of an azimuthally polarised input light beam. We show that for low atomic densities, circular birefringence dominates over dichroism, and azimuthal polarisation is rotated towards radial polarisation. The rotation angle increases with increasing optical densities. At high vapour temperatures, dichroism becomes more and more relevant, leading to intricate variations of both alignment and ellipticity.
arXiv:2607.14331v1 Announce Type: new
Abstract: Long interaction histories are among the most informative inputs for click-through rate (CTR) prediction, yet in online advertising they collide with a hard serving constraint: ads must be scored within a few hundred milliseconds to enter the auction, which rules out running a large sequence encoder at request time. We describe how a production advertising system resolves this conflict by decoupling history encoding from real-time inference. A high-capacity offline transformer asynchronously encodes the user's full cross-surface interaction history into a compact representation cached in a feature store, while a lightweight runtime model combines this cached representation with the user's most recent events and the request context at serving time. The offline encoder is pre-trained autoregressively on large-scale interaction logs with a dual objective - feedback prediction and next-item prediction - and the two-stage architecture is then fine-tuned for CTR prediction on the target advertising surface. Offline, the split design recovers 72-80% of the quality of a full-history runtime transformer that would be too expensive to deploy, and the cached representation is robust enough to staleness to permit inexpensive refresh policies. In production A/B experiments, the system improves the primary ranking metric by +2.77% in search advertising and +2.1% on the Yandex Advertising Network, with revenue gains of +2.26% and +0.43% respectively - without increasing serving latency.
arXiv:2607.14706v1 Announce Type: new
Abstract: We design and analyze \underline{M}echanism-\underline{E}nforced \underline{S}equential \underline{HA}lving (MESHA), an algorithm for Best Arm Identification (BAI) in strategic linear bandits. In this setting, each arm may strategically misreport its feature vector to maximize the probability of being identified as the best arm, when rewards are generated from the arms' true but unobservable features. The design of MESHA applies the na\"ive uniform sampling rule and an epoch-wise Grim Trigger Condition (GTC): the former reduces the impact of arms' strategic behaviours and the latter eliminates arms whose reported features severely deviate from the ground truth. Considering an arbitrary Nash Equilibrium, we prove that any arm would attempt to pass the GTC check to maximize its identified probability and derive an upper bound on the failure probability of MESHA within a fixed budget $T$. We also show that state-of-the-art linear BAI algorithms with $G$-optimal design would fail in such strategic environment, as the optimal design (OD)-based sampling rule based on strategically reported features may {\it starve} the optimal arm of any sampling budget. Finally, extensive numerical experiments indicate that MESHA
outperforms baselines that rely on OD-based sampling rules as well as the feature-agnostic baselines, corroborating the efficacy of MESHA.
arXiv:2607.14392v1 Announce Type: new
Abstract: We present an uncertainty quantification (UQ) framework for application level benchmarking and characterization of noisy quantum backends. The framework compares two workload classes under one statistical pipeline: noisy intermediate scale quantum (NISQ) variational quantum algorithms (VQAs) and Quantum Singular Value Transformation (QSVT) based Green's function reconstruction. For the VQA branch, we evaluate ten benchmark families spanning chemistry, optimization, simulation, compiling, linear solving, partial differential equations, metrology, error correction, tomography, and channel fidelity estimation. For the QSVT branch, we reconstruct orbital resolved Green's functions and spectral peaks from a block encoded real time propagator. The workflow combines Bayesian optimization, posterior distribution refinement, sensitivity analysis, robust parameter density estimation, backend ranking, noise correlation, and resource estimation analysis. Instead of reporting only one best parameter vector, the framework identifies robust parameter regions, residual gaps to ideal behavior, backend specific failure modes, and calibration sensitive uncertainty. The result is a common benchmark for variational and non-variational workloads that measures how reliably each backend reaches useful task level behavior.