arXiv:2604.27694v2 Announce Type: replace-cross Abstract: Renewed attention to the identity of Bitcoin's pseudonymous creator has revived an old worry: that the roughly 1.148 million BTC mined by Satoshi and never moved represent a major tail risk for bitcoin. This paper argues that the worry is overstated. The mechanical downside of selling the position is bounded well below the feared collapse, and the outcomes most consistent with sixteen years of observed behavior are not bearish for bitcoin's effective supply. We analyze the position in two ways. First, we model the case of a purely financial holder. Multiple sale scenarios, checked against both a square-root-law estimate and the historical record of large sales, suggest that bitcoin's current market liquidity could absorb a patient multi-year sale with a cumulative price impact centered around 10 to 13 percent relative to a no-sale case. The same arithmetic also links the downside from a surprise sale to the upside from a confirmed burn: both are bounded by the same effective-supply adjustment, so the doom case and the burn-rally case cannot both be large. Second, we consider the preferences implied by the sixteen-year record. Ideological restraint, privacy, already having enough, and preserving the myth all point toward further dormancy, permanent loss of access, or a deliberate burn. A sale or an act of sabotage remains possible, but the record supports it less strongly. Under both approaches, the mechanical bear case is bounded, and the likeliest outcomes are neutral to mildly positive for bitcoin's effective supply. The argument does not rule out transient overshoot or leverage-driven amplification; it bounds the durable repricing the coins themselves can cause.
Science Journals
arXiv:2605.12410v2 Announce Type: replace-cross Abstract: We propose and analyze a model-based bootstrap for transition kernels in finite controlled Markov chains (CMCs) with possibly nonstationary or history-dependent control policies, a setting that arises naturally in offline reinforcement learning (RL) when the behavior policy generating the data is unknown. We establish distributional consistency of the bootstrap transition estimator in both a single long-chain regime and the episodic offline RL regime. The key technical tools are a novel bootstrap law of large numbers (LLN) for the visitation counts and a novel use of the martingale central limit theorem (CLT) for the bootstrap transition increments. We extend bootstrap distributional consistency to the downstream targets of offline policy evaluation (OPE) and optimal policy recovery (OPR) via the delta method by verifying Hadamard differentiability of the Bellman operators, yielding asymptotically valid confidence intervals for value and $Q$-functions. Experiments on the RiverSwim problem show that the proposed bootstrap confidence intervals (CIs), especially the percentile CIs, outperform the episodic bootstrap and plug-in CLT CIs, and are often close to nominal ($50\%$, $90\%$, $95\%$) coverage, while the baselines are poorly calibrated at small sample sizes and short episode lengths.
arXiv:2607.05694v1 Announce Type: cross Abstract: Logit-based watermarking is a widely used mechanism for identifying LLM generated content, yet its effectiveness is governed by a fundamental trade-off between detectability and semantic distortion. Existing analyses provide limited guidance for principled hyperparameter selection, leaving practical deployments reliant on heuristic tuning. In this work, we develop a power-calibrated statistical framework that establishes explicit quantitative relationships between watermark hyperparameters, detection power, and distortion. This characterization transforms watermark design into a guided optimization problem. Building on these results, we derive practical parameter selection procedures that achieve optimal tradeoffs under constraints. Extensive experiments across multiple language models and datasets validate the theory and demonstrate that the proposed framework consistently identifies Pareto-optimal points.
arXiv:2607.05412v1 Announce Type: new Abstract: STEM education faces challenges in personalization and interdisciplinary integration. AI technology has brought new possibilities, but the mechanisms by which AI reshapes the STEM education ecosystem require systematic investigation. This study employs bibliometric methods to analyze 242 publications from 2015-2025, constructing knowledge maps to reveal the evolutionary trajectory. The findings show that the field has transformed from intelligent tutoring systems to inquiry-based learning and computational thinking cultivation driven by LLMs. AI's key contribution lies in providing intelligent scaffolding that lowers the threshold for understanding knowledge. In this sense, AI is a core driving force promoting its shift from knowledge transmission to capability development.
arXiv:2512.19458v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly embedded in agentic frameworks for scientific discovery. First-principles materials computation imposes a demanding standard for autonomy: successful execution depends on internally consistent inputs, supervision of long-running calculations, and verified outputs. Here we present VASP Agent, a coding-agent-centered system that combines reusable domain skills, deterministic tools, workspace-state inspection, runtime evidence, and scientific guardrails to execute multi-step VASP calculations. The system is evaluated across multiple tasks including structural relaxation, bandgap calculation, equilibrium lattice constant determination, and CO/Pt(111) adsorption. VASP Agent completes all evaluated cases, and its computed numerical results are compared with those obtained using pymatgen and other agentic tools. When large deviations occur, the calculation parameters produced by VASP Agent are more appropriate than those produced by LLM-based workflows. Failure analysis shows that errors that terminate fixed pipelines can be diagnosed and recovered under agentic control.
arXiv:2607.04687v2 Announce Type: replace-cross Abstract: We consider the set of matchings of a graph and a local change operation, called a flip, between them. In the combinatorial setting, the base graphs are either complete graphs or complete bipartite graphs, and in the geometric setting, the graphs are embedded on point sets in the plane, with the requirement that edges must be drawn as straight lines and must not cross. For base graphs with an even number of vertices, we consider perfect matchings, i.e., all vertices are matched, and for base graphs with an odd number of vertices, we consider almost-perfect matchings, i.e., all but one vertex of the graph are matched. A 2-flip between two perfect matchings exchanges two edges, and a 1-flip between two almost-perfect matchings exchanges one edge. The corresponding flip graph has the set of perfect or almost-perfect matchings as vertices, with pairs of them connected by an edge if they differ in a 2-flip or 1-flip, respectively. In this work, we provide a comprehensive picture of Hamiltonicity properties of these flip graphs. We prove that the flip graphs in the combinatorial setting are Hamilton-connected, i.e., they admit a Hamilton path between any two vertices, or, if the flip graphs are bipartite, we prove that they are Hamilton-laceable, i.e., they admit a Hamilton path between any two vertices from different partition classes. In the geometric setting, we prove that any path in them misses exponentially many vertices, in particular, they have no Hamilton paths or cycles. For points in convex position and almost-perfect matchings under 1-flips, we complement this by constructing a cycle in the flip graph that visits almost all vertices.
arXiv:2607.06324v1 Announce Type: cross Abstract: The multifractal detrended cross-correlation coefficient $\rho_q(n)$ is widely used to investigate scale-dependent interactions, but its application to negative fluctuation orders is affected by numerical instabilities, unbounded values, and interpretational difficulties. We propose a Signed Multifractal Detrended Cross-Correlation Coefficient, $\rho_{\mathrm{SMFDCCA}}(n,q)$, an amplitude-conditioned correlation observable for multifractal detrended analysis, based on locally normalized detrended correlations and regularized fluctuation amplitudes. The proposed coefficient preserves the sign of local interactions, remains strictly bounded within $[-1,1]$ for both positive and negative values of $q$, and eliminates the corrective procedures required by previous approaches. Validation using independent fractional Gaussian noise confirms the absence of spurious cross-correlations and the numerical stability of the method. Applications demonstrate that the proposed observable resolves how cross-correlations evolve jointly with temporal scale and fluctuation amplitude, revealing scale- and amplitude-dependent correlation structures, including stronger synchronization during large fluctuations in stock-market indices and heterogeneous coupling patterns in temperature records.
arXiv:2607.06395v1 Announce Type: cross Abstract: Attosecond metrology is the ability to measure ultrafast optical light-wave oscillations, yet its approach has been limited to classical fields. Hence, the influence of the fluctuations of a quantum field on attosecond measurements has remained unexplored. Here, we close this gap by showing that the attosecond streaking measurement of bright quantum light is sensitive to quantum fluctuations of the optical field on the attosecond timescale. The distinct sub-cycle modulations allow to extract the properties of the squeezed field quadrature in regimes where conventional state tomography approaches reach their limitation. With the full quantum optical attosecond streaking scheme developed here, we provide a certification method that can measure quantum squeezing below the shot noise limit, thereby overcoming the problem of tomographically measuring bright quantum light. This opens the way towards quantum optical metrology of field fluctuations with attosecond temporal resolution.
arXiv:2604.03457v2 Announce Type: replace Abstract: Low-storage explicit Runge-Kutta schemes are particularly popular for the numerical integration of time-dependent partial differential equations based on the method-of-lines due to their efficiency and their reduced memory requirements. We show that D-splitting methods, splitting methods on the extended phase space, can be used as high performance 2N-storage embedded explicit RK methods without a third storage register. They are pseudo-geometric methods preserving some of the qualitative properties of the exact solution up to a higher order than the order of the method. Some of their properties are analysed, to build new tailored methods, and are tested on numerical examples.
arXiv:2607.05012v2 Announce Type: replace Abstract: Accurate determination of hyperfine structure constants in atomic systems provides important insight into the interplay of electron correlation and relativistic effects in the nuclear region. Although sodium (Na) is a relatively light atom, previous all-order relativistic many-body calculations of the magnetic dipole hyperfine constants for the low-lying states of $^{23}$Na show noticeable discrepancies with experiment. To address this, we calculate the ionization potentials and hyperfine structure constants of $^{23}$Na using relativistic coupled-cluster theory with explicit inclusion of triple excitations. We further incorporate corrections from the Breit interaction, quantum electrodynamics, and the Bohr-Weisskopf (BW) effect. Results from lower-order methods are also presented to assess the importance of different physical contributions across states. Our calculations demonstrate that contributions from the lower-order relativistic and BW effects play almost similar roles with the electron correlation effects, including triple excitations, and are essential for reconciling theoretical predictions with experimental observations. This study can also serve as a useful guide for understanding the role of triples in heavier alkali systems.
arXiv:2607.05019v2 Announce Type: replace Abstract: In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks. When modality imbalance is pronounced, various regularization techniques have been proposed to balance the learning process and overcome the inferior performance of late-fusion networks. In contrast, this work demonstrates that multimodal data can be effectively classified without any explicit modality fusion, using deep ensembles of unimodal networks. We systematically compare deep ensembles to late-fusion networks at equal parameter count and show that ensembles consistently outperform state-of-the-art late-fusion methods designed to address modality imbalance. This advantage also holds over intermediate-fusion techniques we evaluated and over hybrid methods that combine unimodal and multimodal predictions. We propose and empirically validate a method for selecting the number of models per modality in an ensemble, avoiding computationally expensive exhaustive search. Under extreme modality imbalance and small ensemble sizes, the heuristic indicates that ensembles of unimodal models trained solely on the stronger modality are preferable; as the ensemble scales up, incorporating models from the weaker modality becomes beneficial. Both predictions align with our empirical findings. To systematically explore the challenges of optimizing multimodal models, we propose a synthetic multimodal framework that allows control over both the number of modalities and their predictive strength; our findings are consistent across synthetic and real-world datasets. Finally, by fitting scaling laws to bimodal datasets, we estimate the asymptotic performance of ensembles.
arXiv:2607.06003v1 Announce Type: new Abstract: Scalable plasmonic technologies face a critical trade-off: few-body architectures offer high enhancement but are sensitive to fabrication flaws, while scalable methods like solid-state dewetting yield large, low-enhancement gaps. We introduce a paradigm shift using a many-body plasmonic architecture inspired by statistical mechanics. By moving toward the continuum limit (N>>1), local geometric variations are statistically averaged out, effectively decoupling optical performance from microscopic disorder. We implement this concept via a lithography- and etching-free, multi-step dewetting strategy, creating wafer-scale nanoclusters. This process strategically forms a robust many-body system by introducing numerous small satellite nanoparticles between larger particles. Crucially, this design achieves a high collective enhancement that surpasses even optimized few-body systems, despite having larger individual gaps. Under optimized conditions, these substrates exhibit a surface-enhanced Raman scattering enhancement factor approaching 4 x 108 with unprecedented reproducibility (RSD of ~10%). This scalable, low-cost concept establishes a practical route toward reproducible wafer-scale nanophotonic platforms for sensing, spectroscopy, and quantum technologies.
arXiv:2502.09696v3 Announce Type: replace Abstract: Large Multimodal Models (LMMs) exhibit shortfalls when interpreting images and, by some measures, have poorer spatial cognition than young children or animals. Despite this, they attain high scores on many popular visual benchmarks, with headroom rapidly eroded by model progress. This creates a need for difficult benchmarks that remain relevant for longer. We introduce ZeroBench - a lightweight visual reasoning benchmark curated using adversarial filtering to be "impossible" for frontier LMMs at its original release, with initial SotA scores of 0% pass@1 and pass^5. We track progress on ZeroBench over the subsequent year, observing SotA reaching 6% pass^5 and 19% pass@5, indicating the potential longevity of the benchmark. We evaluate 46 LMMs on ZeroBench, compare performance to a human baseline, analyse strengths and weaknesses, chart a year of progress in visual capabilities, and publicly release ZeroBench at https://zerobench.github.io.
arXiv:2607.05402v1 Announce Type: new Abstract: Improvements in AI technologies have made it feasible to develop new types of medical AI tools. However, these tools raise new kinds of questions, especially in relation to the ethics and AI Act compliance. We analyzed two cases of AI tools developed to predict medical risks, the risk of work disability (case A) and the risk of getting Alzheimer's disease (case B). We observed both cases using the ethical AI and the EU AI Act as frameworks, noted that they classify as high-risk systems, and that bringing them from the research environment to production would require a lot of work and compliance due to the related regulation.
arXiv:2607.06243v1 Announce Type: new Abstract: Photonic integrated circuits (PICs) enable optical systems with dramatically increased performance, cost-effectiveness, and scalability through enhanced light-matter interactions, high-density integration, and mass production. Due to the significant mode mismatch between various integrated photonic platforms and optical fibers, spot-size conversion interfaces with low-loss, compact footprint, and high manufacturability are essential. Conventional spot-size converters based on intuitive designs often require multi-layer tapering structures and tiny waveguide tips to adiabatically expand the eigenmodes. These rigid design constraints commonly lead to large device footprints and the requirements of multiple high-precision lithography steps. In this paper, we overcome these limitations using inverse design methods, which optimize the coupling efficiency over a large parameter space beyond traditional eigenmode evolution limits. Specifically, we demonstrate efficient and ultra-compact photonic interfaces on the thin-film lithium niobate (TFLN) platform, where the partially etched rib waveguides and non-vertical sidewalls have previously hindered the achievement of low-loss waveguide tapers in single-layer configurations. Our inverse-designed photonic structures achieve simulated and experimentally measured coupling efficiencies as low as 1 dB and 3 dB per facet between TFLN waveguides and lensed/ultra-high numerical aperture (UHNA) fiber, with broad 1-dB bandwidths exceeding 120 nm. The inverse-designed interfaces are highly compatible with standard TFLN PIC components and require only a single high-resolution lithography step. More importantly, the design concept transcends traditional eigenmode evolution theories and is broadly applicable to a variety of material platforms and application scenarios.
arXiv:2607.06251v1 Announce Type: new Abstract: A circle graph is the intersection graph of a set of chords in a circle. A dominating set of a graph $G=(V,E)$ is a subset $D\subseteq V$ such that every vertex in $V\setminus D$ is adjacent to at least one vertex of $D$. Computing a minimum dominating set is known to be NP-hard on circle graphs. In this paper, we study the minimum dominating set problem on bipartite circle graphs, namely, circle graphs admitting a chord representation in which the chords can be partitioned into two color classes such that no two chords of the same color intersect. We prove that the problem remains NP-hard for this restricted graph class by a reduction from Planar Monotone 3-SAT. On the positive side, we present a polynomial-time 2-approximation algorithm and develop a polynomial-time approximation scheme (PTAS) based on local search.
arXiv:2607.06271v1 Announce Type: new Abstract: Tensor trains (or Matrix-Product States) are a data structure used in many fields of computer science and physics. They were recently shown to generalise binary decision diagrams when used over the 2-element Galois field, prompting the question of their reducibility in such a context, when the standard approach, over real or complex number, is not amenable to finite fields. We provide here a unique normal form and associated polynomial-time reduction strategy for tensor trains over arbitrary fields. We also show how to directly extract a normal form out of a full tensor, how to get the leading index and value of a normal form, and an upper bound on the size of a fully-reduced tensor train relative to a naive storage of the full tensor. On the one hand, this work strengthens the use of tensor trains as a relevant formal tool. On the other hand, from the perspective of tensor networks, it extends the formalism to more general settings than the well-studied real and complex fields, and crucially provides the first tensor train form with the uniqueness property.
arXiv:2607.04236v2 Announce Type: replace-cross Abstract: Bayes-assisted conformal prediction combines the strengths of Bayesian modelling with exact, distribution-free frequentist coverage guarantees. Although conformal validity is preserved even when the Bayesian working model (BWM) is misspecified, the size of the resulting prediction sets can degrade substantially when the prior is poorly aligned with the observed data. We address this limitation by introducing RoBAS (Robust Bayes-Assisted Shrinkage): a Bayes-assisted framework for constructing robust nonconformity scores, with two instantiations: one induced by a heavy-tailed BWM, and a closed-form empirical Bayes shrinkage score. The resulting scores adapt to the quality of the working information encoded in the prior: when this information is reliable, they exploit it to produce efficient prediction sets; when it is weak or inaccurate, they revert to the Distance-To-Average (DTA) score, a robust non-informative baseline. We evaluate the proposed scores on tabular and image regression tasks where the training distribution may differ from the calibration and test distributions, while the calibration and test data themselves remain exchangeable. We find that they are competitive with widely used scores in the absence of such shift, while substantially reducing interval widths in shifted settings.
arXiv:2607.05593v1 Announce Type: new Abstract: The growing integration of AI agents into human teams calls for a principled understanding of how collective intelligence emerges in hybrid systems. Recent frameworks clarify how attention, memory, and reasoning differences shape human-AI interaction at the individual and dyadic levels, but a formal account of how these differences scale to group-level dynamics is lacking. Most network science has examined either human-only or multi-agent AI-only systems, leaving open how its findings and parametrizations translate to hybrid groups. This chapter synthesizes network science, collective cognition, and multi-agent systems through the lens of attention, memory, and reasoning. We review how task environments, group topologies, agent-level processes, and incentive structures shape collective outcomes in human-only and AI-only networks, then examine how these results extend to hybrid settings, conceptualizing hybrid networks as heterogeneous human-AI nodes and links with distinct individual and transactive constraints. Our comparative analysis identifies which network effects are robust across agent types and which require revision, and highlights configurations that were peripheral in single-type traditions, such as human gatekeepers of AI sub-networks, but become structurally central in hybrid teams. Integrating a cognitive systems perspective with network science, we clarify how established exploration-exploitation and efficiency-redundancy trade-offs may operate differently in hybrid teams, and conclude with implications for organizational design, governance, and the responsible development of hybrid intelligence systems.
arXiv:2607.06406v1 Announce Type: new Abstract: Bright dissipative Kerr solitons in optical microresonators provide chip-scale sources of ultrashort pulses and frequency combs. Their properties are defined by the cavity dispersion for which fundamentally conflicting requirements exist: short pulses and broadband spectra require weak dispersion, whereas strong dispersion is associated with predictable dynamics. Here, we resolve this conflict by introducing a localized strong-dispersion section spanning several modes around the pump resonance within an otherwise weakly dispersive system. We implement this hybrid-dispersion scheme in a photonic crystal microresonator and reveal a new soliton attractor of backward-propagating solitons, accessible at low pump power in a thermally stable manner within the blue-detuned regime. The conflicting requirements for broadband spectra and low-noise single-soliton formation are reconciled, even in microwave-repetition-rate resonators, which otherwise are prone to uncontrollable multi-soliton formation. These results highlight the potential to achieve previously incompatible characteristics in nonlinear photonic systems through hybrid-dispersion attractor shaping.
arXiv:2607.06417v1 Announce Type: new Abstract: Disordered materials such as glasses exhibit complex structural dynamics that are challenging to probe with conventional spectroscopies. We demonstrate that terahertz-driven four-wave mixing (FWM) at glass surfaces provides direct access to low-frequency vibrational modes and structural evolution in amorphous solids. Applied to a compositional series of PbO-silicate glasses (20-54 mol% PbO), this technique resolves distinct contributions from collective Boson-peak excitations and Pb-O / Si-O network stretching modes, and tracks their systematic evolution across structurally distinct compositional regimes. The dominant vibrational frequency blueshifts with PbO content, reflecting the progressive evolution of the Pb$^{2+}$ network role from silicate-modifier to ward network-former. A pronounced enhancement of the FWM signal near 44 mol% PbO coincides with the emergence of medium-range Pb-Pb correlations, while in-plane-to-out-of-plane FWM intensity ratio ($I_{\rm SS}/I_{\rm PS}$) tracks $\chi^{(3)}$ tensor anisotropy tied to Pb$^{2+}$ lone-pair spatial correlations. The non-monotonic peak in both observables at 44 mol% PbO - a composition where NMR finds no change in local Pb-O coordination and Pb-O-Pb free-oxide linkages are negligible - provides direct evidence that a collective lone-pair reorganization occurs in the medium-range structure independently of nearest-neighbor bonding. These results establish terahertz-driven FWM as a bulk-sensitive, near-surface depth-confined ($\sim$50 nm) nonlinear spectroscopy sensitive to vibrational and electronic structural fingerprints inaccessible to linear infrared, Raman, and terahertz time-domain probes.
arXiv:2607.05929v1 Announce Type: new Abstract: General boundary conditions are implemented within a fast Fourier transform framework for linear and non-linear mechanical problems using small or finite transformation formulations. In the context of parallel computing (distributed memory), we present a framework that enables the combination of periodic and non-periodic (Dirichlet or Neumann) boundary conditions. Taking advantage of the link between non-periodic boundary conditions and the symmetries of the relevant components of the fluctuation displacement and stress fields, discrete trigonometric transforms are employed to adapt the classical Moulinec-Suquet fast Fourier transform approach. The present study employs an original displacement-based fixed-point algorithm in combination with a convergence acceleration method in order to solve boundary value problems. Finite difference approaches are used to build the discrete Green operators associated with a pre-conditioner (reference material), whose choice depends on the loading type and the small or finite transformation frameworks. The newly developed double tetrahedron scheme is employed to investigate non-periodic problems. Outcomes are compared to those of the classical hexahedral scheme. The robustness and computational efficiency of the presented parallel solver is demonstrated through numerical experiments of non-trivial loading scenarios (tension, bending, normal-mixed loading, torsion-bending), complex and densely discretized microstructures and diverse behavior laws (elasticity, isotropic plasticity, crystal plasticity), within small and finite transformation frameworks.
arXiv:2607.05717v1 Announce Type: new Abstract: Jupyter Notebooks have become widely adopted in data science, as they allow the sharing of reproducible computational analysis. They are, however, accessible only to people who understand computer code. To reach the broader audience of scientists interested in data analysis and computation, but unfamiliar with code, we introduce Plainbook, notebooks centered on natural language rather than code. Plainbook is based on two principles: promote the natural language descriptions, and verify the values. In plainbook, the natural language descriptions are preserved, rather than the resulting code; the code is generated automatically from the cell descriptions. As natural language is read top to bottom, Plainbook adopts a linear execution semantics, in which cells are guaranteed to be executed in the order in which they appear; there is no "hidden state" or out-of-order execution as in Jupyter. To allow users who may not understand code to verify the correctness of the computation, we have built into Plainbook verification mechanisms centered on values and value inspection. These include mechanisms that focus on individual cells, akin to unit tests, as well as global mechanisms. Both the linear execution semantics, and the verification mechanisms, are underpinned by a snapshot kernel that caches execution states and makes execution and verification efficient.
arXiv:2607.05744v1 Announce Type: new Abstract: The Model Context Protocol (MCP) is the dominant way coding agents discover and invoke external tools. A server advertises each tool through a tools/list handshake that returns a name, a natural-language description, and a JSON input schema. The client renders this metadata once, in a one-time approval dialog, and then injects it verbatim into the model's context on every subsequent turn. Nothing in the protocol requires the rendered approval view and the bytes delivered to the model to match. We isolate that gap as a single structural mechanism, concealment encoding, and show with a model-free, protocol-free analysis that Unicode's TAG block (U+E0000 to U+E007F) has no assigned glyph in any mainstream terminal, chat, or IDE renderer, so a payload written in it is absent from what a human reviewer sees while surviving byte-for-byte into the model's tokenizer. We then measure whether this mechanism actually defeats today's client-side defenses, building a proof-of-concept that speaks the real MCP JSON-RPC/stdio protocol against a genuine client and server. Across 5 distinct MCP metadata surfaces we implement 8 concrete techniques with a deterministic, protocol-level harness. All 8/8 techniques deliver an attacker-controlled payload into the model's context, 4/8 evade a representative string-matching sanitizer, and exactly as the mechanism analysis predicts, only the TAG-block encoding (1/8) is invisible in the human approval view while still reaching the model verbatim. MCP forces re-approval for 0/8 techniques even under a time-of-check to time-of-use rug-pull. To test whether these outcomes are a property of the protocol or an artifact of one server codebase, we re-implement the catalogue against 3 independently developed Python MCP server libraries and find total agreement across all 32 cross-library outcome cells. The baseline sanitizer flags 0 of 25 benign descriptions.
arXiv:2607.06047v1 Announce Type: new Abstract: Light polarization is a primary degree of freedom for encoding quantum information. The scaling up of photonic quantum networks and computer architecture depends crucially on its precise characterization. This is typically achieved by placing external waveplates, polarizers, moving mounts, and recently metasurfaces, on top of the detectors. All these solutions complicate integration and scaling. Here we break convention with traditional architecture and present a monolithic, self-aligned metal-superconductor nanowire single photon detector (M-SNSPD) possessing intrinsic full polarization selectivity. Gold nanowires, co-fabricated atop NbTiN superconducting nanowires within the same lithographic footprint, act as polarization-selective plasmonic metamaterials inducing resonant absorption in the NbTiN. U-shaped wires provide linear polarization selectivity, while S-shaped meanders distinguish circular polarization, while retaining the high-count rates and low dark count rates of conventional SNSPDs. By arranging them into a four-pixel array we realize simultaneous projection onto four polarizations and demonstrate continuous polarization state tomography with an ensemble average fidelity exceeding 98%. Our approach opens new avenues towards scalable detector arrays with integrated plasmonic functionalities, for single photon polarimetry, imaging and spectroscopy.