arXiv preprints from January 1, 2026 through September 21, 2026 — 06:03:38 EST
Incorporating dynamical fermions is a central challenge for diffusion samplers of lattice gauge theories. We propose an analytic pseudofermion sampler based on continuous Hasenbusch transport as a component for gauge diffusion. The construction uses shifted linear solves and a finite-path correction, requiring neither explicit fermion...
Despite their promise as structural and electronic analogs to cuprates, nickelate thin films consistently exhibit broadened superconducting transitions in many experiments that remain poorly understood. Local measurements are ideal for revealing the presence of defects or competing phases, which may cause this transition broadening...
We propose a Landau-analysis-inspired strategy for directly computing the symbol of integrals that evaluate to multiple polylogarithms in twisted cohomology. The central idea is to identify all ways in which singular hypersurfaces, twisted hypersurfaces, and integration boundaries can interact when external parameters are varied,...
Doping a quantum spin liquid is expected to give a superconductor. On kagome it gives charge order first, because of band structure rather than interaction. In the chargon (Higgs) construction of its U(1) Dirac spin liquid the two gauge components, degenerate on square and triangular lattices, are split by a line-graph effect: one...
We are in an era of unprecedented spectroscopic data availability, with surveys such as APOGEE, GALAH, and DESI observing millions of stars. However, data are heterogeneous and to date, efforts to exploit synergies between surveys have been limited. In particular, there is a lack of principled methods that combine spectroscopic data...
We present the first application of neural-network-based baryon acoustic oscillation (BAO) reconstruction to real galaxy survey data, restoring the acoustic signature damped by nonlinear structure growth. {\texttt{NERV}} ({\bf N}eural-network {\bf E}nhanced {\bf R}econstruction of the Uni{\bf V}erse) augments standard reconstruction...
Heavy-quarkonium hadroproduction at the LHC can access kinematic configurations in which the partonic center-of-mass energy is much larger than the quarkonium mass and transverse momentum. Fixed-order predictions may become unstable in this regime because large logarithms of the collision energy are not resummed. Extending high-energy...
Spiral spin liquids are magnetic states whose classical ground-state manifold consists of planar incommensurate spin spirals with wave vectors lying on a continuous ring or surface in reciprocal space. The resulting subextensive degeneracy suppresses magnetic long-range order and gives rise to liquid-like behavior. Despite numerous...
Coherent and efficient light-matter interfaces between an atom and a single mode of the electromagnetic field are essential for quantum technologies. Traditionally, these systems employ optical cavities or waveguides to isolate a specific mode of light, but a more recent approach is to engineer antennas from ordered configurations of...
We investigate dark-matter-induced neutron disappearance as an interpretation of the high-energy nuclear recoil event reported by LUX-ZEPLIN. An incoming dark matter (DM) particle annihilates a bound neutron into an invisible scalar, leaving a recoiling daughter nucleus. For each accessible daughter state, two-body kinematics fixes...
Next-generation gravitational-wave observatories like Cosmic Explorer will provide an unprecedented opportunity to probe the cosmological stochastic gravitational-wave background, providing insight into the early Universe that is inaccessible with other means. We investigate the prospects for measuring a Gaussian cosmological...
We identify the spatial projection of a conserved string charge density with a bit-thread flow. This correspondence gives bit threads a string worldsheet description and allows us to compute black hole entropy from the maximal entropy flux of a worldsheet-derived current. We first reproduce the BTZ entropy and then extend the...
We develop a decision tree classifier that recovers 3D magnetic field geometry from synthetic polarization and position angle maps of a magnetized, turbulent field. We test the classifier for a series of geometries: Uniform, Wavy, Helical, and Hourglass. The classifier is calibrated across a range of Alfvénic Mach numbers and injected...
Quantum error correcting (QEC) plays a crucial role in protecting quantum information against decoherence and enabling scalable, reliable quantum computing. One of the most realistic and ubiquitous sources of noise affecting quantum hardware today is generalized amplitude-damping (GAD) noise. Conventional, deterministic QEC codes...
Long-running AI agents create a control problem: each action they take changes the state, which in turn affects the trajectory of future actions. If the agent is not fully aligned, then guaranteeing safety requires approving consequential actions before allowing them to be executed. But requiring human approval at every step makes...
We model the economics of recursive self-improvement (RSI) and assess its plausibility and impacts. First, we build a sequence of increasingly rich models of AI progress to highlight the feedback loops behind RSI. We represent our models as directed graphs and show that net acceleration in AI capabilities depends on the product of...
We provide an introduction to real-world asset (RWA) tokenization, and develop two economic models connecting this emerging market to classical financial theory. In search for a theory of RWA tokenization, we classify RWA tokenization according to the role of the underlying asset, distinguishing standard RWA tokenization, stablecoins,...
The random-coefficient demand model of Berry, Levinsohn, and Pakes (1995) is commonly estimated by the generalized method of moments (GMM), using an unconditional moment restriction with a fixed set of instruments. Identification of the model, however, rests on a conditional moment restriction. The two are not equivalent: the...
We study revenue-maximizing attention allocation on an ad-funded social media platform governed by recommendation algorithms. Attention is costly and can be monetized through advertising or allocated to increase creators' exposure, creating a trade-off between monetization and production incentives. In a two-sided model with...
Understanding how supply chains endogenously transform requires a parametric model of processing networks with non-neutral substitution elasticities. While the Cascaded CES (CCES) production function provides a rigorous framework for these multi-layered linkages, dynamically calibrating its structural parameters from time-series data...
Predict-then-optimize methods such as Smart "Predict, then Optimize" (SPO+) of Elmachtoub and Grigas (2022) learn a mapping from contextual features to unknown edge costs and then solve the induced combinatorial problem on the predicted costs. This approach is powerful but relies on the predictive model being well specified: when the...
A principal faces a decision problem under uncertainty and can contract with a researcher to provide relevant information. The cost of acquiring information is only known to the researcher, and, moreover, by privately making an investment the researcher can reduce their expected cost. We show that this contracting problem can be...
Hierarchical orchestration, in which a Manager agent reviews worker output and can send it back for revision, is the default coordination pattern in production multi-agent LLM frameworks. Classical organizational theory predicts that the authority link speeds convergence on decisive output; work on sycophancy and...
After the arrival of transformative artificial intelligence (TAI), broad-based automation is expected to decrease the labor share and increase income and wealth inequality. Although economic growth is likely to accelerate, most of its gains may accrue to a narrow group of individuals and firms. Hence, if unmitigated by redistributive...
Inferring intelligence from observable behavior is a foundational challenge in artificial intelligence. We develop a theory of Bayesian intelligence for agents such as language models. Each prompt induces a possibly imperfect internal experiment; the agent updates a full-support prior by Bayes' rule and faithfully reports its...