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Oct 16

The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems

Modern CI/CD pipelines integrating agent-generated code exhibit a structural failure in responsibility attribution. Decisions are executed through formally correct approval processes, yet no entity possesses both the authority to approve those decisions and the epistemic capacity to meaningfully understand their basis. We define this condition as responsibility vacuum: a state in which decisions occur, but responsibility cannot be attributed because authority and verification capacity do not coincide. We show that this is not a process deviation or technical defect, but a structural property of deployments where decision generation throughput exceeds bounded human verification capacity. We identify a scaling limit under standard deployment assumptions, including parallel agent generation, CI-based validation, and individualized human approval gates. Beyond a throughput threshold, verification ceases to function as a decision criterion and is replaced by ritualized approval based on proxy signals. Personalized responsibility becomes structurally unattainable in this regime. We further characterize a CI amplification dynamic, whereby increasing automated validation coverage raises proxy signal density without restoring human capacity. Under fixed time and attention constraints, this accelerates cognitive offloading in the broad sense and widens the gap between formal approval and epistemic understanding. Additional automation therefore amplifies, rather than mitigates, the responsibility vacuum. We conclude that unless organizations explicitly redesign decision boundaries or reassign responsibility away from individual decisions toward batch- or system-level ownership, responsibility vacuum remains an invisible but persistent failure mode in scaled agent deployments.

  • 2 authors
·
Jan 21 2

Can an Anti-de Sitter Vacuum in the Dark Energy Sector Explain JWST High-Redshift Galaxy and Reionization Observations?

The James Webb Space Telescope's (JWST) discovery of an unexpectedly high abundance of UV-bright galaxies at redshifts z > 10 poses a significant challenge to the standard LambdaCDM cosmology. This work tests whether this tension can be resolved solely by modifying the cosmological background, without invoking significant evolution in the astrophysical properties of early galaxies. We investigate an alternative framework featuring the presence of an anti-de Sitter vacuum in the dark energy sector, a model that naturally arises in quantum gravity models like string theory and can enhance early structure formation. Using a self-consistent semi-analytical model that couples galaxy evolution with reionization, we confront this scenario with a wide range of observations. We first show that while a model tailored to fit the high-z UV luminosity functions (UVLFs) shows promise, it is in strong tension with well-established cosmological constraints from the CMB and other low-redshift probes. Conversely, models within this framework that are consistent with these constraints provide only a modest boost to structure formation and fail to reproduce the observed JWST galaxy abundances at z > 10. While these models remain consistent with the cosmic reionization history, our primary result is that this class of cosmological modifications is insufficient on its own to explain the galaxy excess. Our study underscores the critical importance of holistic testing for any beyond-LambdaCDM proposal; apparent success in one observational regime does not guarantee overall viability. By demonstrating the limitations of a purely cosmological solution, our results strengthen the case that evolving astrophysical properties are a necessary ingredient for solving the challenge of early galaxy formation.

  • 4 authors
·
Sep 2, 2025

Global strong solutions to the 1D full compressible Navier--Stokes equations with temperature-dependent coefficients and vacuum

We study the initial--boundary value problem for the one-dimensional full compressible Navier--Stokes system whose viscosity and heat conductivity are given by μ(θ)=μθ^α and κ(θ)=κθ^β, respectively. The initial density is nonnegative and is allowed to vanish. For α>2 and β>2α+4, we prove the existence of a global strong solution for arbitrarily large compatible H^2 data, provided that the prescribed boundary temperature is sufficiently high relative to their size. No velocity or temperature perturbation is required to shrink as the boundary temperature tends to infinity. A shifted initial-layer weight and an exact symmetrization of the two material-derivative equations separate the fast thermal mode from the momentum mode. The resulting estimates are uniform with respect to the positive lower bound used in the approximation and therefore survive the limit to genuine vacuum. The proof further combines a relative-entropy identity centered at the conserved mean density, the physical effective viscous flux, a Zlotnik-type estimate along particle trajectories, and finite-time transport estimates for the density derivatives. The density remains uniformly bounded and the temperature stays uniformly comparable with the prescribed boundary temperature. After the initial layer we establish vacuum-compatible exponential relaxation: the density converges to its conserved mean in every finite L^p, the velocity and temperature perturbation decay in H^1, and the weighted material-derivative energy decays at rate cbarθ^{1-α}.

  • 3 authors
·
Oct 3

SOUS VIDE: Cooking Visual Drone Navigation Policies in a Gaussian Splatting Vacuum

We propose a new simulator, training approach, and policy architecture, collectively called SOUS VIDE, for end-to-end visual drone navigation. Our trained policies exhibit zero-shot sim-to-real transfer with robust real-world performance using only onboard perception and computation. Our simulator, called FiGS, couples a computationally simple drone dynamics model with a high visual fidelity Gaussian Splatting scene reconstruction. FiGS can quickly simulate drone flights producing photorealistic images at up to 130 fps. We use FiGS to collect 100k-300k image/state-action pairs from an expert MPC with privileged state and dynamics information, randomized over dynamics parameters and spatial disturbances. We then distill this expert MPC into an end-to-end visuomotor policy with a lightweight neural architecture, called SV-Net. SV-Net processes color image, optical flow and IMU data streams into low-level thrust and body rate commands at 20 Hz onboard a drone. Crucially, SV-Net includes a learned module for low-level control that adapts at runtime to variations in drone dynamics. In a campaign of 105 hardware experiments, we show SOUS VIDE policies to be robust to 30% mass variations, 40 m/s wind gusts, 60% changes in ambient brightness, shifting or removing objects from the scene, and people moving aggressively through the drone's visual field. Code, data, and experiment videos can be found on our project page: https://stanfordmsl.github.io/SousVide/.

  • 5 authors
·
Dec 20, 2024