Mechanism
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Related Articles from SNS
Asymptotic Optimality of the High-Dimensional Gaussian Mechanism and Improved Low-Dimensional Mechanisms for Differential Privacy
arXiv:2606.08681v1 Announce Type: new Abstract: The additive noise mechanism is a foundational tool for differential privacy (DP) of $T$-dimensional real-valued vector queries. The Gaussian mechanism, utilizing Gaussian noise, is the mostly widely used such mechanism, due to its simplicity and strong privacy guarantees. In this work, we provide justification for this choice, showing that as the dimension $T\to\infty$, no additive-noise mechanism can asymptotically improve on the Gaussian...
Mechanism Design Without Disclosure: Committing to and Running Hidden Mechanisms
arXiv:2302.05590v3 Announce Type: replace-cross Abstract: A central tenet in mechanism design is the ability to irrevocably commit to a mechanism. Commitment is achieved by public declaration, letting players verify incentive properties in advance and the outcome in retrospect. However, public declaration can reveal superfluous information that is private to the mechanism designer, such as her target function or costs.
Nonbossy Mechanisms: Mechanism Design Robust to Secondary Goals
arXiv:2307.11967v2 Announce Type: replace Abstract: We study mechanism design when agents may have hidden secondary goals which will play a role when the primary utility of the outcomes is the same. We show that in such cases, a mechanism is immune to strategic manipulation if and only if it is incentive compatible with regard to primary utility -- a property we term "primary incentive compatibility" -- and nonbossy -- a well-studied property in the context of matching and allocation...
Shared Semantics, Divergent Mechanisms: Unsupervised Feature Discovery by Aligning Semantics and Mechanisms
Announce Type: new Abstract: As large language models are increasingly deployed in high-stakes settings, there is a growing need for tools that audit not only model outputs but also the internal computations that produce them. Circuit analysis is a central approach in mechanistic interpretability, but it is typically target-conditioned, explaining a single prompt paired with a chosen completion. This target-conditioned setup can obscure heterogeneity across a model's continuation distribution.
Do Matching Mechanisms Work with LLM Agents?
Announce Type: new Abstract: This study examines whether standard matching mechanisms function as intended in LLM-agent markets, where LLM agents make allocation-related decisions as delegated decision-makers. We compare decentralized free-negotiation markets with centralized mechanism-based markets including several representative mechanisms. Across controlled one-to-one matching environments, mechanism-based markets generally outperform free negotiation in terms of stability and efficiency.
A Unified Framework for Uniform-Price Resource Allocation Mechanisms
arXiv:2606.06151v1 Announce Type: new Abstract: Mechanisms for allocating a divisible resource among strategic agents have been widely studied. The prominent paradigm is the proportional (Kelly) mechanism, which elicits a scalar bid per agent, allocates the resource proportionally, and charges payments equal to the bids. Follow-up mechanisms improve social welfare, but sacrifice simplicity by introducing complex allocation rules or unintuitive payments.
Tuning Cross-stream Lift in Viscoelastic Shear: Distinct Hydrodynamic Signatures of Force-bearing and Force-free Mechanisms
arXiv:2604.07906v2 Announce Type: replace Abstract: We investigate the lift and drag corrections acting on a particle suspended in a planar viscoelastic shear flow when the particle is tuned to translate relative to the flow by an external mechanism. A cross-stream lift force arises when particle is driven in streamwise direction; we find that the nature of the driving mechanism dictates the lift direction: force-bearing mechanisms (such as gravity acting on non-neutrally buoyant particles)...
EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learning
arXiv:2606.05942v1 Announce Type: cross Abstract: Neural network (NN)-based nonlinear causal discovery methods recover DAG structure but leave each causal mechanism as a black box. Waxman et al. argued that extracting causal mechanisms from NN weights is ill-posed. We propose EML-CD, a framework that integrates the EML operator (capable of composing elementary functions from a single binary operator) into causal structure learning, with interpretable mechanism recovery as the primary objective.
A Voxel-Based Quantum Computing Method (VBQC) for Solid Mechanics Problem
new Abstract: Quantum computing presents a promising method to overcome the efficiency and memory constraints in large-scale mechanical problems, with numerous successful applications demonstrated in fluid mechanics. However, solid mechanics problems usually require irregular grids for spatial discretization, due to the Lagrange formulations and complex boundaries, which makes the quantum simulation of the system matrix, e.g., the mass or stiffness matrix which is often referred to as the...
In vivo measurements of fascia lata effective mechanics combined to a memory fiber recruitment viscoelastic modeling approach
arXiv:2606.03353v1 Announce Type: cross Abstract: The fascia lata plays a central role in force transmission and body mechanics, yet its in vivo mechanical behavior remains poorly characterized. Existing approaches -- shear wave elastography and direct force measurements alike -- share a fundamental limitation: none simultaneously captures both the elastic and viscous components of fascial mechanics within a single experiment. The primary aim of this study is therefore to develop an...