arXiv subjects

Aayush Agarwal

Publications and source records attributed to Aayush Agarwal.

3 recordsLinked to original sources

arXiv2026

Many-body activity emerging in a monolayer of air-fluidized hollow pentagons

Particles governed by many-body interactions exhibit remarkably complex structures and dynamics. We experimentally investigate a monolayer of pentagon particles subjected to an up-lifting air flow which induces many-body aerodynamic interactions and stochastic motion akin to a thermal bath. To minimize air flow resistance, particles move collectively with interactions dictated by their geometry: hollow particles exhibit effective attraction, whereas solid particles repel each other. Under sufficiently large air flow, sparsely packed hollow pentagons overcome substrate friction and undergo long-time diffusive motion. Under lower air flow, we see a coexistence of isolated, static pentagons and densely packed, "active" clusters, whose particles display super-diffusivity. This "emergent activity" arises collectively when locally disordered structures interact with the air flow, resulting in correlated motion across broad temporal and spatial scales. Using Langevin dynamics simulations of two-dimensional attractive active pentagons, whose activity is an effective result of the local packing density, we further unravel the basic features of this emergent activity.

cond-mat.soft↗

arXiv2025

Private Map-Secure Reduce: Infrastructure for Efficient AI Data Markets

The modern AI data economy centralizes power, limits innovation, and misallocates value by extracting data without control, privacy, or fair compensation. We introduce Private Map-Secure Reduce (PMSR), a network-native paradigm that transforms data economics from extractive to participatory through cryptographically enforced markets. Extending MapReduce to decentralized settings, PMSR enables computation to move to the data, ensuring verifiable privacy, efficient price discovery, and incentive alignment. Demonstrations include large-scale recommender audits, privacy-preserving LLM ensembling (87.5\% MMLU accuracy across six models), and distributed analytics over hundreds of nodes. PMSR establishes a scalable, equitable, and privacy-guaranteed foundation for the next generation of AI data markets.

cs.CR↗

arXiv2023

Learning high-dimensional causal effect

The scarcity of high-dimensional causal inference datasets restricts the exploration of complex deep models. In this work, we propose a method to generate a synthetic causal dataset that is high-dimensional. The synthetic data simulates a causal effect using the MNIST dataset with Bernoulli treatment values. This provides an opportunity to study varieties of models for causal effect estimation. We experiment on this dataset using Dragonnet architecture (Shi et al. (2019)) and modified architectures. We use the modified architectures to explore different types of initial Neural Network layers and observe that the modified architectures perform better in estimations. We observe that residual and transformer models estimate treatment effect very closely without the need for targeted regularization, introduced by Shi et al. (2019).

cs.LG↗