Amrish Baskaran

Amrish Baskaran, Nirbhay Pherwani, Raghul Krishnan

Recent standards such as RSL address AI content policy declaration -- telling AI systems what the licensing terms are. However, no existing system provides audit infrastructure -- tamper-evident licensing transaction records with independently verifiable proofs that those records have not been retroactively modified. We describe Aegon, a protocol that extends standard JWT tokens with content-specific licensing claims and maintains a Certificate Transparency-style Merkle tree over an append-only transaction ledger, enabling third-party auditors to independently verify that specific content licensing transactions were recorded and have not been retroactively modified. Publishers validate tokens at the edge using standard JWKS with no broker dependency in the content delivery path. A signed provenance event log tracks content through AI transformation stages (chunking, embedding, retrieval, citation), bound to ledger entries by transaction ID. We further describe hardware-attested compliance receipts for on-device Android AI agents using StrongBox secure element attestation -- to our knowledge, the first application of hardware-attested compliance receipts to AI content licensing. Existing DRM systems use hardware-backed keys for content decryption but do not produce verifiable compliance receipts for audit trails. We describe a reference architecture and an evaluation methodology for measuring protocol overhead. The protocol runs entirely over standard HTTPS and is designed to complement existing licensing standards rather than replace them.

Jingxi Chen, Amrish Baskaran, Zhongshun Zhang et al.

The Visibility-based Persistent Monitoring (VPM) problem seeks to find a set of trajectories (or controllers) for robots to persistently monitor a changing environment. Each robot has a sensor, such as a camera, with a limited field-of-view that is obstructed by obstacles in the environment. The robots may need to coordinate with each other to ensure no point in the environment is left unmonitored for long periods of time. We model the problem such that there is a penalty that accrues every time step if a point is left unmonitored. However, the dynamics of the penalty are unknown to us. We present a Multi-Agent Reinforcement Learning (MARL) algorithm for the VPM problem. Specifically, we present a Multi-Agent Graph Attention Proximal Policy Optimization (MA-G-PPO) algorithm that takes as input the local observations of all agents combined with a low resolution global map to learn a policy for each agent. The graph attention allows agents to share their information with others leading to an effective joint policy. Our main focus is to understand how effective MARL is for the VPM problem. We investigate five research questions with this broader goal. We find that MA-G-PPO is able to learn a better policy than the non-RL baseline in most cases, the effectiveness depends on agents sharing information with each other, and the policy learnt shows emergent behavior for the agents.