arXiv subjects

Jakov Mitrovski

Publications and source records attributed to Jakov Mitrovski.

3 recordsLinked to original sources

arXiv2026

Bitcoin-IPC: Scaling Bitcoin with a Network of Proof-of-Stake Subnets

Despite holding over $1.7T in value, Bitcoin scales poorly: Layer-1 (L1) processes only a few transactions per second, and Layer-2 (L2) solutions suffer from operational and liquidity fragmentation that requires external bridges for cross-chain connectivity. We present Bitcoin-IPC, a protocol that horizontally scales Bitcoin via permissionless, interconnected, programmable Proof-of-Stake L2 subnets staked in L1 BTC. Subnets leverage Bitcoin L1 for interconnectivity, settlement, and security, including Sybil protection, equivocating validator slashing, unilateral exit for honest stake, long-range attack protection, and compromised subnet firewalling. Bitcoin-IPC cross-subnet transfers involve only source/destination subnet validators and L1, with no external bridges or per-path liquidity lock-up. Embedded and batched in Bitcoin's witness mechanism, cross-subnet settlements can approach 6 vB per transfer, 23x less than a native L1 payment. Filling Bitcoin blocks with such batches corresponds to L1 monetary throughput of about 273 transfers per second, without modifying Bitcoin.

cs.DC↗

arXiv2026

Pod: An Optimal-Latency, Censorship-Free, and Accountable Generalized Consensus Layer

This work addresses the inherent issues of high latency in blockchains and low scalability in traditional consensus protocols. We present pod, a novel notion of consensus whose first priority is to achieve the physically-optimal latency of $2δ$, or one round-trip, i.e., requiring only one network trip (duration $δ$) for writing a transaction and one for reading it. To accomplish this, we first eliminate inter-replica communication. Instead, clients send transactions directly to all replicas, which independently process transactions and append them to local logs. Replicas assigns a timestamp and a sequence number to each transaction in their logs, allowing clients to extract valuable metadata about the transactions and the system state. Later on, clients retrieve these logs and extract transactions (and associated metadata) from them. Necessarily, this construction achieves weaker properties than a total-order broadcast protocol, due to existing lower bounds. Our work models the primitive of pod and defines its security properties. We then show pod-core, a protocol that satisfies properties such as transaction confirmation within $2δ$, censorship resistance against Byzantine replicas, and accountability for safety violations. We show that single-shot auctions can be realized using the pod notion and observe that it is also sufficient for other popular applications.

cs.DC↗

arXiv2021

Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World

The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP). The VRSP is concerned with scheduling trips in traffic networks and the re-scheduling of vehicles when disruptions occur, for example the breakdown of a vehicle. While solving the VRSP in various settings has been an active area in operations research (OR) for decades, the ever-growing complexity of modern railway networks makes dynamic real-time scheduling of traffic virtually impossible. Recently, multi-agent reinforcement learning (MARL) has successfully tackled challenging tasks where many agents need to be coordinated, such as multiplayer video games. However, the coordination of hundreds of agents in a real-life setting like a railway network remains challenging and the Flatland environment used for the competition models these real-world properties in a simplified manner. Submissions had to bring as many trains (agents) to their target stations in as little time as possible. While the best submissions were in the OR category, participants found many promising MARL approaches. Using both centralized and decentralized learning based approaches, top submissions used graph representations of the environment to construct tree-based observations. Further, different coordination mechanisms were implemented, such as communication and prioritization between agents. This paper presents the competition setup, four outstanding solutions to the competition, and a cross-comparison between them.

cs.AI↗