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Rijul Jain

Publications and source records attributed to Rijul Jain.

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Centrality-Based Deployment of Queue Policies in Acyclic Multipath Routing Networks

Excessive queueing delays constitute a significant impediment to latency-sensitive network applications. Although effective deployment of Active Queue Management (AQM) strategies has been proposed as a necessary solution, deployment remains sparse. This paper studies AQM deployment in a specific class of networks where routers/switches have a topological hierarchy, form acyclic paths, and adopt multipath routing. Our approach rests on the well-established premise that AQM deployment impacts packet-forwarding dynamics in networks carrying TCP flows, thus establishing a direct link between stability and network performance. We use fluid models for TCP and queue dynamics in the network, along with a simple threshold-based queue policy to outline a closed-loop model for the network. Stability analyses reveal that while the network is vulnerable to instability as the average round-trip time (RTT) of the TCP flows increases, it tolerates a much larger RTT without losing stability when the threshold-based AQM is deployed in an appropriate router. We then define a Katz centrality-based metric to choose the most appropriate router for AQM deployment, and argue that doing so ensures the greatest stabilising effect. Finally, packet-level simulations corroborate that the proposed deployment strategy ensures low-latency operation of the network.

cs.NI

What's in a Proof? Analyzing Expert Proof-Writing Processes in F* and Verus

Proof-oriented programming languages (POPLs) empower developers to write code alongside formal correctness proofs, providing formal guarantees that the code adheres to specified requirements. Despite their powerful capabilities, POPLs present a steep learning curve and have not yet been adopted by the broader software community. The lack of understanding about the proof-development process and how expert proof developers interact with POPLs has hindered the advancement of effective proof engineering and the development of proof-synthesis models/tools. In this work, we conduct a user study, involving the collection and analysis of fine-grained source code telemetry from eight experts working with two languages, F* and Verus. Results reveal interesting trends and patterns about how experts reason about proofs and key challenges encountered during the proof development process. We identify three distinct strategies and multiple informal practices that are not captured final code snapshots, yet are predictive of task outcomes. We translate these findings into concrete design guidance for AI proof assistants: bias toward early specification drafting, explicit sub-goal decomposition, bounded active errors, and disciplined verifier interaction. We also present a case study of an F* proof agent grounded in these recommendations, and demonstrate improved performance over baseline LLMs

cs.SE