Researcher profile

Srivatsan Ravi

Srivatsan Ravi contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate

Trust snapshot

Quick read

Trust 17 - UnverifiedVerification L1Unclaimed author
4works
0followers
6topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

4 published item(s)

preprint2026arXiv

Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On

The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution. As these agents transition from isolated operation to collaborative ecosystems, we witness the emergence of the Agent-to-Agent (A2A) network, a paradigm where heterogeneous agents autonomously coordinate to solve multi-step tasks. While these networks may offer better task performance compared to simply using one agent to complete the entire task, they introduce systemic vulnerabilities, such as adversarial composition, semantic misalignment, and cascading operational failures, that existing agent alignment techniques cannot address. In this vision paper, we argue that the trustworthiness of A2A networks cannot be fully guaranteed via retrofitting on existing protocols that are largely designed for individual agents. Rather, it must be architected from the very beginning of the A2A coordination framework. We present a comprehensive conceptual framework that situates trust in A2A systems through four design pillars.

preprint2023arXiv

Data-Driven Template-Free Invariant Generation

Automatic verification of concurrent programs faces state explosion due to the exponential possible interleavings of its sequential components coupled with large or infinite state spaces. An alternative is deductive verification, where given a candidate invariant, we establish inductive invariance and show that any state satisfying the invariant is also safe. However, learning (inductive) program invariants is difficult. To this end, we propose a data-driven procedure to synthesize program invariants, where it is assumed that the program invariant is an expression that characterizes a (hopefully tight) over-approximation of the reachable program states. The main ideas of our approach are: (1) We treat a candidate invariant as a classifier separating states observed in (sampled) program traces from those speculated to be unreachable. (2) We develop an enumerative, template-free approach to learn such classifiers from positive and negative examples. At its core, our enumerative approach employs decision trees to generate expressions that do not over-fit to the observed states (and thus generalize). (3) We employ a runtime framework to monitor program executions that may refute the candidate invariant; every refutation triggers a revision of the candidate invariant. Our runtime framework can be viewed as an instance of statistical model checking, which gives us probabilistic guarantees on the candidate invariant. We also show that such in some cases, our counterexample-guided inductive synthesis approach converges (in probability) to an overapproximation of the reachable set of states. Our experimental results show that our framework excels in learning useful invariants using only a fraction of the set of reachable states for a wide variety of concurrent programs.

preprint2022arXiv

Evaluating the Feasibility of a Provably Secure Privacy-Preserving Entity Resolution Adaptation of PPJoin using Homomorphic Encryption

Entity resolution is the task of disambiguating records that refer to the same entity in the real world. In this work, we explore adapting one of the most efficient and accurate Jaccard-based entity resolution algorithms - PPJoin, to the private domain via homomorphic encryption. Towards this, we present our precise adaptation of PPJoin (HE-PPJoin) that details certain subtle data structure modifications and algorithmic additions needed for correctness and privacy. We implement HE-PPJoin by extending the PALISADE homomorphic encryption library and evaluate over it for accuracy and incurred overhead. Furthermore, we directly compare HE-PPJoin against P4Join, an existing privacy-preserving variant of PPJoin which uses fingerprinting for raw content obfuscation, by demonstrating a rigorous analysis of the efficiency, accuracy, and privacy properties achieved by our adaptation as well as a characterization of those same attributes in P4Join.

preprint2021arXiv

A Concurrency-Optimal List-Based Set

Designing an efficient concurrent data structure is an important challenge that is not easy to meet. Intuitively, efficiency of an implementation is defined, in the first place, by its ability to process applied operations in parallel, without using unnecessary synchronization. As we show in this paper, even for a data structure as simple as a linked list used to implement the set type, the most efficient algorithms known so far are not concurrency-optimal: they may reject correct concurrent schedules. We propose a new algorithm for the list-based set based on a value-aware try-lock that we show to achieve optimal concurrency: it only rejects concurrent schedules that violate correctness of the implemented set type. We show empirically that reaching optimality does not induce a significant overhead. In fact, our implementation of the concurrency-optimal algorithm outperforms both the Lazy Linked List and the Harris-Michael state-of-the-art algorithms.