3 citations · 3 across the 4 of their papers we have counts for
4 papers
Heterogeneous LLM Debate Under Adversarial Peers: Honest Gains, Replacement Costs, and Resilience
Prashanti Nilayam, Kiran Kumar Ramanna, Prashil Tumbade +1
Heterogeneous LLM debate is motivated by the promise that diverse peers correct one another, but the same exchange that carries correction also carries adversarial influence. We me…
Detection Without Correction: A Two-Parameter Decomposition of Multi-Stage LLM Pipelines
Prashanti Nilayam, Kiran Ramanna, Prashil Tumbade
Multi-stage LLM pipelines that perform multi-agent debate, intrinsic self-correction, or retrieval-augmented verification exhibit puzzling aggregate behaviors: accuracy plateaus an…
QUIVER: A Formal Framework for Quantifying Perturbation Propagation and Bifurcation in Compound AI Systems
Prashanti Nilayam, Sankalp Nayak
Compound AI systems that chain multiple LLM calls into directed computation graphs are now the dominant architecture for production AI. Although these architectures leverage hetero…
FedSS: Federated Learning with Smart Selection of clients
Ammar Tahir, Yongzhou Chen, Prashanti Nilayam
Federated learning provides the ability to learn over heterogeneous user data in a distributed manner while preserving user privacy. However, its current client selection technique…