Skip to content

Author

Rajiv Ramnath

The Ohio State University

We have 4 of 226 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Preprint Aug 2026

When Does Latent Communication Pay? A Causal Audit of Relayed KV Caches in Multi-Agent LLMs

Multi-agent LLM systems relay key-value caches instead of text and credit their gains to exchanged"latent thoughts". That credit is a claim about which example's cache is relayed, not merely that one is. We audit it causally in released systems. The cache is replaced with deranged (mismatched-example), zeroed, and moment-matched random counterparts, under two regimes defined by whether the receiver needs the sender's private information. Where it does, the battery reads ceiling: 100% against 23-25% for answer-irrelevant relays on the primary backbone, a contrast replicated across three families, five checkpoints, and a prose document-QA surface. Where it does not, a pre-registered five-seed protocol establishes equivalence within 2.8 points, a margin anchored to the audited system's reported gain, under Holm-corrected TOST on GSM8K and ARC-Challenge across three Qwen3 scales and on MedQA at 8B (one cell shows a small detected advantage inside the margin); a second family shows no detected advantage. A large cache effect need not be a pairing effect. In one natural cell, zeroing the relay costs 14.7 points; a mismatched cache, 0.4. Nor is need sufficient: under the same test, delivered channels span ceiling (LatentMAS's native relay), partial (KVComm's layer subset), and no detected example-specific transfer (C2C's released projector). Benchmark deltas do not by themselves establish latent-thought transmission; establishing it takes a mismatched-cache audit, which we release.

Jiaming Cheng, Subhransu Das, Rajiv Ramnath · 3 citations
Book Open access Jul 2026

Phase-Wise Analysis of LLM Inference Acceleration on GPU, CPU, and Edge Device

This study presents a cross-platform, multi-model empirical study, where several important observations are brought, including the contrastive effect of quantization under different hardware bottlenecks, along with a quantification of runtime delays caused by the lack of parallelism in the ARM architecture.

Subhransu Das, Jiaming Cheng, Swathi Vallabhajosyula et al. · 0 citations
Book Open access Jul 2026

From HPC to Edge: A Web-Based Workflow for AI Model Testing and Deployment

An integrated management framework powered by Tapis is shown how a unified UI-driven workflow streamlines the transition from initial evaluation to deployment, ensuring operational consistency and reproducibility without manual script porting.

Manikya Swathi Vallabhajosyula, Gautam Gururaj Molakalmuru, Samuel Khuvis et al. · 0 citations
Review Aug 2026

Large Models for Small Devices: Recent Advances and Empirical Analysis of Edge AI Deployment

This work surveys dozens of recent works that report compression results on real hardware and extracts practical deployment guidelines from them, and deploys compact language and image models on GPU, CPU, and Raspberry Pi platforms across question answering and image segmentation.

Subhransu Das, Jiaming Cheng, Arnav Kumar et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.