publications

2026

1. Preprint

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

   Subhransu Das, Jiaming Cheng, Arnav Kumar, and 6 more authors

   2026

   Preprint

   arXiv Bib

   @misc{das2026edgeai,
     title = {Large Models for Small Devices: Recent Advances and Empirical Analysis of Edge {AI} Deployment},
     author = {Das, Subhransu and Cheng, Jiaming and Kumar, Arnav and Afrose, Sadia and Han, Mingzhe and Silagy, Michael and Palande, Shreya and Soni, Brijesh and Ramnath, Rajiv},
     year = {2026},
     archiveprefix = {arXiv},
     primaryclass = {cs.AI},
     note = {Preprint}
   }

2. Preprint

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

   Jiaming Cheng, Subhransu Das, and Rajiv Ramnath

   2026

   Preprint

   Abs arXiv Bib

   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, replacing the cache 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, 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 under Holm-corrected TOST. Benchmark deltas do not by themselves establish latent-thought transmission; establishing it takes a mismatched-cache audit, which we release.

   @misc{cheng2026latentaudit,
     title = {When Does Latent Communication Pay? A Causal Audit of Relayed {KV} Caches in Multi-Agent {LLMs}},
     author = {Cheng, Jiaming and Das, Subhransu and Ramnath, Rajiv},
     year = {2026},
     archiveprefix = {arXiv},
     primaryclass = {cs.CR},
     note = {Preprint},
   }

3. PEARC

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

   Subhransu Das, Jiaming Cheng, Swathi Vallabhajosyula, and 2 more authors

   In Practice and Experience in Advanced Research Computing (PEARC ’26), 2026

   To appear

   Bib

   @inproceedings{das2026phasewise,
     title = {Phase-Wise Analysis of {LLM} Inference Acceleration on {GPU}, {CPU}, and Edge Device},
     author = {Das, Subhransu and Cheng, Jiaming and Vallabhajosyula, Swathi and Soni, Brijesh and Ramnath, Rajiv},
     booktitle = {Practice and Experience in Advanced Research Computing (PEARC '26)},
     year = {2026},
     note = {To appear},
   }

4. CCNC

   SPICE: Structured Pruning for Inference on Constrained Edge Devices

   Subhransu Das, Jiaming Cheng, Aniruddha Rakshit, and 3 more authors

   In IEEE Consumer Communications & Networking Conference (CCNC), 2026

   Bib

   @inproceedings{das2026spice,
     title = {{SPICE}: Structured Pruning for Inference on Constrained Edge Devices},
     author = {Das, Subhransu and Cheng, Jiaming and Rakshit, Aniruddha and Soni, Brijesh and Boubin, Jayson and Ramnath, Rajiv},
     booktitle = {IEEE Consumer Communications \& Networking Conference (CCNC)},
     year = {2026},
   }

2025

1. PEARC

   EPIC: Efficient Pruning for Inference on Constrained Devices

   Subhransu Das, Jiaming Cheng, Aniruddha Rakshit, and 2 more authors

   In Practice and Experience in Advanced Research Computing (PEARC ’25), 2025

   Bib

   @inproceedings{das2025epic,
     title = {{EPIC}: Efficient Pruning for Inference on Constrained Devices},
     author = {Das, Subhransu and Cheng, Jiaming and Rakshit, Aniruddha and Boubin, Jayson and Ramnath, Rajiv},
     booktitle = {Practice and Experience in Advanced Research Computing (PEARC '25)},
     year = {2025},
   }