Papers by Pratik Prabhanjan Brahma
AdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing routing strategies rely on static heuristics or external controllers to optimize performance. |
| Approach: | They propose a framework that leverages intrinsic generation confidence to estimate solvability. |
| Outcome: | Empirical results show that confidence-driven selection yields favorable Pareto frontier . computational cost of state-of-the-art large language models remains a key barrier to scalable deployment . |
TaDA: Training-free recipe for Decoding with Adaptive KV Cache Compression and Mean-centering (2025.acl-industry)
Copied to clipboard
| Challenge: | key-value caches in large language models consume memory, posing a major challenge for scalable deployment. |
| Approach: | They propose a training-free recipe for KV cache compression with quantization precision that adapts to error sensitivity across layers and a mean centering to eliminate separate outlier handling. |
| Outcome: | The proposed technique reduces the KV cache memory footprint to 27% of the original 16-bit baseline while achieving comparable accuracy. |