3 papers
cs.LG2025
TNT: Improving Chunkwise Training for Test-Time Memorization
Zeman Li, Ali Behrouz, Yuan Deng +5
Recurrent neural networks (RNNs) with deep test-time memorization modules, such as Titans and TTT, represent a promising, linearly-scaling paradigm distinct from Transformers. Whil…
cs.CL2025
ATLAS: Learning to Optimally Memorize the Context at Test Time
Ali Behrouz, Zeman Li, Praneeth Kacham +5
Transformers have been established as the most popular backbones in sequence modeling, mainly due to their effectiveness in in-context retrieval tasks and the ability to learn at s…
cs.LG2025
PolarQuant: Quantizing KV Caches with Polar Transformation
Insu Han, Praneeth Kacham, Amin Karbasi +2
Large language models (LLMs) require significant memory to store Key-Value (KV) embeddings in their KV cache, especially when handling long-range contexts. Quantization of these KV…