5 papers
EnvDreamer: Large-Scale Multimodal-to-Environment Generation for Embodied AI
Kabir Swain, Sijie Han, Antonio Torralba
Large datasets and high capacity models have accelerated progress in vision and language. This work introduces a platform aimed at bringing comparable gains to embodied learning, w…
Tensor Memory: Fixed-Size Recurrent State for Long-Horizon Transformers
Kabir Swain, Sijie Han, Daniel Karl I. Weidele +2
Transformers process images and videos by flattening space and time into long token sequences. While attention and KV caching preserve past features, their memory grows with sequen…
Hurwitz Quaternion Multiplicative Quantization for KV Cache Compression
Kabir Swain, Sijie Han, Daniel Karl I. Weidele +3
We propose \textbf{Hurwitz Quaternion Multiplicative Quantization (HQMQ)}, a \textbf{calibration-free} method for KV cache compression of large language models. HQMQ treats each 4-…
Tensor Cache: Eviction-conditioned Associative Memory for Transformers
Kabir Swain, Sijie Han, Daniel Karl I. Weidele +2
Autoregressive Transformer KV caches grow linearly with context length; sliding-window caching bounds memory but discards evicted tokens entirely, so relevant evidence outside the…
VirtualEnv: A Platform for Embodied AI Research
Kabir Swain, Sijie Han, Ayush Raina +4
As large language models (LLMs) continue to improve in reasoning and decision-making, there is a growing need for realistic and interactive environments where their abilities can b…