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cs.CL2025
Mirror Speculative Decoding: Breaking the Serial Barrier in LLM Inference
Nikhil Bhendawade, Kumari Nishu, Arnav Kundu +3
Speculative decoding accelerates LLM inference by using a draft model to look ahead, but gains are capped by the cost of autoregressive draft generation: increasing draft size elev…
cs.CL2025
EpiCache: Episodic KV Cache Management for Long-Term Conversation on Resource-Constrained Environments
Minsoo Kim, Arnav Kundu, Han-Byul Kim +2
Modern large language models (LLMs) extend context lengths to millions of tokens, enabling coherent, personalized responses grounded in long conversational history. However, the Ke…
cs.CL2025
Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential
Mohammad Samragh, Arnav Kundu, David Harrison +4
Autoregressive language models are constrained by their inherently sequential nature, generating one token at a time. This paradigm limits inference speed and parallelism, especial…