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Raincheck: Deferred Multimodal Prefill for Efficient Multi-Turn MLLM Inference

Paper recorded by Signals 4 on 2026-10-29 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.CL · 自然语言处理 · first seen 2026-10-30

Abstract

Multi-turn conversations with multimodal large language models (MLLMs) reuse input context, but long multimodal inputs increase time to first token (TTFT). Query-aware hard pruning reduces prefill tokens but permanently excludes information that later questions may require. We propose Raincheck, a framework for deferred multimodal prefill that decouples initial token selection from later informati

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#81 most recent of 628 cs.CL papers we have recorded · ↑ newer: Red-Teaming Large Language Models in Low-Resource African Languages: A · ↓ older: Lost in the Latent Space: How Code Embeddings Represent Functional Int
Cite this page: Raincheck: Deferred Multimodal Prefill for Efficient Multi-Turn MLLM Inference: the #81 most recent of 628 cs.CL papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/raincheck-deferred-multimodal-prefill-for-efficient-multi-turn-mllm-inference.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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