Teng Mei, Chengxuan Pei, Weijun Wang, Jie Wu, Marco Canini, Yunming Xiao
Large language models (LLMs) increasingly run on edge devices alongside tasks that continuously process sensor data. In many applications, these foreground tasks prepare observations as inputs to the LLM, whose outputs guide subsequent processing or actions. The observations must be processed by foreground deadlines, and LLM feedback must arrive while its source observations remain useful. Yet the CPU and accelerators share a unified-memory system, so LLM inference can delay foreground processing through memory-bandwidth contention even when compute resources remain available. We present EdgePace, a bandwidth-aware runtime that protects foreground deadlines while sustaining timely LLM feedback. EdgePace creates safe suspension points across prefill and decode, monitors foreground memory demand and stalls at low cost, and schedules inference according to contention risk, deadline slack, and output freshness. We evaluate EdgePace on a OnePlus 13 and three NVIDIA Jetson platforms using multi-camera processing and robotic target tracking. Relative to unmanaged inference, it reduces foreground deadline misses by 24.1%--96.1% while retaining 64.9%--98.5% of LLM throughput. All measured LLM outputs arrive within their task-defined freshness windows. On OnePlus 13, for example, camera deadline misses fall by 91.3% while 90.4% of LLM throughput is retained.
Web retrieval-augmented generation (RAG) splits a page into fixed-size text chunks or, in recent visual pipelines, screenshot tiles, and the retrieved item serves at once as the unit of indexing, ranking, and reading. We trace answer evidence from source HTML to reader input over the full English Wikipedia and find that this coupling loses evidence in text and screenshot pipelines alike: evidence present in the source page often fails to reach the reader intact, particularly structured content such as tables and infoboxes, which chunking separates from the headers and context that give it meaning. Stronger extraction, context expansion, and larger reader budgets leave the loss in place. We present Tessera, a training-free evidence delivery layer that decouples what a pipeline retrieves from what its reader reads. Given the pages behind any ranked list, Tessera rebuilds their HTML into fine-grained evidence units, each completed with its page title, section path, and table headers, selects the units relevant to the query, and packs them into the reader context at an unchanged token budget. The index, the retriever, and the reader stay as they are, so Tessera applies to text, screenshot, and agentic pipelines. Across three question answering benchmarks, Tessera improves answer accuracy by 5 to 6 points on text and screenshot pipelines, consistently across readers, retrievers, and reader budgets, and outperforms strong HTML-aware and structure-aware RAG baselines under matched budgets. The gains carry over to multi-step agentic search. Our code is publicly available at https://github.com/sarendis56/Tessera.