SmoothAgent: Efficient Long-Horizon LLM-Based Agent Serving with Lookahead Context Engineering

2026-07-02T08:52:18Zf0d8a9b596ea7af0e1ef28235378fc391de1a43d063a91393fdcafa44c5d3ab8
AReaL2.0agentic-rlasync-runtimescloud-autoscalingcloudyguicontext-engineeringdiscrete-diffusioneldrentropy-regularizationfederated-learninggpu-energy-efficiencykv-cache-compressionllm-servinglookahead-schedulingmicroservice-availabilitymoe-routingmosaickvmpi-one-sidedparallel-samplingpromise-futureself-evolving-agentssparse-modelsstochastic-connectivity

What happened

This document is a batch of recent arXiv CS papers (July 2, 2026) covering systems and ML infrastructure advances. Key contributions include: SmoothAgent — a lookahead context-engineering model and runtime that precomputes segment-decomposable context transformations to eliminate transformation overhead and reduce time-to-first-token (TTFT) up to 11.9×; MosaicKV — dynamic two-dimensional KV-cache compression for very long-context LLM serving delivering up to 16× attention speedup, ~3× memory reduction and only ~1.8% accuracy loss; ELDR — expert-locality-aware decode routing for PD-disaggregatd

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_cs_dc
Record identifier
f0d8a9b596ea7af0e1ef28235378fc391de1a43d063a91393fdcafa44c5d3ab8
Enrichment time
2026-07-02T08:52:18Z
AI-assisted enrichment
Yes

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