Haystack Engineering: Context Engineering for Heterogeneous and Agentic
  Long-Context Evaluation
Authors: name: Mufei Li, name: Dongqi Fu, name: Limei Wang, name: Si Zhang, name: Hanqing Zeng, name: Kaan Sancak, name: Ruizhong Qiu, name: Haoyu Wang, name: Xiaoxin He, name: Xavier Bresson, name: Yinglong Xia, name: Chonglin Sun, name: Pan Li
Organization: Meta Research

Modern long-context large language models (LLMs) perform well on synthetic
"needle-in-a-haystack" (NIAH) benchmarks, but such tests overlook how noisy
contexts arise from biased retrieval and agentic workflows. We argue that
haystack engineering is necessary to construct noisy long contexts that
faithfully capture key real-world factors -- distraction from heterogeneous
biased retrievers and cascading errors in agentic workflows -- to test models'
long-context robustness. We instantiate it through HaystackCraft, a new NIAH
benchmark built on the full English Wikipedia hyperlink network with multi-hop
questions. HaystackCraft evaluates how heterogeneous retrieval strategies
(e.g., sparse, dense, hybrid, and graph-based) affect distractor composition,
haystack ordering, and downstream LLM performance. HaystackCraft further
extends NIAH to dynamic, LLM-dependent settings that simulate agentic
operations, where models refine queries, reflect on their past reasonings, and
decide when to stop. Experiments with 15 long-context models show that (1)
while stronger dense retrievers can introduce more challenging distractors,
graph-based reranking simultaneously improves retrieval effectiveness and
mitigates more harmful distractors; (2) in agentic tests, even advanced models
like Gemini 2.5 Pro and GPT-5 suffer cascading failures from self-generated
distractors or struggle to perform early stops. These results highlight
persistent challenges in agentic long-context reasoning and establish
HaystackCraft as a valuable testbed for future progress.