Long-term memory systems often organize experience before future queries are known, but whether this improves evidence access under a fixed read budget remains unclear. We evaluate inexpensive query-agnostic segmentation and hierarchical retrieval on AMA-Bench agent trajectories and BEAM conversations spanning 128K to 10M tokens. Experiments use exact serialized-context budgets, development-only tuning, two sentence encoders, and uncertainty estimates clustered by trajectory or conversation. In an exploratory post-plan analysis of 1,117 step-anchored trajectory questions, fixed three-turn windows outperform all tested alternative boundary methods. On BEAM, primary-encoder flat retrieval recall falls from 0.564 at 128K to 0.331 at 10M. Single-vector hierarchical routing further reduces recall; multiple local vectors recover part of this loss but do not surpass flat retrieval. Doubling the read budget from 8,000 to 16,000 characters improves evidence recall at every scale with both encoders. However, the 10M evaluation, comprising 130 questions from eight conversations, detects no corresponding QA improvement. Single-vector routing reduces QA accuracy, while multi-vector routing is not distinguishable from flat retrieval. These findings identify coarse representation quality as a bottleneck in the tested hierarchies and show that improved evidence access need not yield better answers.

Text Segmentation (TS) is the idea of segmenting bodies of text into coherent blocks, mostly defined by the topics each segment contains. Historically, techniques in this area have been unsupervised, with more success recently coming from supervised methods instead. Although these approaches see better performance, they require training data and upfront training time. We propose a new method called Coherence, where we use strong sentence embeddings to pull representational keywords as the main constructor of sentences when comparing them to one another. Additionally, we include a storage of previously found keywords for the purposes of creating a more accurate segment representation instead of just the immediate sentence in question. With our system, we show improved results over current state-of-the-art unsupervised techniques when analyzed using Pk and WindowDiff scores. Because its unsupervised, Coherence requires no fine-tuning.