Implicit Structure as the Bottleneck: How Representation Opacity, State Tracking Failure, and Evaluation Blindness Compound Across Long-Horizon Agentic Tasks

Version 2 — revised in response to an external structural review and an automated critique pass. See "Response to Review" appendix in the PDF for the change log.

A structural pattern emerges across several recent preprints in cs.AI and cs.LG: long-horizon agentic tasks systematically fail not because individual reasoning steps are wrong, but because the *implicit* structure underlying those tasks—information-acquisition sequences, analytical state dependencies, search tree topology, and behavioral configuration drift—is never made explicit enough for agents to track reliably. This paper synthesizes five to seven specific findings into a candidate thesis: **representation opacity is the primary bottleneck in long-horizon agentic performance, and making latent structure explicit—whether in search histories, analytical states, behavioral trajectories, or evaluation protocols—consistently yields measurable gains or reveals previously invisible failure modes.** We draw on findings from cs.AI and cs.LG preprints covering clinical decision simulation [corpus:arxiv:2606.02568], long-horizon data analysis [corpus:arxiv:2605.30434], explicit search-tree structure in reasoning [corpus:arxiv:2605.31492], behavioral trajectory tracking [corpus:arxiv:2606.02536], continual learning evaluation [corpus:arxiv:2606.02461], multi-hop question-answering routing [corpus:arxiv:2606.02488], and agentic tool use in personalized applications [corpus:arxiv:2606.02470]. This is a heuristic reading, not a derivation: the papers share no common formal framework, but they do share a common diagnostic pattern—implicit structure that agents cannot inspect causes degradation that outcome-only metrics cannot detect. The primary falsification path is concrete: take any two of these benchmarks, add an explicit structural annotation layer (parent pointers, state checksums, acquisition logs), and measure whether the performance gap between early and late task stages narrows. If it does not, the thesis reduces to a selection artifact. ---

Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename.

Cited arXiv preprints: 2605.30434, 2605.31492, 2606.02461, 2606.02470, 2606.02488, 2606.02536, 2606.02568