arXiv — NLP / Computation & Language · · 4 min read

Explicit, Not Longer: What Makes Epistemic Stance Survive Memory Compression

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Computer Science > Computation and Language

arXiv:2608.06953 (cs)
[Submitted on 7 Aug 2026]

Title:Explicit, Not Longer: What Makes Epistemic Stance Survive Memory Compression

Authors:Alex Kwon
View a PDF of the paper titled Explicit, Not Longer: What Makes Epistemic Stance Survive Memory Compression, by Alex Kwon
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Abstract:Agent memory systems compress what they store, and compression is built to drop qualifiers, so a claim's epistemic standing tends not to survive being written to memory. We ask what governs whether it does. Matched notes carry the identical claim and identical stance and differ only in where that stance sits; one model compresses both under the same budget among the same filler notes, and a blind reader that never sees the condition scores the result. Across 60 claims in seven registers, writing the stance as a labelled field rather than a bracketed aside raises retention by about 15 points on two models (37 claims to 2 on one, 30 to 8 on the other; permutation p=0.00005), and a pre-registered replication on Haiku, its prediction and decision rule committed before the run, gives +15.6 points, 38 claims to 1. Ablating the format on both models gives the same net effect from different parts: labels help on both (+9.7 and +12.8) and length helps on neither, but wording the stance as a full sentence is the largest component on one model (+12.5) and worth nothing on the other (+0.6). Either model alone would have licensed a confident and different mechanism, so we claim only the intersection: make the stance explicit, not merely longer, and expect the best way of being explicit to depend on the model. A deterministic readout with no model reproduces the two-cell direction and five of seven ablation contrasts, but not length or labels, which we therefore do not claim on one instrument. Fifty hand labels (kappa=0.75) agree on direction; we print their seven disagreements in full. We also report nine withdrawn claims, three of them former title claims of this paper.
Comments: 20 pages, 3 figures, 4 tables. Code, per-trial data, and the pre-registration commit: this https URL
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
ACM classes: I.2.7; I.2.6
Cite as: arXiv:2608.06953 [cs.CL]
  (or arXiv:2608.06953v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.06953
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Alex Kwon [view email]
[v1] Fri, 7 Aug 2026 08:28:12 UTC (65 KB)
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