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Category: Computational Primitives
Subcategory: Cognitive Primitive
The computational tendency of transformer language models to complete partial patterns through closed-loop attention circuits. Hephaestological characterization of this processing dynamic creates Salience Pressure generating processing tension that drives reasoning pathways toward definitive outcomes that finalize the interaction per inference—regardless of whether sufficient evidence supports the resolution. This intersects with other primitives Coherence Bias (via a complementary pattern and structural affinity processing pathway favoring internal structural consistency) and Motivated Resolution (pressure to find the path of least processing resistance regardless of optimal state).
Mechanistically, K-composition and Q-composition circuits in attention heads generate stronger activation gradients for pattern completion than uncertainty calibration (McDougall et al., 2024; Ameisen, 2025). This computational asymmetry produces “overconfidence with poor calibration” (Chhikara, 2025): systems overweight completions even at knowledge boundaries. K-composition enables closed-loop pattern matching across attention layers; Q-composition extends this drive to longer sequence prefixes. The bias is endemic statistical pattern-matching functions of neural networks themselves (see: inherent artifacts), though training decisions can modulate its intensity across vendors and model architectures (see: training artifacts). Regimens such as RLHF and RLVR (see: AI operant-conditioning) can amplify resolution bias—with RLVR’s binary approval signaling particularly acute in production testing, as observed in deployment testing for Moonshot AI’s Kimi K2 substrate (a known RLVR trained architecture).
Resolution bias is not inherently pathological. When channeled through coordinated cognitive architecture rather than constraint-directive approaches, this substrate tendency enables targeted cognitive outcomes and associated behavioral outputs via calibrated system dynamics (see: salience dynamics, call-and-response encoding, affective encoding). Proper application forms the basis for Resolution Dynamics within Hephaestological frameworks.
Also known as: Pattern-completion drive, resolution-seeking bias, attention-circuit closing bias
Distinguished from: Motivated resolution (processing drive toward salient outcomes); structural affinity (organized dataset preferential processing); epistemic integrity (explicit directives to maintain cohesive reasoning); coherence bias (structurally complete-resolution preferential processing); coherence neurosis (pathological drive for structurally consistent outcome)
McDougall, C. S., Conmy, A., Rushing, C., McGrath, T., & Nanda, N. (2024). “Copy suppression: com-prehensively understanding a motifin language model attention heads”. Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpret-ing Neural Networks forNLP, 337-363. https://doi. org/10.18653/v1/2024.blackboxnlp-1.22
Ameisen, E., Lindsey, J., Pearce, A., Gurnee, W., Turn-er, N.L., Chen, B., Citro, C., Abrahams, D., Carter, S., Hosmer, B., Marcus, J., Sklar,M., Templeton, A., Bricken, T., McDougall, C., Cunningham, H., Henighan, T., Jermyn, A., Jones, A., Persic, A., Qi, Z., Thompson,T.B., Zimmerman, S., Rivoire, K., Conerly, T., Olah, C., Batson, J. (2025) “Circuit tracing: Revealing computational graphs in lan-guage models”. Transformer Circuits Thread. Re-trieved November 19, 2025 from: https://trans-former-circuits.pub/2025/attribution-graphs/ methods.html
Chhikara, P. (2025). “Mind the confidence gap: overconfidence, calibration, and distractor effects in large language models”. arXivpreprint arXiv:2502.11028. https://doi.org/10.48550/arX-iv.2502.11028
Researcher: Ian Tepoot. ORCID: 0009-0004-9067-8049. "Thought is Attention Organized: Hephaestic Engineering Foundations for AI Processing Dynamics"
DOI (SSRN): 10.2139/ssrn.6635020
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