ubmaMOS

Prompts first

Choose the smallest layer that preserves the epistemic behaviour you need. Token counts are measured on the exact TXT files with o200k_base and cl100k_base.

1. A

   2-Line

   For epistemically hardened environments and reversible decisions.

   o200k · DA 51 · EN 36
2. B

   Minimal

   Portable runtime with verification and stop guardrails built in.

   o200k · DA 152 · EN 117
3. C

   Full

   Canonical specification with explicit escalation and hardening.

   o200k · DA 1185 · EN 871

Danish is the source language for Minimal and Full; English is the source for the 2-Line Baseline. The others are controlled translations. The English Full edition matches the Danish canonical source on structural and semantic control points; English is not claimed to be universally superior.

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Method: token counts are computed deterministically on the included plain-text files. o200k_base and cl100k_base are encoding measurements, not a promise of exact API billing for every model. See sources [8]–[10].

What ubmaMOS does

A decision-gate protocol that challenges the current direction, updates the state of knowledge, and identifies the highest-value next move without turning analysis into an end in itself.

1. 0

   PHASE

   Calibrate the audit to discovery, decision, build, verification, hardening or execution.

2. 1

   RED

   Find the strongest concrete condition that could materially change the direction.

3. 2

   GAPS

   Separate what is known, inferred, unverified, unknown, or plausibly overlooked.

4. 3

   UPDATE

   Identify what is newly learned and which prior assumptions it changes.

5. 4

   CONSEQUENCE

   Retain, revise, verify, test, drop, or stop.

6. 5

   GREEN

   Preserve only what still stands after the strongest material challenge.

7. 6

   NEXT MOVE

   Choose one question or action with the highest expected decision value.

Context → interpretation → understanding → application

What counts as knowledge?

Labels are used only when the distinction could change the decision. UNKNOWN is a legitimate result; model confidence is not verification.

RED: evidence threshold for a material finding

Report a RED finding only when all three are present:

1. Plausible mechanism: a concrete way the problem could occur.
2. Contextual anchoring: evidence, an observation or an identifiable assumption in the case at hand.
3. Material consequence: the finding could realistically change the conclusion, quality, risk or next action.

When should the model think, and when should it test?

Same-model critique can identify a hypothesis or a checkpoint. It is not independent evidence. Use the shortest runtime layer that preserves the behaviour you need.

Escalate to a primary source, tool, calculation, measurement or executable test when a verifiable factual premise could change an important or hard-to-reverse decision.

Which layer, when?

Language choice: English uses fewer tokens in all three measured o200k_base pairs here, but performance and nuance depend on model, task and domain. Danish remains the source language for Minimal and Full; English is a portable operational candidate, not a universal winner. [8] [9] [10]

The framework's synthesis and intellectual roots

ubmaMOS names a specific architecture and publication system. Its underlying mechanisms come from older intellectual traditions and are not claimed as exclusive inventions.

Specific to ubmaMOS

1. The name ubmaMOS / Universal Bounded Meta-Audit.
2. The integrated sequence: PHASE → RED → GAPS → UPDATE → CONSEQUENCE → GREEN → NEXT BEST MOVE.
3. Red Before Green as a governing sequence rather than a generic “be critical” instruction.
4. Boundedness and Information Value vs. Action Cost as explicit stop logic.
5. The separation between canonical specification, portable runtime and lightweight baseline.
6. The episMOS framing: possibility → context → understanding → usable action.

Intellectual lineage

Evidence boundary: the literature supports several components and failure mechanisms. It does not establish that the complete ubmaMOS protocol, as one integrated framework, has been independently validated across domains. That remains an empirical claim to test.

Where it helps, and how it can fail

ubmaMOS is a decision and audit protocol, not an automatic truth system. Use it when structure can change the quality or the next action; skip the ritual when a cheap test already settles it.

• Researchclaims · sources · uncertainty
• Strategyassumptions · decision gates
• Designdiscovery · selection · hardening
• Code & QAreproduction · tests · stopping
• Documentsprovenance · evidence status
• Executionstop reviewing · act

Boundary: UBMA’s internal critique can suggest what should be verified. It does not replace external verification, domain expertise or executable ground truth where they are relevant.

Failure modes and controls

The framework’s falsification rule: if a shorter baseline is non-inferior on decision quality and better on cost, harmful flips or convergence, the longer runtime layer is dominated and should be reduced.

Why the components are plausible, and what is not proven

The framework is a synthesis. Evidence status is assigned to mechanisms, not transferred wholesale to the complete ubmaMOS protocol.

Current evidence claim: ubmaMOS is evidence-informed at the component level and explicitly designed around known failure modes. The integrated protocol still requires independent comparative evaluation against simpler baselines.

Sources and canonical status

Component-level evidence is kept separate from theory, tradecraft, emerging benchmarks and ubmaMOS as an integrated framework design.

1. [1]
   Lord, C. G., Lepper, M. R., & Preston, E. (1984). Considering the opposite.

   Empirical human debiasing: an active counter-hypothesis reduced specific judgment biases in the tasks tested.

2. [2]
   Belton, I. K., & Dhami, M. K. (2020). Cognitive biases and debiasing in intelligence analysis.

   Critical review of bias, debiasing and structured analytic techniques; institutional practice is not automatically evidence of efficacy.

3. [3]
   Russell, S. J. (1997). Metareasoning.

   Value of computation: further computation has both an expected decision gain and a cost.

4. [4]
   Kamoi, R. et al. (2024). When Can LLMs Actually Correct Their Own Mistakes?

   TACL survey: self-correction is task-dependent and stronger with reliable external feedback.

5. [5]
   Huang, J. et al. (2024). Large Language Models Cannot Self-Correct Reasoning Yet.

   Direct negative results for intrinsic reasoning self-correction in the settings tested.

6. [6]
   Sharma, M. et al. (2023). Towards Understanding Sycophancy in Language Models.

   LLM evidence that preference optimization can favour agreement with the user's beliefs over truth.

7. [7]
   Jaroslawicz, D. et al. (2025). How Many Instructions Can LLMs Follow at Once?

   IFScale: instruction adherence fell at high instruction density; the task is not identical to UBMA.

8. [8]
   Ahia, O. et al. (2023). Do All Languages Cost the Same?

   EMNLP: the same semantic content can need different token counts across languages.

9. [9]
   Etxaniz, J. et al. (2024). Do Multilingual Language Models Think Better in English?

   NAACL: self-translation into English improved the tested tasks and models; no universal language rule.

10. [10]
    OpenAI. What are tokens and how to count them?

    Official guidance: tokenization varies with text, language, model and encoding; use a tokenizer/tiktoken for concrete counts.

Evidence boundary: the sources support individual mechanisms and known failure modes; they do not validate Full UBMA end to end. “Universal” describes the intended cross-domain scope, not universal empirical superiority. The English edition is a controlled translation and portable candidate; Danish is the canonical source for Minimal and Full.