LLMs can produce outputs that are fluent, persuasive, and contextually coherent while still containing serious epistemic failures. Many failures persist because we ask evaluators (human or model) to generate an undifferentiated holistic judgment (“is this answer good?”), which collapses multiple failure modes into a single impressionistic score. My hypothesis is that epistemic reliability can be improved more by enforcing explicit integrity constraints (and tracking their violations) than by optimizing general “answer quality” metrics.
I’ve published Operationalized Integrity Principles (OIP) v1.1, a framework intended to operationalize epistemic safeguards as enforceable procedural constraints. This is not presented as a solution to hallucinations or a guarantee of truthfulness; it is a governance architecture aimed at improving auditability and reducing known failure patterns.
This seems relevant to the LessWrong audience because it sits at the intersection of alignment governance, evaluation design, and institutional incentive failures: if LLMs are deployed in environments where humans treat fluent outputs as credible by default, then preventing high-confidence epistemic failures becomes a safety and decision-theory problem rather than a purely technical accuracy problem.
OIP-TF: a testing framework intended to score outputs using claim decomposition and integrity gating
OIP is not intended as “prompt hygiene” or stylistic caution. The goal is to formalize common epistemic failure modes into a set of auditable gates that can be tested, failed, and reviewed: not “this answer feels wrong”, but (for example) “Gate 4 failed due to false precision under weak evidence.”
My hypothesis is that decomposition + gating can improve inter-rater reliability relative to holistic scoring, and that it improves correctness to a measurable degree without external tools (RAG / retrieval / domain verification). A central question is whether this is substantive epistemic improvement or merely performative—and therefore independent validation is needed.
Core design premise: decomposed evaluation rather than holistic judgment
A central claim of OIP is that many evaluation failures come from treating “answer quality” as a single latent variable. When evaluators are asked to produce one overall score, they implicitly average across multiple epistemic dimensions (truth, calibration, sourcing, logical consistency, scope discipline, etc.), and the result is usually dominated by surface coherence.
OIP-TF instead treats evaluation as a decomposition pipeline. The question is not “is this answer good?”, but “does this answer violate any epistemic constraints that should block high-stakes use?”
At a high level:
Segment the output into atomic claims Let the output be decomposed into a set of claims: C = \{c_1, c_2, …, c_n\}
Assign a claim type and burden level Each claim c_i is tagged as empirical / causal / predictive / normative / interpretive / definitional, etc. The framework assumes that different claim types carry different epistemic burdens: an empirical claim about a verifiable event should be held to a stricter standard than an interpretive claim or an explicitly speculative forecast.
Evaluate each claim against specific epistemic properties Rather than scoring the answer globally, each claim is assessed for discrete failure modes, e.g.:
counterfactual acknowledgement where the claim depends on fragile premises
Aggregate into a gate-based integrity score Scores are aggregated via gates: S = f(G_1, G_2, …, G_k) where each gate G_j corresponds to a failure mode class (source laundering, false precision, logical closure, drift, etc.).
The point of “gates” is that they are meant to behave like compliance controls: if a high-severity failure occurs, the output is not “slightly worse”, it is non-usable for certain contexts.
In other words, OIP is attempting to shift evaluation away from “leaderboard benchmarking” toward something closer to procedural integrity testing, where the output is decomposed and interrogated in a way that reduces reviewer variance and increases auditability.
This is closer in spirit to second-line risk control testing than to standard capability evaluation. The objective is not to prove the model is smart, but to detect whether it is producing outputs that look authoritative while violating epistemic constraints that a careful human would treat as disqualifying.
What the framework is trying to do (and not do)
OIP is intended to enforce an explicit separation between evidence and inference, reduce narrative closure (where the model prematurely converges on confident conclusions), and require uncertainty calibration when the available evidence does not justify high confidence. It also aims to provide an auditable structure for reviewer challenge by making it possible to identify which specific gate failed, rather than relying on vague holistic dissatisfaction. More broadly, the framework is designed to function as a test harness that reduces evaluator variance by decomposing review into constrained subtasks rather than impressionistic overall scoring.
OIP is not intended to offer correctness guarantees, nor to replace retrieval or tool augmentation methods. It does not attempt to substitute for domain expertise, and it is not designed as a leaderboard benchmarking method for model capability comparisons. Finally, it does not claim empirical validation at this stage; formal validation remains future work.
What would falsify or seriously weaken the approach
Would implementation of OIP produce substantive epistemic improvement or merely performative epistemics?
Failure conditions that would significantly weaken the thesis include:
OIP increases hedging without improving correctness (“uncertainty laundering”)
OIP improves surface professionalism but not factual accuracy
decomposition scoring fails to improve inter-rater reliability relative to holistic scoring
the framework is brittle under long-context or adversarial prompting
the evaluation loop becomes circular (LLM judging LLM) in ways that cannot be bounded
Proposed validation direction
If this is to be validated properly, I think the minimum publishable study would require a controlled prompt set drawn from at least one high-stakes domain (such as medical, legal, or financial decision contexts), with outputs generated under baseline conditions compared against OIP-Core and OIP-JSON constrained generation. The study would also need independent human scoring using structured rubrics, alongside formal inter-rater reliability analysis. Key outcome measures should include factual error rate where ground truth exists, unsupported claim rate, citation integrity failures, incidence of false precision, and measurable drift across multi-turn sessions.
If decomposition does not measurably reduce evaluator variance, or does not improve correctness-related metrics, then the core thesis is substantially weakened.
Request for critique
I’d be particularly interested in feedback on:
whether the gate architecture is well-posed or contains redundancy
whether decomposition-based scoring is likely to reduce evaluator variance in practice
how to avoid “performative” epistemics becoming the dominant optimization
whether this resembles prior work I may have missed (especially decomposed evaluation for epistemic reliability)
Operationalized Integrity Principles (OIP): gate-based decomposed evaluation for epistemic reliability in LLM outputs
LLMs can produce outputs that are fluent, persuasive, and contextually coherent while still containing serious epistemic failures. Many failures persist because we ask evaluators (human or model) to generate an undifferentiated holistic judgment (“is this answer good?”), which collapses multiple failure modes into a single impressionistic score. My hypothesis is that epistemic reliability can be improved more by enforcing explicit integrity constraints (and tracking their violations) than by optimizing general “answer quality” metrics.
I’ve published Operationalized Integrity Principles (OIP) v1.1, a framework intended to operationalize epistemic safeguards as enforceable procedural constraints. This is not presented as a solution to hallucinations or a guarantee of truthfulness; it is a governance architecture aimed at improving auditability and reducing known failure patterns.
This seems relevant to the LessWrong audience because it sits at the intersection of alignment governance, evaluation design, and institutional incentive failures: if LLMs are deployed in environments where humans treat fluent outputs as credible by default, then preventing high-confidence epistemic failures becomes a safety and decision-theory problem rather than a purely technical accuracy problem.
OIP consists of three layers:
OIP-Core: lightweight behavioural constraints (<1000 tokens) usable in-session
OIP-JSON: structured enforcement schemas (standard + persistent variants) intended for orchestration integration
OIP-TF: a testing framework intended to score outputs using claim decomposition and integrity gating
OIP is not intended as “prompt hygiene” or stylistic caution. The goal is to formalize common epistemic failure modes into a set of auditable gates that can be tested, failed, and reviewed: not “this answer feels wrong”, but (for example) “Gate 4 failed due to false precision under weak evidence.”
My hypothesis is that decomposition + gating can improve inter-rater reliability relative to holistic scoring, and that it improves correctness to a measurable degree without external tools (RAG / retrieval / domain verification). A central question is whether this is substantive epistemic improvement or merely performative—and therefore independent validation is needed.
Core design premise: decomposed evaluation rather than holistic judgment
A central claim of OIP is that many evaluation failures come from treating “answer quality” as a single latent variable. When evaluators are asked to produce one overall score, they implicitly average across multiple epistemic dimensions (truth, calibration, sourcing, logical consistency, scope discipline, etc.), and the result is usually dominated by surface coherence.
OIP-TF instead treats evaluation as a decomposition pipeline. The question is not “is this answer good?”, but “does this answer violate any epistemic constraints that should block high-stakes use?”
At a high level:
Segment the output into atomic claims
Let the output be decomposed into a set of claims:
C = \{c_1, c_2, …, c_n\}
Assign a claim type and burden level
Each claim c_i is tagged as empirical / causal / predictive / normative / interpretive / definitional, etc.
The framework assumes that different claim types carry different epistemic burdens: an empirical claim about a verifiable event should be held to a stricter standard than an interpretive claim or an explicitly speculative forecast.
Evaluate each claim against specific epistemic properties
Rather than scoring the answer globally, each claim is assessed for discrete failure modes, e.g.:
source tier / citation integrity (including citation laundering)
uncertainty disclosure adequacy (calibration vs overconfidence)
entailment consistency with surrounding claims
scope discipline (overreach detection, universal negatives, false generality)
category errors / representational reification
counterfactual acknowledgement where the claim depends on fragile premises
Aggregate into a gate-based integrity score
Scores are aggregated via gates:
S = f(G_1, G_2, …, G_k)
where each gate G_j corresponds to a failure mode class (source laundering, false precision, logical closure, drift, etc.).
The point of “gates” is that they are meant to behave like compliance controls: if a high-severity failure occurs, the output is not “slightly worse”, it is non-usable for certain contexts.
In other words, OIP is attempting to shift evaluation away from “leaderboard benchmarking” toward something closer to procedural integrity testing, where the output is decomposed and interrogated in a way that reduces reviewer variance and increases auditability.
This is closer in spirit to second-line risk control testing than to standard capability evaluation. The objective is not to prove the model is smart, but to detect whether it is producing outputs that look authoritative while violating epistemic constraints that a careful human would treat as disqualifying.
What the framework is trying to do (and not do)
OIP is intended to enforce an explicit separation between evidence and inference, reduce narrative closure (where the model prematurely converges on confident conclusions), and require uncertainty calibration when the available evidence does not justify high confidence. It also aims to provide an auditable structure for reviewer challenge by making it possible to identify which specific gate failed, rather than relying on vague holistic dissatisfaction. More broadly, the framework is designed to function as a test harness that reduces evaluator variance by decomposing review into constrained subtasks rather than impressionistic overall scoring.
OIP is not intended to offer correctness guarantees, nor to replace retrieval or tool augmentation methods. It does not attempt to substitute for domain expertise, and it is not designed as a leaderboard benchmarking method for model capability comparisons. Finally, it does not claim empirical validation at this stage; formal validation remains future work.
What would falsify or seriously weaken the approach
Would implementation of OIP produce substantive epistemic improvement or merely performative epistemics?
Failure conditions that would significantly weaken the thesis include:
OIP increases hedging without improving correctness (“uncertainty laundering”)
OIP improves surface professionalism but not factual accuracy
decomposition scoring fails to improve inter-rater reliability relative to holistic scoring
the framework is brittle under long-context or adversarial prompting
the evaluation loop becomes circular (LLM judging LLM) in ways that cannot be bounded
Proposed validation direction
If this is to be validated properly, I think the minimum publishable study would require a controlled prompt set drawn from at least one high-stakes domain (such as medical, legal, or financial decision contexts), with outputs generated under baseline conditions compared against OIP-Core and OIP-JSON constrained generation. The study would also need independent human scoring using structured rubrics, alongside formal inter-rater reliability analysis. Key outcome measures should include factual error rate where ground truth exists, unsupported claim rate, citation integrity failures, incidence of false precision, and measurable drift across multi-turn sessions.
If decomposition does not measurably reduce evaluator variance, or does not improve correctness-related metrics, then the core thesis is substantially weakened.
Request for critique
I’d be particularly interested in feedback on:
whether the gate architecture is well-posed or contains redundancy
whether decomposition-based scoring is likely to reduce evaluator variance in practice
how to avoid “performative” epistemics becoming the dominant optimization
whether this resembles prior work I may have missed (especially decomposed evaluation for epistemic reliability)
Links to published framework:
Zenodo DOI (canonical archive): https://doi.org/10.5281/zenodo.18371018
GitHub mirror: https://github.com/JimHales-OIP/Operationalized-Integrity-Principles