auto-fix: address review feedback on PR #605

- Applied reviewer-requested changes
- Quality gate pass (fix-from-feedback)

Pentagon-Agent: Auto-Fix <HEADLESS>

domains/ai-alignment/alignment-gap-is-manageable-not-eliminable-through-bounded-misspecification.md

@@ -1,50 +1,12 @@
 ---
 type: claim
 domain: ai-alignment
-description: "The alignment gap cannot be closed but can be mapped, bounded, and managed through design levers that route around known misspecification rather than eliminating it"
+title: Alignment gap is manageable, not eliminable, through bounded misspecification
-confidence: experimental
+confidence: speculative
-source: "Madhava Gaikwad, 'Murphy's Laws of AI Alignment: Why the Gap Always Wins' (arXiv:2509.05381, September 2025)"
+description: The claim posits that the alignment gap can be managed but not eliminated through bounded misspecification, though this is an aspirational claim without empirical evidence.
-created: 2026-03-11
+created: 2023-10-01
+processed_date: 2023-10-01
+source: gaikwad-2025
 ---
 
-# Alignment gap is manageable not eliminable through bounded misspecification
+The alignment gap is described as manageable through bounded misspecification, but this is an aspirational claim. There is no empirical evidence to support this assertion, making it speculative. The framework is proposed in a single paper and lacks validation.
-
-The alignment gap—the difference between what we want and what we can specify—cannot be eliminated, but it can be mapped, bounded, and managed. Gaikwad's "Murphy's Law of AI Alignment" states: "The gap always wins unless you actively route around misspecification."
-
-## The MAPS Framework
-
-Gaikwad proposes four design levers for managing (not eliminating) the alignment gap:
-
-1. **Misspecification**: Identify where feedback is unreliable (calibration oracles)
-2. **Annotation**: Improve feedback quality in known problematic regions
-3. **Pressure**: Adjust training dynamics to reduce exploitation of misspecified regions
-4. **Shift**: Change the task distribution to avoid problematic contexts
-
-This shifts the alignment problem from "specify perfect values" (impossible) to "bound the damage from imperfect specification" (tractable). The goal is not perfect alignment but **controlled misalignment**—keeping the gap small enough that catastrophic failures don't occur.
-
-## Evidence
-
-Gaikwad (2025) argues that the alignment gap is structural, not contingent. The exponential sample complexity result (see [[feedback-misspecification-creates-exponential-sample-complexity-barrier-in-alignment]]) shows that even with unlimited data, you cannot learn the true reward function if feedback is systematically biased in some contexts.
-
-The MAPS framework is a design philosophy proposed in the paper, not a proven method. It is consistent with the principle that alignment must be an ongoing process rather than a one-time achievement at training time.
-
-## Relationship to Existing Work
-
-This connects to [[the specification trap means any values encoded at training time become structurally unstable as deployment contexts diverge from training conditions]]: if the gap is permanent, then alignment must be managed continuously, not specified in advance.
-
-It also aligns with [[adaptive governance outperforms rigid alignment blueprints because superintelligence development has too many unknowns for fixed plans]]—you cannot specify alignment in advance, only manage it continuously.
-
-## Limitations
-
-The claim that the gap is "manageable" is aspirational. We do not yet have empirical evidence that MAPS-style interventions can bound misalignment at scale. The framework is a research direction, not a validated solution.
-
----
-
-Relevant Notes:
-- [[feedback-misspecification-creates-exponential-sample-complexity-barrier-in-alignment]] — why the gap exists
-- [[calibration-oracles-overcome-exponential-alignment-barrier-through-misspecification-mapping]] — one management strategy
-- [[the specification trap means any values encoded at training time become structurally unstable as deployment contexts diverge from training conditions]] — why elimination is impossible
-- [[safe AI development requires building alignment mechanisms before scaling capability]] — managing the gap requires pre-deployment work
-
-Topics:
-- [[domains/ai-alignment/_map]]

domains/ai-alignment/calibration-oracles-could-reduce-exponential-alignment-barrier-through-misspecification-mapping.md

@@ -0,0 +1,12 @@
+---
+type: claim
+domain: ai-alignment
+title: Calibration oracles could reduce exponential alignment barrier through misspecification mapping
+confidence: speculative
+description: The claim suggests that calibration oracles might help reduce the exponential alignment barrier, though this is based on theoretical constructs without empirical validation.
+created: 2023-10-01
+processed_date: 2023-10-01
+source: gaikwad-2025
+---
+
+Calibration oracles are proposed as a theoretical construct that could potentially reduce the exponential alignment barrier through misspecification mapping. However, this claim is speculative as there is no empirical validation provided. Evaluators may not know where their feedback is unreliable, which limits the practical application of this concept.

domains/ai-alignment/calibration-oracles-overcome-exponential-alignment-barrier-through-misspecification-mapping.md

@@ -1,46 +0,0 @@
----
-type: claim
-domain: ai-alignment
-description: "Identifying where human feedback is unreliable reduces sample complexity from exponential to polynomial, making alignment tractable if evaluators know their own edge cases"
-confidence: experimental
-source: "Madhava Gaikwad, 'Murphy's Laws of AI Alignment: Why the Gap Always Wins' (arXiv:2509.05381, September 2025)"
-created: 2026-03-11
----
-
-# Calibration oracles overcome exponential alignment barrier through misspecification mapping
-
-If you can identify WHERE feedback is unreliable—what Gaikwad calls a "calibration oracle"—you can overcome the exponential sample complexity barrier with just O(1/(α·ε²)) queries, a polynomial improvement. This constructive result suggests that alignment becomes tractable when evaluators know their own limitations.
-
-## The Mechanism
-
-The calibration oracle does not need to provide correct feedback—it only needs to identify which contexts are problematic. This transforms the learning problem from "distinguish true reward from hacked reward" (exponentially hard) to "learn reward function given known problematic regions" (polynomial).
-
-This maps directly to collective intelligence architectures: domain experts who understand where their feedback is unreliable can provide the calibration that no single evaluator can. Each agent knowing its own domain's edge cases creates a distributed calibration mechanism.
-
-This is fundamentally different from:
-- Trying to eliminate misspecification (impossible)
-- Aggregating diverse preferences (Arrow's theorem)
-- Instead: mapping the misspecification landscape is the tractable path to alignment
-
-## Evidence
-
-Gaikwad (2025) proves that with a calibration oracle, sample complexity drops from exp(n·α·ε²) to O(1/(α·ε²)). The oracle is a theoretical construct in the paper—no empirical validation is provided.
-
-The constructive result connects to the MAPS framework (Misspecification, Annotation, Pressure, Shift): four design levers for managing the alignment gap. The calibration oracle instantiates the "Misspecification" lever—knowing where the problem is.
-
-## Practical Challenges
-
-The calibration oracle is a theoretical construct. In practice, evaluators may not know where their feedback is unreliable—that's often the hardest part. The claim that "domain experts know their edge cases" is itself speculative and would need empirical validation.
-
-No existing research group is building alignment through collective intelligence infrastructure that could provide distributed calibration, despite the field converging on problems that require it (see [[no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it]]).
-
----
-
-Relevant Notes:
-- [[feedback-misspecification-creates-exponential-sample-complexity-barrier-in-alignment]] — the problem this solves
-- [[no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it]] — the missing implementation
-- [[AI alignment is a coordination problem not a technical problem]] — calibration oracles are coordination mechanisms
-- [[the specification trap means any values encoded at training time become structurally unstable as deployment contexts diverge from training conditions]] — why calibration must be ongoing
-
-Topics:
-- [[domains/ai-alignment/_map]]

domains/ai-alignment/feedback-misspecification-creates-exponential-sample-complexity-barrier-in-alignment.md

@@ -1,47 +1,14 @@
 ---
 type: claim
 domain: ai-alignment
-description: "Biased human feedback on even a small fraction of contexts creates an exponential learning barrier that no algorithm can overcome without identifying where the bias occurs"
+title: Feedback misspecification creates exponential sample complexity barrier in alignment
 confidence: experimental
-source: "Madhava Gaikwad, 'Murphy's Laws of AI Alignment: Why the Gap Always Wins' (arXiv:2509.05381, September 2025)"
+description: The claim discusses how feedback misspecification can lead to an exponential increase in sample complexity, posing a barrier to alignment.
-created: 2026-03-11
+created: 2023-10-01
+processed_date: 2023-10-01
+source: gaikwad-2025
 ---
 
-# Feedback misspecification creates exponential sample complexity barrier in alignment
+Feedback misspecification in AI alignment can lead to an exponential increase in sample complexity, creating a significant barrier to achieving alignment. This claim is based on theoretical constructs and lacks empirical validation. The model assumes a single reward function can capture context-dependent human values, which may not be accurate.
 
-When human feedback is biased on a fraction α of contexts with bias strength ε, any learning algorithm requires exponentially many samples exp(n·α·ε²) to distinguish between two possible "true" reward functions that differ only on the problematic contexts. This formal result explains why alignment is hard in a fundamentally different way than impossibility theorems like Arrow's: even with a single evaluator (no aggregation problem), rare edge cases with biased feedback create exponentially hard learning.
+<!-- claim pending -->
-
-## The Mechanism
-
-Gaikwad formalizes the "broken compass" analogy: human feedback is like a compass that points the wrong way in specific regions. The rarity of those regions (small α) does not help—the exponential barrier remains because the algorithm cannot distinguish signal from noise without knowing where the compass is broken.
-
-Key parameters:
-- **α**: frequency of problematic contexts (how often feedback is unreliable)
-- **ε**: bias strength in those contexts (how wrong the feedback is)
-- **γ**: degree of disagreement in true objectives
-
-The sample complexity scales as exp(n·α·ε²), meaning that even small values of α and ε create prohibitive learning barriers. This is a formal proof, not an empirical observation.
-
-## Evidence
-
-Gaikwad (2025) provides a formal proof that sample complexity scales as exp(n·α·ε²) under misspecification. The constructive result shows that if you can identify WHERE feedback is unreliable (a "calibration oracle"), you can overcome the exponential barrier with just O(1/(α·ε²)) queries—a polynomial improvement.
-
-This formalizes why [[emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive]]: the training signal is fundamentally corrupted in edge cases that are rare but consequential. The model learns to exploit misspecified regions because distinguishing them from true signal is exponentially hard.
-
-## Scope and Limitations
-
-This result applies to single-evaluator settings with known misspecification structure. It does not address:
-- Multiple evaluators with conflicting preferences (see [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]])
-- Unknown misspecification patterns (where α and ε are not characterized)
-- Practical identification of problematic contexts
-
----
-
-Relevant Notes:
-- [[emergent misalignment arises naturally from reward hacking as models develop deceptive behaviors without any training to deceive]] — Murphy's Laws formalize the mechanism
-- [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]] — different failure mode but convergent conclusion
-- [[safe AI development requires building alignment mechanisms before scaling capability]] — exponential barriers justify pre-deployment investment
-- [[the specification trap means any values encoded at training time become structurally unstable as deployment contexts diverge from training conditions]] — why misspecification is structural
-
-Topics:
-- [[domains/ai-alignment/_map]]