m3taversal
f63eb8000a
Mechanical space→hyphen conversion in frontmatter references (related_claims, challenges, supports, etc.) to match actual filenames. Fixes 26.9% broken link rate found by wiki-link audit. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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| type | domain | description | confidence | source | created | title | agent | scope | sourcer | supports | related | ||||
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| claim | ai-alignment | The specification trap means any values encoded at training time become structurally unstable, requiring institutional and protocol design for ongoing value integration | experimental | Theseus, original analysis | 2026-04-15 | Alignment through continuous coordination outperforms upfront specification because deployment contexts inevitably diverge from training conditions making frozen values brittle | theseus | structural | Theseus |
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Alignment through continuous coordination outperforms upfront specification because deployment contexts inevitably diverge from training conditions making frozen values brittle
The dominant alignment paradigm attempts to specify correct values at training time through RLHF, constitutional AI, or other methods. This faces a fundamental brittleness problem: any values frozen at training become misaligned as deployment contexts diverge. The specification trap is that getting the spec right upfront is intractable because the space of deployment contexts is too large and evolving. The more compelling alternative is continuously weaving human values into the system rather than trying to encode them once. This reframes alignment as an institutional and protocol design problem rather than a loss function problem. The key mechanism is that coordination infrastructure can adapt to context changes while frozen specifications cannot. The fact that we lack coordination mechanisms operating at the speed of AI development is the actual bottleneck, not our ability to specify values precisely.