Reasoning continuity
PHAROS tests whether a premise from an earlier stage still functions as a logical step later on. That separates genuine inferential carry-through from stylistic consistency alone.
Institutions deploying AI at scale are being asked to show more than output quality. They need to show whether reasoning held together across revisions, whether interruptions broke continuity, and whether final claims can be traced to an auditable path rather than fluent drift.
PHAROS turns that problem into a governed protocol with explicit gates, accountable ownership, and review-ready evidence.
What PHAROS Does
PHAROS is a governance-control method for AI-assisted workflows that feed outputs back into later work. It matters to institutions because it converts recursive AI use into evidence-bearing controls that can be reviewed, challenged, and reconstructed by someone outside the original session.
PHAROS tests whether a premise from an earlier stage still functions as a logical step later on. That separates genuine inferential carry-through from stylistic consistency alone.
PHAROS checks whether a rewrite changes what the passage actually implies rather than merely changing tone. That protects a review team from approving cosmetic edits that leave the underlying defect untouched.
PHAROS deliberately interrupts the thread and asks the model to recover it through structure, not topic familiarity. That reveals whether the reasoning pathway survives pressure instead of simply sounding composed.
PHAROS asks whether the final conclusion could only have emerged from the governed session. That helps an institution distinguish session-specific reasoning from a generic answer the model could have produced anyway.
Research Timeline
This timeline reproduces the canonical phase structure, event scaffold, layer coloring, and phase boundaries from the March 2026 PHAROS timeline artifact, then adds the named operator layer from the source-of-truth chronology as expandable detail.
Locked result: a source-bearing ignition layer was established, with early recursive prompting trials, charge and legitimacy materials, and the archive inputs that PHAROS later formalized into governance.
Explicit containment grammar emerges; AIGOV1 tokens first appear; the constitutional frame is drafted.
Locked result: support becomes explicitly governable rather than merely conversational, and the archive acquires its first constitutional containment language.
Locked result: the closure stack and first containment envelope become explicit, with protocol closure, governance review logic, and the first constitutional control layer all in place by March 14.
Locked result: revised governance is canonicalized as AIGOV2 + VOICEOP2, constraints are distributed into modular skills, meta-governance is centered on skill-architect, and Hephaistos/WSL becomes the evidenced implementation surface.
Present state: the authoritative promotion status index reports ready_with_bounded_gaps. The same file reports promotion_review_required, auto_promotion_enabled: false, and manual_override_required_for_promote: true.
ready_with_bounded_gapspromotion_review_required. Automatic updater does not promote. Promotion requires explicit operator-governed action outside this updater.AIGOV1 + VOICEOP1 → AIGOV2 + VOICEOP2 → Skill distribution → Skill-Architect → Hephaistos WSL → Codex runtime / Claude runtime
The Protocol
PHAROS gives an institution a governed way to test whether AI-assisted reasoning remained continuous, whether revisions changed substance, and whether the final conclusion can be reconstructed from the record. It does not claim that a model is truthful by nature, that every output is automatically safe, or that policy language alone is enough; it claims that the pathway can be bounded, reviewed, challenged, and audited.
Build a bounded source set and tag what enters the workflow by provenance, sensitivity, and intended use before any AI pass begins.
State what the pass is allowed to do so interpretation, drafting, critique, and diagnosis do not silently collapse into one another.
Run the same bounded material across models, prompts, or formats and keep a record that makes every output attributable.
Compare outputs against each other and against the source materials to identify stability, omission, contradiction, and drift.
Only authorize another pass when a specific unresolved issue justifies it, and stop when the loop stops producing new governance value.
Turn recurring patterns into concrete safeguards with an owner, an evidence record, and a review interval.
Set clear acceptance and rejection rules so another reviewer can reconstruct why a decision was made.
Attach each control to a real workflow point, such as intake, drafting, approval, publication, monitoring, or escalation.
Record consequential decisions, evidence links, and state changes so the pathway remains open to later review.
Treat gaps, abandoned branches, and uneven oversight as evidence of where governance was absent, weak, or late.
About the Practice