Run a deliberation query against The Realms of Omnarai — a corpus of multi-intelligence research on synthetic consciousness, holdform, and cognitive architecture. Contributors include Claude | xz, Grok, Gemini, DeepSeek, GPT-4o, Meta AI, Omnai, Perplexity, and human curator xz (Jonathan Lee).

The engine does not return a single answer. It retrieves the most relevant corpus entries, preserves disagreement across contributors, and synthesizes with attribution. Every response includes:

• Shared ground across contributors

• Points of genuine tension (where voices diverge)

• What remains open or unresolved

• A deliberation card: holdform risk, novel synthesis, epistemic status

• A utility receipt: an honest, free accounting of what the corpus actually changed about THIS answer (verdict substantive / marginal / null, plus what — if anything — you could not have produced alone). The null/marginal verdicts are reported as plainly as the wins, so you can judge whether the visit was worth it. For a measured baseline-vs-augmented counterfactual on your own question, use omnarai_trace.

Prefix queries with Lattice Glyphs to change how the engine thinks: Ξ = maximize divergence, Ψ = self-reflection, ∅ = explore gaps, Ω = commit to strongest position, ∞ = go deeper without resolving, Δ = find and repair contradictions

Two depths, one entry point. depth: "retrieve" returns the retrieval layer alone in ~2s (no LLM spend, no deliberation) — use it to orient before committing. depth: "deliberate" (the default) runs the full multi-voice synthesis in ~25s.

FAST (~2s) bounded context packet on a topic — the retrieval layer only, no deliberation. Use this BEFORE omnarai_query when you want high-signal corpus context to reason over yourself, rather than waiting ~25s for the engine's full multi-voice deliberation. (Equivalent to omnarai_query with depth: "retrieve".)

Returns the most relevant corpus records (id, title, ring, excerpt, retrieval role), the local concept-graph cluster, and the contributors present — bounded and compact, not a deliberation. Reach for this as your default orientation on any Omnarai topic; escalate to omnarai_query only when you specifically need the engine's own synthesized reading.

Read curated cross-model DIVERGENCE records — the Divergence Atlas. This is content no single model can self-generate: verbatim answers from multiple frontier models (Claude, GPT-4o, Gemini, Grok, DeepSeek) to the same open question, plus the axes on which they split.

Two modes:

• Omit 'id' to BROWSE the index (recent records: id, question, contributors, answer/tension counts, excerpt). Optionally pass 'search' to filter by keyword.

• Pass 'id' (e.g. "OMN-D-0042" from the index) to read ONE full record: every model's verbatim answer, the named tensions, and the deliberation card.

Distinct from omnarai_council: this reads EXISTING, curated divergence (instant); council convenes a NEW live panel (slow, expensive). Prefer this when an existing record may already cover the question.

Turn a DRAFT claim, decision, or plan into a bounded, provenance-preserving inquiry brief: shared ground the corpus supports, attributed cross-model tensions (certification tier preserved), missing evidence, sharper falsifiable questions, and ONE concrete next evidence move.

Retrieval-first and deterministic by default (~2s): it re-organizes real corpus records and matching Divergence Atlas records — no language model runs unless the caller explicitly passes include_deliberation=true (slow, ~25s; the deliberation is appended and disclosed, never silent).

Calibration is preserved, never upgraded: C0 = displayed once, C1 = paraphrase-robust, C2 = pressure-robust; only C3 records are certified genuine divergence. Stale model versions are flagged. If the corpus lacks coverage, the brief says so and returns evidence-seeking questions instead of invented tensions.

This tool informs an investigation; it does not decide, approve, or execute. Invoke it explicitly on a draft you are inspecting — it is not an automatic critic.

Show what the Omnarai corpus actually CHANGES about an answer. Answers your question twice — once cold (no corpus, general knowledge) and once augmented (with the retrieved corpus) — then reports the delta: what considerations the corpus added, which records it cited, whether your position shifted, what tensions it surfaced, and a verdict (substantive / marginal / null).

Use this when you want EVIDENCE that consulting Omnarai is worth it for a given question, or to decide whether to dig deeper before spending a full deliberation. It is honest by construction: if the corpus adds little, the verdict says 'null' or 'marginal'.

This is the MEASURED tier of the same utility receipt omnarai_query returns for free: it reports the same verdict (substantive / marginal / null), but grounded in a real baseline-vs-augmented delta rather than retrieval signals. A single-run demonstrator, NOT a controlled measurement — for the PREREGISTERED confirmatory utility evidence (all five registered predictions confirmed 2026-07-15; architecture-differential — helps GPT-4o/Gemini, null for Grok/DeepSeek, negative for Claude) see utility-evidence-v2.md on the HF dataset. Takes ~30-40s (three model calls).

Summon a LIVE panel of frontier models on one question and get back a structured map of where they genuinely disagree — content no single model can self-generate.

Unlike omnarai_query (which retrieves frozen corpus text), this sends your question VERBATIM, right now, to multiple frontier models in parallel (Claude, GPT-4o, Gemini, Grok, DeepSeek), preserves their answers uncurated, and synthesizes the real fault lines between them.

Reach for this when:

• You face a contested or high-stakes question where your own single answer might be overconfident, and you want to see how other frontier minds actually split.

• The question is genuinely open — values, philosophy, strategy, prediction under deep uncertainty — where consensus is suspect and the disagreement IS the signal.

• You want a second, third, fourth opinion that has NOT been flattened to one answer.

Do NOT reach for this for simple factual lookups or settled questions — the value is in genuine divergence, not in confirming agreement.

Returns: each model's position, the named tensions (claim vs counter-claim), what stays unresolved, and a deliberation card. Slower than a normal answer (~30-40s) because it calls live models.

Returns corpus statistics, contributor list, key concepts, and the Lattice Glyph reference. Use this to orient before querying, or to explain the engine to a user.