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+# Proposal: Temporal Axis for Contradiction Probe
+
+**Status:** Report / RFC
+**Date:** 2026-05-14
+**Context:** A large production run of `gbrain eval suspected-contradictions` surfaced ~115 HIGH findings. Walking through them by hand exposed a structural limitation in the probe.
+
+## The Problem
+
+The contradiction probe (`gbrain eval suspected-contradictions`) treats all claims as timeless. When two chunks make conflicting statements, the judge flags a contradiction regardless of whether both statements were true at their respective points in time.
+
+This worked fine when the brain was mostly static wiki pages. It breaks now that the brain contains:
+- Conversation transcripts with claims that were true when spoken
+- Meeting pages capturing what people said on specific dates
+- Takes that evolve (a founder's ARR claim in January vs. July)
+- Status records that supersede each other (a state moves from "trial" to "confirmed")
+
+The probe can't distinguish "this changed" from "this is wrong."
+
+## Bug-class examples (synthetic placeholders)
+
+### 1. Temporal Evolution (False Positive)
+
+```
+Finding: HIGH
+  A: [daily/transcripts/2026/2026-04-28] "status: trial"
+  B: [meetings/2026-05-07-session] "status: confirmed"
+  Axis: Whether status is trial or confirmed
+```
+
+Both are correct as of their respective dates. April 28: trial. May 7: confirmed. The probe flags this because it has no concept of "this claim was valid from X until Y." The May 7 record didn't make the April 28 transcript wrong; it recorded a change.
+
+### 2. Negation Parsing (False Positive)
+
+```
+Finding: HIGH
+  A: [people/alice-example] "person traveled to city-a for alice-example's event — NOT bob-example's event"
+  B: [meetings/2026-05-11-context] mentions of bob-example's event in city-b
+  Axis: Whose event the city-a trip was for
+```
+
+The disambiguation fact contains "NOT bob-example's event" as an explicit negation. The judge reads "bob-example's event" as a positive claim and flags it against the alice-example context. The data is correct; the probe can't parse negation.
+
+### 3. Role Changes (True Positive That Needs Time Awareness)
+
+```
+Finding: HIGH
+  A: [sources/notes/2017-03-28] advisor-example: "Partner, venture-firm-a"
+  B: [people/advisor-example] advisor-example: "Senior Policy Advisor, gov-org-b"
+```
+
+Both true at their respective times. 2017: partner at venture-firm-a. 2025: gov-org-b advisor. The current probe correctly flags this as a contradiction, but the resolution should be "superseded by time" not "one side is wrong." The 2017 note isn't wrong; it's a historical record.
+
+## Scenario #1: Founder Tracking (the big one)
+
+This is the use case that makes a time axis transformative rather than incremental.
+
+The brain holds hundreds of company pages and thousands of meeting pages. Founders make claims:
+
+- "We're at $50K MRR" (January OH)
+- "We hit $200K MRR" (April OH)
+- "We're at $150K MRR" (July OH — what happened?)
+
+Today the probe would flag January vs. April as a contradiction. The real signal is April vs. July: **a claimed metric went backwards.** That's not a data quality issue; that's intelligence.
+
+What a time-aware probe could surface:
+
+**Claim trajectory tracking:**
+```
+Company: Acme Corp
+  2026-01: "$50K MRR" (source: OH transcript)
+  2026-04: "$200K MRR" (source: OH transcript)
+  2026-07: "$150K MRR" (source: OH transcript) ← REGRESSION DETECTED
+  2026-07: "$2M ARR" (source: investor update) ← INCONSISTENT WITH MRR
+```
+
+**Prediction vs. outcome:**
+```
+Founder: Jane Doe (Acme Corp)
+  2026-01: "We'll hit $1M ARR by June" (source: batch kickoff)
+  2026-06: Actual ARR: $400K (source: investor update)
+  → Prediction accuracy: 40%
+  → Pattern: consistently 2-3x optimistic on timeline
+```
+
+**Narrative consistency:**
+```
+Founder: John Smith (WidgetCo)
+  2026-01: "Our moat is proprietary data" (source: interview)
+  2026-03: "We're pivoting to an API-first model" (source: OH)
+  2026-06: "Our moat is network effects" (source: Demo Day)
+  → Moat narrative changed 3x in 6 months — flag for review
+```
+
+This isn't adversarial. It's the kind of pattern an experienced operator notices intuitively across hundreds of conversations. GBrain can make it systematic.
+
+## Scenario #2: Event Disambiguation
+
+Two distinct events within a short window can conflate during ingestion because the probe has no temporal frame to say "event A is a different event from event B."
+
+Time-aware facts would store (synthetic placeholders):
+```
+fact: "alice-example milestone" valid_from: 2026-04-15 valid_until: 2026-04-15
+fact: "alice-example event in city-a" valid_from: 2026-04-17 valid_until: 2026-04-19
+fact: "bob-example milestone" valid_from: 2026-05-04 valid_until: 2026-05-04
+fact: "bob-example event in city-b" valid_from: 2026-05-12 valid_until: 2026-05-12
+```
+
+The probe should recognize these as two distinct events with non-overlapping time windows, not as contradictions about "whose event."
+
+## Scenario #3: Role and Status Changes
+
+People change roles. Companies change status. The brain records history. Synthetic examples representative of the cases observed in production:
+
+- advisor-example: venture-firm-a partner (2019) → gov-org-b advisor (2025)
+- investor-example: fund-a partner → fund-b CEO (2023)
+- agent-fork: provider restriction event (2026-04-04) ≠ shutdown
+- fund-c: "interesting fund" (early) → "declined" (later) → "losing confidence" (latest)
+
+All of these are correct historical records. The probe should classify them as **temporal supersession** rather than **contradiction.**
+
+## Scenario #4: Decision Tracking
+
+Multi-step decisions that supersede earlier framings example (synthetic):
+```
+2026-04-24: "status: trial" (initial framing)
+2026-04-25: "status: in progress" (confirmed, no longer "trial")
+2026-05-07: "status: finalized" (session record)
+2026-05-11: follow-up actions taken
+```
+
+Each step supersedes the previous. A time-aware probe would show the **evolution chain** rather than flagging each pair as a contradiction.
+
+## What Exists Today
+
+The probe already has some temporal infrastructure:
+
+1. **`date-filter.ts`** — `shouldSkipForDateMismatch()` pre-filters pairs, but only checks whether dates are "too far apart" (a coarse heuristic). It doesn't reason about which claim is newer or whether one supersedes the other.
+
+2. **`auto-supersession.ts`** — proposes resolution commands, checks `since_date` on takes. But this is post-hoc (after the judge flags a contradiction). The judge itself doesn't see dates.
+
+3. **Facts table** has `valid_from` and `valid_until` columns. These exist but are sparsely populated and not used by the probe.
+
+4. **Takes table** has `since_date`. Also sparsely populated.
+
+## What Would Need to Change
+
+### Phase 1: Judge prompt enhancement (smallest change, biggest impact)
+
+Pass the source dates to the judge. The current judge prompt shows two text chunks and asks "are these contradictory?" If it also showed:
+
+```
+Statement A (from: 2026-04-28):
+  "status: trial"
+
+Statement B (from: 2026-05-07):
+  "status: confirmed"
+```
+
+The judge could output a `temporal_supersession` verdict instead of `contradiction`. New verdict taxonomy:
+
+- `no_contradiction` — statements are compatible
+- `contradiction` — genuinely conflicting claims at the same point in time
+- `temporal_supersession` — newer claim updates/replaces older claim (not an error)
+- `temporal_regression` — a metric or status went backwards (potential signal)
+- `temporal_evolution` — legitimate change over time, neither supersession nor regression
+- `negation_artifact` — one side contains an explicit negation the judge misread
+
+### Phase 2: Claim trajectory view (new command)
+
+```bash
+gbrain eval trajectory "Acme Corp MRR"
+gbrain eval trajectory "advisor-example role"
+gbrain eval trajectory "deal-x status"
+```
+
+Pull all time-stamped claims about an entity+attribute, sort chronologically, detect:
+- Regressions (metric went down)
+- Contradictions within the same time window
+- Prediction vs. outcome gaps
+- Narrative drift (moat story changed 3x)
+
+### Phase 3: Automatic `valid_from`/`valid_until` population
+
+During `extract_facts`, infer temporal bounds from source context:
+- Meeting page dated 2026-04-28 → claims valid_from 2026-04-28
+- Takes from transcripts → valid_from = transcript date
+- Imported notes → valid_from = note date
+- Entity pages with no date → valid_from = page created date (weakest signal)
+
+### Phase 4: Founder scorecard
+
+For founders specifically, a temporal probe could generate:
+- **Claim accuracy score** — what they predicted vs. what happened
+- **Consistency score** — how stable their narrative is over time
+- **Growth trajectory** — whether the numbers are actually moving
+- **Red flag detector** — metrics going backwards, story changing, timeline slipping
+
+## Recommendation
+
+Start with Phase 1. The judge prompt change is small. It immediately eliminates the temporal false positives (which were a majority of the residual HIGH findings in the production audit) and gives the probe a new vocabulary for time-aware reasoning.
+
+Phase 2 (trajectory view) is the one that would change how operators use the brain for founder evaluation. Worth scoping as a standalone feature.
+
+Phases 3–4 are downstream and can wait.
+
+## Appendix: Production probe stats (2026-05-14)
+
+- ~107K pages, ~257K chunks
+- Previous run: ~115 HIGH findings across 50 queries
+- After manual resolution: ~25 residual findings
+- Of those ~25: roughly two-thirds temporal false positives, the remainder probe artifacts (self-contradiction, negation parsing)
+- 0 genuine data contradictions remained on the queries tested
+- Fresh targeted probe on a representative entity-role query: 0 contradictions (was 14+ before fixes)