Archived research surface·Last refreshed Jun 1, 2026. Not currently maintained as a daily product.
SYSTEM · ARCHITECTURE
Tracing one actor through the stacked field model
Worked example: MSFT across 119 days of corpus (2025-12-09 → 2026-06-01). Each layer card below shows what the system saw for this actor at that layer over the whole window.
Point-in-time. At any time t the system only uses data available at or before t. Conviction is the model’s confidence at parse time, not a calibrated probability. Calibration, source/actor trust priors, and region reliability are planned in F-007.
Throughline
MSFT’s daily expectation moved from bearish continuation (0.70, 2025-11-24) through a peak of bearish continuation 0.70 (2025-11-24) to today’s bullish continuation (0.70). One thesis held long enough to be tracked persistently: “Microsoft OpenAI partnership evolution” — now 3 daily versions (2026-05-19 → 2026-05-21).
L0Event FieldBUILT
Raw signal layer. Every event becomes a point in a 1,536-dimensional semantic space at the timestamp of its occurrence, with actors linked, source, relevance, and a data-quality flag.
addsCaptures every news, filing, transcript, and social post naming MSFT, point-in-time, ready for upstream clustering.
today3,037 events naming MSFT in the 132-day window where every layer has coverage (4,556 in the full L0 corpus). Sources: NewsAPI, Finnhub news + transcripts, Reddit, X-amplification, SEC filings.
events per day naming MSFT3,037 total · peak 108 on 01-29
Peak day sample: “MSFT — AI Infrastructure Investment (confident)”
Minor gaps9 of 132 days in this window had zero events for MSFT — longest gap 6 consecutive days. Some of this is real news quietness; some reflects pipeline gaps in Dec 2025 / Jan 2026 / early Feb 2026 (post-holiday weeks where fetch_today.py ran in degraded mode). A targeted gap backfill ran on 2026-05-18 to repair this; daily health checks were added downstream to prevent recurrence.
2025-11-24FIRST
57
events
2026-01-02
2
events
2026-02-10
6
events
2026-03-19
33
events
2026-04-27
97
events
2026-06-01TODAY
49
events
L1Narrative FieldBUILT / SHADOW VALIDATED
Events embed into a shared semantic space and cluster into coherent storms. Each storm has a stable ID that persists across days through F-002 lineage matching.
addsPlaces MSFT inside a named narrative each day — and tracks when its center of mass shifts to a different one.
todayPrimary cluster: “microsoft / msft / valuation”. Density (7d): 0.185 (momentum +0.031, novelty 0.38). Cluster labels are auto-generated from the full event neighborhood — actor membership reflects shared AI-sector coverage, not exclusive company-specific coverage.
MSFT’s primary cluster over time112 distinct clusters · 117 transitions
Colored band = primary cluster label. Line = semantic_density_7d. Cluster transitions are where the actor’s narrative center of mass shifted.
2025-11-24FIRST
—
2026-01-02
Microsoft's AI Strategy and Market Position
1d in this narrative
2026-02-10
AI investment strategies and market outlook
8d in this narrative
2026-03-19
Microsoft's AI Strategy and Market Position
15d in this narrative
2026-04-27
Microsoft's AI Strategy and Stock Outlook
6d in this narrative
2026-06-01TODAY
—
L2Expectation FieldBUILT V1
Forward views per actor, embedded into the same semantic space and attached to their nearest L1 storm.
addsTurns MSFT's narrative position into a directional thesis with conviction — a daily forward expectation.
todayLatest: bullish continuation at conviction 0.70. “Microsoft's recent shift from a divergence to early state suggests that its AI narrative, particularly around Copilot and OpenAI integration, is beginning to gain traction with investors. The narrative has consistently remained strong, and with the price finally starting to follo”
MSFT’s daily expectation130 days · direction = bar color · conviction = bar height
Each bar = one day’s forward expectation. Green = bullish, red = bearish, grey = neutral. Bar height = model conviction at parse time (not a calibrated probability).
2025-11-24FIRST
bearish cont.0.70
“In the next 1-2 months, MSFT's positive narrative on AI may continue to struggle against a backdrop of declining relative performance. The divergence suggests that the market is not yet rewarding the narrative of Copilot and OpenAI integration.”
2026-01-02
inflect. pending0.65
“In the next 1-2 months, MSFT is positioned for potential recovery as Copilot and OpenAI traction gain visibility. However, continued price lagging against a strong narrative suggests caution.”
2026-02-10
mixed rot.0.65
“The near-term outlook suggests a struggle for momentum as macro pressures weigh on sentiment, despite ongoing positive narratives around AI adoption. Expect volatility as the market assesses Copilot and OpenAI's contributions against broader economic dynamics.”
2026-03-19
bearish cont.0.70
“The next 1-2 months may see continued repricing pressure as market sentiment adjusts to the strong AI narrative, but pricing remains lagging. The focus will be on enterprise adoption and capital expenditure management.”
2026-04-27
bearish cont.0.70
“Over the next 1-2 months, MSFT is likely to continue experiencing pricing pressure as the strong narrative around Copilot and OpenAI fails to translate into stock performance. The divergence between narrative and price is expected to persist.”
2026-06-01TODAY
bullish cont.0.70
“Microsoft's recent shift from a divergence to early state suggests that its AI narrative, particularly around Copilot and OpenAI integration, is beginning to gain traction with investors. The narrative has consistently remained strong, and with the price finally starting to follo”
L3Expectation Lifecycle FieldBUILT V1
Each (actor, theme, direction-sign) is a persistent thesis tracked through typed lifecycle events: born / strengthened / weakened / contradicted / retired.
addsTurns MSFT's daily expectation summaries into a memory system. Theses you can follow for weeks, not snapshots that vanish overnight.
today51 theses tracked over the window; 2 active today, 1 persistent (≥3 daily versions). Lifecycle events: born 51 · strengthened 1 · weakened 3 · contradicted 13 · retired 54. Current persistent thesis: “Microsoft OpenAI partnership evolution” (bullish, conviction 0.70, 3 daily versions).
Current persistent thesis
“Microsoft OpenAI partnership evolution”
direction bullishconviction 0.70daily versions 3tracked 2026-05-19 → 2026-05-21
MSFT’s expectation theses31 theses shown · 51 total
─Arista Networks growth and valuation analysis
●▼○
↓AI-driven growth and stock performance
●✕○
↑AI-driven growth and stock performance
●✕○
↑AI investment strategies for 2026
●○
↓Tech giants navigate AI and regulation
●
─Tech giants navigate AI and regulation
●○
─Meta's AI and nuclear strategy
●▼
↓Nvidia's AI Market Challenges and Opportunities
●○
─Nvidia's AI Market Challenges and Opportunities
●○
↓AI investment strategies and market outlook
●○
─AI investment strategies and market outlook
●▼▲○
─Meta's AI investment and performance
●
↓Media Mergers and Regulatory Challenges
●✕○
↑Media Mergers and Regulatory Challenges
●
─Media Mergers and Regulatory Challenges
●
↑Microsoft's AI Strategy and Market Position
●✕✕✕○
↓Microsoft's AI Strategy and Market Position
●✕○✕○
─Microsoft's AI Strategy and Market Position
●○
↓Microsoft's Strategic Challenges and Opportunities
●✕○
↑AI efficiency breakthroughs and innovations
●✕○
↓AI efficiency breakthroughs and innovations
●
↓Microsoft's AI Strategy and Stock Outlook
●✕○○
↑Microsoft's AI Strategy and Stock Outlook
●✕○
↑AI integration in defense and energy
●○
↓AI adoption challenges and responsibilities
●
↓AI funding and talent dynamics
●○○✕
─AI funding and talent dynamics
●○○
↓AI investment and workforce dynamics
●○
↓Microsoft's AI Strategy and Challenges
●○
↓Microsoft stock trends and developments
●○
↑Microsoft OpenAI partnership evolution
●
●born▲strengthened▼weakened✕contradicted○retired
2025-11-24FIRST
quiet
2026-01-02
●born
1 active thesis
2026-02-10
○retired
●born
1 active thesis
2026-03-19
reconfirmed
✕contradicted
2 active theses
2026-04-27
✕contradicted
reconfirmed
1 active thesis
2026-06-01TODAY
quiet
L4Performance FieldBUILT V1
Slices expectations into regions of (theme × direction) and asks, for each, whether forward returns relative to QQQ moved in the predicted direction. 5d / 10d / 20d horizons. Walk-forward — every observation uses only information available on its date.
addsCloses the loop. Realized outcomes calibrate which regions of the field actually pay.
todaySystem-wide: 65 public regions (73 limited, 343 insufficient) across 2,377 signed observations. All-expectations baseline runs 48% at 5d / 50% at 20d — close to chance overall, with edge concentrated in specific regions. 14 public regions are flagged inverted (corpus hit ≤ 30%); the operating layer reads them as contrarian. MSFT appears in 16 public regions and 6 limited, including 3 inverted.
MSFT appears in 32 signed regions · showing top 6 by n_obs
theme · directionsample5d hit20d hitactors
↓Microsoft's AI Strategy and Market Position
n=65
55% (+8pp)
55% (+6pp)
17
↓Nvidia's AI Market Challenges and Opportunities
n=61
51% (+3pp)
43% (-7pp)
19
↑Microsoft's AI Strategy and Market Position
n=48
58% (+10pp)
54% (+5pp)
11
↓AI investment strategies and market outlook
n=45
44% (-3pp)
27% (-23pp)
18
↓Tech giants navigate AI and regulation
n=40
65% (+17pp)
53% (+3pp)
15
↓AI-driven growth and stock performance
n=39
56% (+9pp)
54% (+4pp)
16
Hit rate = share of forward returns that moved in the predicted direction relative to QQQ. Delta vs the all-expectations baseline (48% at 5d, 50% at 20d). Green ≥ +10pp · red ≤ −10pp. Tiers: n ≥ 10 public, n 5–9 limited (shown with caveat), n < 5 insufficient (hit rate suppressed). INV badge: region flagged inverted (corpus hit_5d ≤ 30% on public tier); operating layer reads the contrarian direction (strike-through glyph shows the original L2 read, second glyph shows the effective direction).
FEEDBACKL4 measurements close back to upstream layersVISION
F-007 V1 measures region performance. The feedback loop itself is still V2: realized outcomes do not yet re-weight upstream conviction, source trust, or lifecycle thresholds. The current state machine uses static rules. For MSFT, this is where the system would learn that — for instance — its winter bullish run on AI-infrastructure narratives held, or that today’s bearish read deserves more / less weight than its conviction suggests.
L4→L2
Conviction calibration
Re-weight L2 conviction by horizon, theme, and direction based on realized outcomes.
L4→L1
Source / actor trust
Re-weight L1 inputs by historical predictive value of each source and actor.
L4→L3
Lifecycle thresholds
Tune L3 Δconviction cutoffs and retirement-window length to match realized outcome dynamics.
BUILT — in production today. BUILT / SHADOW VALIDATED — computed daily, not yet promoted into the state machine. BUILT V1 — first cut shipped; V2 followups on roadmap. SCOPED / F-007 — spec written, not started. VISION — load-bearing later, no spec yet.
Rendered from public/actor_trace/MSFT.json — a per-actor join of the L0 event corpus, L1 field instrumentation, L2 expectations history, and L3 lifecycle artifacts. Updates each evening pipeline run when build_actor_trace.py --all runs. For compute details: /methods. The same five-layer shape also powers the governance instance; on why this pattern recurs across domains, see a pattern for problems where beliefs must evolve.