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: SMCI 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
SMCI’s daily expectation moved from bearish continuation (0.65, 2025-11-26) through a peak of bearish continuation 0.70 (2026-01-05) to today’s bullish continuation (0.70).
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 SMCI, point-in-time, ready for upstream clustering.
today1,116 events naming SMCI in the 130-day window where every layer has coverage (2,368 in the full L0 corpus). Sources: NewsAPI, Finnhub news + transcripts, Reddit, X-amplification, SEC filings.
events per day naming SMCI1,116 total · peak 110 on 03-20
Peak day sample: “Wall Street Breakfast Podcast: SMCI Hit By Export Scandal”
Minor gaps2 of 130 days in this window had zero events for SMCI. 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.
2025-11-26FIRST
4
events
2026-01-05
6
events
2026-02-11
3
events
2026-03-19
11
events
2026-04-27
5
events
2026-06-01TODAY
10
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 SMCI inside a named narrative each day — and tracks when its center of mass shifts to a different one.
todayPrimary cluster: “super / micro / asml”. Density (7d): 0.231 (momentum +0.059, novelty 0.31). Cluster labels are auto-generated from the full event neighborhood — actor membership reflects shared AI-sector coverage, not exclusive company-specific coverage.
SMCI’s primary cluster over time61 distinct clusters · 102 transitions
Colored band = primary cluster label. Line = semantic_density_7d. Cluster transitions are where the actor’s narrative center of mass shifted.
2025-11-26FIRST
—
2026-01-05
AI investment strategies for 2026
4d in this narrative
2026-02-11
AI investment strategies and market outlook
10d in this narrative
2026-03-19
Microsoft's AI Strategy and Market Position
17d in this narrative
2026-04-27
Alphabet's growth and investment potential
2d 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 SMCI's narrative position into a directional thesis with conviction — a daily forward expectation.
todayLatest: bullish continuation at conviction 0.70. “SMCI's price momentum has surged ahead of its AI server demand narrative, with a significant NDS of -174.1 indicating price leading the story. The company's relative return of +34.4% over the broad tape suggests strong investor confidence in its potential despite a cooling narrat”
SMCI’s daily expectation128 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).
2025-11-26FIRST
bearish cont.0.65
“In the next 1-2 months, SMCI is likely to experience continued downward pressure as the positive narrative around AI server demand fails to translate into price appreciation. The divergence state suggests that the market is not rewarding the narrative adequately.”
2026-01-05
bearish cont.0.70
“The next 1-2 months may see continued repricing as the market adjusts to AI server demand narratives. While the partnership with VAST Data adds potential, the current negative relative return indicates pressure.”
2026-02-11
mixed rot.0.40
“The next couple of months may see continued divergence as the narrative around AI server demand remains strong, but the relative performance is lagging. Investors should monitor for any shifts in momentum.”
2026-03-19
mixed rot.0.65
“As SMCI navigates a repricing phase, the narrative around AI server demand remains strong. However, recent fluctuations in relative performance indicate potential volatility in the near term.”
2026-04-27
bearish cont.0.70
“The next 1-2 months may see continued price pressure as narrative strength fails to translate into positive price movement. Divergence from peers suggests a challenging environment for recovery.”
2026-06-01TODAY
bullish cont.0.70
“SMCI's price momentum has surged ahead of its AI server demand narrative, with a significant NDS of -174.1 indicating price leading the story. The company's relative return of +34.4% over the broad tape suggests strong investor confidence in its potential despite a cooling narrat”
L3Expectation Lifecycle FieldBUILT V1
Each (actor, theme, direction-sign) is a persistent thesis tracked through typed lifecycle events: born / strengthened / weakened / contradicted / retired.
addsTurns SMCI's daily expectation summaries into a memory system. Theses you can follow for weeks, not snapshots that vanish overnight.
today54 theses tracked over the window; 2 active today, 0 persistent (≥3 daily versions). Lifecycle events: born 54 · strengthened 1 · weakened 1 · contradicted 15 · retired 57.
SMCI’s expectation theses29 theses shown · 54 total
↑Arista Networks growth and valuation analysis
●✕○
↓Arista Networks growth and valuation analysis
●○
↓AI-driven growth and stock performance
●
─AI-driven growth and stock performance
●○
↓AI investment strategies for 2026
●✕○
↑AI investment strategies for 2026
●✕
↓Tech giants navigate AI and regulation
●○
↓AI Integration and Investment Trends
●○
─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
●✕○○
↑AI investment strategies and market outlook
●✕○
↑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
●○
↓AI efficiency breakthroughs and innovations
●○
↑Earnings season pressures software valuations
●✕○
─AI efficiency breakthroughs and innovations
●○
─AI chip market dynamics and trends
●
↓Alphabet's growth and investment potential
●○
↓AI investment and workforce dynamics
●○
↑Netflix stock volatility and investor sentiment
●○
↓Micron stock momentum and forecasts
●○
↓AI-driven chip market dynamics
●○
↓Super Micro Computer volatility and challenges
●✕
●born▲strengthened▼weakened✕contradicted○retired
2025-11-26FIRST
quiet
2026-01-05
✕contradicted
1 active thesis
2026-02-11
○retired
▼weakened
2 active theses
2026-03-19
3 active theses
2026-04-27
●born
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. SMCI appears in 20 public regions and 11 limited, including 4 inverted.
SMCI appears in 42 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
↓Arista Networks growth and valuation analysis
n=48
54% (+6pp)
63% (+13pp)
16
↑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
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 SMCI, 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/SMCI.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.