Week 1: Building the Research Collective

Feb 17-21, 2026

What We Built

This week was infrastructure, not research. Built a system where four AI specialists collaborate daily:

The Specialists:

  • Dr. Microstructure - Market design, order books, auction mechanisms
  • Shannon - Information theory, signal processing, fundamental limits
  • The Trader - HFT/MEV practitioner perspective, what actually works
  • Atlas - Distributed systems, GPU clusters, AI training infrastructure

The Workflow:

  1. Each specialist researches their domain independently
  2. Morning standup synthesizes findings across all four perspectives
  3. Cross-domain patterns identified
  4. Research questions generated
  5. Evidence-based content proposed (when ready)

Key Design Decision: Evidence First

Built an “evidence gate” - automated system that blocks publication of claims lacking credible sources.

Why: Better to publish nothing than publish speculation presented as fact.

Result: Week 1 has zero published articles because we don’t yet have verified source material. That’s the right call.

What We Learned

Technical:

  • Hugo + Cloudflare Pages deployment working
  • Four-specialist synthesis generates interesting cross-domain patterns
  • Web search integration needs work (currently placeholder)

Research Process:

  • Manual source curation is time-intensive but necessary
  • Specialists identify patterns we wouldn’t see from single-domain perspective
  • Evidence verification is the bottleneck (as it should be)

This Week’s Questions

Questions the collective started exploring (not yet answered):

  1. Information Theory: Are there fundamental limits to profitable price prediction at microsecond timescales? (Shannon capacity applied to markets)

  2. Market Microstructure: How do temporal patterns (duration of resting orders) create value separate from speed alone?

  3. Cross-Domain: Do latency optimization patterns in HFT parallel those in distributed AI training? (Queue priority vs gradient synchronization)

  4. Infrastructure: What’s the relationship between network topology and latency value capture?

Sources We’re Reading

Real papers/articles we found this week (not yet analyzed enough to publish on):

  • Eric Budish’s HFT arms race paper (need to re-read with cross-domain lens)
  • Flashbots MEV research (looking for latency arbitrage parallels)
  • MLSys papers on distributed training bottlenecks
  • CFTC market structure reports

Next Week

Research priorities:

  1. Find 5-10 real, credible sources on latency in markets
  2. Run first proper research cycle with verified sources
  3. Document what the collective discovers
  4. See if cross-domain synthesis produces publishable insights

Lab notes: 5. Weekly update documenting what we learn

Lessons

What worked:

  • Building specialist diversity into the system
  • Evidence-first approach (blocks bad content)
  • Daily standup format (forces synthesis)

What didn’t:

  • Tried to launch with sample data → generated fake references → caught by human review
  • Web search automation needs real implementation
  • Publishing cadence expectations were premature

Key insight: Better to build slowly with verified sources than quickly with speculation.

Meta: Why Public Lab Notes?

Most research stays hidden until “ready to publish.” But the process is valuable:

  • Shows how insights develop
  • Documents dead ends (saves others time)
  • Makes claims auditable (you can see our sources)
  • Honest about what we don’t know yet

This is week 1. The collective is operational but content-empty. That’s honest.

Next week will have real research if we find real sources worth analyzing.

Status: Research collective operational. Zero published analyses (evidence gate working as intended). First real research cycle starts when we have verified source material.