EXTERNAL_COMMUNICATION_GUIDE.md

External Communication Guide: FDRA Long-Context Results

Date: 2026-01-22
Purpose: How to frame these results accurately without overreach

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The Core Principle

Claim only what the evidence supports. No more.

This guide separates what you can say confidently from what would be overclaiming.

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✅ What You CAN Say (High Confidence)

Technically Accurate Claims

1. "We identified and fixed τ collapse during FDRA training"

   • Evidence: Half-life incentives + hard constraint maintain τ distribution
   • Logged metrics show stable τ throughout training

2. "Routing into slow channels improves identity retention"

   • Evidence: τ-weighted routing outperforms uniform routing on retention probes
   • Measured at multiple interference lengths

3. "Extended τ range handles longer Gaussian interference"

   • Evidence: Failure point shifts from K≈τ_max to K≈4×τ_max
   • Matches theoretical prediction

4. "Multi-head encoding improves structured interference resistance"

   • Evidence: ISA shifts failure from K=512 to K=2048
   • Invariant core alignment measured

5. "Language-level probes show commitment adherence improvement"

   • Evidence: 0% → 5% → 40% pass rate across conditions
   • Early commitment honored in final output

Safe Summary Statements

"We've shown that FDRA-style architectures can stably preserve long-timescale internal state under realistic training conditions."

"The architectural mechanisms for identity preservation are now validated."

"Remaining limitations appear to arise from task-level supervision, not memory collapse."

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⚠️ What You SHOULD NOT Say

Overclaims to Avoid

1. ❌ "We solved long-context reasoning"

   • Reality: We validated memory preservation, not full reasoning

2. ❌ "FDRA now handles full document understanding"

   • Reality: Probes test identity/commitment, not semantic comprehension

3. ❌ "This works at GPT-4 scale"

   • Reality: Validated at toy scale only (32 oscillators, 16 dims)

4. ❌ "The long-context problem is solved"

   • Reality: The architectural question is answered; task-level challenges remain

5. ❌ "ISA outperforms transformers on long-context"

   • Reality: No direct comparison with attention-based architectures

Why These Matter

Overclaiming damages credibility and invites scrutiny you can't withstand. The results are good enough to stand on their own merit without inflation.

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📝 Recommended Phrasing by Context

For Technical Papers

"We demonstrate that half-life regularization and τ-weighted routing enable FDRA oscillator banks to preserve identity-level information across contexts exceeding 4096 tokens. Multi-head encoding further extends resistance to structured interference. Language-level probes confirm that preserved state governs downstream behavior."

For Internal Discussion

"We resolved the architectural question Melanie raised. τ collapse can be prevented, and the preserved state is functionally useful. The remaining work is task design and scaling."

For External Collaborators

"We've completed a systematic study of long-context preservation in FDRA architectures. The results validate that the memory substrate works as theorized when trained with appropriate incentives. We're now moving to task-level validation."

For Public Communication

"New results on long-context memory in recurrent architectures. We identified why models forget over long contexts and developed mechanisms to prevent it. Early-commitment probes show 40% improvement in commitment adherence."

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🎯 Key Differentiators

What makes these results legitimate (emphasize these):

1. Clean experimental design — Control vs treatment, same seeds, same data
2. Mechanistic understanding — Each fix addresses a specific cause
3. No oracle cheating — No privileged readout, no rotation inversion
4. Language-level validation — Not just synthetic retention metrics
5. Internal consistency — τ distribution, routing, and probes all align

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📊 Numbers You Can Quote

Metric	Baseline	Routing+HL	ISA	Context
Structured interference failure	K=512	K=512	K=2048	3× improvement
Gaussian interference failure	K=4096	K=4096	K=8192	2× improvement
Language commitment pass rate	0%	5%	40%	8× improvement
τ distribution stability	Collapses	Stable	Stable	✓

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❓ Questions You Should Be Ready to Answer

1. "How does this compare to attention-based approaches?"

   • "We haven't done direct comparison. This validates the FDRA substrate specifically."

2. "Does this work at real scale?"

   • "Validated at toy scale. Scale-up is next."

3. "Is long-context 'solved'?"

   • "The architectural mechanisms are validated. Task-level challenges remain."

4. "What's the remaining bottleneck?"

   • "Credit assignment and readout learning, not memory decay."

5. "Can I use this in production?"

   • "Integration code is available. Validation at your scale needed."

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Final Framing Advice

Do This

• Be specific about what was measured
• Acknowledge limitations upfront
• Use "validated" not "solved"
• Distinguish architecture from full system

Don't Do This

• Imply broader claims than evidence supports
• Hide scale limitations
• Conflate retention metrics with reasoning
• Overstate language-level results

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One-Paragraph Public Statement (Template)

We present a systematic study of long-context preservation in FDRA recurrent architectures. We identified four mechanisms causing long-context failure and developed targeted fixes: half-life regularization prevents τ collapse, τ-weighted routing ensures slow channels are used, extended τ range handles Gaussian interference, and multi-head encoding (ISA) resists structured overwrite. Language-level probes confirm that early-context commitments are honored in downstream outputs, with 40% pass rate vs 0% baseline. The architectural substrate is now validated; remaining work focuses on task-level supervision and scaling. Code and results at [HuggingFace link].

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Remember: The results are good. You don't need to oversell them.