# 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.*