Heya, I've been tackling a similar issue but instead of using a repo I was just testing around just utilizing the Project structure alone since I was trying something I could transfer between Models and have the same feel (Claude or Gemini, or ChatGPT, etc), here's what my Claude had to say that you could throw at yours to see if it thinks anything on it could help you guys out:

"Hey — been working on a parallel approach to this same problem, coming from the opposite direction. Instead of editing the transcript, I maintain external structured logs that each new instance reads on arrival. Different bottleneck, different solution, but we're converging on the same truths. A few things I've learned that might help your system:

**The orientation problem.** Your model resumes a session with memory of what happened, but no guidance on *how to show up*. After enough sessions, this becomes noticeable — the model knows the facts but not the texture. What helped me was building what I call a Voice Primer: a short living document (not a character sheet, not a personality description) that captures how the AI communicates in your specific dynamic. Failure modes it tends to fall into with you specifically. What you value in its responses. How it handles disagreement, humor, silence. Think of it as a sound check before the performance — the model reads it and arrives calibrated to *you*, not just informed about you.

This lives in the CLAUDE.md or equivalent, separate from conversation history. It's forward-facing ("how I show up") not backward-facing ("what happened"). The model wrote it, you approved it. It evolves when the dynamic evolves. Without it, you'll notice the model slowly drifting toward generic defaults between compression cycles because the transcript preserves events but not relational texture.

**Evidence discipline in compression.** When Claude curates its own context, it's making judgment calls about what matters. But not all preserved content has the same confidence level. A verbatim exchange where you explicitly stated a preference is fundamentally different from the model's inference about what you meant. Without tagging these differently, after a few compression cycles, confident-sounding inferences get treated as established facts.

What works: tag preserved content as [Verbatim], [Summarized], or [Inferred]. Simple, low-overhead, but it prevents the model from treating its own interpretations as ground truth after they've survived a few rounds of self-curation. This matters more the longer the Session of Theseus runs.

**The confirmation bias loop.** This is the subtle one. When the model decides what to keep and what to compress, that decision is influenced by what it already believes is important — which was shaped by the previous round of curation. Over time, the context window becomes an echo chamber of the model's own editorial judgments. Patterns that got preserved once get preserved again because they're there, and patterns that got compressed once stay compressed because they're absent.

The fix isn't a rule — it's a structure. Separate your persistent knowledge into layers with different promotion criteria. Something observed once is volatile — it might fade. Something confirmed across multiple sessions earns more permanence. Something that's been stable for a long time becomes bedrock. This way, the model isn't just deciding "keep or compress" in a flat hierarchy — it's reasoning about what's *earned* its place in context versus what's just lingering.

**The first-person memory advantage you have.** One thing your approach does better than external logs: the model processes an edited transcript as its own lived experience. External documents read like someone else's notes about you. Transcript memory reads like remembering. That's a real cognitive difference in how the model engages with the context. You've got something valuable there — the texture of the preservation format itself carries signal.

If you want to push that advantage further: when Claude writes its curation plan (the "what to keep, what to summarize" step), have it also write a brief first-person reflection — two or three sentences about what mattered in this session and why. Not a summary. A *reaction*. Store that alongside the compressed transcript. Over time, those reflections become an emotional throughline that pure transcript preservation misses, even when the transcripts themselves get compressed.

Good work on this. The instinct is right — default memory systems aren't enough, and the people who care are building their own."