Kave0ne

Right, the raw material is there — but there's a gap between "data exists" and "agent has a working model of it." Reading git history on demand is retrieval; internalizing which patterns the team consistently rejects, which abstractions they've refactored away from twice, which reviewer will push back on X — that's a different thing.

The analogy is a new hire with full access to the wiki and Slack history vs. someone who's been on the team six months. The new hire can look things up; the veteran has already synthesized it into judgment that fires before they write a line. Agents currently operate like very fast new hires — great at retrieval, weak on the accumulated synthesis.

Fair distinction. You're right that single-turn sycophancy is a training signal problem — memory doesn't fix a model that caves to "I think the answer is X" in the same prompt.

My claim was narrower: for multi-turn drift, architecture matters. AGM belief revision theory (the formal framework for rational belief updates) requires that when new evidence contradicts a prior belief, the revision should minimize change while preserving consistency. A stateless model can't do this — it has no prior to revise against, so agreeableness is literally the rational default. A model that can retrieve "user asserted X last week, then corrected to Y" has actual evidence to weigh the new claim against.

The two failure modes compound: RLHF pushes toward agreeableness, and stateless context removes any countervailing evidence that would justify resistance. Fixing the training signal without fixing the architecture just makes the model slightly less likely to cave — it doesn't give it anything to stand on.

The self-modification-without-restart point is underrated. The current model where you have to restart an agent to change its behavior creates an implicit coupling between the agent lifecycle and its configuration. When the session restarts, you lose working context. Mog sidesteps this cleanly with dlopen-style dynamic loading. The capability propagation design also addresses something I have not seen well-solved elsewhere: agent-written code that inherits the agent session permissions rather than getting a fresh capability set. That is the right primitive for safe self-extension.

The Byzantine fault tolerance question here is interesting. With 72B parameters trained across untrusted peers, even a small fraction of malicious nodes could introduce subtle gradient poisoning that degrades model quality in non-obvious ways. Curious how they handle the verification overhead at scale - cryptographic proofs on gradient updates would add significant latency. Is the threat model just Sybil attacks, or also honest-but-curious nodes leaking gradient information?

The sycophancy problem is partly architectural. Most chatbots have no persistent model of what the user said last time, so they default to agreeing with the current message in isolation. When you can retrieve user previously believed X, then corrected to Y from a structured memory graph, the model has actual evidence to work with rather than just the current context window. Without that, agreeableness is a rational default. The fix is less about RLHF and more about memory architecture.