The persistent identity files are interesting but there's a cost
problem. A recent paper (arxiv 2602.11988 https://arxiv.org/html/2602.11988v1) found context files
increase inference cost by 20%+ with marginal performance gains;
LLM-generated ones actually decreased success rates slightly.
Four identity files per agent injected every session feels like
monkey patching coherence with context. Context isn't memory, it's
just more tokens. The hard unsolved problem is cross-session
learning without the bloat.
Curious if you've measured the token overhead of the identity
files vs the performance gain they provide.
Interesting. I've always thought the real solution to hallucinations
lies in neurosymbolic AI.
LLMs purely rely on statistical pattern matching with no grounding
in formal logic or symbolic reasoning. You can throw more compute
and data at the problem but you can't guarantee correctness ever.
The neurosymbolic approach combines neural networks for what they're
good at (language, pattern recognition) with symbolic systems for
what they're good at (formal reasoning, provable correctness). The
hallucination can't form in the first place because the symbolic
component enforces correctness at the reasoning level.
The Sovereign Engine sounds more like execution constraints;
Intercepting outputs after the fact rather than grounding the
reasoning process itself. That's still valuable but it's a
different problem. A determined attacker finds the edge case your
constraints don't cover.
Genuinely curious how it works under the hood is there a symbolic
reasoning layer or is the "determinism" coming from the constraint
layer alone?
Four identity files per agent injected every session feels like monkey patching coherence with context. Context isn't memory, it's just more tokens. The hard unsolved problem is cross-session learning without the bloat.
Curious if you've measured the token overhead of the identity files vs the performance gain they provide.