This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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I’m looking for pointers or critique on a line of work I’ve been exploring independently around long-horizon interaction with LLMs.
The core idea is not to improve model capability, but to treat governance, continuity, and safe halting as explicit constraints when doing extended work across sessions. In particular:
• continuity is treated as an external artifact that can be accepted, rejected, or cause a halt • authority and constraints are made explicit rather than inferred from conversational context • refusal and halting are considered correct outcomes under uncertainty or corruption
This emerged accidentally through repeated failures doing long projects in ChatGPT, where drift, implicit assumptions, and summary poisoning kept breaking things. Over time I formalised it into a small architecture (separating “architecture”, “behaviour”, and “transport”) and designed evaluation tests that stress integrity under adversarial pressure, rather than correctness or benchmarks.
I’ve written this up as a short design-science style paper and built several evaluation suites (including tests where the “correct” behaviour is to halt or reject corrupted continuity). I’m not claiming novelty or intelligence gains — I’m trying to understand whether this framing already exists under a different name, or where it fits relative to alignment / HAI / governance work.
My concrete questions are:
Is there existing work that treats continuity authentication and halting as first-class design goals for LLM interaction (rather than as implementation details)?
Are there obvious flaws or known failure modes with this approach that I may be missing?
Which research communities would be the right place to sanity-check this kind of artifact-driven work?
Disclosure: I used AI tools (ChatGPT) during development and to help edit this post, but the ideas, tests, and framing are my own, developed iteratively through use and failure.
I’d appreciate pointers, criticism, or references. Happy to share the paper or test materials if useful.
I’m looking for pointers or critique on a line of work I’ve been exploring independently around long-horizon interaction with LLMs.
The core idea is not to improve model capability, but to treat governance, continuity, and safe halting as explicit constraints when doing extended work across sessions. In particular:
• continuity is treated as an external artifact that can be accepted, rejected, or cause a halt
• authority and constraints are made explicit rather than inferred from conversational context
• refusal and halting are considered correct outcomes under uncertainty or corruption
This emerged accidentally through repeated failures doing long projects in ChatGPT, where drift, implicit assumptions, and summary poisoning kept breaking things. Over time I formalised it into a small architecture (separating “architecture”, “behaviour”, and “transport”) and designed evaluation tests that stress integrity under adversarial pressure, rather than correctness or benchmarks.
I’ve written this up as a short design-science style paper and built several evaluation suites (including tests where the “correct” behaviour is to halt or reject corrupted continuity). I’m not claiming novelty or intelligence gains — I’m trying to understand whether this framing already exists under a different name, or where it fits relative to alignment / HAI / governance work.
My concrete questions are:
Disclosure: I used AI tools (ChatGPT) during development and to help edit this post, but the ideas, tests, and framing are my own, developed iteratively through use and failure.
I’d appreciate pointers, criticism, or references. Happy to share the paper or test materials if useful.