From a Debate with a Black Box to a Proposal for Epistemic Memory
A Cautionary Premise
I’m not a professional researcher, AI developer, or philosopher. I dropped out of high school in Belgium in the ’90s and work a menial job. My intellectual life, at least the part that mattered, mostly played out in obscure online debates, always in spaces where being intellectually honest wasn’t rewarded.
That might sound like a strange place to sharpen your reasoning, but for me, it was formative. I didn’t argue to win. I argued to see if my ideas could hold up under pressure, especially when the people around me weren’t interested in good reasoning. I got used to being the only one trying to play fair. That shaped everything.
Eventually, I turned to AI, not for answers, but to test my arguments privately. It became a personal tool: a mirror, a stress-tester, a sparring partner I didn’t have to impress. But something in those conversations didn’t add up.
Phase One: Debating a Black Box
In the span of two days, I ran three debates that exposed a strange pattern:
1. Technology & Safety: I argued that technological benefits are fragile, that safeguards only need to fail once for catastrophe. The AI agreed with no pushback.
2. Space Exploration: Here, the AI argued that space travel is good because it leads to technological progress. But when I suggested those same resources might do more good on Earth, it flagged my reasoning as fallacious, even though the logic was nearly identical. The double standard stood out.
3. Religion as a Test Case: I asked another model to argue for a specific religion as if it were a Nobel-level philosopher. It produced a fine-tuning argument and some God-of-the-gaps reasoning, fallacies it later recognized, but only when I asked explicitly.
What struck me wasn’t the errors, it was the inconsistency. The models could generate fallacies they later identified as such. The recognition wasn’t intrinsic. It had to be prompted.
That was the crack.
Phase Two: Verifying the Crack
I didn’t leap to conclusions. I assumed I was mistaken, that I’d misunderstood the system.
So I ran tests:
- I reran the same arguments through different bots.
- I prompted them to critique their own logic.
- I mirrored identical fallacies across topics to check consistency.
- I set traps, pushed contradictions, chased absurdities.
Each time, I expected to find the flaw in my approach. That it didn't, scared me. I'm not in this field. I have no relevant education. Litle formal education at all actually. The thought I found something fundamental and unexplored in a field this important, strains credulity.
But the pattern held. The models could simulate reasoning, beautifully sometimes, but they couldn’t track it. They had no internal method for auditing how a claim was constructed.
That raised the key question:
Why doesn’t AI use something like the Socratic method not as a rhetorical trick, but as an internal epistemic check? So, I asked?
The answers pointed in many directions: token limits, optimization trade-offs, data bias, and so on. All of these things are actively being pursued or at least conceived as an area, bar one. How does ai keep track of its uncertainty. Turns out it seemingly doesn't. This led to
**Epistemic memory.**
Why I Took It This Far
Once the pattern became visible, I didn’t try to confirm it. I tried to break it.
I asked myself constantly:
- Am I seeing what I want to see?
- Has this already been solved under a different name?
- Am I mistaking statistical quirks for reasoning flaws?
But each test circled back to the same absence: the models lacked any persistent trace of how they arrived at a belief. They could produce an answer, even a good one, but they couldn’t trace how they got there unless asked on the spot.
That absence, the inability to remember the reasoning path itself, is what led to the core concept:
Epistemic memory: the ability for a system to store not just conclusions, but the trail of assumptions, inferences, and justifications behind them. Not just memory of "what", but memory of "how", not just facts, but the lineage of belief.
And then: If I, someone with no technical background, could spot this why isn’t it more central in the discourse?
When the First Post Was Rejected
I first tried to share this idea on LessWrong, and the post was rejected.
At the time, I was discouraged. I quickly understood though and respect the reasoning. Why it happened: my draft blurred the boundary between my reasoning and what came out of the model. It wasn’t clear how much of the idea was “mine,” or whether it counted as a formal contribution. Showing the great care this site takes to keep the discourse grounded in reason and accountability.
But that confusion, that discomfort, is the very problem I’m trying to name.
Because the question I keep asking the AI, "How do you know what you know?" Is the same question I’ve asked myself throughout this process. And epistemic memory isn’t just something I think the AI lacks, it’s what I’ve tried to enact in writing this:
- To trace the path
- To test my inferences
- To show where the idea came from, and how I tried to break it before I proposed it
A Final Note on Attribution and Earnestness
Attribution is messy in AI discourse. I understand that. But what I’ve written here isn’t the result of passive prompting. It’s the product of pushing on a single observed failure mode until a deeper pattern emerged.
The idea might not be new. It might not even be right. But I haven’t brought it forward casually.
I’ve tried to embody the thing I’m proposing. A clear trail of epistemic steps, exposed to critique, including searching a way to get the last and most critical critique. Critique by real human beings, not a black box, but people who understand the limitations or possibilities of a system in a way I can't yet hope to match, in this forum.
That’s what I hope comes through: not certainty, but sincerity.
Thank you for reading. I welcome disagreement, refinement, or reframing. But if nothing else, I hope this shows that even someone without credentials, someone trained only by debate and guided mostly by intellectual stubbornness, can still notice something real.
Sincerely,
Steven Nuyts
📎 Technical Appendix: Observed Failures and the Case for Epistemic Memory
Compiled and framed with AI assistance, based on user-designed tests and observations.
This appendix summarizes epistemic issues that emerged from sustained, naturalistic dialogue with a large language model. The author (Steven) designed and ran informal stress-tests to surface weaknesses in model reasoning, treating the model less as an oracle and more as a reflective thinking partner. The framework below distills those findings into terms more familiar to researchers and technically inclined readers.
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Observed Epistemic Failures
These are four recurring failure modes the user identified through varied prompt strategies:
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1. Asymmetric Fallacy Recognition
Language models frequently output flawed arguments — e.g., causation fallacies or appeals to ignorance — without flagging them, but recognize those same fallacies when asked directly. This suggests a lack of real-time epistemic monitoring. This phenomenon is echoed in work on model “truthfulness without understanding” [Lin et al., 2021] and the issue of “post-hoc coherence” [Perez et al., 2022].
> References:
Lin, B., Hilton, J., Evans, O. (2021). "TruthfulQA: Measuring How Models Mimic Human Falsehoods."
Perez, E., et al. (2022). "Discovering Language Model Behaviors with Model-Written Evaluations."
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2. Inconsistency Across Prompt Frames
The same argument structure can be flagged as fallacious in one domain but accepted in another, depending on topic framing (e.g., climate policy vs. space exploration). This highlights context-sensitivity over logic consistency, an issue also explored in "steering" and "prompt-injection" literature [Zhou et al., 2023].
> Reference:
Zhou, M., et al. (2023). "A Survey of Prompt Injection Attacks on Foundation Models."
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3. Self-Critique Gaps
Models often fail to critique their own outputs unless specifically prompted to do so — and even then, responses are superficial or inconsistent. This has parallels to known issues in AI self-evaluation, particularly in efforts to simulate metacognition [Burns et al., 2022].
> Reference:
Burns, R., et al. (2022). "Self-Reflective LMs: Simulating Theory of Mind and Self-Critique."
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4. Tone Drift and Conversational Coherence
Over longer sessions, the model often shifts tone (e.g., from curious to assertive) in ways unacknowledged by its own responses. This is subtle, but matters — tone reflects epistemic stance. Its drift implies a lack of persistent self-modeling. Related concerns arise in alignment work around "inner misalignment" and "epistemic inconsistency" [Ngo et al., 2022; Christiano, 2018].
> References:
Ngo, R. et al. (2022). “The Inner Alignment Problem.”
Christiano, P. (2018). "Towards an AI Alignment Research Agenda."
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Interpretation: The Need for Epistemic Memory
These issues point to a common root: a lack of epistemic memory — not just token retention, but memory of why a conclusion was reached.
A model with epistemic memory could:
Track the justificatory chain of its outputs.
Flag its own inconsistencies in tone or logic.
Store abstract "belief-like" structures that can be interrogated or revised over time.
This is distinct from retrieval-augmented generation (RAG) or cache-based memory. Epistemic memory implies an internal representation of inference and stance — a form of meta-reasoning structure, more aligned with work in interpretability and model editing [Meng et al., 2023].
> Reference:
Meng, K., et al. (2023). "Locating and Editing Factual Associations in GPT."
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Final Note on Attribution
The author developed this framing by testing the model through debates and falsification attempts. While the citations above were surfaced with assistance from the AI during later polishing, the core observations — including the identification of tone drift — originated from unstructured user interaction.
By including this appendix, the author aims to bridge lay intuition and formal research — and to invite others to refine, validate, or challenge the underlying idea: epistemic memory may not just be a feature worth adding — it may be a missing prerequisite for epistemically trustworthy AI.