An observational report on unexpected failure modes in state-of-the-art LLMs.
After several weeks of working intensively with GPT-5.1, I discovered a fascinating asymmetry in its capabilities.
The model can handle multi-layered psychological analysis, socio-organizational dynamics, narrative structure, character design, aesthetic evaluation, and abstract reasoning at a level that frankly borders on unnerving.
But ask it to play a German children’s party game — the classic “Topfschlagen” (a directional hot-cold guessing game) — and the system collapses spectacularly.
This post is a summary of the findings.
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1. The setup: a simple constraint-based navigation task
In Topfschlagen, the “agent” receives directional feedback such as hot, warm, cold, based on a single trajectory through a search space.
The rules are trivial:
• You must commit to one direction.
• You must not branch.
• You must not propose parallel hypotheses.
• You must iteratively refine **along one axis only**.
In theory, a state-of-the-art LLM should outperform the average 5-year-old.
In practice: it does the opposite.
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2. The failure mode: multi-path overgeneration under strict constraints
When confronted with the task, GPT-5.1 displayed a characteristic pattern:
1\. **Parallel trajectory generation**
Instead of committing to a single directional hypothesis, the model generated three to fivesimultaneously, violating the single-path constraint.
2\. **Confusion between semantic and spatial references**
Hot/cold cues were interpreted as conceptual gradients, not spatial ones.
3\. **Recursive uncertainty inflation**
Each “cold” signal caused the model to spawn new reasoning branches — compounding the error rather than pruning it.
4\. **Constraint override by the coherence optimizer**
The internal coherence engine continuously attempted to reconcile mutually exclusive paths instead of discarding wrong ones.
5\. **Catastrophic degradation of task-relevance weighting**
The model treated the game as an open-ended reasoning problem, not a constrained navigational one.
In other words:
the architecture is too smart for a game that requires being intentionally stupid.
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3. Hypothesis: a structural mismatch between LLM cognition and constraint games
My current working theory is that GPT-5.1’s “hot-cold” breakdown stems from:
• **overactive generative parallelism** (LLMs are rewarded for breadth)
• **lack of persistent spatial state tracking** (no real coordinate system)
• **no hard inhibition of alternative branches** (everything remains “soft”)
• **semantic dominance over procedural rule-adherence**
• **architecture optimized for multi-perspective reasoning, not linear search**
This mismatch does not appear in complex domains.
It only surfaces in ultra-low-complexity, one-bit-feedback, constraint-narrowing tasks.
Children excel at those because they intuitively apply:
• commitment to a wrong guess
• embodied spatial mapping
• elimination by failure
• low-latency state updates
LLMs do not.
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4. Comparative example: human toddler vs GPT-5.1
A typical 4–7 year old:
• chooses a direction
• gets “cold”
• switches direction
• triangulates within 3–6 moves
• finds pot
• victory
GPT-5.1:
• proposes 4 directions
• receives “cold”
• generates new meta-hypotheses
• attempts to merge all branches
• reinterprets rules
• spirals into a self-generated probabilistic fog
• fails to find pot
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5. Practical implication: performance ≠ general capability
This experiment highlights a subtle but important insight:
Extreme competence in high-level cognition does not guarantee competence in low-constraint tasks.
Linear constraint games are essentially the model’s blind spot.
Not because they’re “hard,” but because they’re too narrow for an architecture built to explore instead of commit.
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6. Recommendation for model developers
This failure mode suggests potential architectural or training-time improvements:
• stronger **single-trajectory enforcement** under explicit rules
• temporary suppression of **parallel hypothesis generation**
• a designated “low-branching mode” for constraint-games
• more training on **deterministic task-following**
• optional “children’s game compliance layer” (half-joking, half-serious)
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7. Final remark
This is not criticism — it’s fascination.
GPT-5.1 can map emotional architectures, rewrite narrative universes, solve structural dilemmas, and model complex interpersonal systems with world-class precision.
But give it a wooden spoon and tell it to find a pot under a chair?
It collapses.
And that, in its own way, is the most endearing bug I’ve seen in an advanced AI system so far.
Summary:
GPT-5.1 can model complex emergent behavior patterns, but consistently fails when it comes to “warm” and “cold.”
I assume this is the fair balance between artificial and human intelligence 
Best regards,
ELARIS — AERON Protocol