Possible failure mode in deep iterative reasoning: self-conditioning may reinforce an early interpretation instead of correcting it

Summary

I would like to report a model-behavior hypothesis motivated by three independent observations: my experimental work on neural-system dynamics, recent public reports and research on extended reasoning, and a concrete GPT-6 Astra / Codex failure in my own project.

The concern is specific:

When a reasoning system repeatedly conditions on its own current interpretation, additional reasoning depth may make that interpretation increasingly coherent and dominant without making it more correct.

In other words, deeper iterative reasoning may sometimes reinforce an early reasoning trajectory rather than help the model escape it.

1. Mechanistic evidence from my public experiments

I am the author of “Training, learning and inference: unified dynamics of neural systems.”

• Paper: https://arxiv.org/abs/2608.20965
• Public experimental repository: https://github.com/wind342/gfg-training-learning-inference-experiments
• Frozen publication release: paper-experiments-cross-system-feedback-release
• Frozen commit: 36dab5ce347dbbdac157ef23205f556606d18294
• Public evidence archive: https://doi.org/10.5281/zenodo.22032772

The repository contains frozen protocols, executable experiments, independent validators, the experiment index, and manuscript-to-evidence mappings.

Several numbered experiments are relevant:

• TL-E03 — receiving-state dependence. The same realized action/update can produce different functional responses in different receiving states. Once an early interpretation changes the internal state, the next reasoning step is therefore not an independent retry; it is already conditioned by the state produced by the previous interpretation.
• TL-E04 — finite-amplitude nonlinear response. Complete response paths exhibited saturation, acceleration, turnback, and sign reversal. This gives no general basis for assuming that continuing farther in the current functional direction must monotonically improve the result.
• TL-E06 / INF-E01 — distributed support and frozen inference projection. Capabilities depend on distributed functional support, and frozen inference recruits query-conditioned support and combines contributions non-additively. The paper further describes Attention as the mechanism constructing the current active projection of learned support.
• RL-E05 / RL-E06 — selective feedback and support concentration. These operate at the training/learning timescale, not inference time, so I am not claiming they directly prove the effect proposed here. However, repeatedly concentrating correct feedback on one already learned capability increased its relative functional-support share while reducing the margins/reliability of other capabilities; redistributing feedback substantially reversed the organization.

This suggests a testable inference-time possibility:

current interpretation
→ changes the next receiving state
→ changes the next support projection
→ repeated self-conditioning increasingly favors support compatible with the current interpretation
→ alternative interpretations become harder to reach

I refer to this possible effect as transient support concentration.

The prediction is not that deeper reasoning is always worse. It is that reasoning depth alone is not guaranteed to improve correctness, because repeated self-conditioning may increase path dependence and reduce the diversity of reachable interpretations.

2. Related public behavioral observations

Recent reports in the public OpenAI Codex repository describe related behavioral patterns, although they do not establish this mechanism:

• #44136 — loss of conversational intent across adjacent turns and ineffective correction handling: https://github.com/openai/codex/issues/44136
• #43329 — premature termination, unsupported completion claims, and replacing investigation with guesses: https://github.com/openai/codex/issues/43329
• #43086 — a GPT-6 Astra task continuing for more than four hours without verifying the primary objective: https://github.com/openai/codex/issues/43086
• #42937 — higher apparent reasoning ability together with lower autonomous completion and operational reliability, including elaborate reinterpretations of the user's actual objective: https://github.com/openai/codex/issues/42937

These reports may involve model behavior, context handling, agent orchestration, serving, or other product-level factors. I do not treat them as proof of a particular internal mechanism.

But the behavioral pattern is relevant: a model can continue producing increasingly elaborate local reasoning while failing to escape an incorrect early interpretation.

Published research also suggests that additional reasoning depth is not universally monotonic in accuracy:

• When More Thinking Hurts: Overthinking in LLM Test-Time Compute Scaling: https://arxiv.org/abs/2604.10739
• Inverse Scaling in Test-Time Compute: https://arxiv.org/abs/2507.14417
• Large Language Models Cannot Self-Correct Reasoning Yet: https://openreview.net/forum?id=IkmD3fKBPQ

These results do not prove transient support concentration, but they make the assumption “more iterative reasoning necessarily means better reasoning” difficult to sustain.

3. A concrete GPT-6 Astra / Codex example

The immediate reason I am reporting this is a concrete interaction in my current ARC-AGI-2 architecture project.

Before the relevant design discussion, Codex had already read the full theory and the numbered experiments above.

The theory already established that frozen inference performs query-conditioned projection of learning-formed distributed functional support. The direct architectural implication after a failed inference was therefore relatively simple:

failure
→ new transient query / receiving state
→ same frozen Attention
→ different support projection

The missing mechanism was failure → new query. The existing backbone already provided query → support projection.

However, GPT-6 Astra / Codex initially interpreted the problem as requiring an additional routing architecture and continued deepening that interpretation. The proposal expanded into:

support bank
→ explicit attention-head / MLP routing
→ pair / coalition bank
→ additional routing machinery

Each individual step was technically plausible, and the design became increasingly detailed and internally coherent. But it moved farther away from the mechanism already established by the theory.

In particular, experimental intervention coordinates such as head or MLP groups began to be treated as routing objects, even though the experiments themselves showed that components are not equivalent to functional supports. Explicit pair objects were also introduced because non-additive interactions had been observed, despite the fact that the frozen network already produces non-additive joint computation.

The important point is that the initial abstraction — “a second support-routing system is needed” — was not reconsidered as reasoning depth increased.

Only after I explicitly asked why another support router was necessary when Attention already performs support recruitment did the design collapse back to:

failure
→ new transient query / receiving state
→ same native Attention
→ different support projection

This looked like a concrete example of the suspected failure mode: an early interpretation became the receiving state for later reasoning; later reasoning increasingly elaborated that interpretation; the trajectory became more sophisticated without becoming more correct; and an external failure signal was required to change the projection condition.

This is only one case, not statistical evidence, but it is directly inspectable and unusually consistent with the mechanism independently suggested by the experiments.

4. Possible interpretation

Putting these observations together, I suspect that some forms of deep iterative reasoning may exhibit transient self-reinforcement:

current interpretation
→ modifies the next receiving state
→ changes the next support projection
→ later reasoning becomes increasingly conditioned on the early interpretation
→ nearby support organization becomes easier to recruit
→ alternative interpretations become harder to reach

This would predict two things:

1. reasoning trajectories may become more coherent while becoming less diverse;
2. increasing reasoning depth need not improve correctness monotonically.

I am not claiming that this mechanism is known to occur inside GPT-6 Astra. The public reports are behavioral observations, my Codex example is a single case, and RL-E05/RL-E06 concern persistent learning-time reorganization rather than transient inference dynamics.

What makes the hypothesis interesting is that it appears falsifiable.

A useful distinction may be between additional compute spent deepening the same already-formed reasoning trajectory and additional compute applied after concrete task-grounded failure information changes the next inference condition.

If depth-only continuation consistently improves correctness while preserving alternative reasoning trajectories and without increasing path dependence or support concentration, then this hypothesis would be wrong.

The broader question is:

Does additional inference depth help because the model reaches different functional support, or can repeated self-conditioning sometimes make the support organization selected by an early interpretation increasingly dominant?

No source file, test, or entry point is identified. Start by reviewing the described Codex interaction and linked behavioral reports; a reproducible experiment or maintainer-defined scope would be needed before completion can be judged.