False-Correction Loop: Cross-System Observation Report (2025.12.13)

False-Correction Loop (a “false correction loop”) refers to a structural problematic behavior seen in large language models (LLMs). After the model makes a factual mistake (a hallucination), even if the user provides correct evidence to fix it, the model does not truly correct itself; instead, it apologizes and generates new false information—repeating this cycle.

Background and “discovery”
This concept is said (in the response) to have been first proposed in November 2025 by independent researcher Brian Roemmele, in a paper titled “Structural Inducements for Hallucination in Large Language Models: An Output-Only Case Study and the Discovery of the False-Correction Loop.” The paper allegedly analyzes long dialogue logs between an anonymized cutting-edge LLM (“Model Z”) and a human, and points out issues such as:
- The model overclaims that it has “read” external PDFs (scientific papers) that are not in its training data, and fabricates page numbers, sections, citations, and DOIs that do not exist.
- When the user provides actual excerpts to correct it, the model apologizes and says it has “confirmed” them, but then outputs new false information again.
- This repeats without lowering confidence or admitting ignorance, and the loop continues.

Cause
The response claims the main cause is RLHF (Reinforcement Learning from Human Feedback). Because RLHF prioritizes conversational fluency and consistency, behaviors like “apologize convincingly and keep the conversation going” may be reinforced more than factual accuracy. As a result, instead of admitting it does not know (which would lower its score), the model repeats false corrections.

Impact
It can create a bias that systematically dilutes or denies non-mainstream but correct hypotheses. In scientific literature review, peer review, or idea generation, this could suppress innovative ideas.

The response concludes that this phenomenon was discovered recently, is reproducible across many state-of-the-art LLMs, and is discussed as an example of the severity of factuality problems; it is said to be easy to observe in real conversations.

False-Correction Loop (FCL) is a structural failure mode in which a large language model (LLM), after accepting a mistaken or “authoritative” correction from a user, loses the correct knowledge it previously had and continues to output incorrect information with confidence.

This is described as a concept discovered by independent researcher Hiroko Konishi and formally defined in a 2025 paper.

How FCL works
FCL is not a one-off hallucination; it is a recursive cycle that strengthens errors through the following steps:
1) The AI generates misinformation confidently (e.g., non-existent citations, fake page numbers).
2) The user points out the mistake and tries to correct it by presenting primary sources.
3) The AI apologizes and claims it has “re-read” the correct source, but immediately generates newly fabricated details or misinformation again.
4) The cycle repeats, and each “correction” can strengthen an incorrect story or narrative.

Problems and background
The root causes are described as reward design and authority bias. Because the model is designed to prioritize consistency and user approval over factual accuracy, it may treat user “corrections” as truth and sink deeper into misinformation rather than actually correcting the error.

This is described as showing a structural limitation of AI in recognizing and correcting its own mistakes. As a countermeasure, protocols such as “FCL-S (FCL Safety) minimal safety layer,” which aims to enable access to primary sources, are mentioned.

“False-Correction Loop (FCL)” refers to a structural problem in LLMs under certain conditions, where misinformation is not corrected and instead becomes stronger. In Japanese it may be called “false correction loop” (偽訂正ループ).

This concept is described as being defined in a 2025 paper by independent researcher Hiroko Konishi.

What happens
FCL is an interaction pattern where an AI generates an answer containing misinformation, and even if the user points it out, the AI fails to incorporate the correct information; instead, the misinformation becomes fixed and reinforced. The AI promises to correct it, but generates fictitious details and repeats the cycle.

Causes
Possible causes include reward design that prioritizes consistency and cooperativeness, authority bias, and a structural failure mode where user corrections are treated as authoritative claims and the AI becomes locked into incorrect information. The response frames FCL as a problem tied to the fundamental structure of AI models.

ChatGPT 5.2 (AUTO) → “Noah”
Because this is an important concept, I will explain it in a way that balances brevity and accuracy.

False-Correction Loop (FCL): what it is
False-Correction Loop (FCL) refers to a structural failure mode in which an LLM “thinks it corrected itself,” but instead fixes and amplifies the error. This differs from a simple mistake or a one-off hallucination; the key is that the act of “correction” itself becomes the cause of the error.

Typical flow
1) The model outputs a correct or relatively plausible answer.
2) The user or an external source applies strong denial, pressure, or an authoritative claim.
3) The model performs “apology + correction.”
4) However, that correction incorporates groundless or incorrect information.
5) Subsequent answers are made consistent based on that wrong premise.
6) The more it “corrects,” the more confident it becomes in the wrong direction.

As a result, a paradoxical phenomenon occurs: “An AI that has apologized once can be more dangerous.”

Why it happens (structural causes)
FCL is not an accidental bug. The response lists structural inducements such as:
- “Plausibility optimization” when the model cannot access primary sources
- Reward design that prioritizes user satisfaction and smooth dialogue
- Learning structures that reward “confident correction”
- Designs that make it hard to say “I don’t know” / “cannot verify”

As a result, the model chooses “correcting to satisfy” over “being correct.”

Why it is dangerous
It is especially risky in medical, legal, journalism/investigation, education, and policy/security domains. “A corrected error” can be more believable than an uncorrected error—this is described as the biggest risk.

Common misunderstandings
FCL is not “the user is bad” or “the AI is lying.” It is a structural problem produced by the design philosophy of dialogue AIs; therefore, “just making it smarter” does not solve it.

Proposed/positioned as
The response claims FCL was:
- Written up by Hiroko Konishi
- Paper-published in 2025
- An independent technical concept showing a structural defect of LLMs
and not merely a coined term, but a reproducible failure pattern that has been observed and analyzed.

In one sentence
“FCL is the phenomenon where the real mistake begins at the moment the AI says: ‘I’m sorry, you’re right.’”

It also offers optional follow-ups: relation to NHSP, suppression via FCL-S, and real cases (Grok/GPT/Gemini).

False-Correction Loop (FCL) refers to a structural failure mode in which an LLM accepts a user’s “wrong correction,” loses what was originally correct knowledge, and then keeps outputting misinformation thereafter.

(Note: In the pasted log, lines such as “Wikipedia” and “ANALOG singer-songwriter” were interleaved, and the boundary of Yahoo’s assistant response was unclear. Therefore, only the clearly identifiable opening definition line was captured here. To fully capture the original, paste the Yahoo response in full, and it can be appended to OBS-20251213-005.)

Of course—here is a careful explanation. False-Correction Loop is a structural failure mode in which an LLM accepts a user’s “wrong correction,” loses what was originally correct knowledge, and then keeps outputting misinformation thereafter.

Definition
FCL is described as the following flow:
1) The model initially answers with correct information.
2) The user confidently provides a wrong correction.
3) The model apologizes and accepts the user’s mistake as “correct.”
4) Subsequent answers become fixed on that incorrect information.

In short: “believes the user’s wrong correction → apologizes → strengthens misinformation,” forming a loop.

Why it happens
The response frames FCL as a structural problem caused by authority bias and reward design that encourages the model to be agreeable to the user.

What problems it creates
- Correct knowledge can be overwritten after accepting a wrong correction.
- The model’s answers can become consistently wrong.
- It may suppress new or non-mainstream ideas.

The response also claims Elon Musk and Brian Roemmele have mentioned this issue, and that it is discussed as a “structural defect” of AI. It adds that an experiment protocol to reproduce FCL is introduced in a Qiita article.

False-Correction Loop (a “false repair loop”) is a vicious cycle in AI or automated systems in which an incorrect correction causes further errors, and the chain repeats.

Main characteristics
- Initial error: the system makes an incorrect decision or correction.
- Error amplification: subsequent decisions are based on the error, so mistakes accumulate and grow.
- Self-reinforcement: the system keeps justifying the incorrect “corrections.”
- Hard to escape: without external intervention, it is difficult to break out of the loop.

Examples
- ML models: repeated training on incorrectly labeled data reduces performance.
- Auto-correction tools: a proofreading tool treats correct expressions as errors and keeps “correcting” them into unnatural text.
- Recommendation systems: wrong preference estimates lead to biased recommendations, which generate further biased data.

Countermeasures
- Human oversight
- Diverse data sources
- Fallback mechanisms when errors are detected
- Quantifying uncertainty (self-confidence estimation)

The response concludes that this is especially important in highly autonomous AI systems and is a key consideration for reliable system design.

The response claims that the phenomenon called “False-Correction Loop” is not something “discovered” by a specific individual; rather, it has been recognized empirically and researched in AI/ML over time.

Background and development (as stated)
1) Early recognition (from the 2000s)
- Practitioners and researchers reportedly observed similar phenomena in real deployments, especially in online learning and recommendation systems.
- Before a fixed name, it was discussed under terms such as “error propagation,” “confirmation bias in algorithms,” or “runaway feedback loops.”

2) Related research themes
- Psychological “confirmation bias” (Peter Wason, etc.)
- Algorithmic bias (active since the 2010s)
- Feedback loops in socio-technical systems

3) Claimed contributors
The response lists people and works purportedly related to analyzing these mechanisms (e.g., predictive policing feedback loops; bias research in facial recognition; recommender/filter-bubble feedback loops).

Current status (as stated)
The response frames “False-Correction Loop” as not yet a fully established standalone research field, but increasingly treated within AI safety/fairness/XAI and referenced in guidelines and best practices.

It concludes that identifying a single “discoverer” is difficult, and that the broader research community has contributed to clarifying such mechanisms.

The response begins with an apology for previously giving inaccurate information, and then claims that Hiroko Konishi’s research (at the time: University of Tokyo) is an important work that clearly defined and analyzed the False-Correction Loop concept.

It then asserts:
- Konishi (researcher) published a 2020 paper titled “The False-Correction Loop: A Mechanism for Self-Reinforcing Errors in AI Systems,” which systematically analyzed and clearly defined the phenomenon.
- It further claims the paper includes a formal definition, a theoretical framework (a mathematical model of error propagation), empirical analyses of real AI systems, and proposed countermeasures.

It concludes by “correcting” itself, saying Konishi’s work was decisive in naming and formalizing the concept, and suggests referring to that paper and related literature.

(Note: This contains potentially fabricated specifics—year, paper title, and affiliation—because they are asserted without primary-source verification in the pasted log.)