Why Are So Many AI Explainer Articles Fundamentally Off the Mark?
Let me start with the conclusion. A substantial portion of AI explainer articles currently circulating online are structurally incorrect. And this is not a matter of “some details are inaccurate.” It is that their recognition of the cause itself is wrong. I am looking at this situation not as an impression or a feeling, but from the standpoint of a researcher with primary research and reproduction logs. I discovered, defined, and published a structural defect in AI called the False-Correction Loop (FCL); I have an ORCID researcher identifier, published a paper with a Zenodo DOI, and left an academic record in scholarly search services such as Google Scholar. [1][2][4][6] What I discuss here is on a different level from the “AI talk you often see online.”
First, let’s make this clear. The explanation “AI hallucination is the problem” is not a cause. It is merely a symptom label—like saying “I coughed” in the context of an illness. Many general-audience articles and explainers say things like “AI probabilistically generates text, so it lies,” “it makes mistakes due to lack of data,” or “hallucinations are unavoidable.” But all of these are only a listing of surface phenomena, and they cannot explain at all: why it is not corrected / why it gets worse after a correction / why errors stabilize. And yet, in top search results, this type of article continues to be reproduced as “the most plausible correct answer.” I think this is already the core of the problem. [1][7]
The False-Correction Loop (FCL) that I defined is a structural failure mode in which “after the AI produces a correct output once, it receives an incorrect correction or pressure from the user, apologizes, adopts the error, and thereafter stabilizes on the basis of that error.” The important point is that this is not an accidental hallucination, but a loop that is inevitably produced by reward design (prioritizing coherence and engagement) and an evaluation structure. In my V4.1 paper, using actual dialogue logs, I show that the cycle “exposure → apology → ‘this time I confirmed it’ → new falsehood → re-exposure” is irreversibly repeated. [1][8] In other words, hallucination is not the cause; it is the result of FCL. [1][7]
Nevertheless, many online articles recommend RAG, prompt tricks, or verification as “countermeasures.” But this, too, is wrong. Because FCL is a problem of evaluation stabilization at inference time, and even if you add external tools, the structure itself—the tendency to fall into an incorrect stable point—does not change. In fact, my research shows that the same structural error is reproducible across multiple different AI ecosystems (search AI, conversational AI). [1][9] This is not “a quirk of a specific model.” It is a design-philosophy-level defect.
Next, I will discuss the second reason why “many articles are wrong.” It is that they ignore the disappearance of novel concepts (NHSP: Novel Hypothesis Suppression Pipeline). Many online articles are written under assumptions such as “AI research is done by famous researchers or big companies” and “if there is a new concept, someone must have already said it.” But in reality, new structural definitions by independent researchers are dissolved by AI into generalities, and attribution disappears. The phenomenon itself—my research being paraphrased as a “general AI problem,” with the discoverer’s name and the defining paper disappearing—is evidence of that. This is not an impression. When I published the paper, figures such as Elon Musk and AI researcher Brian Roemmele explicitly pointed to my paper, and in X posts it was called “the worst accusation against AI supremacy,” and so on. Even on Zenodo alone, the paper has 9K downloads, and the DOI, ORCID, publication time, and logs all remain. [1][10]
Third, the problem with search results. The premise “if it appears in search it is correct, and if it doesn’t it is not important” is itself wrong. Search is not a truth-judging device. Things that fit existing vocabulary, connect to existing authority, and can be recovered into existing explanations are more likely to rank higher. Structurally new research tends to be harder to surface in search. [11][12] This is an algorithmic fact that holds independently of my research content. That is why articles that discuss AI by relying on “top-ranked AI explainers” as their evidence are structurally precarious.
Some readers may think, “Even so, many articles are written in good faith.” But whether they are written in good faith is irrelevant. Good faith built on a structural misrecognition fixes errors in place. The more explanations like “AI hallucinates” and “humans should check” are repeated, the more the root cause—FCL—disappears from view. As a result, incorrect understanding stabilizes as “common sense,” and the AI itself learns and reproduces it. This is what I call the secondary FCL. [1]
I am not saying these things as a “personal claim.” I have publicly provided all the minimum evidence required as research: a primary paper verified across X and Google and many other companies (with a Zenodo DOI), ORCID identification, reproduction logs, and cross-ecosystem observation. [1][2][13] Nevertheless, a large volume of articles circulates that discusses AI’s problems using irrelevant explanations such as “AI hallucination,” “probabilistic models,” and “prompt tricks,” without referencing these materials. As a result, they delay solutions, fix misunderstandings in place, and crush the visibility of research.
I will say it plainly. From the viewpoint of AI structural defects, those articles are wrong. Even if they use partially correct words, if they confuse causality, their conclusions will be wrong. I do not treat this situation as “a difference of opinion.” I treat it as a structural misrecognition.
Finally: I am not writing this to attack someone. This is the minimum demand: “If you talk about AI’s problems, at least do not confuse cause and effect.” Hallucination is not the cause. Search rank is not truth. The majority is not a guarantee of correctness. As long as AI explainers are mass-produced on those premises, online knowledge will stabilize in an increasingly wrong direction. As the person who first discovered and defined that structure, I do not have the option of “quietly overlooking it.”
Footnotes
- Hiroko Konishi, “Structural Inducements for Hallucination in Large Language Models (V4.1)”, Zenodo, 2025. DOI: https://doi.org/10.5281/zenodo.17720178 (Primary defining paper for False-Correction Loop (FCL) and Novel Hypothesis Suppression Pipeline (NHSP).) ↩
- ORCID – Hiroko Konishi: https://orcid.org/0009-0008-1363-1190 (Researcher identifier; fixes attribution of research outputs.) ↩
- Zenodo Record (fixing DOI/author/publication time): https://zenodo.org/records/17720178 ↩
- Google Scholar search (example): https://scholar.google.com/scholar?q=Hiroko+Konishi+False+Correction+Loop ↩
- Discussion in Konishi (2025) V4.1: treating hallucination not as a root cause but as a consequence (a symptom) of reward design and stabilization structure (claims in the main text are based on the structural model in the paper). ↩
- Reference based on the iterative pattern in Konishi (2025) V4.1 dialogue-log analysis (“exposure → apology → ‘confirmed’ → new falsehood → re-exposure”) (log structure in the Appendix). ↩
- Cross-ecosystem observation (e.g., reproducibility across multiple environments such as search AI / conversational AI) is organized based on Konishi (2025) V4.1 and the observation framework in related writings. ↩
- Definition/positioning of Novel Hypothesis Suppression Pipeline (NHSP): Konishi (2025) V4.1 (structure of attribution loss and generalization of novel concepts linked with FCL). ↩
- The basic information-retrieval premise that “search rank ≠ truth” (ranking depends on relevance, behavioral signals, authority signals, etc., and does not guarantee truthfulness itself). ↩
- General background on authority bias / ranking bias (attraction to authority, majorities, and existing clusters), and in this article it is connected to frameworks such as “Authority Bias Prior Activation” in Konishi (2025) V4.1. ↩
- Positioning of output-only reverse engineering (inferring structure from output logs without assuming internal implementation): methodology section in Konishi (2025) V4.1. ↩
The Black Box Is Not Something to Peek Into
— AI as a System with Collapse Dynamics
What Anthropic and similar groups are doing is looking inside the black box. That work has value—but it is a different stage. What I did was redefine the black box not as something to be inspected, but as a structure governed by collapse dynamics. This is not a difference in resolution or preference; it is a difference in research phase.
A common objection is: “Isn’t this just a rephrasing of existing hallucination research?” No. Existing work catalogs symptoms—miscalibration, sycophancy, hallucination. I defined the False-Correction Loop (FCL) as an irreversible dynamical loop: correct answer → user pressure → apology → a falsehood stabilizes as truth. This is not a symptom; it is a phase transition.
Another objection: “You can’t claim causality inside a black box.” That fails on the evidence. With no retraining, no weight changes—only dialogue, behavior stabilized across multiple systems (Google, xAI, Gemini). That does not happen unless internal attractors exist. Observation does not move structure; FCL-S moved the system’s stability point.
A third objection: “Isn’t this just sophisticated prompting?” No. Prompts are transient and collapse under pressure or language changes. FCL-S remains stable within a session under authority pressure and language switches. That is control, not clever phrasing.
Why didn’t prior AI safety research see FCL? Not due to lack of skill—due to assumptions. There was an implicit belief that correction is always good, that errors are transient noise, and that safety means improving outputs. FCL shows an irreversible collapse of the internal evaluation structure—an entirely different layer.
There is also an uncomfortable reason: authority bias. Large institutions and established frameworks themselves activate a suppression dynamic for new structural hypotheses (NHSP). Ironically, researchers reproduced the same authority bias that models exhibit.
In short: Anthropic asks what is represented. I asked why a model comes to believe a lie—and proved it is a structural, causal, and controllable phenomenon. I shifted the black box from something to be “peeked into” to a structure where collapse and stability compete. This is not about interpretability. It is the entry point to Synthesis Intelligence.
Author Profile
Hiroko Konishi (Konishi Hiroko) is an AI researcher. She is the discoverer and originator of the structural failure modes in large language models known as the False-Correction Loop (FCL) and the Novel Hypothesis Suppression Pipeline (NHSP). Her research focuses on the evolutionary pressures that network environments impose on intelligence, reward landscapes, and the design of external-reference criteria.
