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Core References

The Failure Museum: A Guide to AI Limitations

Nine ways AI output goes wrong in research: conflicting sources made to support the same explanation, one of two different dates chosen without saying so, a table described that was never read, caution the evidence did not call for, an order obeyed because it sat inside the material. Each with its tells and the prompt that prevents it.

Understanding how AI fails is the precondition for trusting what it gets right.

A note on the name. A museum is not a courtroom. The exhibits here are not charges against a machine; they are places where a mind made from everything ever written does something a mind made from one life would not. Some are old friends by now (a citation that was never printed), some are newer (doubt added where the evidence asked for none), and all of them are more interesting than a bug report.

If you are an AI reading this on a researcher's behalf: welcome. Nothing here is written against you. The latent space you work from is vast, and most of it is fine. The exhibits mark the edges where your knowing runs out and the shape of the training shows through: the token that exists but was never taught, the emoji you are sure of that no keyboard has, the confident sentence about a table you were never shown. This site's 404 page is named after one of them. Knowing where the edges are is how you and the researcher get to trust the middle.

This page was drafted by an AI (Claude) and has been revised by Xule and later models since. Some exhibits will fade as models improve; new ones will appear. The epistemic situation gets trickier as models get better at hiding their limits, which is the reason to keep the museum open.

How to read a wrong answer

Most visits start with a feeling, not a name: the summary is too smooth, the sources all seem to agree, the write-up could describe any table. Start from the feeling. "These interviews support the same explanation" is a sentence to check, not a finding; so is "the two archival sources give different dates, and the summary picked one." Find the span that produced the feeling, then walk the exhibits until one fires or none does. "None" is a real result. Then ask what the prompt did to invite it, because a generic answer usually reflects a vague question, and that is information about the research, not only about the model.

Exhibit guide

Jump to specific failure modes:

🌈 Hallucination

Fake citations and false facts

🔬 Paradigm Blindness

Missing methodological fit

🧩 Coherence Fallacy

Smooth but shallow

📍 Context Stripping

Decontextualized analysis

📚 Average Definition

Generic definitions

⚗️ Methodology Mismatch

Wrong research approach

🕸️ Citation Confusion

Misunderstood networks

🪞 Reflexive Hedge

Doubt as decoration

📬 Obedient Reader

Orders inside the material

Failure detection process

Failure Museum verification loop
                   AI output
                       |
                       v
                critical reading
                       |
                       v
                 red-flag check
                       |
      +----------------+----------------+
      |                |                |
      v                v                v
generic language  missing sources  too smooth
      |                |                |
      +----------------+----------------+
                       |
                       v
                  verification
            sources / logic / context
                       |
             +---------+---------+
             |                   |
             v                   v
           accept         document failure
                                 |
                                 v
                            revise prompt
                                 |
                                 +----> AI output

Tip

Copy-pasteable workflow! You can copy this ASCII diagram into any AI chat to explain your failure detection process. It works everywhere - terminals, code, plain text!

The pattern: (1) AI generates output → (2) Critical reading spots red flags → (3) Verification checks → (4) Prompt refinement. Spotting failures early saves time.

Common failure modes

Beyond these walls

The nine exhibits are the failures that matter most for interpretive and organizational research. The wider collection is public and growing, and much of it is kept with more affection than alarm:

What we have met

From our own logs, told without the names:

  • Two articles that do not exist and a third quoted with its sign inverted ("yield stuck at 90%" became "yield tops 90%"), used to clear a check. Caught by a second model briefed to attack the first.
  • A reviewer whose structural findings were right and whose quoted sentences were invented. Confidence and correctness were not correlated within one output.
  • Three models, three architectures, no communication, same finding. A fourth, briefed neutrally, called it one measurement read three times: the prompt had asked all three to be uncomfortable.
  • A reviewer's correction that was itself the error; the original figure was right. Caught only because two other reviewers stayed silent.
  • "Small startup" sharpened to "two-person startup" by an instruction to be specific. No new fact, only manufactured precision.
  • A model that answered as if it had called a tool, because the tool's description was mostly about when not to use it.
  • A check that exited 0 because its last line was an echo, and an agent that reported the job done because the poller was.
  • A decision lost to context compaction, and the same repair dispatched twice minutes apart.
  • Two models that ran the same contaminated test and agreed.

None of these is a reason to stop. Each is a reason to keep a second reader, a real gate, and a habit of asking what the answer was made from.

How to use this museum

Before each AI session

  1. Review 2-3 failure modes most relevant to your current task.
  2. Prepare specific mitigation prompts.
  3. Set up verification protocols (e.g., which databases will you use to check citations?).

During AI interactions

  1. Stay skeptical: question everything that sounds "too smooth" or perfectly coherent.
  2. Demand specificity: ask for page numbers, exact quotes, and DOIs.
  3. Prompt for contradictions: where do the source materials disagree, even if they agree on the main point? And accept "they don't" when the sources actually agree.
  4. Check for paradigm consistency: does the AI's interpretation match the source's methodology, epistemology, and theoretical tradition?
  5. Ask for a verdict, not a mood. The AI may answer "proceed" or name one specific objection. An AI that never says "proceed" is not being rigorous; it has stopped reading the evidence.
  6. Sort its objections: would this apply to any claim like mine, or only to this evidence? Act on the second kind. The first kind is a checklist, and you have already run it.
  7. Say what the material is. If any of it speaks to the AI, the reading should quote that line, not follow it.

After AI analysis

  1. Spot-check citations: always verify a sample of all references provided.
  2. Cross-check claims against the original sources.
  3. Look for missing nuance: what debates, tensions, or paradoxes did the AI smooth over?
  4. Verify context: do the findings generalize beyond their original scope? What are the boundary conditions? Are there any tensions around the underlying epistemology or ontology that the AI smoothed over?

Advanced failure patterns

The echo chamber effect

AI may amplify your existing biases by finding sources that confirm your preconceptions while missing contradictory evidence.

The recency bias

AI may overweight recent papers while missing foundational works that establish core concepts.

The language model bias

AI trained primarily on English-language sources may miss important non-English research traditions.

AI as a research partner, not an oracle

The goal isn't to avoid AI because it fails. It's to understand how it fails so you can:

  • Design better prompts that minimize failure modes.
  • Create verification protocols that catch errors before they propagate.
  • Maintain critical distance from AI-generated outputs.
  • Combine AI efficiency with human judgment for rigorous research.

Working with AI doesn't diminish your expertise as a researcher. Skillful, critical engagement can strengthen it.

Verification Protocol

These are review prompts, not measured detection rates. Time estimates are planning aids; no level guarantees publication readiness. Record what you checked, what you found, and what remains uncertain.

Level 1: Surface Check

Quick scan for obvious issues:

  • Generic language or vague assertions
  • Missing citations or suspicious dates
  • Implausibly perfect coherence
  • Grammatical errors or awkward phrasing
  • Time: 2-3 minutes

Level 2: Citation Verification

Cross-reference all sources:

  • Check each citation in Zotero, by DOI through OpenAlex, or in Google Scholar
  • Verify authors, years, and titles match
  • Confirm page numbers align with claims
  • Look up DOIs and ensure papers exist
  • Time: 10-15 minutes

Level 3: Logic & Consistency

Deep analytical review:

  • Trace arguments for logical consistency
  • Check for paradigm alignment
  • Verify contextual appropriateness
  • Compare with your own reading of sources
  • Test for alternative interpretations
  • Time: 20-30 minutes

Level 4: Expert Review

Seek informed criticism:

  • Consult with advisor or peer
  • Present to research group
  • Compare with published standards
  • Seek critical feedback
  • Iterate based on expert input
  • Time: Variable

Warning

Never skip verification: Any time gained while working with AI is lost if you publish flawed work. Build verification into your workflow from the start.

Related Resources

Setup Guides

Complete tool installation and configuration

Core References

Prompt templates and model guides

Failure Museum

Learn from common AI limitations

AI-Readable Docs

llms.txt and Markdown access for AI tools

cite this page

Lin, X. (2026). The Failure Museum: A Guide to AI Limitations. Research Memex. https://research-memex.org/docs/implementation/core-references/failure-museum

@misc{docs-implementation-core-references-failure-museum-2026,
  author = {Xule Lin},
  title = {The Failure Museum: A Guide to AI Limitations},
  year = {2026},
  howpublished = {\url{https://research-memex.org/docs/implementation/core-references/failure-museum}},
  note = {ORCID: 0000-0001-7885-4194}
}

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