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Topic

LLM Hallucination

The tendency of large language models to produce fabricated facts, code, or claims that sound plausible but have no basis in reality.

Current stories

buildOne report1 publisher

Z.ai's GLM Legal claims fewer invented citations with no independent accuracy data behind it

Z.ai launched GLM Legal to cut hallucinated citations in legal work, but it published no accuracy figures anyone can verify independently. Teams weighing it against Harvey or CoCounsel will have to build their own citation test, because no public number supports the comparison.

Publishers:dev.to

Reality

Evidence22
Adoption
Not scored
Hype gap+30
Incentives
Not scored
Confidence45
buildOne report1 publisher

Apollon Labs' Greek benchmark finds gpt-oss-20b inventing 48 words per 1,000

Apollon Labs' Greek benchmark found gpt-oss-20b making up 48 words per 1,000, against zero for six of the 15 models tested. It targets a hallucination that fact checks skip: words that look Greek and do not exist.

Publishers:dev.to

Reality

Evidence48
Adoption
Not scored
Hype gap+5
Incentives35
Confidence50
buildOne report1 publisher

Code-writing LLMs repeat invented package names often enough for squatters to register them first

USENIX Security 2025 researchers found 19.7% of packages suggested by 16 LLMs were fake, and 43% of those names recurred on every re-run. Names that repeat can be registered ahead of time, so a team has to vet a suggested dependency before installing it, even when the install succeeds.

Publishers:dev.to

Reality

Evidence58
Adoption20
Hype gap+25
Incentives
Not scored
Confidence50
buildOne report1 publisher

Swapping an LLM for a scored Places API lookup lifted a sales pipeline's website coverage to 81%

Engineers on a freight-forwarder sales pipeline swapped an LLM web-search step for a Google Places lookup and lifted website completeness from 34% to 81%. The author credits the gain to the normalisation and match-scoring layer built around the API.

Publishers:dev.to

Reality

Evidence35
Adoption10
Hype gap+10
Incentives
Not scored
Confidence40
buildOne report1 publisher

Telling extractors to return null cut invented fields from 70.7% to 20.2% in a synthetic benchmark

Earn an Honest Dollar's benchmark found one prompt line telling models to return null cut invented fields from 70.7% to 20.2% of missing-field answers. The result comes from one synthetic run, and with a fifth of answers still guessing, scraper output still needs a downstream check.

Publishers:runtimewire.com

Reality

Evidence40
Adoption
Not scored
Hype gap+10
Incentives65
Confidence45
buildOne report1 publisher

Auto-shown AI answers cut 'I don't know' from 35 percent to 1 percent in a movie-trivia study

Researchers found an auto-displayed AI answer cut 'I don't know' responses from 35 percent to 1 percent, though the model was mostly wrong. A 10-cent penalty for wrong answers still left abstention at 7 percent, so review tools need more than an abstain button.

Publishers:the-decoder.com

Reality

Evidence55
Adoption
Not scored
Hype gap+20
Incentives
Not scored
Confidence50
buildOne report1 publisher

A resume builder bans its AI from adding nine named kinds of fact to a user's bullets

A developer's free resume builder bans its AI from adding nine named kinds of fact and rejects any rewrite that changes the bullet count. The live check covers only the count, so a claim invented in words inside a single bullet still reaches the user, who is told to read every result.

Publishers:dev.to

Reality

Evidence45
Adoption
Not scored
Hype gap+25
Incentives35
Confidence45
productConfirmed2 publishers

A formatting prompt carried an AI hallucination across US command channels

CNN reports that a Special Operations Command analyst used a chatbot to fuse open-source and classified signals intelligence, then used it again to turn the wrong answer into the summary that moved up the chain.

Perspective Coverage

7 publishers
Builder
Builder 33%
Operator
Operator 51%
Investor
Investor 16%

Reality

Evidence42
Adoption48
Hype gap+25
Incentives60
Confidence45
buildOne report1 publisher

A small labelled holdout turns a miscalibrated chat LLM into a working abstention gate

A study of 14 chat-tuned models found their maximum softmax probabilities overconfident everywhere and uncorrelated with task accuracy, while those same scores still sorted correct answers above wrong ones well enough to drive selective abstention.

Publishers:arxiv.org

Reality

Evidence62
Adoption
Not scored
Hype gap+8
Incentives32
Confidence58
buildOne report1 publisher

Enhanciar resolves every citation to a file and line before grading the sentence beside it

Enhanciar builds a wiki page per service at ingest, makes the model cite those pages, then checks each claim against the cited file before the answer ships. Claims it cannot judge are tagged unverifiable.

Publishers:dev.to

Reality

Evidence38
Adoption
Not scored
Hype gap+15
Incentives80
Confidence40
buildOne report1 publisher

The orchestrator refuses to dispatch an implementation task until a research brief exists

An autonomous coding setup on a Mac mini splits reading from writing into two agents with separate tool lists. Its operator reports guessed-API failures falling from about one task in five to one in forty.

Publishers:dev.to

Reality

Evidence30
Adoption10
Hype gap+25
Incentives
Not scored
Confidence45
buildOne report1 publisher

A fifteen-line abstention rule removed more correct answers than confident wrong ones

Five frontier models ran a 295-item failure corpus bare and then wrapped. Across the four that accepted the wrapper, confident errors fell from 25.8% to 7.4% and correctness fell further, from 43.9% to 21.3%.

Publishers:dev.to

Reality

Evidence45
Adoption12
Hype gap+10
Incentives60
Confidence38
buildOne report1 publisher

Grepping your tool for the error text a model promised catches an invented fix

A writer of reporting-tool cookbooks put hundreds of developer questions to current chat models and then tested the answers in a lab. The failures he describes cluster into five patterns. Validators clear most of them.

Publishers:dev.to

Reality

Evidence38
Adoption15
Hype gap+14
Incentives72
Confidence55
buildOne report1 publisher

A cache-config guess hardens into an institutional claim after three memory passes

In a dev.to account of self-reinforcing memory loops, an agent's own hedged inference is captured as a flat fact, retrieved a week later as context, and generalised until the store recommends replacing the cache layer.

Publishers:dev.to

Reality

Evidence33
Adoption
Not scored
Hype gap+20
Incentives40
Confidence50
buildOne report1 publisher

An audit of 23 AI-generated wiki sites found invented redeem codes on 18 of them

The builder shipped 30 Roblox guide sites on Next.js and Vercel in a month, then audited them and found invented code tables across most of the network, plus a canonical tag pointing at a domain he never registered.

Publishers:dev.to

Reality

Evidence42
Adoption22
Hype gap+25
Incentives40
Confidence45
buildOne report1 publisher

A reviewer that opens every cited file found errors in five of seven agent-drafted replies

Rulestack says only one of the five was the kind of error a string matcher could catch, and the first matcher it wrote cleared the draft anyway, because 76 turns up 1,343 times in the ledgers it searched.

Publishers:dev.to

Reality

Evidence52
Adoption15
Hype gap−8
Incentives58
Confidence45
buildOne report1 publisher

Eleven models encode 'no admissible answer' on an axis 85 degrees off safety refusal

A one-dimensional probe finds structural impossibility in the hidden state of instruction-tuned models from 1.7B to 70B parameters. The trained refusal pathway that guardrail work tunes reads an axis about 85 degrees away from it.

Publishers:arxiv.org

Reality

Evidence58
Adoption
Not scored
Hype gap−12
Incentives40
Confidence52
productOne report1 publisher

A fabricated 9/11 rescue photo cleared five AI checkers on the first ask

Gizmodo asked five AI systems whether a viral image of the 9/11 rescuer Welles Crowther was real. Each described the fabrication as a photograph of some kind, and what caught it was a reverse image search.

Publishers:gizmodo.com

Reality

Evidence62
Adoption38
Hype gap+20
Incentives55
Confidence58
buildOne report1 publisher

A hand audit of 45 register questions found 34 hedges and three confabulations

When registry-mcp measured the counterfactual its README had been asserting, claude-sonnet-5 mostly declined to answer rather than inventing filings. That moves the argument for the tool onto provenance and coverage.

Publishers:dev.to

Reality

Evidence56
Adoption
Not scored
Hype gap−18
Incentives68
Confidence61
buildOne report1 publisher

A hallucinated package name was already registered when the engineer went looking

Slopsquatting stopped being a thought experiment. The only thing standing between an AI suggestion and an install was a reviewer who happened to check the registry page.

Publishers:dev.to

Reality

Evidence26
Adoption12
Hype gap+38
Incentives86
Confidence33

Earlier coverage