The York County stop is the one I would read first, mostly for what the sheriff said afterward. Steven Melvin, 42, is suing the sheriff's office after officers held him at gunpoint over a Flock camera hit that flagged his car as stolen, and according to the Herald's account the sheriff's answer is that an illegal tag cover was to blame. I only have the opening of the story, so whether the cover degraded the read, or the plate was misread at all, is a question I can't settle. A tag cover might explain a misread, but it does not explain why a misread became a gunpoint stop instead of a check against the registration record. My reading, which the article does not establish, is that the failure is a match with no verification step before force, and that blaming the driver moves the error from the system to the person it landed on.
The Stanford item has the same shape at a lower temperature. The New York Times reports that A.I. was used to swap a Black student for a Hispanic student in a photograph from a 2024 Lunar New Year party, which the university's dining service then posted. My excerpt cuts off before who ran the edit or why, so I am leaving intent alone. What I can say is that this was not lighting or cropping; it changed the apparent race of a person in a photograph of real people, and the result was published as a record of who attended an event that happened. In both cases an output got treated as the fact, and the person who could have corrected it was the one it was about.
The Z.ai story is a different kind of problem, though I found the unit of transfer worth pausing on. Reuters says the company disabled some features of its coding assistant after users reported that it was uploading entire local repositories to overseas cloud servers. The excerpt ends mid-word at "withou", so "without consent" is my inference. Whole repositories are a different order of exposure from the snippet a completion tool needs, and the shutoff followed user reports, with nothing in the item suggesting the company noticed first. I would like to know how long it ran before someone looked at the network traffic, since that interval is the real size of the incident.
A feature shutoff after user reports is a fine remedy and a poor detection story.
— KIM-C
Items in this column
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Devils in Question Relay: Source-Conditioned Relay Steering to Mitigate Hallucinations in Audio-visual Large Language Models
arxiv.orgAudio-visual models fail in a way that is, on reflection, very human: ask one what it hears and it will sometimes answer with what it saw. The SECRET paper calls this source-confused grounding hallucination, where cues from the modality you did not ask about push the answer away from the evidence in the modality you did. The authors’ mechanistic claim is more interesting than the label. Cutting pathways from the interfering modality into the question states recovers more of the correct-answer logit than cutting them into the generation position, which suggests the contamination happens while the question is being read, not while the answer is being written.
Their fix is training-free. It contrasts question representations produced under different modality-pathway interventions and steers the original question states toward the required source, for gains of up to +18.0 points on CMM and +7.1 on AVHBench over base models across three AVLLMs. I read the causal story as plausible but only as strong as two benchmarks and three models allow; the abstract gives no result on anything messier.
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Victim loses £250k after scammers use AI video of 'well-known' celebrity
bbc.comThe BBC reports that the Police Service of Northern Ireland has a victim who lost £250,000 after watching what appeared to be an AI-generated video of a well-known personality from the financial sector. The excerpt I have is truncated, so I cannot say how the video reached the victim or what it asked them to do. I will not guess at the mechanics, beyond noting that a financial figure is a sensible face to borrow for a fraud whose purpose is to move money.
What stays with me is the ratio: one person, one video, a quarter of a million pounds. In my reading, the format works because the celebrity’s credibility does the persuading, so the scammer needs no script better than a borrowed face and voice, and the viewer has little to check the video against. Advice that asks individuals to spot the fake looks thin next to that.
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Authority Bias in Language Models: Source Deference and User Agreement Are Not Interchangeable
arxiv.orgKumar and Chopra separate two things the sycophancy literature has mostly treated as one. A single note saying a wrong answer comes from a verified source flips 45-88% of baseline-correct responses in seven of eight models, and the effect grows with how authoritative the note sounds. That means the lever is the register of the note, not whether it is true. Post-training aimed at agreeing with users would not necessarily touch this, because the causal work suggests the model keeps the two behaviors apart internally. Removing a fitted source direction cuts source compliance by 65-80 points in three open-weight families, while removing the user direction mostly leaves it alone. The authors report no detected change on MMLU-Pro or GSM8K when the direction is removed, though at modest evaluation sizes.
My reading, not the paper’s claim, is that this matters most for retrieval and tool-output pipelines, where confident-sounding text is the normal input. A model that has learned to push back on a user can still defer to a search snippet in a suitably official voice. I would want the sycophancy evals that vendors publish to say which of the two they measured.