AI model captures how humans read, paving the way to personalised text
News AI model captures how humans read, paving the way to personalised text and better augmented reality Published: 7.8.2026 Researchers now understand not just how our eyes move when we read, but also how we build meaning from text. The new model could hold the key to developing highly customisable applications that adapt to different types of reader. This AI model follows the logic humans use when choosing where to direct our aattention when reading.
- ▪News AI model captures how humans read, paving the way to personalised text and better augmented reality Published: 7.8.2026 Researchers now understand not just how our eyes move when we read, but also how we build meaning from text.
- ▪The new model could hold the key to developing highly customisable applications that adapt to different types of reader.
- ▪This AI model follows the logic humans use when choosing where to direct our aattention when reading.
Hacker News (AI / LLM) files mainly under ai. We currently carry 4,253 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Aalto |
| Canonical URL | https://www.aalto.fi/en/news/ai-model-captures-how-humans-read-paving-the-way-to-personalised-text-and-better-augmented-reality |
| Publication time | Mon, 10 Aug 2026 11:45:58 +0000 |
| Retrieval time | 2026-08-10T11:55:46.090Z |
| Last seen | 2026-08-10T11:55:46.090Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 2-IrfefWIN-S · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
News AI model captures how humans read, paving the way to personalised text and better augmented reality Published: 7.8.2026 Researchers now understand not just how our eyes move when we read, but also how we build meaning from text. The new model could hold the key to developing highly customisable applications that adapt to different types of reader. This AI model follows the logic humans use when choosing where to direct our aattention when reading. Image: Kalle Kataila / Aalto University Researchers at Aalto University, together with international partners, have developed the most accurate model yet of how humans read. The new model uses reinforcement learning, a type of AI used in robotics, to explain—and recreate—the choices readers make as they move through text.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Aalto.
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