---
title: "Slides: New York Times' Connections: A Case Study on NLP in Word Games — Shafik Quoraishee, NYT Games"
category: "slides"
video_id: "P_uhFGH4J9Y"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: New York Times' Connections: A Case Study on NLP in Word Games — Shafik Quoraishee, NYT Games

## Source Video
[New York Times' Connections: A Case Study on NLP in Word Games — Shafik Quoraishee, NYT Games](https://www.youtube.com/watch?v=P_uhFGH4J9Y)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/P_uhFGH4J9Y/slide-001.jpg]]

OCR text:

> INNOVATIONPARTNER
> aws
> PLATINUMSPONSORS
> Graphite
> WWindsurf
> MongoDB
> daily
> augment code
> Workos

![[assets/slides/P_uhFGH4J9Y/slide-002.jpg]]

OCR text:

> About Me
> ae
> e Game/Al Developer at The New York |
> Times
> e Worked previously for Business Insider. Shafik Quorarshee
> The NBA, MTV and the Department of .
> Defense 7 ;
> fe) ee Le
> ey i |
> ay a
> Pom | -
> ri i a
> an eae
> de
> ~~
> P ; ; PS
> _ a Microsoft ary
> —"|

![[assets/slides/P_uhFGH4J9Y/slide-003.jpg]]

OCR text:

> Caveats to the Work You Are About To See
> e This is all my own independent research and experimentation,
> and not currently specifically based on New York Time's
> internal research
> re
> | a Microsoft §=S7nou®
> _

![[assets/slides/P_uhFGH4J9Y/slide-004.jpg]]

OCR text:

> Introduction To New York Times Connections
> e Connections was launched by the New
> York Times in beta in June 2023, and
> officially released in August 2023.
> e The game is edited by Wyna Liu, who is
> awesome
> e It quickly became one of NYT's
> most-played games, second only to
> Wordle, with hundreds of millions of plays
> within its first year.
> e ALL CONNECTIONS PUZZLES AND
> E GAME ITSELF, ARE HUMAN
> a ADE NOW AND FOREVER
> | re WAV
> )
> ao \ eet

![[assets/slides/P_uhFGH4J9Y/slide-005.jpg]]

OCR text:

> Reinforcement Learning Solver Using
> Hyperdimensional Semantic Clusters
> © Applied reinforcement leaming to treat
> group selection as a sparse-reward
> decision process.
> e Used hyperdimensiona! semantic
> embeddings to structure the word space.
> e Trained agents to learn grouping policies
> from historical puzzle solutions.
> e Incorporated lexical and semantic
> coherence as input features for state
> evaluation
> =
> || u

![[assets/slides/P_uhFGH4J9Y/slide-006.jpg]]

OCR text:

> Current Performance LLMs against of ARC-AGI 2
> System ARC-AGI-1 Score ARC-AGI-2 Score Efficiency (cost/task)
> Human panel (at least 2 humans) 98% 100% $17
> Human panel (average) 64.2% 60% $17
> o3-preview-low (CoT + Search/Synthesis) 75.7% 4%* $200
> o1-pro (CoT + Search/Synthesis) ~50% 1%* $200*
> ARChitects (Kaggle 2024 Winner) 53.5% 3% $0.25
> o3-mini-high (Single CoT) 35% 0.0% $0.41
> r1 and r1-zero (Single CoT) 15.8% 0.3% $0.08
> gpt-4.5 (Pure LLM) 10.3% 0.0% $0.29

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.