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Gould & Ward et al.: Measuring Puzzle Difficulty in Units of Human Solve Time — Fukai Reads
An AI evaluation paper by Gould, Ward and colleagues. They attached human solve times to 43 benchmarks and over 30,000 problems, and found that the human time of tasks a model completes at 50% success without externalising its reasoning has doubled roughly every 373 days over six years, reaching about three minutes for GPT-5.5. Their difficulty-measurement craft, built partly on Sudoku and crosswords, transfers directly to puzzle design.
Wang et al.: An LLM Agent That Reads Mental Busyness From Gaze — Fukai Reads
A paper from Meta Reality Labs and collaborators that estimates cognitive load (mental busyness) from eye gaze. It tackles the poor generalization and low interpretability of prior methods with GazeMind, a framework that structures gaze and has an LLM reason over it with context, individual traits, and worked examples, reporting 62.73% accuracy on three-way classification (over 20 points above prior methods).
Özkan: Co-Training the Level-Generating AI and the Level-Solving AI — Fukai Reads
A paper by Miraç Buğra Özkan that trains level generation and level solving together via reinforcement learning. In Unity, a hummingbird (solver) and a floating island (generator) learn while watching each other's results, reaching about 90.2% success across 100 unseen layouts.
Feng et al.: Can LLM Agents Bargain Well in a Trading Game? — Fukai Reads
A Tsinghua University team's benchmark, SidConArena, for evaluating LLM agents in a cooperative-yet-competitive trading game. Built on the board game Sidereal Confluence, it scores agents across negotiation, production, and sealed-bid auction phases, finding that frontier models are stronger but still misprice resources, bargain passively, and plan poorly over long horizons.
McConnell & Zhao: Generating Just-Right Puzzles in Real Time with a Genetic Algorithm — Fukai Reads
A paper by McConnell and Zhao on adaptive puzzle generation using a genetic algorithm. It generates Cosmic Express-style path puzzles in real time (about 7 seconds each) to match a player model built from how the player solves, and shows in an 18-person study that a time-only version lags on felt difficulty and sense of progression.
