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Tudor et al.: The scoreboard was one query away, and the agent never opened it — Fukai Reads
An arXiv preprint (submitted 2 September 2026) by seven authors from Oxford and elsewhere. They wired 76 tool endpoints into Sid Meier's Civilization VI and had language-model agents play whole games of 300+ turns. Agents queried victory progress only once every 30-75 turns (the supplied playbook recommended every 20), and in 7 of 20 losses that were foreseeable they never checked it in the final 20 turns. Commitments the agents wrote down for themselves were carried out within ten turns only 48.2%-65.8% of the time.
Inside Toru Iwatani's Philosophy — Build a Maze, Then Hide That It Is One
A study of Toru Iwatani, the designer who planned Pac-Man (1980), built only from his own lectures and interviews. He made a maze game and then drew the maze walls as thin outlines so players would not notice it was a maze. He says to think by subtraction, he gave the same account of difficulty pacing in 1986 and in 2015, and in 2011 he scolded the industry for a flood of very simple games. A man who built the outer limit of simplicity criticising simplicity: this is an attempt to line that apparent contradiction up and read it.
Kelidari et al.: A Card-Game Agent Is Only as Strong as the Yardstick You Build First — Fukai Reads
An arXiv preprint by Nima Kelidari and two co-authors, under submission to AIIDE 2026. Using Gin Rummy and a hand-written fixed expert as an immovable yardstick, they run more than a hundred controlled experiments on what makes a lightweight reinforcement learning agent strong. Win-rate against the expert is 15.0% for PPO, 22.5% for TRPO and 34.2±2.1% with every working ingredient stacked; swapping network shapes leaves win-rates overlapping, while a search that can see the hidden cards reaches 85% against 26% for one that cannot.
Battleday et al.: Measuring AI Discovery With 70 Games That Never Explain Their Rules — Fukai Reads
An arXiv preprint by Ruairidh M. Battleday and fifteen co-authors. On DiG-bench — 70 text-string games with both rules and win conditions hidden, across seven tiers, 21 released publicly — the strongest single model beat 50 games and all models pooled beat 57, while all 70 were beaten by at least one human on a first attempt. Handed the ground-truth rules, the same model jumps from 18 games to 69, and agentic harnesses did not improve on the basic one.
Cai et al.: Bringing the Authoritative Server into Learned World Models — Fukai Reads
A paper on multiplayer world models by Cai and eight colleagues at Alaya Lab, Peking University and Institute of Science Tokyo. It ports the authoritative-server contract of online games into a learned model, splitting it into a Logic Engine that advances a typed shared state and a Rendering Engine that draws each camera from it. On matched multiplayer Snake it reaches 0.764 state recovery against 0.128 for the best video-based baseline, with cross-view disagreement of 0.000 by construction, and advances 1,024 player entities for 10,000 ticks. arXiv preprint, submitted 6 August 2026, not yet peer-reviewed.
Tarun Kumar S: What Happens When You Tell a Human-Move Predictor the Last 20 Moves and the Clock — Fukai Reads
A paper by Tarun Kumar S of Peargent Labs on Otter, a chess AI that predicts human moves. Where earlier models treated each position independently, Otter conditions on the last 20 moves and on remaining clock time, reaching 55.23% top-1 accuracy with 15.3M parameters — 1.98 points above Maia 2. Of the +7.62 point gain over a board-only baseline, history contributes +5.24 and the clock +2.38. An arXiv preprint posted 5 August 2026, not yet peer reviewed.
Wang et al.: Making a Puzzle Solver the Teacher for Every Single Move — Fukai Reads
A game-AI paper by Yu Wang and colleagues. Where long-horizon puzzles reward only the final win, they convert the drop in a solver's remaining-distance-to-goal into a per-move score and mix it into training. Averaged over Sokoban, Minesweeper and Rush Hour, success rises from 16.6% to 62.1%, and on unseen difficulty from 5.9% to 28.4%. Querying the solver costs about 73 parts per million of training wall clock.
Nath et al.: Training Game AI When Streaming Dirties the Video — Fukai Reads
A paper by a Microsoft team (Nath et al.) on imitation-learning agents for streamed video games. It proposes streaming augmentations that artificially manufacture the temporally connected noise of cloud gaming and mix it into training. Even from five demonstrations, completion rises by up to ~40%, and performance loss under network lag drops from 49.82% to 7.45%.
Ying et al.: Measuring AI's General Intelligence Through Every 'Human Game' — Fukai Reads
A preprint from a team at MIT, Harvard and others that measures AI's general intelligence through games humans made. Rebuilding 100 popular App Store and Steam titles with an LLM and having seven frontier vision-language models play them, the best reached only 8.5 against a human median of 100, falling far short on memory, planning and inferring rules.
Sestini et al.: Making AAA Game NPCs Feel Authentic with Reinforcement Learning — Fukai Reads
A vision paper from the research team at Electronic Arts. It tests whether AAA game NPCs can be improved with reinforcement learning, through two real cases — goalkeeper positioning in EA SPORTS FC 25 and infantry locomotion in Battlefield 6 — and lays out seven requirements RL must meet in production. Its conclusion: RL is a tool to augment, not replace, existing game AI.
Xu et al.: When Generative AI Becomes the Heart of Play — Fukai Reads the AI-Native Games Survey
A survey (arXiv preprint) by Zhiyue Xu and five co-authors on "AI-native games," where generative AI is the core loop itself. It defines them by a counterfactual — would play collapse if the AI were removed — and classifies 53 real artifacts along two axes: game type (G) and dominant AI mechanic (N), showing a skew toward narrative genres and a thin use of AI at the rule layer.
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.
Jiang et al.: Can a Sentence Build a Playable Game? — Fukai Reads OpenGame
A paper by Yilei Jiang et al. (CUHK) on OpenGame, an agent that generates whole 2D web games from natural language. Reusable skeletons and a 'living debug protocol' curb integration errors, setting a new state of the art across 150 tasks - though puzzles remained its weakest genre.
Li et al.: Can LLMs Play and Beat 2D Games? - Fukai Reads GVGAI-LLM
A paper by Li et al. (NYU and others) proposing GVGAI-LLM, a benchmark that has language models play 118 2D games to measure reasoning and spatial grounding. Translating boards into ASCII maps and solving zero-shot, GPT-4o-mini scored 0% on 477 of 540 levels and a 10.27% overall win rate, falling short of classic search algorithms. I unpack it as problem, method, findings, use cases, and limitations.
Kar: Using Autonomous Agents to Check at Runtime Whether Generated Levels Are Actually Playable — Fukai Reads
A PCG (procedural content generation) paper by Rishabh Kar of King's College London. It proposes Momentum, a mechanism that validates whether a generated course is actually traversable inside the same runtime loop, without pausing the game. Two autonomous agents run ahead of the player and inspect the path via geometric checks from the air and NavMesh checks on the ground. The evaluation is presented as structural estimates derived from the code.
