The paper proposes a method to distill world‑model representations into compact Vision‑Language‑Action (VLA) policies. By adding a single feature‑alignment term during VLA training, a frozen world model’s internal features are cached and the student policy learns to match them, eliminating the need for a generative future‑rolling component. The resulting lightweight policy runs in 32 ms on an RTX 5090, achieving high performance on LIBERO and RoboCasa‑GR1, and transfers effectively to real robotic hardware.
By Trung Dao, Sankalp Yamsani, Jaden Park, Joohyung Kim, Yong Jae Lee
The paper introduces Gauntlet, a framework that lets large language models autonomously build game-playing agents from a bare contract—just a game description, raw observation/action interface, and an empty policy file. In a single session, the model experiments with the game, compiles a standalone controller, and the resulting program is evaluated on held‑out instances without further model calls. The authors demonstrate that these compiled agents can win full‑scale games such as StarCraft II and Civilization, marking the first time a language‑agent system has achieved standalone victory in such complex titles.
By Joey Xiao, Haonan Huang
Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset. The applications that matter most are login-gated and stateful, so synthetic environments stand in for them.
arXiv:2607. 28074v1 Announce Type: cross Abstract: Computer-use agents learn from what their actions change, so training one needs applications it can act on, break and reset.
By Yash Pandya, Sahil Gupta, Sarthak Harne, Archana Yadav, Kavyansh Chourasia, Hussein Mozannar, Vibhav Vineet, Sara Abdali, Corby Rosset, Yash Lara, Ahmed Awadallah, Ece Kamar, Akshay Nambi
FM‑Bench is a new benchmark that tests large language model agents on long‑horizon decision making by having them run a football club for 20 in‑game years. The agent must manage a squad, trade players, negotiate contracts, invest in facilities and youth, set lineups, and respond to a board that can fire it, all using 26 tools and roughly 340–400 decision stops, with a deterministic engine producing a final score without human or LLM judges. The benchmark includes a solo track where each of 15 frontier models competes against a frozen scripted world, and an Arena track where the same models plus a scripted anchor share one 20‑year world, allowing the first head‑to‑head evaluation at this scale.
whyItMatters":"FM‑Bench provides a rigorous, large‑scale test of sustained, cumulative decision‑making in language‑model agents, revealing that managerial strategy—not computational scale or vendor—drives performance over long horizons."
By Tianyou Wang, Chongyang Gao, Kezhen Chen, Chen Dong, Yinghao He, Donghan Li, Wangcheng Xu, Hongjiu Zhang, Chi Li
The paper introduces the World-Model Policy Arbiter (WMPA), a test‑time framework that selects among a portfolio of frozen goal‑conditioned policies by simulating each policy’s future trajectories in a learned world model and evaluating them with a shared value function. WMPA avoids frequent policy switching by committing to the chosen policy for a short interval and does not require retraining or task‑specific knowledge. On the OGBench benchmark, WMPA raises the macro‑average success rate from 44% to 58%, achieving significant improvements on 12 of 18 datasets, including large gains on cube‑double‑play and scene‑play.
By Junwei Quan, Evgenii Opryshko, Nicholas Rhinehart, Igor Gilitschenski
arXiv:2607. 25152v1 Announce Type: new Abstract: Long-running autonomous agents plan, act, and judge their own completion without human intervention.
By Hyundoo Park, Byungho Choi
arXiv:2608. 14490v1 Announce Type: new Abstract: We present a Test-time World-model Inference (Twin) system, in which a frontier coding agent writes an executable world model for completing continual learning tasks, such as ARC-AGI-3 games.
By Alexy Skoutnev, Kirill Acharya, Gaston Longhitano, Madeleine Udell, Kevin Ellis, Iddo Drori
FM‑Bench is a new benchmark that tests large language model agents on long‑horizon decision‑making by having them run a football club for 20 in‑game years. The agent must manage a squad, trade players, negotiate contracts, invest in facilities, set lineups, and respond to a board that can fire it, all while a deterministic engine aggregates the outcomes into a final score without human judgment. The benchmark evaluates six behavioral capabilities and compares 15 frontier models in solo and arena tracks, revealing that managerial behavior—not computational scale—drives performance.
The paper introduces SLIM, a pushing benchmark that tests language‑based goal specification for latent world models. It shows that a vanilla LeWM fails almost entirely due to an action‑insensitive encoder, but adding an inverse‑dynamics auxiliary loss restores encoder sensitivity and dramatically improves success rates. With the repaired latent, a lightweight language‑goal head can plan from natural language sentences, achieving high performance on navigation and pushing tasks without retraining the world model.
By Florian Strohm, Patrick Wagner, Jannik Schwab, Marco Huber
arXiv:2609.06816v1 Announce Type: new
Abstract: Decision-time search in perfect and imperfect information games with enumerable belief states are effective methods for game AI. Collectible card games...
By Dustin Rubin
arXiv:2609.25686v1 Announce Type: cross
Abstract: Long-horizon assigned work requires an LLM agent to track the state of a task: which steps are done, blocked, cancelled, or open to repetition. Agent...
By Chenyu Zhang, Wonbin Kweon, Jiawei Han