SkillGuard extracts executable environment contracts from LLM skill documents to detect only relevant drifts, reporting zero false positives on 599 cases, 100% precision in known-drift tests, and raising one-round repair success from 10% to 78%.
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Scaling instructable agents across many simulated worlds
11 Pith papers cite this work. Polarity classification is still indexing.
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COSPLAY co-evolves an LLM decision agent with a skill bank agent to improve long-horizon game performance, reporting over 25.1% average reward gains versus frontier LLM baselines on single-player benchmarks.
PokeGym is a new benchmark that tests VLMs on long-horizon tasks in a complex 3D game using only visual observations, identifying deadlock recovery as the primary failure mode.
Retrocausal transformer decomposes multi-agent motion forecasts into marginals and pairwise joints, models uncertainty with compressed exponentials, achieves strong Waymo results, generalizes to Argoverse 2 and V2X-Seq, and enables implicit instruction following from standard training.
SPIKE dual-controller framework raises success rates 5-9 points and cuts tokens 55% in StarDojo agents by reusing strategic plans across stable segments and escalating only at detected events.
CA2 integrates call stack information into RL agents for game testing and shows consistent gains over baselines that ignore code signals.
Odysseus adapts PPO with a turn-level critic and leverages pretrained VLM action priors to train agents achieving at least 3x average game progress over frontier models in long-horizon Super Mario Land.
The paper organizes research on generalist game AI into Dataset, Model, Harness, and Benchmark pillars and charts a five-level progression from single-game mastery to agents that create and live inside game multiverses.
A configurable framework called GamePals enables shared control via human cooperation or partial automation to improve video game accessibility for people with upper-limb impairments, evaluated in a study with 13 participants.
UI-TARS-2 reaches 88.2 on Online-Mind2Web, 47.5 on OSWorld, 50.6 on WindowsAgentArena, and 73.3 on AndroidWorld while attaining 59.8 mean normalized score on a 15-game suite through multi-turn RL and scalable data generation.
Interviews with 14 accessible gamers identify essential shared-control practices, key limitations of human assistance, and design requirements for software agents that could automate support.
citing papers explorer
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Skill Drift Is Contract Violation: Proactive Maintenance for LLM Agent Skill Libraries
SkillGuard extracts executable environment contracts from LLM skill documents to detect only relevant drifts, reporting zero false positives on 599 cases, 100% precision in known-drift tests, and raising one-round repair success from 10% to 78%.
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Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks
COSPLAY co-evolves an LLM decision agent with a skill bank agent to improve long-horizon game performance, reporting over 25.1% average reward gains versus frontier LLM baselines on single-player benchmarks.
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PokeGym: A Visually-Driven Long-Horizon Benchmark for Vision-Language Models
PokeGym is a new benchmark that tests VLMs on long-horizon tasks in a complex 3D game using only visual observations, identifying deadlock recovery as the primary failure mode.
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RetroMotion: Retrocausal Motion Forecasting Models are Instructable
Retrocausal transformer decomposes multi-agent motion forecasts into marginals and pairwise joints, models uncertainty with compressed exponentials, achieves strong Waymo results, generalizes to Argoverse 2 and V2X-Seq, and enables implicit instruction following from standard training.
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SPIKE: An Adaptive Dual Controller Framework for Cost-Efficient Long-Horizon Game Agents
SPIKE dual-controller framework raises success rates 5-9 points and cuts tokens 55% in StarDojo agents by reusing strategic plans across stable segments and escalating only at detected events.
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CA2: Code-Aware Agent for Automated Game Testing
CA2 integrates call stack information into RL agents for game testing and shows consistent gains over baselines that ignore code signals.
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Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning
Odysseus adapts PPO with a turn-level critic and leverages pretrained VLM action priors to train agents achieving at least 3x average game progress over frontier models in long-horizon Super Mario Land.
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Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse
The paper organizes research on generalist game AI into Dataset, Model, Harness, and Benchmark pillars and charts a five-level progression from single-game mastery to agents that create and live inside game multiverses.
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Video Game Accessibility through Shared Control for People with Upper-Limb Impairments
A configurable framework called GamePals enables shared control via human cooperation or partial automation to improve video game accessibility for people with upper-limb impairments, evaluated in a study with 13 participants.
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UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
UI-TARS-2 reaches 88.2 on Online-Mind2Web, 47.5 on OSWorld, 50.6 on WindowsAgentArena, and 73.3 on AndroidWorld while attaining 59.8 mean normalized score on a 15-game suite through multi-turn RL and scalable data generation.
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Shared Control for Game Accessibility: Understanding Current Human Cooperation Practices to Inform the Design of Partial Automation Solutions
Interviews with 14 accessible gamers identify essential shared-control practices, key limitations of human assistance, and design requirements for software agents that could automate support.