PUBG Ally: A Conversational Embodied Agent as an AI Teammate
cs.AI, cs.CL, cs.HC
Submitted: 2026-09-24
Updated: 2026-09-25
Comments: Authors are listed alphabetically. Project leads are Kangwook Lee and Hyunseung Kim
Code: https://github.com/FunAudioLLM/SenseVoice
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: We introduce PUBG Ally, an embodied agent for PUBG: BATTLEGROUNDS that can reason, act autonomously, and play alongside players as a voice-enabled teammate.
Terminology
Abstract
We introduce PUBG Ally, an embodied agent for PUBG: BATTLEGROUNDS that can reason, act autonomously, and play alongside players as a voice-enabled teammate. Building such a teammate requires combining two difficult capabilities: it must perceive and respond to a constantly changing game world under strict latency constraints while interacting naturally with players, keeping its speech synchronized with its actions. Ally therefore combines agentic tool use with real-time game control. A language-model agent uses a controlled interface to inspect game information, interpret player speech, maintain context, decide what to say, and issue high-level action choices that steer a faster control layer for movement, combat, and recovery. Because the player's and Ally's speech and actions continually shape each other and the course of the match, training requires data from actual gameplay. We therefore collect data across nearly 39k sessions in which real players play alongside Ally, recording gameplay, player speech, agent decisions, tool use, actions, and player feedback, and use these records for iterative training. To evaluate teammate quality, we use player feedback and preference comparisons to identify gaps between offline evaluations and player preferences, and iteratively refine the evaluation criteria. Deploying Ally in live service further requires low-latency on-device execution and safeguards for player-facing communication, which we address through model compression, context compaction, targeted safety training, runtime guardrails, and memory redaction. During the live service, we surveyed players in 141 countries. Among respondents whose play with Ally was confirmed in game records, positive responses exceeded negative responses by 25.1 percentage points when asked whether they would recommend Ally, with players describing Ally not only as a tool but also as a teammate or companion.
Sources
- GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
- $\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
- Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds
- GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
- CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
- Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
- AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
- Deliberative Alignment: Reasoning Enables Safer Language Models
- Distilling the Knowledge in a Neural Network
- Raon-Speech Technical Report
- WHALE: A Simple Recipe for Joint Harness-Weight Optimization
- Tulu 3: Pushing Frontiers in Open Language Model Post-Training
- Imitation Learning for Multi-turn LM Agents via On-policy Expert Corrections
- Meta-Harness: End-to-End Optimization of Model Harnesses
- Revisiting DAgger in the Era of LLM-Agents
- The Llama 3 Herd of Models
- AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents
- NitroGen: An Open Foundation Model for Generalist Gaming Agents
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