GPTNT: Benchmarking Real-Time Collaboration Between Multimodal Agents on Keep Talking And Nobody Explodes
arXiv:2606.28514v1 Announce Type: new Abstract: Multimodal models are increasingly deployed to solve tasks collaboratively with humans or other artificial agents. Existing benchmarks show that these models possess many of the required component capabilities, but the conditions that coincide in collaboration, including time pressure, information asymmetry, and imperfect communication, are usually studied in isolation. We introduce GPTNT, a benchmark built on the cooperative video game Keep Talking and Nobody Explodes, in which two agents must coordinate to defuse procedurally generated bomb puz
The proliferation of multimodal models and the increasing demand for collaborative AI systems necessitate robust benchmarks that reflect real-world complexities like time pressure and imperfect information.
This benchmark provides a critical tool for evaluating and accelerating the development of truly collaborative and robust AI agents, moving beyond isolated capabilities to integrated performance under stress.
The focus for AI agent development will shift towards integrating diverse capabilities and addressing communication and coordination challenges in dynamic, high-pressure environments.
- · AI research labs
- · Multimodal model developers
- · AI agent platform providers
- · AI models lacking strong collaborative capabilities
GPTNT enables more accurate assessment of AI's collaborative intelligence against human-level performance.
Improved collaborative agents will accelerate automation in complex, multi-stakeholder workflows currently requiring human intervention.
The development of highly adaptive and communicative AI agents could lead to new paradigms in human-AI teaming and autonomous system design across various industries.
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Read at arXiv cs.AI