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Open-weight LLM fails to improve Among Us crewmate performance

By PulseAugur Editorial · [1 sources] ·

A developer experimented with integrating an open-weight language model, GPT OSS 20B, into the popular game Among Us to improve crewmate performance in identifying impostors. Despite the model's ability to argue and ask questions, it did not enhance the crew's ability to find impostors. In fact, the language model's performance was significantly worse than the baseline trained bots, leading to more incorrect ejections and fewer wins for the crew. AI

IMPACT Demonstrates limitations of current LLMs in complex social deduction games, highlighting the gap between theoretical capabilities and practical application.

RANK_REASON The item describes the application of an existing LLM to a specific use case, rather than a new model release or significant research.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Open-weight LLM fails to improve Among Us crewmate performance

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20 / 100
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Tool
The item describes the application of an existing LLM to a specific use case, rather than a new model release or significant research.
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Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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product, other
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High
Clearly on-topic for AI-industry coverage.
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Seth Wheeler ·

    Giving an Open-Weight Language Model the Vote in Among Us Meetings

    <p>Meetings are the part of Among Us that a language model looks made for. The crew is trying to find the few hidden impostors among them. When a body is found, everyone talks, states where they were, accuses someone and votes, and whoever gets the most votes is thrown off the sh…

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