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Qwen3.5-9B agent diagnosed for Pac-Man failures in perception and decision-making

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to diagnose failures in vision-language agents, using a Qwen3.5-9B model trained to play Pac-Man. The study distinguishes between perception errors (reading the screen) and decision-making errors (choosing actions). By isolating these components, the team found that while an agent could accurately read static game screens, it still made poor action choices during gameplay. This highlights the need for more nuanced post-training evaluation that separates perception from policy performance and considers full-game validation rather than a single selected score. AI

IMPACT Provides a framework for identifying and addressing specific failure modes in AI agents, potentially improving training and evaluation processes.

RANK_REASON Academic paper detailing a new diagnostic method for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Qwen3.5-9B agent diagnosed for Pac-Man failures in perception and decision-making

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Academic paper detailing a new diagnostic method for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xinglu Wang, Edward Wang, Yong Zhang, Zhenan Fan ·

    Where It Breaks: Diagnosing Perception, Memory, and Decision Failures of a Vision-Language Agent in Pac-Man

    arXiv:2611.06704v1 Announce Type: new Abstract: Training an agent to play Pac-Man with only up, down, left and right actions raises a basic question: when performance is poor, what should be improved? Difficulty can arise from reading the screen or from choosing actions over a lo…

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