CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
arXiv preprint arXiv:2308.10032 , year=
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Multi-turn multi-agent dialogue improves VLM spatial reasoning in collaborative reconstruction only marginally, with text descriptions outperforming visual inputs.
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Common-agency Games for Multi-Objective Test-Time Alignment
CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
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Multi-Turn Multi-Agent Dialogue for Collaborative Reconstruction Improves VLM Performance on Spatial Reasoning, But Only Barely
Multi-turn multi-agent dialogue improves VLM spatial reasoning in collaborative reconstruction only marginally, with text descriptions outperforming visual inputs.