CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
Stackelberg game preference optimization for data-efficient alignment of language models
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
GTKA uses adversarial game training to generate privacy-safe sub-queries for external LLMs, then integrates answers locally, reducing intent leakage while preserving answer quality on new biomedical and legal benchmarks.
citing papers explorer
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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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Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition
GTKA uses adversarial game training to generate privacy-safe sub-queries for external LLMs, then integrates answers locally, reducing intent leakage while preserving answer quality on new biomedical and legal benchmarks.