CAGE uses common-agency games and an EPEC algorithm to compute equilibrium policies that balance multiple conflicting objectives for test-time LLM alignment.
Gtbench: Uncovering the strategic reasoning limitations of llms via game-theoretic evaluations
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
VS-Bench is a new benchmark of ten visual multi-agent environments that measures VLMs on element recognition, next-action prediction, and normalized episode return, showing strong perception but large gaps in reasoning and decision-making with the best model at 46.6% prediction accuracy and 31.4% of
A game-theoretic heterogeneous multi-agent architecture with three cloud LLMs and a local verifier achieves 77.2% F1, 100% recall, and 3x speedup for code vulnerability detection at $0.002 per sample on the NIST Juliet suite.
citing papers explorer
-
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.
-
VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments
VS-Bench is a new benchmark of ten visual multi-agent environments that measures VLMs on element recognition, next-action prediction, and normalized episode return, showing strong perception but large gaps in reasoning and decision-making with the best model at 46.6% prediction accuracy and 31.4% of
-
Strategic Heterogeneous Multi-Agent Architecture for Cost-Effective Code Vulnerability Detection
A game-theoretic heterogeneous multi-agent architecture with three cloud LLMs and a local verifier achieves 77.2% F1, 100% recall, and 3x speedup for code vulnerability detection at $0.002 per sample on the NIST Juliet suite.