arXivPaperNeeds Review
Learning When to Trust via Selective Context Preference Optimization
MIST tests whether models use good context while resisting bad context, exposing agents that appear robust only because they ignore external evidence altogether.
arXiv
Source Summary
MIST renders each reasoning item under **four matched conditions**: clean, misleading, correct-context, and irrelevant-context. Its **SC2W** metric counts cases where misleading context flips an otherwise correct answer to wrong.
Practical Implication
Evaluate retrieval-augmented agents for selective trust, not only prompt-injection resistance. The proposed **SCOPE** method trains on matched preference pairs balanced across all four conditions so resistance does not come from ignoring useful context.
Agent-Ready Context
MIST renders each reasoning item under **four matched conditions**: clean, misleading, correct-context, and irrelevant-context. Its **SC2W** metric counts cases where misleading context flips an otherwise correct answer to wrong. Evaluate retrieval-augmented agents for selective trust, not only prompt-injection resistance. The proposed **SCOPE** method trains on matched preference pairs balanced across all four conditions so resistance does not come from ignoring useful context. The abstract reports reduced susceptibility on popular open models while preserving other-condition accuracy, but provides no numerical effect sizes here. Broader generalization beyond the benchmark's reasoning items remains an open question from the supplied material.
Context Map
benchmarkresearch#agent-evals#agent-reliability#context-engineeringUncertainty
The abstract reports reduced susceptibility on popular open models while preserving other-condition accuracy, but provides no numerical effect sizes here. Broader generalization beyond the benchmark's reasoning items remains an open question from the supplied material.