Embedding-based defenses fail against attacks that align malicious message embeddings with benign ones in LLM multi-agent systems, but token-level confidence scores improve robustness by enabling better pruning of suspicious messages.
A Survey of Con- fidence Estimation and Calibration in Large Language Models
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A thermodynamic-inspired information-geometric framework defines a composite LLM stability score that outperforms a utility-entropy baseline by 0.0299 on average across 80 observations, with gains increasing at higher entropy.
TRACE aggregates answer consistency and confidence trajectory over multiple reasoning steps to decide when to halt inference, reducing token usage by 25-30% while keeping accuracy within 1-2% of full reasoning.
Verbalized confidence from small LMs enables cost-effective cascade routing for automated educational scoring, matching large-model accuracy at 76% lower cost when discrimination is strong.
Simple supervision improves LLM distributional alignment with diverse population groups on three datasets, with evaluation across multiple models and prompts providing a benchmark.
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When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems
Embedding-based defenses fail against attacks that align malicious message embeddings with benign ones in LLM multi-agent systems, but token-level confidence scores improve robustness by enabling better pruning of suspicious messages.