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Xidian University
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LLM-based multi-agent systems are riddled with hidden failure modes that traditional testing misses, but FLARE uncovers them with coverage-guided fuzzing.
LLMs can slash token usage by 80% and "thinking rate" by 95% without sacrificing accuracy, simply by learning when *not* to reason.
Ditch the deterministic databases: this LLM-driven simulation framework evaluates tool-calling agents with surprisingly reliable proxy states, offering a scalable alternative to costly benchmarks.
