Harness-MU is a zero-tuning infrastructure that decouples safety orchestration from language generation in multi-user LLM agents, achieving full privacy preservation on Muses-Bench while improving utility and instruction-following over baselines.
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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
Presents a new benchmark and role-sensitive policy gate for agentic relationship harm that outperforms generic safety prompting with zero harmful compliance in tests.
citing papers explorer
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Harness-MU: A Safe, Governed, and Effective Harness for Multi-User LLM Agents
Harness-MU is a zero-tuning infrastructure that decouples safety orchestration from language generation in multi-user LLM agents, achieving full privacy preservation on Muses-Bench while improving utility and instruction-following over baselines.
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Agentic Relationship Harm: Benchmarking and Gating Relational Manipulation in AI Agents
Presents a new benchmark and role-sensitive policy gate for agentic relationship harm that outperforms generic safety prompting with zero harmful compliance in tests.