VACS: Value-Aligned Compositional Shielding for Multi-Agent Reasoning
Multi-agent reasoning systems in high-stakes domains must be both accurate and safe, yet agents often follow heterogeneous value priorities (e.g., rigor, conciseness, safety), causing conflicting recommendations. Existing methods do not jointly provide: (i) principled inference of each agent's implicit values from behavior, (ii) compositional formal safety guarantees without full online communication, and (iii) value-aware conflict resolution with faithful explanations. We present VACS (Value-Aligned Compositional Shielding), a four-layer framework addressing all three. Layer 1 learns value-dimension rewards from pairwise preferences using Bradley-Terry modeling and infers per-agent value...
| Authors | Yiyao Zhang, Diksha Goel, Hussain Ahmad et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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