LedgerMind Tackles Multimodal Agent Hallucination via Structured Evidence Ledgers
Enjun Du · hf · 2026-07-31
LedgerMind introduces a provenance-constrained reasoning framework for multimodal agents, addressing the issue that traditional evaluations focus solely on final answer accuracy while ignoring intermediate reasoning reliability.
The framework's core mechanisms include:
- Structured Evidence Ledger: Normalizes tool outputs into trajectory states, ensuring downstream reasoning cites only active ledger entries.
- Three-Layer Grounding Protocol: Strictly checks model grounding at both entity and numeric levels.
- Event-Triggered Verification & Repair Engine: Uses typed state transitions during repair to prevent introducing unprovenanced content (formal non-amplification guarantee).
Experiments show this design effectively identifies and repairs four hidden failure modes (e.g., unsupported intermediate reasoning, citation-backed entity hallucination, over-reasoning). It significantly improves both answer accuracy and trajectory-level faithfulness across multiple benchmarks.
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