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LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger

Research LLM Agents

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TL;DR - LedgerMind structures multimodal-agent trajectories as provenance-constrained evidence ledgers, making intermediate reasoning auditable rather than evaluating only final answers. It improves answer accuracy and trajectory-level faithfulness across multiple benchmarks and MLLM backbones.

  • Tool outputs become normalized ledger entries that downstream claims must cite.
  • Entity- and numeric-level checks detect unsupported reasoning and “Phantom Grounding.”
  • Typed repair transitions prevent verification steps from introducing unproven content.
  • Adaptive routing adjusts reasoning depth to query complexity, reducing over-reasoning.

Sources (1)

LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger

arXiv cs.LG Enjun Du, Hange Zhou, Chenxu Du, Siyi Liu, Zirong Chen, Ziyu Zheng, Yongqi Zhang 2026-07-30 arXiv:2607.28374
Public signals Hugging Face upvotes 15
Providers: Hugging Face · Upvotes 15 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-30 14:28:56.540639 UTC

TL;DR - LedgerMind structures multimodal-agent trajectories as provenance-constrained evidence ledgers, making intermediate reasoning auditable rather than evaluating only final answers. It improves answer accuracy and trajectory-level faithfulness across multiple benchmarks and MLLM backbones.

  • Tool outputs become normalized ledger entries that downstream claims must cite.
  • Entity- and numeric-level checks detect unsupported reasoning and “Phantom Grounding.”
  • Typed repair transitions prevent verification steps from introducing unproven content.
  • Adaptive routing adjusts reasoning depth to query complexity, reducing over-reasoning.
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