Full-horizon planning with on-demand replanning achieves accuracy parity with single-step planning in tool-calling agents for knowledge base and multi-hop question answering while consuming 2-3 times fewer tokens.
In: Proceedings of the 34th ACM International Conference on Information and Knowledge Management
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A neuro-symbolic system converts legal clauses into deterministic typed graphs for consistent, auditable adjudication that cuts compute costs by over 90% versus direct large reasoning model use.
Structured reasoning artifacts enable coordination in LLM multi-agent systems by preventing approval and welfare collapse under asymmetric information while keeping bad-approval rates low across audit regimes.
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Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling
Full-horizon planning with on-demand replanning achieves accuracy parity with single-step planning in tool-calling agents for knowledge base and multi-hop question answering while consuming 2-3 times fewer tokens.
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Accurate Legal Reasoning at Scale: Neuro-Symbolic Offloading and Structural Auditability for Robust Legal Adjudication
A neuro-symbolic system converts legal clauses into deterministic typed graphs for consistent, auditable adjudication that cuts compute costs by over 90% versus direct large reasoning model use.
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Toward Explanatory Equilibrium: Verifiable Reasoning as a Coordination Mechanism under Asymmetric Information
Structured reasoning artifacts enable coordination in LLM multi-agent systems by preventing approval and welfare collapse under asymmetric information while keeping bad-approval rates low across audit regimes.