Reversible Evidence-Graph Action Projection for Frozen Language Agents

Grounded language-agent systems must act under delayed and reversible evidence, because observations confirm effects only after execution and later interactions can invalidate established facts. Overcoming this difficulty requires an evidence-synchronized action interface backed by transactionally verified and revocable state, rather than fluent reasoning and syntactic executability alone. Planning and reasoning agents are well suited to adaptive decision generation, yet their reliance on textual histories leaves stale and premature actions available when interaction evidence changes, especially in environments that enumerate grounded actions. Within this setting, StructuThink addresses task ordering through transition knowledge, but its task graph is derived from expert refinements and does not revise the environment-supplied feasible set from delayed receipts and revoked support. We therefore propose Reversible Evidence-Graph Action Projection (REGAP), which maintains receipt-gated typed state and revocable evidence to construct a compact, nonempty action interface for a frozen selector, together with guarantees for interface totality, evidence soundness, and conditional path preservation. Experiments on embodied household and scientific-interaction benchmarks show that REGAP substantially improves completion and context efficiency over complete-action, planning, and reasoning baselines.