Logically Redundant, Behaviorally Different: Omitting Derivable Information in LLM Agents

LLM agents use tools over multiple steps, accumulating observations, tool outputs, and memory as they act. Existing work therefore compresses or omits observations and interaction history to manage growing context. Logically, information derivable from what remains can be omitted without loss. However, how such omission affects agent behavior remains underexplored. This paper uses paired Explicit and Derivable conditions to study this question in multi-step tool-use tasks. Explicit provides selected derived information directly in the current state representation, while Derivable omits the same information only when it remains exactly derivable from retained facts and rules. In TextWorld, Explicit improves task success by 61.7 percentage points, completing 58/60 tasks compared with 21/60 under Derivable. The effect grows as more derived conclusions are stated, is not reproduced by repeating retained premises, and also appears in next-action choices from the same environment state. The difference weakens or disappears when additional reasoning or preparation is available before action and also varies across executors. Derivability alone does not guarantee behavioral preservation. Whether omission preserves behavior depends on the executor, inference settings, and pre-action processing.