Echoes Are Not Votes: Provenance-Aware Contexts for Tool-Using Agents

Tool-using language-model agents routinely encounter several artifacts that descend from one source: generated clients, copied documentation, cached outputs, and summaries. Current contexts present these artifacts as separate evidence, allowing one origin to acquire many apparent votes. We identify this failure as lineage amplification and isolate it with EchoBench, a controlled benchmark that adds claim-preserving derivatives while holding provenance roots fixed. Across 120 stratified tasks and four models, Raw accuracy falls from 100.0% with no derivatives to 55.2% with seven, although no root changes. A symmetric-replication control remains at 97.5%. When derivatives are rewritten with the same templates as roots, a surface-only heuristic falls from 100.0% to 50.0% and Raw agents reach 50.8%, exposing a sharp load-dependent preference for the amplified answer. We introduce duplication invariance as a paired measure of this behavior and present EchoLedger, a training-free reference context layer that records the derivation DAG, exposes one representative per root-set signature, and retains an expandable receipt. On the shared policy matrix, a calibrated metadata-free MinHash baseline reaches 97.5%, while full-context tags and compact EchoLedger reach 100%; compact EchoLedger uses 33.5% fewer characters. Across the four-model main study, EchoLedger reaches 99.8% versus 81.0% Raw (+18.75 points; 95% CI [12.29, 25.42]). On 36 overlapping-root tasks evaluated with Claude, Codex, and OpenCode runners, it reaches 100.0% versus 41.7% Raw (+58.33 points; [42.59, 73.15]) and recovers the correct unique-root-union decision in every task. On replay-validated public code with wording and path cues removed, Raw/EchoLedger accuracy is 1.3/100.0%. The analysis establishes derivation lineage as a distinct context relation and makes lineage amplification measurable and controllable.