BINDMEM: Executable Memory for Long-Horizon Reasoning

Long-horizon agents must interpret and combine information across past interactions, yet access to supporting evidence does not ensure correct reasoning. Even a successfully computed value may fail to satisfy the question's required scope or precision. We introduce BINDMEM, a neuro-symbolic memory interface that preserves source-local interpretations (WRITE), executes query-dependent programs (READ), and checks answer requirements before disclosing derived values (DISCLOSE). WRITE materializes source-local interpretations as provenance-linked typed records whenever their inputs are available from the source context. READ compiles each question into a typed program and an answer contract, then executes it over the retrieved records while tracking supporting evidence and unresolved conditions. DISCLOSE supplies a computed value only when it meets the requested precision and scope; otherwise it supplies the selected evidence alone. The final reader retains the original retrieved text throughout. Across six benchmark variants with two instruction-tuned readers, BINDMEM attains the highest overall score in all 12 comparisons against seven memory baselines, and it also improves a frontier reader. Fixed-retrieval controls isolate the contributions of stored interpretations, execution-selected evidence, and selective disclosure, and with identical stored records BINDMEM outperforms the evaluated Python pipelines for both readers.