CHARTA: A Recurrent Language Model with Routed Writes and Addressable Memory

Fixed-state recurrent models provide constant-memory processing, but maintaining structured, persistent task-relevant state under memory load is not generally an explicit objective. We introduce CHARTA, an attention-free language model that structures delta-rule recurrent memory through routed and addressable writes. CHARTA retains the delta update while adding a block-level organiser that routes write strength across blocks, an event router that conditions write content and strength, and contextual and token-stable addressing paths. It can be trained with auxiliary supervision of task-relevant state while its recurrent matrices, which we call boards, remain accessible through fitted or learned readouts. We evaluate CHARTA along three complementary axes: persistence under load, state accessibility and causal relevance, and data-efficient language modelling. After training with at most four bindings, CHARTA reaches 96.7% accuracy with sixteen, versus 82.3% for DeltaNet, and retains once-stated facts across unseen window boundaries. A linear decoder recovers held-out item locations from boards at 49.0%, versus 26.1% for DeltaNet; board transplantation causally changes predictions. On BabyLM strict-small, CHARTA achieves a higher aggregate score than the top entry (55.9 vs. 53.6). Together, our results show that fixed-size recurrent state can be structured to retain, expose, and reuse task-relevant information without attention over the preceding context.