Scaling-Aware Adapter for Structure-Grounded LLM Reasoning

Abstract

Large language models (LLMs) enable reasoning over biomolecular structures, yet existing methods remain modality-specific and typically compress structural inputs via sequence-based tokenization or fixed-length query connectors. Such architectures either omit geometric grounding required to mitigate structural hallucinations or impose inflexible modality-fusion bottlenecks that both over-compress and misallocate structural tokens, impeding generalized all-atom reasoning. We introduce **Cuttlefish**, a unified all-atom LLM that grounds language reasoning in geometric cues while scaling modality tokens with structural complexity. First, **Scaling-Aware Patching** uses an instruction-conditioned gating mechanism to generate variable-size patches over structural graphs, adaptively scaling the query-token budget with structural complexity to mitigate fixed-length connector bottlenecks. Second, **Geometry Grounding Adapter** refines these adaptive tokens via cross-attention to modality embeddings and injects the resulting modality tokens into the LLM, exposing explicit geometric cues to reduce structural hallucination. Experiments across diverse all-atom benchmarks show that Cuttlefish achieves superior performance in heterogeneous structure-grounded reasoning.