Reconfigurable Embodied Reasoning: Offline Composition of Replaceable Capability Modules

Generalist embodied models typically integrate capabilities such as grounding and interaction, spatial reasoning, temporal understanding and planning, and video understanding into a single checkpoint. We explore how these capabilities can be independently maintained, updated, and recombined after training while retaining deployment as a single model. We organize independently maintained LoRA modules into a capability library and introduce ReCAP, a weight-only composition method that integrates selected modules into one static checkpoint. ReCAP incorporates capability updates through module replacement and recomposition, without retraining the remaining modules. ReCAP anchors composition in one complete module update and expresses the others as residual edits in shared coordinates. It aggregates these edits through magnitude-weighted voting in a joint residual subspace and controls their injection with a support mask. ReCAP achieves the highest Full-11 score among the evaluated training-free merging methods across 11 embodied reasoning benchmarks, two backbones, and three capability-library settings. On libraries trained from a shared base, it scores 66.53 on InternVL3.5-14B and 68.27 on Qwen3-VL-8B, exceeding the strongest single modules by 1.86 and 0.85 points. Under a shared evaluation pipeline, the Qwen3-VL-8B composition outperforms all nine public embodied models, leading the strongest comparator by 2.89 points. Further experiments show that task-specific recomposition improves target performance, local module improvements benefit the composed model without retuning, and incremental updates obtained through continued training of an individual capability can enter an existing capability library. These results demonstrate the viability of building high-performing embodied models from independently evolving capability modules while preserving single-checkpoint deployment.