NAPA: Natively Multimodal Autoregressive Perception Architecture

Perception-centric systems are typically implemented with a modular encoderdecoder pipeline: a vision backbone for feature extraction and a separate decoder (or late-fusion module) for task prediction. We ask whether this separation is necessary for tasks such as openvocabulary segmentation and OCR. We present Falcon Perception, a single, early-fusion dense Transformer that processes image patches, text, and task tokens in a single shared parameter space. Falcon Perception uses a hybrid attention mask: bidirectional for image tokens and causal for text/task tokens, and keeps dense outputs practical by emitting only a small number of task tokens per instance and decoding continuous spatial outputs with lightweight heads, enabling parallel high-resolution mask prediction. On SA-Co, Falcon Perception achieves 68.0 F , compared to 62.3 for a strong state-of-the-art promptable segmentation baseline. 1 To measure compositional grounding beyond saturated referring benchmarks, we introduce PBench and show large gains on spatial and dense long-context regimes (e.g., 53.5 vs. 31.6 on spatial and 72.6 vs. 58.4 on Dense). Finally, we extend the same architecture to document OCR: a compact Falcon-OCR model with 300M parameters achieves 80.3% on olmOCR and 88.64% on OmniDocBench. Code: GitHub, PBench: HF Dataset, Blog: Project Blog.