Falcon Perception

TL;DR — Falcon Perception is a 0.6B-parameter early-fusion Transformer for open-vocabulary grounding and segmentation from natural language prompts. The model processes image patches + text in one sequence using a hybrid attention mask, and produces variable numbers of instances with a small, structured token interface and lightweight output heads. On SA-Co, Falcon Perception reaches 68.0 Macro-F1 (vs. 62.3 for SAM 3) with the main remaining gap being presence calibration (MCC 0.64 vs. 0.82). We also introduce PBench, a diagnostic benchmark that breaks down performance by capability (attributes, OCR-guided disambiguation, spatial constraints, relations) and by dense long-context crowded scenes.

We also relase Falcon OCR, a 0.3B-parameter model which reaches a score of 80.3 and 88.6 on the olmOCR benchmark and OmniDocBench respectively, while having the highest throughput of any open source OCR model.

This post is a brief, practical write-up of what we built, why we built it this way, and what we learned along the way.

The problem: why do perception systems end up as pipelines?

Many open-vocabulary perception systems are built as modular pipelines: a (often frozen) vision backbone extracts features, a separate fusion/decoder stage combines them with language, and additional components handle matching and post-processing. This family of designs works well in many settings, but it comes with trade-offs: it can be hard to scale cleanly, hard to attribute improvements to the right component, and easy to accumulate complexity as we add a new fix for each failure mode.

We asked a simpler question: can a single early-fusion Transformer backbone handle both perception and language modeling, if we choose the right attention pattern, output interface, and training signal?

In our experiments, the answer is largely yes. The rest of this post describes the main design choices and the evidence behind them.

: Falcon Perception reads text on objects to disambiguate; SAM 3 cannot. : Falcon Perception resolves spatial constraints; SAM 3 returns false positives. : Falcon Perception understands interactions; SAM 3 ignores relational constraints. : Falcon Perception scales to hundreds of instances; SAM 3's decoder runs out of query tokens. : Accurate transcription of handwritten text and in-the-wild captures under adverse conditions. : Faithful reproduction of tabular structure and cell content across diverse formats. : Accurate recognition of equations across varying levels of symbolic complexity. : Faithful text extraction from multi-column, mixed-content documents.