Model Detail
Falcon-Perception
—Falcon-Perception is a code generation model released by TII. The model is registered under the mask-generation pipeline tag on Hugging Face, distributed under the permissive apache-2.0 license.
The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Falcon-Perception is best fit for code completion, repository-scale Q&A, and pair-programming integrations. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Introducing Falcon ASR
Falcon-Emirati: When an LLM Learns the Dialect, the Culture, and the Nuance
FALCON: A Model and Dataset Agnostic Framework for Synthetic Data Generation for NL2SQL Pairs
arXiv:2610.03625v1 Announce Type: new Abstract: Relational databases are among the most widely deployed forms of structured knowledge, and natural language access to them requires grounding language onto schema entities and relations while handling the ambiguity inherent in how people phrase request
FALCON-S: Fixed-wing ground-effect Aerodynamics Simulator and Flight Control Learning Suite
arXiv:2609.06046v1 Announce Type: cross Abstract: We present a modular, high-fidelity simulation framework for the development and benchmarking of flight control strategies in fixed-wing aerial robots operating near the ground. Unlike existing simulators that rely on simplified or hover-oriented dyn
FALCON-Discover: Discovering Concentrated False-Confidence Regions for Calibration
arXiv:2607.18278v1 Announce Type: cross Abstract: Calibration is usually evaluated in aggregate, but the most dangerous failures are often local: predictions that remain highly confident despite being wrong. We study this failure mode as false-confidence concentration, the extent to which confident
FALCON: Transforming Cyber Threat Intelligence into Deployable IDS Rules with Self-Reflection
arXiv:2508.18684v2 Announce Type: replace-cross Abstract: Signature-based Intrusion Detection Systems (IDS) detect malicious activity by matching network or host events against predefined rules. Security analysts manually develop these rules from Cyber Threat Intelligence (CTI). As threats evolve, t
