Many teams building autonomous agents hit a wall when the model has to make quick, reliable choices—routing a request, triaging an alert, picking a tool, or enforcing a guardrail. Independent classifications often give contradictory answers, forcing extra code to reconcile them and slowing down the pipeline. Deploying a separate reasoning step adds latency and complexity, especially in environments with limited compute or strict air‑gap requirements.
GLiNER2.5‑Decide tackles this problem directly. It is a 340‑million‑parameter classifier that receives raw text together with a schema of typed questions and returns a single, jointly consistent answer set. Each answer ships with a probability, a confidence score and feasibility metadata, so downstream logic can act on the numbers without extra post‑processing. Because the model is non‑generative, there is no need for prompt templates or token generation; the encoder scores every allowed label in one forward pass and a constrained decoder picks the highest‑scoring combination that satisfies any declared rules (implications, exclusions, cardinality limits, ordinal bounds).
The weights are released under Apache 2.0 and install with a simple pip command. They run on CPU, GPU or in fully offline settings, and a hosted API is also available for teams that prefer a managed service. By moving the decision step into a single model call, agents avoid duplicated inference, eliminate conflicting outputs and keep latency low—often under a few tens of milliseconds on modest hardware.
In practice this means faster routing, cleaner triage, more reliable guardrails and simpler tool selection pipelines, all without sacrificing interpretability or adding heavyweight reasoning layers.
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