causal-filtering

Causal Neural Set Filtering (CNSF) is a proposed neural network method for online multi-target tracking that improves efficiency and performance by reducing error metrics by 19.3% and 30.4% compared to Track-MT3, with fewer parameters and faster inference.

This paper introduces Contract2Tool, a framework for automatically inferring lightweight tool contracts (preconditions, effects, risk) from tool metadata, documentation, and execution traces, enabling reliable causal tool filtering for LLM agents. Experiments show learned contracts achieve near-gold contract performance in downstream multi-step agent tasks, significantly reducing token usage.