Results for "structured tool invocation"

Agent calls external tools dynamically.

Models trained to decide when to call tools.

Letting an LLM call external functions/APIs to fetch data, compute, or take actions, improving reliability.

Constraining model outputs into a schema used to call external APIs/tools safely and deterministically.

Enables external computation or lookup.

Forcing predictable formats for downstream systems; reduces parsing errors and supports validation/guardrails.

Neural networks that operate on graph-structured data by propagating information along edges.

Removing weights or neurons to shrink models and improve efficiency; can be structured or unstructured.

Central log of AI-related risks.

Methods for breaking goals into steps; can be classical (A*, STRIPS) or LLM-driven with tool calls.

A table summarizing classification outcomes, foundational for metrics like precision, recall, specificity.

A structured collection of examples used to train/evaluate models; quality, bias, and coverage often dominate outcomes.

Ordering training samples from easier to harder to improve convergence or generalization.

Mechanisms for retaining context across turns/sessions: scratchpads, vector memories, structured stores.

Structured dataset documentation covering collection, composition, recommended uses, biases, and maintenance.

Structured graph encoding facts as entity–relation–entity triples.

Probabilistic graphical model for structured prediction.

Software pipeline converting raw sensor data into structured representations.

Standardized documentation describing intended use, performance, limitations, data, and ethical considerations.

Measures a model’s ability to fit random noise; used to bound generalization error.

Updating beliefs about parameters using observed evidence and prior distributions.

Formal model linking causal mechanisms and variables.

GNN using attention to weight neighbor contributions dynamically.

Interleaving reasoning and tool use.

Sampling-based motion planner.

Ability to correctly detect disease.

Testing AI under actual clinical conditions.

Simulating adverse scenarios.

Maximum expected loss under normal conditions.

Networks using convolution operations with weight sharing and locality, effective for images and signals.