P (44 terms)

PAC Learning A model is PAC-learnable if it can, with high probability, learn an approximately correct hypothesis from finite samp... Intermediate Parameter Sharing Using same parameters across different parts of a model. Intermediate Parameter-Efficient Fine-Tuning Techniques that fine-tune small additional components rather than all weights to reduce compute and storage. Intermediate Parameters The learned numeric values of a model adjusted during training to minimize a loss function. Intermediate Pareto Optimality No agent can improve without hurting another. Advanced Particle Filter Monte Carlo method for state estimation. Intermediate Path Planning Finding routes from start to goal. Advanced Perception Stack Software pipeline converting raw sensor data into structured representations. Advanced Perplexity Exponential of average negative log-likelihood; lower means better predictive fit, not necessarily better utility. Intermediate Physical Safety Ensuring robots do not harm humans. Frontier Physics Engine Software simulating physical laws. Advanced PID Controller Classical controller balancing responsiveness and stability. Intermediate PII Information that can identify an individual (directly or indirectly); requires careful handling and compliance. Intermediate Planner-Executor Separates planning from execution in agent architectures. Intermediate Planning Methods for breaking goals into steps; can be classical (A*, STRIPS) or LLM-driven with tool calls. Intermediate Planning Horizon Number of steps considered in planning. Advanced Plant The physical system being controlled. Intermediate Polarization Groups adopting extreme positions. Advanced Policy Strategy mapping states to actions. Intermediate Policy Gradient Optimizing policies directly via gradient ascent on expected reward. Intermediate Policy Search Directly optimizing control policies. Advanced Positional Encoding Injects sequence order into Transformers, since attention alone is permutation-invariant. Intermediate Posterior Distribution Updated belief after observing data. Advanced Potential Fields Planning via artificial force fields. Advanced Power-Seeking Behavior Tendency to gain control/resources. Advanced PR Curve Often more informative than ROC on imbalanced datasets; focuses on positive class performance. Intermediate Precision Of predicted positives, the fraction that are truly positive; sensitive to false positives. Intermediate Prediction Drift Shift in model outputs. Intermediate Predictive Coding Learning by minimizing prediction error. Frontier Predictive Policing AI predicting crime patterns (highly controversial). Intermediate Prior Distribution Belief before observing data. Advanced Privacy Attack Attacks that infer whether specific records were in training data, or reconstruct sensitive training examples. Intermediate Probability Distribution Describes likelihoods of random variable outcomes. Advanced Prognostic Model Predicting disease progression or survival. Intermediate Prompt The text (and possibly other modalities) given to an LLM to condition its output behavior. Intermediate Prompt Engineering Crafting prompts to elicit desired behavior, often using role, structure, constraints, and examples. Intermediate Prompt Injection Attacks that manipulate model instructions (especially via retrieved content) to override system goals or exfiltrate ... Intermediate Prompt Leakage Extracting system prompts or hidden instructions. Intermediate Prompt Sensitivity Small prompt changes cause large output changes. Intermediate Propensity Score Probability of treatment assignment given covariates. Advanced Proprioception Internal sensing of joint positions, velocities, and forces. Advanced Prosody Temporal and pitch characteristics of speech. Intermediate Protein Folding Predicting protein 3D structure from sequence. Advanced Pruning Removing weights or neurons to shrink models and improve efficiency; can be structured or unstructured. Intermediate