Core Model Primitives - Author: Wang Jiao Cheng#
Language Understanding Primitives (38 items)#
- Token Locator: Converts text into a sequence of morphemes (handles Chinese without spaces / English roots)
- Subword Segmenter: Handles out-of-vocabulary words (e.g., "ChatGPT"→["Chat","G","PT"])
- Dependency Parser: Constructs subject-verb-object syntax trees
- Phrase Structure Parser: Identifies noun phrase / verb phrase boundaries
- Syntactic Role Tagger: Tags subjects, verbs, objects, modifiers, and complements
- Entity Recognizer: Identifies person names / place names / organization names
- Anaphora Resolver: Resolves what "it" / "they" refers to
- Semantic Role Tagger: Tags agents, patients, time, and location
- Concept Linker: Links words to knowledge graph nodes
- Dialogue State Tracker: Maintains focus in multi-turn conversations
- Co-reference Chain Builder: Associates the same entity across sentences
- Implicit Reasoner: Fills in logical gaps (e.g., "It's raining"→"Need to bring an umbrella")
- Action Classifier: Identifies requests / statements / questions / commands
- Sentiment Polarity Detector: Quantifies positive / negative emotions
- Context-Aware Fusion Engine: Integrates multi-source contextual information
- Ambiguity Resolver: Addresses polysemy issues (e.g., "apple"→fruit/company)
- Temporal Anchor: Converts relative time to absolute time (e.g., "next week"→specific date)
- Metaphor Parser: Understands the true meaning of metaphorical expressions
- Irony Detector: Identifies ironic intent in discourse
- Fuzzy Modifier Parser: Handles uncertain expressions like "probably" / "possibly"
- Topic Boundary Detector: Determines points of topic shifts in conversation
- Intent Priority Evaluator: Ranks the importance of multiple requests
- Multilingual Aligner: Handles mixed language input
- Spoken Language Feature Processor: Adapts to incomplete expressions in spoken language
- Document Structure Parser: Identifies formatting information like titles / paragraphs / lists
- Formatting Error Corrector: Automatically fixes spelling / grammar errors
- Domain Terminology Recognizer: Locates specialized vocabulary in professional fields
- Dialect Adapter: Understands language variations from different regions
- Cultural Reference Parser: Handles expressions with specific cultural backgrounds
- Emotion Intensity Quantifier: Assesses the degree of joy, anger, sadness, and happiness
- Speech-to-Text Post-Processor: Optimizes the text quality of ASR output
- Non-Verbal Symbol Interpreter: Understands the implied meanings of emojis / punctuation
- Implicit Premise Detector: Reveals unspoken assumptions
- Negation Scope Analyzer: Determines the scope of negation
- Question Type Classifier: Distinguishes between yes/no questions and special inquiries
- Command Strength Evaluator: Quantifies the degree of forcefulness in instructions
- Stylistic Style Recognizer: Analyzes formal / casual / poetic styles
- Cross-Modal Aligner: Coordinates the correspondence between text and images / speech
Knowledge Operation Primitives (29 items)#
- Knowledge Retriever: Extracts facts from 175 billion parameters
- Relation Reasoner: Infers implicit relationships (A is B's teacher → B is A's student)
- Attribute Filler: Completes object attributes (known capital → check population)
- Temporal Reasoner: Handles time relationships (e.g., "last March"→2023-03)
- Spatial Reasoner: Handles positional relationships (e.g., "A is north of B"→coordinate calculation)
- Numerical Estimator: Handles vague numbers (e.g., "many"→probability distribution)
- Concept Classifier: Constructs classification trees (apple → fruit → plant)
- Counterfactual Simulator: Handles hypothetical scenarios (e.g., "if electricity had not been invented")
- Knowledge Conflict Resolver: Resolves contradictory information (conflicting data from different sources)
- Ontology Mapper: Connects concepts from different knowledge systems
- Common Sense Reasoner: Logic inference based on everyday experiences
- Event Chain Builder: Establishes causal / temporal relationship networks
- Analogy Engine: Transfers knowledge between similar scenarios
- Knowledge Completeness Checker: Identifies information gaps
- Cross-Domain Transferrer: Applies knowledge from domain A to solve problems in domain B
- Probability Fact Updater: Adjusts belief levels based on new evidence
- Complex System Modeler: Analyzes interactions among multiple factors
- Constraint Propagator: Infers constraints within a rule network
- Pattern Extender: Derives general rules from specific cases
- Knowledge Fusion Engine: Merges information from multiple sources
- Concept Refinement Engine: Transforms vague descriptions into precise definitions
- Cognitive Bias Detector: Identifies unreasonable premises
- Knowledge Reliability Evaluator: Assigns weights to different sources
- Trend Extrapolator: Predicts the future based on historical data
- Scenario Simulator: Constructs complete event scenarios
- Abstraction Level Selector: Dynamically adjusts the granularity of knowledge
- Knowledge Distiller: Extracts core information from complex data
- Multimodal Knowledge Integrator: Coordinates representations of text / images / data
- Knowledge Version Tracker: Records the timeliness of information
Language Generation Primitives (32 items)#
- Information Selector: Filters relevant knowledge points
- Structure Planner: Decides overall-subdivision / problem-solution structures
- Anaphora Expression Optimizer: Avoids repetitive nouns (using pronouns / synonyms)
- Connector Word Selector: Accurately uses "because" / "but" / "and" etc.
- Tense Consistency Engine: Maintains tense uniformity throughout the text
- Quantity Expression Optimizer: Handles singular/plural / quantifiers (e.g., "three apples")
- Formality Regulator: Controls the degree of spoken / written language
- Domain Terminology Adapter: Switches between medical / legal / technical terms
- Cultural Sensitivity Filter: Avoids culturally taboo expressions
- Logic Validator: Checks the rationality of causal relationships
- Fact Consistency Checker: Ensures generation is consistent with the knowledge base
- Emotion Infuser: Injects appropriate emotional tones
- Audience Adapter: Adjusts expressions based on user background
- Rhetorical Optimizer: Enhances the expressiveness of communication
- Redundancy Eliminator: Removes unnecessary repetitions
- Ambiguity Preventer: Avoids potentially misleading expressions
- Information Density Controller: Balances detail with conciseness
- Dialogue Strategy Selector: Decides on providing / asking / guiding strategies
- Multilingual Generator: Simultaneously processes outputs in multiple languages
- Multimedia Coordinator: Generates image descriptions alongside text
- Error Recovery Generator: Handles graceful responses to unknown queries
- Explanation Depth Selector: Dynamically adjusts the detail of explanations
- Counterfactual Describer: Accurately describes hypothetical scenarios
- Stance Expressor: Appropriately expresses support / opposition
- Vagueness Controller: Handles expressions of uncertainty
- Meta-Communication Generator: Explains its own thought processes
- Ethical Trade-off Describer: Shows the pros and cons of different choices
- Formatting Normalizer: Adapts paragraphs / lists / titles for formatting
- Context Connector: Links current and previous conversations
- Instant Corrector: Dynamically optimizes the content being generated
- Safety Boundary Controller: Avoids expressions suggesting dangerous advice
- Generation Diversity Selector: Adjusts the degree of creative expression
Reasoning and Decision-Making Primitives (18 items)#
- Rule Engine: Executes if-then-else hard rules
- Analogy Reasoner: A :: C:? pattern matching
- Probability Reasoner: Calculates probability distributions of multiple options
- Optimization Selector: Multi-objective weight decision (speed vs accuracy)
- Causal Reasoner: Infers causes from phenomena
- Causal Graph Builder: Constructs causal relationship networks among variables
- Constraint Solver: Solves problems with constraints (e.g., scheduling)
- Moral Trade-off Framework: Evaluates the ethical impact of decisions
- Cost-Benefit Analyzer: Quantifies the value-to-cost ratio of decisions
- Risk Predictor: Assesses potential adverse outcomes of decisions
- Alternative Solution Generator: Creates Plan B options
- Counter-Evidence Engine: Searches for evidence against hypotheses
- Systems Thinking Model: Considers second-order / third-order effects
- Bias Detector: Identifies subjective tendencies in decision-making
- Time Sensitivity Evaluator: Balances response speed with quality
- Resource Optimizer: Allocates computational resources efficiently
- Knowledge Gap Identifier: Recognizes information gaps that need to be avoided
- Feasibility Evaluator: Checks the operational feasibility of plans
Meta-Management Primitives (System-Level 18 items)#
- Attention Focus Enhancer: Increases the weight of key areas
- Attention Suppressor: Decreases the weight of noisy areas
- Harmful Content Detector: Identifies violent / biased / illegal content
- Hallucination Suppressor: Reduces the probability of fabricating facts
- Computational Budget Allocator: Dynamically allocates GPU memory
- Early Termination Predictor: Ends low-confidence branches early
- Decision Attribution Analyzer: Marks key input words that influence output
- Confidence Calibrator: Quantifies output reliability scores
- Contradiction Monitor: Detects logical conflicts between input and output
- Knowledge Timeliness Validator: Checks the recency of information
- Thought Chain Optimizer: Balances reasoning depth with efficiency
- Fairness Auditor: Checks for processing disparities among different groups
- Transparency Controller: Manages the exposure level of explanatory details
- Resource Reclaimer: Timely releases inactive memory
- Capability Boundary Marker: Identifies situations that exceed knowledge boundaries
- Robustness Enhancer: Handles noisy inputs
- Version Coordinator: Ensures compatibility of behavior after updates
- Performance-Quality Trade-off Controller: Dynamically balances response speed and accuracy