Causal Stack Operating Model

Mon, 01 Jan 0001 00:00:00 +0000

The old fault family index recognized compositional AI systems, including retrieval, memory, tools, state, validators, policy layers, and orchestration. But it still had a problem: it grouped failures by conceptual fault type, while the new product-fault-families.md groups them by product debugging surface.

Layer 0 Alt

Mon, 01 Jan 0001 00:00:00 +0000

0. Outline:

Layer 0 — Natural Language Interface Substrate

0. Front Matter
 0.1 Purpose
 0.2 Scope
 0.3 Core thesis
 0.4 Audience
 0.5 How to read this document
 0.6 Layer 0 invariants

1. Meaning as a Layered Phenomenon
 1.1 Surface form
 1.2 Lexical meaning
 1.3 Constructional meaning
 1.4 Compositional semantics
 1.5 Pragmatic meaning
 1.6 Discourse meaning
 1.7 Social and interactional meaning

2. Indeterminacy and Compression
 2.1 Ambiguity
 2.1.1 Lexical ambiguity
 2.1.2 Syntactic ambiguity
 2.1.3 Scope ambiguity
 2.1.4 Referential ambiguity

 2.2 Vagueness
 2.2.1 Fuzzy boundaries
 2.2.2 Gradable terms
 2.2.3 Context-relative thresholds

 2.3 Underspecification
 2.3.1 Missing parameters
 2.3.2 Recoverable omissions
 2.3.3 Defaults and assumptions
 2.3.4 User-side economy of expression

 2.4 Prototype-Based Categories
 2.4.1 Category resemblance
 2.4.2 Central and peripheral cases
 2.4.3 Domain-specific prototypes

 2.5 Metaphor and Conceptual Transfer
 2.5.1 Technical metaphor
 2.5.2 Evaluative metaphor
 2.5.3 Structural metaphor

 2.6 Summary: Why Natural-Language Meaning Is Partial, Graded, and Compressed

3. Context, Reference, and Grounding
 3.1 Implicit Context
 3.1.1 User context
 3.1.2 Task context
 3.1.3 Domain context
 3.1.4 Organizational context
 3.1.5 Preference context

 3.2 Deixis
 3.2.1 Person deixis
 3.2.2 Time deixis
 3.2.3 Place deixis
 3.2.4 Discourse deixis
 3.2.5 Social deixis

 3.3 Coreference
 3.3.1 Pronoun resolution
 3.3.2 Named-entity resolution
 3.3.3 Repeated descriptions
 3.3.4 Ambiguous referents

 3.4 Ellipsis
 3.4.1 Sentence-level ellipsis
 3.4.2 Turn-level ellipsis
 3.4.3 Task-state ellipsis
 3.4.4 Recoverability conditions

 3.5 Presupposition
 3.5.1 Entity presuppositions
 3.5.2 Event presuppositions
 3.5.3 Authority presuppositions
 3.5.4 State presuppositions

 3.6 Interface Grounding
 3.6.1 Selected object
 3.6.2 Active document
 3.6.3 Cursor position
 3.6.4 Viewport state
 3.6.5 Highlighted text
 3.6.6 Current UI focus

 3.7 Summary: How Meaning Depends on External, Prior, and Situated State

4. Communicative Force and Intent
 4.1 Pragmatics in the Broad Sense
 4.1.1 Meaning as use
 4.1.2 Literal meaning versus intended meaning
 4.1.3 Contextual inference

 4.2 Speech Acts
 4.2.1 Request
 4.2.2 Command
 4.2.3 Question
 4.2.4 Suggestion
 4.2.5 Warning
 4.2.6 Correction
 4.2.7 Confirmation
 4.2.8 Refusal
 4.2.9 Evaluation

 4.3 Indirect Speech Acts
 4.3.1 Ability question as request
 4.3.2 Observation as implied request
 4.3.3 Critique as revision request
 4.3.4 Preference as constraint

 4.4 Implicature
 4.4.1 Relevance implicature
 4.4.2 Scalar implicature
 4.4.3 Consequential implicature
 4.4.4 Domain-specific implicature

 4.5 Relevance Filtering
 4.5.1 Task-relevant interpretation
 4.5.2 Domain-relevant interpretation
 4.5.3 User-goal-relevant interpretation
 4.5.4 Salience and suppression of irrelevant meanings

 4.6 Action Fan-Out
 4.6.1 Single utterance to single action
 4.6.2 Single utterance to parameterized action
 4.6.3 Single utterance to workflow
 4.6.4 Single utterance to multi-step orchestration

 4.7 Summary: How Utterances Become Communicative Acts

5. Discourse State and Conversational Mutation
 5.1 Discourse Structure
 5.1.1 Sequence
 5.1.2 Contrast
 5.1.3 Cause
 5.1.4 Condition
 5.1.5 Exception
 5.1.6 Elaboration
 5.1.7 Summary

 5.2 Discourse Markers
 5.2.1 however
 5.2.2 therefore
 5.2.3 actually
 5.2.4 instead
 5.2.5 unless
 5.2.6 for example
 5.2.7 in short

 5.3 Repair and Correction
 5.3.1 Self-repair
 5.3.2 Other-repair
 5.3.3 Referent correction
 5.3.4 Parameter correction
 5.3.5 Intent correction
 5.3.6 Scope correction

 5.4 Incremental Refinement
 5.4.1 Adding constraints
 5.4.2 Removing constraints
 5.4.3 Narrowing the search space
 5.4.4 Broadening the search space
 5.4.5 Reprioritizing criteria

 5.5 Mixed Initiative
 5.5.1 User-led initiative
 5.5.2 System-led clarification
 5.5.3 Collaborative narrowing
 5.5.4 Reframing and negotiation

 5.6 Redundancy and Emphasis
 5.6.1 Repetition as priority signal
 5.6.2 Repetition as urgency signal
 5.6.3 Repetition as contrast
 5.6.4 Repetition as repair

 5.7 Non-Monotonicity
 5.7.1 Later turns invalidating earlier turns
 5.7.2 Retraction of extracted parameters
 5.7.3 Soft cancellation
 5.7.4 Partial revision
 5.7.5 Conflicting updates

 5.8 Conversational Memory Limits
 5.8.1 Stale references
 5.8.2 Lost salience
 5.8.3 Conflicting constraints
 5.8.4 Overloaded discourse context

 5.9 Summary: How Meaning Evolves Across Turns

6. Social, Epistemic, and Priority Signals
 6.1 Politeness and Face Management
 6.1.1 Softened requests
 6.1.2 Indirect criticism
 6.1.3 Deference
 6.1.4 Mitigation
 6.1.5 Face-saving formulations

 6.2 Epistemic Stance
 6.2.1 Certainty
 6.2.2 Uncertainty
 6.2.3 Inference
 6.2.4 Reported knowledge
 6.2.5 Evidence strength

 6.3 Affective Stance
 6.3.1 Frustration
 6.3.2 Concern
 6.3.3 Skepticism
 6.3.4 Satisfaction
 6.3.5 Reluctance

 6.4 Modality
 6.4.1 Possibility
 6.4.2 Necessity
 6.4.3 Permission
 6.4.4 Obligation
 6.4.5 Prohibition

 6.5 Authority and Role
 6.5.1 Speaker authority
 6.5.2 Addressee authority
 6.5.3 Institutional role
 6.5.4 Ownership
 6.5.5 Approval rights

 6.6 Urgency and Priority
 6.6.1 Explicit deadlines
 6.6.2 Relative deadlines
 6.6.3 Consequence framing
 6.6.4 Repeated emphasis
 6.6.5 Priority tradeoffs

 6.7 Commitment Strength
 6.7.1 Strong assertion
 6.7.2 Tentative assertion
 6.7.3 Preference
 6.7.4 Recommendation
 6.7.5 Hard requirement

 6.8 Summary: How Stance Changes Interpretation

7. Failure, Risk, and Repair Semantics
 7.1 Interpretive Failure Types
 7.1.1 Wrong referent
 7.1.2 Wrong intent
 7.1.3 Missing constraint
 7.1.4 Stale context
 7.1.5 Hallucinated context
 7.1.6 Over-execution
 7.1.7 Under-execution

 7.2 Ambiguity Severity
 7.2.1 Harmless ambiguity
 7.2.2 Recoverable ambiguity
 7.2.3 Execution-blocking ambiguity
 7.2.4 Safety-critical ambiguity

 7.3 Confidence and Commitment
 7.3.1 Infer silently
 7.3.2 Infer with disclosure
 7.3.3 Ask for clarification
 7.3.4 Offer alternatives
 7.3.5 Require confirmation
 7.3.6 Decline execution

 7.4 Repair Protocols
 7.4.1 Clarification question
 7.4.2 Confirmation
 7.4.3 Correction acceptance
 7.4.4 Rollback
 7.4.5 Partial execution
 7.4.6 Safe no-op

 7.5 Summary: When Uncertainty Requires Repair Rather Than Execution

8. Cross-Property Interaction
 8.1 How Properties Co-Occur
 8.2 Ambiguity plus Context Dependence
 8.3 Vagueness plus Urgency
 8.4 Deixis plus Interface Grounding
 8.5 Politeness plus Directive Force
 8.6 Repair plus Non-Monotonicity
 8.7 Modality plus Authority
 8.8 Examples of Multi-Property Utterances

9. Diagnostic Property Index
 9.1 Property Table
 9.2 Core Question Per Property
 9.3 Example Per Property
 9.4 Required Resolution Type
 9.5 Risk Level
 9.6 Downstream Obligation

10. Worked Examples
 10.1 “Move it to tomorrow.”
 10.2 “Make this less aggressive.”
 10.3 “Send that to Sarah before the meeting.”
 10.4 “Actually, not that one — the cheaper option from yesterday.”
 10.5 “We probably should not change the public API before launch.”
 10.6 “Onboard the new contractor.”
 10.7 “Same as before, but only the urgent ones.”

11. Glossary
 11.1 Ambiguity
 11.2 Vagueness
 11.3 Underspecification
 11.4 Deixis
 11.5 Coreference
 11.6 Ellipsis
 11.7 Presupposition
 11.8 Pragmatics
 11.9 Speech act
 11.10 Implicature
 11.11 Discourse repair
 11.12 Mixed initiative
 11.13 Epistemic stance
 11.14 Modality
 11.15 Commitment strength
 11.16 Interface grounding
 11.17 Non-monotonicity

12. Final Summary
 12.1 Layer 0 as an irreducible substrate
 12.2 Meaning is distributed across multiple dimensions
 12.3 Context and pragmatics should remain distinct but adjacent
 12.4 Natural-language interpretation is dynamic, situated, and repairable

0. Front Matter

Layer 0 defines the natural-language substrate that exists before parsing, planning, tool use, validation, execution, or system policy.

Layer 0 Natural Language Properties

Mon, 01 Jan 0001 00:00:00 +0000

Natural-Language Properties: A Linguistic and Semantic Taxonomy

Purpose

This document organizes important properties of natural language from a linguistic and semantic point of view. The focus is on how natural language differs from programming languages, formal languages, and APIs.

Layer 1a Base Llm Mechanisms

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1A - Base LLM Architectural / Inference Mechanisms

This document defines the base mechanisms in a dedicated Layer 1A file.

Layer 1b Final Scheme

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1B – Learned Behavioral LLM Features

Purpose

Layer 1B defines stable learned behavioral properties of large language models that are causal, but are not themselves faults.

Layer 1c Ai System Causal Features

Mon, 01 Jan 0001 00:00:00 +0000

Layer 1C – AI-System-Level Causal Features

Framing principle – AI systems are empirical systems

That means their behavior cannot be fully trusted, specified, or improved from implementation structure alone. Once an LLM is embedded in a real application, quality has to be discovered, measured, and validated through representative scenarios, repeated runs, traces, and production observation.

Layer 3 Control Families

Mon, 01 Jan 0001 00:00:00 +0000

Layer 3 – System Control Families

Purpose

This document defines the canonical Layer 3 system control families for AI systems.

Layer 3 Controls And Faults

Mon, 01 Jan 0001 00:00:00 +0000

Layer 3 – System Controls and System Faults

Purpose

Layer 3 describes the system-level controls, design choices, runtime safeguards, monitoring mechanisms, and operational processes that shape whether Layer 2 behavioral fault modes are prevented, detected, bounded, recovered from, or allowed to reach users.

Layer 3 Semantic Fault View

Mon, 01 Jan 0001 00:00:00 +0000

Layer 3 – Semantic Fault View

Purpose

This document defines an NLP- and semantics-focused view of Layer 3 system faults for LLM-based workflows.

Layer 3 System Fault Families

Mon, 01 Jan 0001 00:00:00 +0000

Layer 3 – System Fault Families

Definition

A system-level fault is a failure in the surrounding LLM application that allows model-derived fault modes to produce incorrect, unsafe, inconsistent, or untrustworthy behavior.

Causal Stack on Tech Foundations

Causal Stack Operating Model

Layer 0 Alt

0. Outline:

0. Front Matter

Layer 0 Natural Language Properties

Natural-Language Properties: A Linguistic and Semantic Taxonomy

Purpose

Layer 1a Base Llm Mechanisms

Layer 1A - Base LLM Architectural / Inference Mechanisms

Layer 1b Final Scheme

Layer 1B – Learned Behavioral LLM Features

Purpose

Layer 1c Ai System Causal Features

Layer 1C – AI-System-Level Causal Features

Framing principle – AI systems are empirical systems

Layer 3 Control Families

Layer 3 – System Control Families

Purpose

Layer 3 Controls And Faults

Layer 3 – System Controls and System Faults

Purpose

Layer 3 Semantic Fault View

Layer 3 – Semantic Fault View

Purpose

Layer 3 System Fault Families

Layer 3 – System Fault Families

Definition