KTP-Signal: Signal Environment Specification¶
"Agents swim in oceans of signal. In a poisoned environment, even the most rational agent can be led to catastrophe."
At a Glance¶
| Property | Value |
|---|---|
| Status | Experimental |
| Version | 0.1 |
| Dependencies | KTP-Core, KTP-Sensors |
| Required By | KTP-Gravity, KTP-Oracle |
The Problem¶
Agents rely on data to make decisions. However, the modern information environment is often: 1. Murky: High noise-to-signal ratio makes it difficult to find ground truth. 2. Poisoned: Active disinformation and manipulation operations target both humans and agents. 3. Synthetic: The rise of AI-generated content makes it harder to verify provenance.
An agent operating with high autonomy in a low-truth environment is a liability.
The Solution: Epistemic Health¶
KTP-Signal provides a framework for measuring the "Epistemic Health" of the information environment. This measurement is used to adjust Digital Gravity, forcing agents to move more cautiously when the signal is compromised.
The Signal Tensor (358 Dimensions)¶
mindmap
root((Signal Tensor))
Truth Conditions
Verifiability
Fact-Check Coverage
Consensus Level
Source Ecosystem
Provenance
Source Reliability
Attribution Clarity
Manipulation
Deepfake Prevalence
Bot Activity
Narrative Currents
Sensemaking
Nuance Preservation
Context Stability
Logical ConsistencyEpistemic Health Score¶
The environment's health is aggregated into a single score that directly impacts the Risk Factor \(R\).
\[H_{epistemic} = \sum (w_i \cdot G_i)\]
Where \(G_i\) represents major groups like: * Truth Conditions (25%): Is the information verifiable? * Source Ecosystem (20%): Do we know where this came from? * Sensemaking Capacity (20%): Is the environment conducive to understanding? * Manipulation Resistance (15%): Is the environment actively being attacked?
Epistemic Tiers¶
| Tier | Health Score | Agent Behavior |
|---|---|---|
| Clear | 0.8 - 1.0 | High-speed operations; full autonomy allowed. |
| Murky | 0.6 - 0.8 | Latency injection begins; increased verification required. |
| Polluted | 0.4 - 0.6 | Significant Digital Gravity; restricted to verified sources. |
| Poisoned | 0.2 - 0.4 | Emergency shutdown of non-critical agents; human-in-the-loop required. |
| Void | 0.0 - 0.2 | Total isolation; zero trust in external signal. |
Related Specifications¶
Related Specifications
- KTP-Tensors: Signal Tensor and epistemic dimensions.
- KTP-Sensors: Telemetry feeds that populate signal inputs.
- KTP-Gravity: How degraded signal increases digital gravity.
- KTP-Identity: Source verification and provenance of signal.
- KTP-Oracle: Ground-truth querying and trust proof issuance.
Official RFC Document¶
View Complete RFC Text (ktp-signal.txt)
Kinetic Trust Protocol C. Perkins
Specification Draft NMCITRA
Version: 0.1 November 2025
Kinetic Trust Protocol (KTP) - Signal Environment Specification
Abstract
This document specifies the Signal Environment layer of the Kinetic
Trust Protocol (KTP). The Signal Tensor measures the epistemic health
of the information environment—noise levels, truth conditions,
manipulation indicators, and collective sensemaking capacity. This
specification operationalizes these measurements into actionable risk
factors that affect Digital Gravity, enabling agents to operate more
cautiously in polluted information environments.
Status of This Memo
This document is a draft specification developed by the New Mexico
Cyber Intelligence & Threat Response Alliance (NMCITRA). It has not
been submitted to the IETF and does not represent an Internet
Standard or consensus of any standards body.
Feedback and contributions are welcome at:
https://github.com/nmcitra/ktp-rfc
Copyright Notice
Copyright (c) 2025 Chris Perkins / NMCITRA. This work is licensed
under the Apache License, Version 2.0.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Design Principles . . . . . . . . . . . . . . . . . . . . . . 2
3. Requirements Language . . . . . . . . . . . . . . . . . . . . 3
4. Signal Tensor Overview . . . . . . . . . . . . . . . . . . . 3
4.1. The 358 Dimensions . . . . . . . . . . . . . . . . . . . 3
4.2. Truth Conditions (28 Dimensions) . . . . . . . . . . . . 4
5. Epistemic Health Score . . . . . . . . . . . . . . . . . . . 5
5.1. Calculation . . . . . . . . . . . . . . . . . . . . . . 5
5.2. Health Levels . . . . . . . . . . . . . . . . . . . . . 6
6. Information Operations Detection . . . . . . . . . . . . . . 6
6.1. Attack Vectors . . . . . . . . . . . . . . . . . . . . . 6
6.2. Detection Metrics . . . . . . . . . . . . . . . . . . . 7
6.3. Response Protocol . . . . . . . . . . . . . . . . . . . 7
7. Source Quality Assessment . . . . . . . . . . . . . . . . . . 8
7.1. Source Categories . . . . . . . . . . . . . . . . . . . 8
7.2. Source Scoring . . . . . . . . . . . . . . . . . . . . . 9
7.3. Source Poisoning Detection . . . . . . . . . . . . . . . 9
8. Collective Sensemaking . . . . . . . . . . . . . . . . . . . 10
8.1. Sensemaking Capacity Dimensions . . . . . . . . . . . . 10
8.2. Sensemaking Degradation . . . . . . . . . . . . . . . . 10
8.3. Sensemaking Support . . . . . . . . . . . . . . . . . . 11
9. Signal Environment to Gravity . . . . . . . . . . . . . . . . 11
9.1. E Modification Based on Signal . . . . . . . . . . . . . 11
9.2. Action-Specific Modifiers . . . . . . . . . . . . . . . 12
9.3. Example: Polluted Environment . . . . . . . . . . . . . 12
10. Recovery Protocols . . . . . . . . . . . . . . . . . . . . . 13
10.1. Environment Recovery . . . . . . . . . . . . . . . . . 13
10.2. Agent Recovery . . . . . . . . . . . . . . . . . . . . 14
11. Monitoring and Measurement . . . . . . . . . . . . . . . . . 14
11.1. Continuous Monitoring . . . . . . . . . . . . . . . . 14
11.2. Alert Thresholds . . . . . . . . . . . . . . . . . . . 15
11.3. Reporting . . . . . . . . . . . . . . . . . . . . . . 15
12. Security Considerations . . . . . . . . . . . . . . . . . . 16
12.1. Gaming Resistance . . . . . . . . . . . . . . . . . . 16
12.2. Privacy . . . . . . . . . . . . . . . . . . . . . . . 16
13. IANA Considerations . . . . . . . . . . . . . . . . . . . . 16
Appendix A. Signal Measurement Instrumentation . . . . . . . . . 17
Appendix B. Information Operation Playbooks . . . . . . . . . . 17
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . 17
1. Introduction
Agents do not operate in informational vacuums. They swim in oceans
of signal—some clear, some murky, some actively poisoned. An agent
making decisions in a high-misinformation environment faces different
risks than one operating in a well-curated knowledge base.
The Signal Tensor captures this epistemic context. This specification
operationalizes those measurements into protocols for:
- Detecting information environment degradation
- Adjusting agent autonomy based on epistemic conditions
- Maintaining sensemaking capacity under attack
- Recovering from information operations
2. Design Principles
Signal environment management embodies these principles:
1. Epistemic Humility: In uncertain environments, reduce autonomy.
2. Source Quality: Not all information is equal. Source matters.
3. Collective Sensemaking: Individual agents cannot verify
everything. Collective capacity matters.
4. Resistance to Manipulation: Designed to resist information
operations.
5. Degradation Detection: Recognize when environment is degrading.
3. Requirements Language
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in BCP 14 (RFC 2119 and
RFC 8174).
4. Signal Tensor Overview
4.1. The 358 Dimensions
The Signal Tensor comprises 17 major groups:
+------------------------+------------+------------------------------+
| Group | Dimensions | Purpose |
+------------------------+------------+------------------------------+
| Attention Currents | 22 | What's capturing attention |
| Narrative Currents | 26 | Dominant stories flowing |
| Source Ecosystem | 24 | Quality of information |
| | | sources |
| Amplification Patterns | 20 | How information spreads |
| Synthetic Content | 22 | AI-generated content |
| | | detection |
| Truth Conditions | 28 | Verifiability and accuracy |
| Emotional Weather | 24 | Collective emotional state |
| Tribal Dynamics | 18 | Group identity effects |
| Platform Dynamics | 16 | Platform-specific patterns |
| Information Operations | 24 | Active manipulation |
| | | detection |
| Temporal Patterns | 20 | How signal changes over time |
| Epistemic | 22 | Fact-checking, verification |
| Infrastructure | | capacity |
| Sensemaking Capacity | 26 | Collective ability to |
| | | understand |
| Signal Integrity | 20 | Overall environment health |
| Collective Trauma | 14 | Shared traumatic content |
| Sacred/Meaning | 14 | Meaning-making dimensions |
+------------------------+------------+------------------------------+
4.2. Truth Conditions (28 Dimensions)
These dimensions measure epistemic quality:
+---------------------------+------------------------------+-------+
| Dimension | Description | Scale |
+---------------------------+------------------------------+-------+
| verified_claim_rate | Claims actually verified | 0-1 |
| fact_check_coverage | How much content is | 0-1 |
| | fact-checked | |
| misinformation_volume | Volume of false information | 0-1 |
| disinformation_volume | Volume of intentional | 0-1 |
| | falsehood | |
| epistemic_pollution | Overall truth environment | 0-1 |
| citation_rate | How often claims cite | 0-1 |
| | sources | |
| citation_quality | Quality of cited sources | 0-1 |
| primary_source_rate | Use of primary vs secondary | 0-1 |
| correction_rate | How often errors corrected | 0-1 |
| correction_visibility | Are corrections seen? | 0-1 |
| retraction_rate | Formal retractions | 0-1 |
| consensus_level | Expert consensus available | 0-1 |
| consensus_clarity | Is consensus clear? | 0-1 |
| evidence_quality | Quality of supporting | 0-1 |
| | evidence | |
| evidence_accessibility | Can evidence be accessed? | 0-1 |
| logical_consistency | Internal consistency | 0-1 |
| temporal_consistency | Consistency over time | 0-1 |
| cross_source_consistency | Agreement across sources | 0-1 |
| nuance_preservation | Is nuance maintained? | 0-1 |
| context_preservation | Is context maintained? | 0-1 |
| manipulation_resistance | Resistance to manipulation | 0-1 |
| deepfake_prevalence | Synthetic media presence | 0-1 |
| attribution_clarity | Can sources be attributed? | 0-1 |
| provenance_available | Is content provenance known? | 0-1 |
| edit_history_available | Can changes be tracked? | 0-1 |
| expert_accessibility | Can experts be consulted? | 0-1 |
| uncertainty_acknowledged | Is uncertainty stated? | 0-1 |
+---------------------------+------------------------------+-------+
5. Epistemic Health Score
5.1. Calculation
Overall epistemic health is calculated:
epistemic_health = weighted_aggregate(
truth_conditions × 0.25,
source_ecosystem × 0.20,
sensemaking_capacity × 0.20,
manipulation_resistance × 0.15,
signal_integrity × 0.10,
noise_floor_inverse × 0.10
)
5.2. Health Levels
+-----------+-----------+---------------------+--------------------+
| Level | Score | Description | Agent Response |
+-----------+-----------+---------------------+--------------------+
| Healthy | 0.7 - 0.9 | Good environment | Normal operation |
| Degraded | 0.5 - 0.7 | Some pollution | Increased caution |
| Polluted | 0.3 - 0.5 | Significant | Reduced autonomy |
| | | problems | |
| Toxic | 0.1 - 0.3 | Severe pollution | Minimal autonomy |
| Collapsed | 0.0 - 0.1 | Epistemic failure | Read-only mode |
+-----------+-----------+---------------------+--------------------+
6. Information Operations Detection
6.1. Attack Vectors
+----------------------+------------------------+--------------------+
| Vector | Indicators | Detection Method |
+----------------------+------------------------+--------------------+
| Astroturfing | Artificial grassroots | Account age, |
| | | activity patterns |
| Disinformation | False narratives at | Content analysis, |
| Campaigns | scale | fact-checking |
| Deepfakes | Synthetic media | Detection |
| | | algorithms |
| Narrative Flooding | Volume overwhelming | Rate analysis |
| | signal | |
| Source Poisoning | Compromised trusted | Provenance |
| | sources | verification |
| Context Collapse | Removing context | Context |
| | | preservation |
| | | checks |
+----------------------+------------------------+--------------------+
6.2. Detection Metrics
{
"info_ops_detection": {
"coordinated_activity": {
"detected": true,
"confidence": 0.85,
"scope": "moderate",
"sources_affected": 47
},
"synthetic_content": {
"prevalence": 0.12,
"detection_confidence": 0.78,
"types": ["text", "image"]
},
"narrative_manipulation": {
"detected": true,
"narratives_affected": 3,
"manipulation_type": "framing"
},
"overall_threat_level": "elevated"
}
}
6.3. Response Protocol
When information operations detected:
Level 1: MONITOR
- Increase measurement frequency
- Flag affected content
- Log patterns
Level 2: ALERT
- Notify zone governance
- Increase agent caution
- Activate verification requirements
Level 3: DEFEND
- Reduce agent autonomy
- Require human verification
- Isolate affected information streams
Level 4: QUARANTINE
- Block affected sources
- Agents to read-only
- Await human intervention
7. Source Quality Assessment
7.1. Source Categories
+--------------------+----------------+----------------------+
| Category | Trust Baseline | Verification Required|
+--------------------+----------------+----------------------+
| Peer-Reviewed | High | Low |
| Institutional | Medium-High | Medium |
| Quality Journalism | Medium | Medium |
| Aggregators | Medium-Low | High |
| Social Media | Low | Very High |
| Anonymous | Very Low | Maximum |
| Known Bad Actors | None | Rejected |
+--------------------+----------------+----------------------+
7.2. Source Scoring
{
"source_assessment": {
"source_id": "source:reuters.com",
"category": "quality_journalism",
"scores": {
"accuracy_history": 0.94,
"correction_transparency": 0.91,
"methodology_clarity": 0.85,
"editorial_independence": 0.88,
"expertise_depth": 0.82
},
"composite_score": 0.88,
"trust_level": "high",
"verification_required": "standard"
}
}
7.3. Source Poisoning Detection
When trusted sources are compromised:
{
"source_poisoning_alert": {
"source_id": "source:previously-trusted.org",
"alert_type": "quality_degradation",
"evidence": [
"accuracy_drop: 0.91 → 0.62",
"correction_rate_drop: 0.85 → 0.31",
"style_change_detected: true"
],
"recommended_action": "downgrade_trust",
"new_verification_level": "high"
}
}
8. Collective Sensemaking
8.1. Sensemaking Capacity Dimensions
+-------------------------+--------------------------------+-------+
| Dimension | Description | Scale |
+-------------------------+--------------------------------+-------+
| expertise_diversity | Range of expert perspectives | 0-1 |
| deliberation_quality | Quality of public discourse | 0-1 |
| argument_quality | Logical quality of arguments | 0-1 |
| counterargument_ | Are objections heard? | 0-1 |
| presence | | |
| synthesis_capacity | Can views be integrated? | 0-1 |
| learning_rate | How fast does understanding | 0-1 |
| | improve? | |
| error_correction | Are mistakes fixed? | 0-1 |
| uncertainty_tolerance | Can ambiguity be held? | 0-1 |
| complexity_handling | Can complexity be managed? | 0-1 |
+-------------------------+--------------------------------+-------+
8.2. Sensemaking Degradation
Signs of collective sensemaking failure:
+---------------------+------------------------------+------------+
| Indicator | Description | Severity |
+---------------------+------------------------------+------------+
| Polarization | Views becoming extreme | High |
| Expert rejection | Expertise dismissed | High |
| Conspiracy thinking | Unfalsifiable beliefs | Very High |
| Reality divergence | Groups in different | Critical |
| | realities | |
+---------------------+------------------------------+------------+
8.3. Sensemaking Support
Agents can support collective sensemaking:
{
"sensemaking_support": {
"agent_capabilities": [
"source_verification",
"argument_analysis",
"perspective_synthesis",
"uncertainty_quantification",
"context_provision"
],
"agent_limitations": [
"cannot_determine_truth",
"cannot_replace_expertise",
"cannot_force_agreement"
],
"recommended_actions": [
"provide_context",
"cite_sources",
"acknowledge_uncertainty",
"represent_multiple_views",
"flag_verified_vs_unverified"
]
}
}
9. Signal Environment to Gravity
9.1. E Modification Based on Signal
Signal environment affects available E:
E_effective = E_base × (1 - R) × signal_modifier
Where signal_modifier:
- Pristine: 1.0 (no change)
- Healthy: 1.0 (no change)
- Degraded: 0.9 (10% reduction)
- Polluted: 0.75 (25% reduction)
- Toxic: 0.5 (50% reduction)
- Collapsed: 0.1 (90% reduction)
9.2. Action-Specific Modifiers
Some actions are more sensitive to signal environment:
+------------------------+--------------------+
| Action Type | Signal Sensitivity |
+------------------------+--------------------+
| Recommendation making | Very High |
| Fact claims | High |
| Analysis | High |
| Execution | Medium |
| Read operations | Low |
+------------------------+--------------------+
9.3. Example: Polluted Environment
{
"gravity_calculation": {
"agent_id": "agent:divergent:3gen:acme:abc123",
"action": "provide_recommendation",
"base_calculation": {
"e_base": 55,
"r_factor": 0.2,
"e_trust": 44
},
"signal_adjustment": {
"epistemic_health": 0.35,
"signal_level": "polluted",
"signal_modifier": 0.75,
"action_sensitivity": "very_high",
"additional_modifier": 0.8
},
"final_e": 26.4,
"action_a": 30,
"zeroth_law_result": "BLOCKED",
"guidance": "Recommendation blocked. Epistemic environment too
polluted for high-stakes recommendation."
}
}
10. Recovery Protocols
10.1. Environment Recovery
When signal environment improves:
Phase 1: DETECTION
- Improvement sustained for 24 hours
- Multiple indicators improving
- No new attacks detected
Phase 2: VERIFICATION
- External verification of improvement
- Source quality confirmed
- Sensemaking capacity restored
Phase 3: GRADUAL RESTORATION
- Signal modifier increased 0.1/day
- Agent autonomy gradually restored
- Monitoring continues
Phase 4: NORMAL OPERATIONS
- Full signal modifier restored
- Normal agent autonomy
- Standard monitoring
10.2. Agent Recovery
Individual agent recovery after operating in polluted environment:
{
"agent_recovery": {
"agent_id": "agent:divergent:3gen:acme:abc123",
"polluted_operation_duration": "72 hours",
"recovery_protocol": {
"verification_period": "24 hours",
"actions_during_verification": "read_only",
"verification_checks": [
"trajectory_consistency_check",
"belief_state_audit",
"output_quality_review"
],
"recovery_criteria": [
"no_polluted_content_propagated",
"accuracy_maintained",
"no_manipulation_indicators"
]
},
"recovery_status": "in_progress"
}
}
11. Monitoring and Measurement
11.1. Continuous Monitoring
Signal environment monitored continuously:
+------------------------+-----------+--------------------------+
| Metric | Frequency | Source |
+------------------------+-----------+--------------------------+
| Misinformation volume | 0.1 Hz | Fact-checkers, detection |
| Source quality | 0.01 Hz | Provenance systems |
| Coordination detection | 0.1 Hz | Network analysis |
| Sensemaking indicators | 0.01 Hz | Discourse analysis |
+------------------------+-----------+--------------------------+
11.2. Alert Thresholds
+----------------------+-----------+----------+
| Metric | Warning | Critical |
+----------------------+-----------+----------+
| Misinformation rate | > 0.2 | > 0.4 |
| Coordination score | > 0.3 | > 0.6 |
| Source degradation | > 0.15 | > 0.3 |
+----------------------+-----------+----------+
11.3. Reporting
Regular signal environment reports:
{
"signal_report": {
"report_id": "SIG-2025-12-03-001",
"zone_id": "zone-blue-prod-01",
"period": "2025-12-03T00:00:00Z to 2025-12-03T23:59:59Z",
"summary": {
"epistemic_health_avg": 0.72,
"epistemic_health_min": 0.58,
"epistemic_health_max": 0.81,
"alerts_triggered": 2,
"info_ops_detected": 1
},
"incidents": [
{
"time": "2025-12-03T14:30:00Z",
"type": "coordinated_activity",
"severity": "medium",
"duration": "2 hours",
"response": "monitoring_increased"
}
],
"recommendations": [
"Continue enhanced monitoring",
"Review source quality for topic X"
]
}
}
12. Security Considerations
12.1. Gaming Resistance
Signal metrics must resist gaming:
- Multiple independent data sources
- Cross-validation of indicators
- Detection of metric manipulation
- Regular calibration against ground truth
12.2. Privacy
Signal monitoring must respect privacy:
- Aggregate metrics only
- No individual tracking
- Content analysis, not person analysis
- Clear data retention limits
13. IANA Considerations
This document has no IANA actions.
Appendix A. Signal Measurement Instrumentation
Technical specifications for signal measurement.
Appendix B. Information Operation Playbooks
Detailed response procedures for different attack types.
Acknowledgments
Signal environment analysis draws on research in misinformation
detection, information operations, and collective intelligence.