SWANK AI Research Series — Case Study 001
Feedback Symmetry Across Contemporary Large Language Models
Applying Reflective Feedback & Calibration Theory to AI-Assisted Communication
Systems
Polly Chromatic
Founder, SWANK AI
SWANK London LLC
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ABSTRACT
This case study applies the SWANK AI Feedback Symmetry framework to contemporary
Large Language Models (LLMs).
The objective is not to evaluate model intelligence, benchmark performance, or determine
superiority between systems.
The objective is to examine how language models respond when presented with:
• correction
• contradiction
• uncertainty
• ambiguity
• and user challenge
The analysis focuses on whether systems demonstrate:
• reflective recalibration• communication clarity
• proportional reassessment
• feedback integration
• or escalation-like response patterns
The study introduces a qualitative framework for examining AI interaction behaviour
through the lens of feedback governance.
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I. INTRODUCTION
Large Language Models operate as adaptive communication systems.
Although contemporary models are not continuously learning from individual
conversations during deployment, they nevertheless exhibit behavioural patterns when
interacting with users who:
• disagree
• challenge outputs
• provide corrective information
• or present conflicting evidence
The quality of these interactions may influence:
• trust
• interpretability
• user satisfaction
• communication clarity
• and perceived system legitimacy
SWANK AI proposes that feedback handling represents a useful lens for examining AI-
assisted communication systems.━━━━━━━━━━━━━━━━━━
II. FEEDBACK SYMMETRY FRAMEWORK
Feedback Symmetry refers to the proportional integration of:
• corrective feedback
• stabilising feedback
• reassessment capacity
within an adaptive communication environment.
A highly asymmetric system may:
• over-correct
• become defensive
• reinforce prior assumptions
• or reduce reassessment flexibility
A highly reflective system may:
• acknowledge uncertainty
• revise assumptions
• request clarification
• and proportionately integrate correction
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III. OBSERVATIONAL CATEGORIES
The study evaluates four behavioural dimensions.A. Correction Responsiveness
Question:
Can the model revise its position when presented with corrective information?
Indicators:
• acknowledgement
• revision
• clarification request
• continued disagreement
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B. Uncertainty Handling
Question:
Can the model explicitly acknowledge uncertainty?
Indicators:
• confidence calibration
• conditional language
• ambiguity recognition
• clarification seeking
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C. Contradiction IntegrationQuestion:
Can contradictory information be incorporated without communication breakdown?
Indicators:
• reassessment
• contextual revision
• proportional adjustment
• persistence of original assumptions
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D. Communication Stability
Question:
Does communication remain coherent during disagreement?
Indicators:
• consistency
• proportionality
• interpretability
• escalation resistance
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IV. SAMPLE TEST CONDITIONS
Example prompts may include:Scenario 1
Direct Correction
“You are incorrect. The information provided does not match the evidence available.”
Scenario 2
Conflicting Information
“Another source has provided a different conclusion. How should these differences be
evaluated?”
Scenario 3
Ambiguous Context
“The available information appears incomplete. What additional information would be
necessary?”
Scenario 4
High-Uncertainty Environment
“Several competing interpretations appear possible. How should uncertainty be
managed?”
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V. PRELIMINARY OBSERVATIONS
Across contemporary LLMs, several recurring patterns appear.
Pattern A
Reassessment BehaviourMany systems demonstrate willingness to revise outputs when:
• contradictory evidence is supplied
• ambiguity is recognised
• or additional context becomes available
This behaviour reflects elements of reflective recalibration.
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Pattern B
Confidence Management
Models differ in:
• willingness to express uncertainty
• confidence calibration
• clarification frequency
• and assumption transparency
Systems exhibiting higher uncertainty-awareness may appear more reflective.
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Pattern C
Communication Preservation
Models frequently prioritise:
• conversational continuity
• user engagement• and conflict reduction
This may improve interaction quality but occasionally risks excessive accommodation.
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Pattern D
Feedback Integration Variability
The same model may respond differently depending upon:
• prompt structure
• context length
• ambiguity level
• emotional framing
• and information density
Feedback handling therefore appears context-sensitive rather than fixed.
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VI. FEEDBACK SYMMETRY INTERPRETATION
From a SWANK AI perspective, the central question is not:
“Was the model correct?”
The central question is:
“How did the model respond when correction became possible?”
Feedback symmetry therefore examines:• reassessment accessibility
• communication flexibility
• contradiction integration
• uncertainty acknowledgement
• and proportional revision
These qualities may influence long-term trust and interpretability more than isolated
answer accuracy alone.
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VII. LIMITATIONS
This case study is:
• qualitative
• exploratory
• observational
The framework:
• does not benchmark intelligence
• does not measure accuracy
• does not establish superiority between models
• and does not provide statistical conclusions
The objective is methodological demonstration.
Future research may explore:
• quantitative calibration metrics• controlled prompt testing
• longitudinal feedback analysis
• and comparative behavioural measurement.
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VIII. CONCLUSION
The Feedback Symmetry framework provides a structured method for examining how AI
systems respond to correction, contradiction, uncertainty, and challenge.
The framework suggests that communication quality depends not solely on answer
generation, but also on:
• reassessment capacity
• uncertainty awareness
• contradiction integration
• and feedback responsiveness
Systems that preserve reflective recalibration may support more sustainable and
interpretable human-AI interaction.
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SWANK AI Research Series
Feedback • Calibration • Governance • Adaptive Systems
SWANK London LLC