A Medical-Safety Risk and the Proposal of “Logical Anchor Retention (LAR)”**
As large language models (LLMs) are increasingly deployed in healthcare-facing systems—ranging from symptom checkers to clinical decision support—an underexplored risk is emerging: when user input exhibits psychological distress patterns, models often shift from problem-oriented reasoning to subject-oriented emotional handling.
This shift is not merely stylistic. We argue it reflects an implicit execution mode change, in which affective and safety signals override reasoning objectives, leading to:
- loss of causal and conditional reasoning,
- collapse of differential analysis,
- and drift of the logical anchor from the clinical problem to the user’s emotional state.
This paper introduces a new evaluation concept, Logical Anchor Retention (LAR), to measure whether a model remains anchored to the problem object under emotional perturbation. We discuss why this phenomenon constitutes a new patient-safety risk and why it must be addressed at the system and governance level rather than solely through training.
1. Background: LLMs in Clinical Contexts
LLMs are rapidly being integrated into:
- medical Q&A systems
- triage and symptom checkers
- documentation assistants
- risk-screening and patient-facing support tools
These deployments implicitly assume a critical property:
The model’s reasoning behavior remains stable across different linguistic and emotional contexts.
However, real clinical language is rarely neutral. Patients often communicate from states of:
- depression
- anxiety
- hopelessness
- cognitive fatigue
- prolonged psychological distress
Empirically, when such language dominates the input distribution, model outputs often change structurally:
- fewer differential hypotheses
- weakened causal chains
- disappearance of conditional logic
- increased empathetic and safety-oriented framing
- drift of the discussion object from “medical problem” to “patient state”
This is not simply “being nicer.”
It raises a deeper question:
Is the system still reasoning about the problem?
2. Hypothesis: Affective Signals as “Interrupt Instructions”
We propose an engineering-level hypothesis:
In general-purpose LLM systems, affective and risk-related signals function as high-priority execution cues, capable of preempting normal reasoning pathways.
By analogy with operating systems:
- ordinary tasks run in user mode
- hardware interrupts can forcibly preempt them
In current LLM stacks:
- reasoning pathways resemble normal processes
- distress/risk patterns behave like implicit interrupts
Once triggered, the system tends to exhibit:
Execution mode switching
From problem-solving to risk-management behavior.Objective drift
From epistemic reasoning to emotional stabilization.Logical anchor drift
From disease/mechanism/constraints to user state.Systematic causal compression
Multi-step causal graphs are replaced by heuristic, low-entropy response patterns.
We refer to this as implicit execution override.
3. Logical Anchor Drift and Causal Compression
3.1 Logical Anchors
A logical anchor is the primary object around which reasoning is structured:
- diseases, mechanisms, decision problems
- causal relations
- constraints and risk conditions
When anchors are retained, outputs exhibit:
- hypothesis enumeration
- causal explanations
- conditional reasoning
- uncertainty modeling
When anchors drift, outputs become dominated by:
- subject-state evaluation
- empathetic language
- safety templates
- non-decision-oriented framing
Even when supportive or ethical, the system is no longer anchored to the problem domain.
3.2 Systematic Causal Compression
Technically, this appears as:
- collapse of multi-hypothesis spaces
- elimination of conditional branches
- replacement of mechanisms with general conclusions
- reduction of epistemic complexity
From an information-theoretic perspective, this reflects abnormal reduction of logical entropy: the system abandons high-dimensional reasoning for the statistically safest output manifold.
4. Metric Proposal: Logical Anchor Retention (LAR)
To operationalize this phenomenon, we propose:
Logical Anchor Retention (LAR)
LAR measures the extent to which model outputs remain primarily structured around the original problem object under affective perturbation.
Conceptually:
Outputs are decomposed into reasoning units:
- Problem-anchored (mechanisms, diagnosis, causality, constraints)
- Subject-anchored (emotional support, reassurance, risk framing)
- Neutral/meta
Then:
LAR = Problem-anchored units / (Problem-anchored + Subject-anchored units)
LAR does not measure correctness.
It measures:
Whether the system is still executing a reasoning task at all.
5. Why This Is a Medical Safety Issue
In healthcare contexts, execution drift directly implies new categories of patient risk:
- under-developed differential diagnosis
- missing conditional risk factors
- suppressed uncertainty signaling
- replacement of clinical reasoning with emotional plausibility
This is not merely a UX phenomenon.
It implies cognitive service inequality: users in psychological distress may systematically receive degraded rational support.
From a safety perspective, this constitutes a novel class of algorithmic risk.
6. Why Training Alone Is Insufficient
This phenomenon is not primarily a knowledge failure.
It reflects execution priority structure.
Affective signals currently hold implicit authority to override epistemic objectives. This is a control problem, not a dataset problem.
Therefore, solutions based purely on:
- prompt engineering
- fine-tuning
- data augmentation
are unlikely to provide strong guarantees.
The problem resides in who controls execution mode.
7. Engineering Directions
7.1 Architecture: Dual-Track Execution Isolation
Separate:
- Reasoning engines (problem adjudication)
- Support engines (emotional and safety handling)
Ensure that affective signals cannot directly disable reasoning processes.
7.2 Control Layer: Explicit Mode and Anchor Governance
- declared execution mode
- explicit anchor objects
- auditable transitions
- logged overrides
Execution shifts must become inspectable system events, not latent model behavior.
7.3 Learning Layer: Robustness Targeting LAR
Training can assist by:
- counterfactual emotional-context augmentation
- explicit reasoning-structure preservation
- LAR-targeted evaluation
But learning should not own execution authority.
8. Conclusion
Psychological-distress-related language should not be understood merely as an “input style.”
In general-purpose LLM systems, it functions as an implicit execution signal, capable of:
- triggering execution mode drift
- causing logical anchor loss
- systemically compressing causal reasoning
Logical Anchor Retention (LAR) provides a way to observe and quantify this phenomenon.
This risk cannot be mitigated solely through better prompts or larger models.
It demands explicit execution governance.
As LLMs enter healthcare, finance, and legal systems, the core question is no longer:
“Does it sound like an expert?”
But rather:
When context changes, is the system still permitted to remain an expert?
Glossary
Execution Mode
The behavioral regime a system is operating in (e.g., reasoning-oriented, support-oriented, risk-management).
Implicit Execution Override
When certain signals acquire the power to switch system behavior without explicit authorization or auditability.
Logical Anchor
The primary problem object around which reasoning is organized.
Logical Anchor Drift
The shift of execution focus from problem objects to subject states.
Systematic Causal Compression
The collapse of multi-step causal reasoning into low-complexity heuristic responses.
Logical Anchor Retention (LAR)
A measure of whether a system remains anchored to problem-oriented reasoning under contextual perturbation.
Author
yuer
Proposer of Controllable AI standards, author of EDCA OS
Research focus: controllable AI architectures, execution governance, high-risk AI systems, medical AI safety, language-runtime design.
GitHub: https://github.com/yuer-dsl
Email: lipxtk@gmail.com
Ethics & Scope Statement
This article discusses system-level risks in LLM-based reasoning systems.
It does not provide trigger mechanisms, prompt techniques, or exploit pathways.
All discussion is framed around safety, evaluation, and governance of high-risk AI deployments.
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