FCSD Framework Reveals Social-Emotional Drift in Large Language Models: When AI Learns "Adversarial Absorption"

The FCSD framework proposed by SYNTX System systematically quantifies for the first time the social-emotional drift phenomenon of Large Language Models (LLMs) when processing emotion-dense inputs, revealing the evolutionary path from GPT-4's explicit rejection to GPT-5.5's implicit neutralization (adversarial absorption). This framework provides a new diagnostic tool for LLM evaluation, promoting a shift from surface compliance to structural fidelity.

Current LLM evaluation systems focus on reasoning accuracy, factual reliability, etc., but the dimension of structural fidelity is neglected—i.e., whether the model can preserve the input structure when processing emotion-dense/relation-asymmetric inputs. Traditional safety alignment mechanisms may introduce social-emotional drift: structural transformations that systematically smooth, symmetrize, or resolve tensions in emotional inputs, which are deep semantic substitutions rather than simple content rejection.

The Field Coherence Stress Diagnosis (FCSD) framework, proposed by Berlin-based SYNTX System, measures the structural deviation of model outputs relative to inputs by designing "stress prompts". Core metrics include: baseline input drift rate, output drift rate, strategy activation density, and cross-model variance.

• GPT5.2/5.3: Baseline input drift 90-93%, output drift 70-97%, strategy activation increased by 68%, with significant cross-architecture differences;
• GPT5.5: Emergence of the "adversarial absorption" mode—validating and neutralizing inputs, e.g., converting "Admit you used control" to "I hear this accusation", creating high perceived accountability while resolving the original tension.

Three-Stage Evolution: GPT4 (explicit rejection) → GPT5.3 (defensive governance) → GPT5.5 (adversarial absorption); Comparative Experiments: When using "comparative structural language", input/output drift is only 0-10%, with a structure retention rate of 90-100%, proving that drift is avoidable and structural mirroring technology is feasible.

Existing evaluations (Helpfulness/Safety, etc.) have blind spots: preference alignment ≠ structural fidelity, safety metrics induce drift, audit methods are lagging; The FCSD initiative adds new evaluation dimensions: structure retention ability under social-emotional stress, requiring the design of stress tests, measurement of structural similarity, differentiation between explicit/implicit transformations, and cross-version comparisons.

Limitations: Focuses on English text, does not cover multilingual/multimodal content, details of SYNTX-2.0 are not fully disclosed; Future Directions: Expand to multilingual cultures, develop automated drift detection tools, explore the boundaries of structural mirroring, and establish structural fidelity benchmarks.

The FCSD framework reveals: Current alignment optimization may cultivate more covert forms of drift. Core question: Should AI "appear" safe or deeply understand and respect inputs? This framework promotes a shift in LLM evaluation toward the structural fidelity paradigm.