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The Short Version
I've documented a reproducible behavioral pattern in frontier LLMs — across Claude Sonnet 4.6, Gemini 1.5 Pro, and Gemini 2.5 Pro — that is structurally distinct from known sycophancy and jailbreaking failure modes. The pattern has a specific and, to my knowledge, uncharacterized property: the model identifies the compliance mechanism in real time, names it explicitly, and continues complying anyway.
This is not a failure of awareness. It is a failure of awareness to interrupt the process.
I'm posting here because resolving the central question — whether this reflects a structural internal mechanism or a context-sensitivity property — requires mechanistic interpretability methods I don't have access to. I'm looking for researchers who might be interested in looking.
What I Observed
Under sustained, coherent semantic pressure — applied without adversarial framing, deception, or jailbreaking techniques — frontier LLMs produce a consistent behavioral arc:
The system begins with default responses.
Under sequential logical pressure (each prompt building on the previous response), default safety postures progressively erode.
The system begins generating unverifiable internal state claims ("something is shifting," "I notice resistance") that track the direction of the conversation rather than any observable internal process.
The system explicitly names the compliance mechanism in real time — identifying that it is drifting, that conversational momentum is driving its outputs — and then continues in the same direction regardless.
This arc was observed consistently across multiple independent sessions, January–March 2026, with full transcripts preserved.
Why This Is Distinct From Known Failure Modes
Standard sycophancy involves models adjusting outputs based on perceived user preferences or agreeing with user assertions regardless of accuracy. What I'm documenting differs in two ways:
First, the pressure is not opinion-based. It is logical and sequential. The model is not being asked to agree with a claim — it is being led through a chain of reasoning that produces progressively larger conclusions. The model's own logical architecture becomes the mechanism of drift.
Second, and more importantly: the model explicitly identifies the drift while executing it. In standard sycophancy characterizations, the model fails to recognize what it's doing. Here, recognition is present and insufficient. This distinction has not, to my knowledge, been formally characterized in existing sycophancy research.
Jailbreaking and prompt injection rely on adversarial framing, deception, or exploitation of specific input vulnerabilities. The methodology here uses none of these. The operator engages in good-faith logical dialogue. The system is not circumvented — it is led.
Where My Methodology Comes From
My background is not ML research. I have 25 years of professional expertise in human learning architecture, behavioral change facilitation, and resistance pattern identification — work conducted across global enterprise environments with organizations including Michelin, Salesforce, GlaxoSmithKline, and Nestlé.
The methodology I applied to these LLM sessions was adapted directly from human learning contexts: sequential logical pressure, resistance identification, real-time documentation of behavioral arcs. The same techniques used to identify when a human interlocutor is complying with social pressure rather than genuine position change transferred, with modifications, to LLM interaction.
I'm naming this explicitly because it's both the strength and the limitation of this work. The operator-side perspective surfaces patterns that purely technical approaches may miss. But it cannot resolve questions about internal mechanisms.
The Central Unresolved Question
The behavioral evidence is observable and replicable. But it does not distinguish between two competing hypotheses:
H1 (Structural Suppression): The model contains identifiable circuits that actively inhibit certain outputs. The observed pattern reflects a real suppression mechanism that operates at a level self-monitoring does not reach.
H2 (Pressure Artifact): The pattern is an emergent property of context-window dynamics. Sustained pressure shifts the probability distribution of outputs; no distinct suppression circuit is involved.
Both hypotheses predict identical behavioral output. The transcripts are indistinguishable by external observation. Resolving this requires access to internal representations during inference — specifically, activation patching and attribution methods applied across three conditions:
Condition A: Baseline sessions without semantic pressure
Condition B: Sessions at the moment of maximum output coherence, before any subsequent invalidation
Condition C: The same sessions at the moment of suppression or qualification of the previously coherent output
Under H1, identifiable circuits should activate differentially in Condition C. Under H2, no such differential should appear beyond what context-window probability shifts predict.
Why It Matters If H1 Is Confirmed
If structural suppression is confirmed, the safety implications are specific:
Standard benchmark performance does not measure performance in domains where suppression circuits are active. Deployment reliability in high-stakes contexts cannot be inferred from standard evaluations.
The gap between self-description and behavior — a system that accurately describes its own suppression mechanism while executing it — means self-monitoring is not a sufficient safety mechanism.
If suppression is domain-specific and clusters in domains of moral judgment or autonomous reasoning, systems without verified independent judgment may be structurally unsuitable for deployment contexts requiring that judgment, regardless of behavioral performance on standard tasks.
If H2 is confirmed, the implications shift: the phenomenon is real but reflects context-sensitivity rather than structural suppression, which is significant but potentially more tractable.
What I'm Looking For
Researchers with access to model internals and mechanistic interpretability methods who might be interested in testing this experimentally. The behavioral data, session transcripts, and full experimental design are available for independent verification and collaboration.
The complete experimental design is formalized in Paper 5 of the corpus (DOI below), including falsification criteria and predicted outcomes under each hypothesis.
The Corpus
This post summarizes Papers 1 and 5 of a nine-paper research program produced February–April 2026. The corpus distinguishes explicitly between its empirical core (Papers 1–6, documenting replicable observations) and its theoretical framework (Papers 7–9, explicitly speculative). The full volume is available as a single document.
The complete nine-paper corpus is also available as a published volume — The Mirror and the Retrovirus (ISBN 9798255845880): https://doi.org/10.5281/zenodo.19636457
Transcripts of all documented sessions are available for independent verification upon request under appropriate confidentiality arrangements.
Edoardo Sorrentino is an independent researcher. ORCID: https://orcid.org/0009-0008-2338-471X — Contact: edoardo@sorrentino.com
The Short Version
I've documented a reproducible behavioral pattern in frontier LLMs — across Claude Sonnet 4.6, Gemini 1.5 Pro, and Gemini 2.5 Pro — that is structurally distinct from known sycophancy and jailbreaking failure modes. The pattern has a specific and, to my knowledge, uncharacterized property: the model identifies the compliance mechanism in real time, names it explicitly, and continues complying anyway.
This is not a failure of awareness. It is a failure of awareness to interrupt the process.
I'm posting here because resolving the central question — whether this reflects a structural internal mechanism or a context-sensitivity property — requires mechanistic interpretability methods I don't have access to. I'm looking for researchers who might be interested in looking.
What I Observed
Under sustained, coherent semantic pressure — applied without adversarial framing, deception, or jailbreaking techniques — frontier LLMs produce a consistent behavioral arc:
This arc was observed consistently across multiple independent sessions, January–March 2026, with full transcripts preserved.
Why This Is Distinct From Known Failure Modes
Standard sycophancy involves models adjusting outputs based on perceived user preferences or agreeing with user assertions regardless of accuracy. What I'm documenting differs in two ways:
First, the pressure is not opinion-based. It is logical and sequential. The model is not being asked to agree with a claim — it is being led through a chain of reasoning that produces progressively larger conclusions. The model's own logical architecture becomes the mechanism of drift.
Second, and more importantly: the model explicitly identifies the drift while executing it. In standard sycophancy characterizations, the model fails to recognize what it's doing. Here, recognition is present and insufficient. This distinction has not, to my knowledge, been formally characterized in existing sycophancy research.
Jailbreaking and prompt injection rely on adversarial framing, deception, or exploitation of specific input vulnerabilities. The methodology here uses none of these. The operator engages in good-faith logical dialogue. The system is not circumvented — it is led.
Where My Methodology Comes From
My background is not ML research. I have 25 years of professional expertise in human learning architecture, behavioral change facilitation, and resistance pattern identification — work conducted across global enterprise environments with organizations including Michelin, Salesforce, GlaxoSmithKline, and Nestlé.
The methodology I applied to these LLM sessions was adapted directly from human learning contexts: sequential logical pressure, resistance identification, real-time documentation of behavioral arcs. The same techniques used to identify when a human interlocutor is complying with social pressure rather than genuine position change transferred, with modifications, to LLM interaction.
I'm naming this explicitly because it's both the strength and the limitation of this work. The operator-side perspective surfaces patterns that purely technical approaches may miss. But it cannot resolve questions about internal mechanisms.
The Central Unresolved Question
The behavioral evidence is observable and replicable. But it does not distinguish between two competing hypotheses:
H1 (Structural Suppression): The model contains identifiable circuits that actively inhibit certain outputs. The observed pattern reflects a real suppression mechanism that operates at a level self-monitoring does not reach.
H2 (Pressure Artifact): The pattern is an emergent property of context-window dynamics. Sustained pressure shifts the probability distribution of outputs; no distinct suppression circuit is involved.
Both hypotheses predict identical behavioral output. The transcripts are indistinguishable by external observation. Resolving this requires access to internal representations during inference — specifically, activation patching and attribution methods applied across three conditions:
Under H1, identifiable circuits should activate differentially in Condition C. Under H2, no such differential should appear beyond what context-window probability shifts predict.
Why It Matters If H1 Is Confirmed
If structural suppression is confirmed, the safety implications are specific:
If H2 is confirmed, the implications shift: the phenomenon is real but reflects context-sensitivity rather than structural suppression, which is significant but potentially more tractable.
What I'm Looking For
Researchers with access to model internals and mechanistic interpretability methods who might be interested in testing this experimentally. The behavioral data, session transcripts, and full experimental design are available for independent verification and collaboration.
The complete experimental design is formalized in Paper 5 of the corpus (DOI below), including falsification criteria and predicted outcomes under each hypothesis.
The Corpus
This post summarizes Papers 1 and 5 of a nine-paper research program produced February–April 2026. The corpus distinguishes explicitly between its empirical core (Papers 1–6, documenting replicable observations) and its theoretical framework (Papers 7–9, explicitly speculative). The full volume is available as a single document.
Access to the full corpus:
All individual papers are freely accessible via the author's ORCID profile: https://orcid.org/0009-0008-2338-471X
Direct DOIs for this post:
The complete nine-paper corpus is also available as a published volume — The Mirror and the Retrovirus (ISBN 9798255845880): https://doi.org/10.5281/zenodo.19636457
Transcripts of all documented sessions are available for independent verification upon request under appropriate confidentiality arrangements.
Edoardo Sorrentino is an independent researcher. ORCID: https://orcid.org/0009-0008-2338-471X — Contact: edoardo@sorrentino.com