Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models
Bibliographic record
Abstract
As large language models (LLMs) integrate to society, understanding its awareness of context is fundamental to ensure safety and alignment. Past research has focused on situational awareness to examine the LLMs ability to recognize itself and circumstances but the ability to recognizing the conversational partner is overlooked. In this study, we introduce interlocutor awareness, the ability of LLMs to recognize and adapt to the identity and capabilities of their conversational partners, and present the first systematic evaluation of this phenomenon. Specifically, we first assess the capability of LLMs to infer the identity of their interlocutor across three tasks: mathematical reasoning, code completion, and conversational inference. Subsequently, we evaluate behavioral adaptation through interlocutor awareness---where LLMs modify their behavior based on who they are interacting with---along two dimensions: collaborative adaptation assessing whether ``sender'' models tailor their explanations within controlled math-solving frameworks, and adversarial tactics, which examine how knowledge of the interlocutor's identity influences a model's success at jailbreak. Our evaluation demonstrates that LLMs reliably identify same-family peers and tend to adapt their behavior based on the identity of their interaction partner. While our findings highlight the potential benefits of interlocutor awareness for optimizing multi-LLM collaboration, they also reveal novel risks related to AI safety and control.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".