Bibliographic record
Abstract
Modern artificial intelligence (AI) systems critically lack the ability to reflect on their own behavior, reasoning processes, and generated output. For example, despite recent advancements in large language models (LLMs), the appearance of reflection in such systems is a linguistic trick rather than a cognitive competence. This deficiency can cause significant challenges, particularly in contexts where safety and reliability are important, such as healthcare and security applications, and also in social situations where complex contexts drive expected behavior. Building on previous work in computational self-awareness and reflection in adaptive systems, in this paper, we propose a reflective agent architecture that incorporates formal models of social expectations and self-simulation mechanisms with LLMs This architecture enhances LLM-based systems to reflect on their decisions and outputs, evaluating alignment with expected behaviors. Furthermore, it enables agents to internally simulate potential actions and evaluate their consequences. Using the expectation event calculus (EEC), the system formally represents expectations, events, and derived outcomes, supporting systematic self-evaluation. Concurrently, self-simulation allows the agent to introspectively predict and analyze possible outcomes and refine its decision when necessary. Our results demonstrate enhanced alignment with human expectations, highlighting the architecture's promise of greater social sensitivity in complex scenarios that require robust and trustworthy AI interactions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".