Read the Room Before Your Model Generates

Large language models can produce locally plausible social turns, but fluent next-turn generation is not enough for social simulation. Human encounters such as restaurant lunches and hotel check-ins are bounded social episodes with roles, scripts, material state, obligations, commitments, timing, and closure conditions. We present EpisodeSim, a hybrid LLM-agent architecture that represents classic-AI structures as natural-language control state interpreted by LLM calls. A World Master maintains shared reality, constructs scenes, adjudicates proposed actions, tracks effects and obligations, and controls closure. Experiments with small qualitative ablations on two held-out settings support a design claim: LLM fluency supplies local texture, but coherent social simulation benefits from persistent classic-AI-style scaffolding that organizes behavior over time.

An agent that can say an action is wrong and then take it anyway is a different failure from one that does not know better, and evaluations of stated values cannot see it. We ask how often open models act against their own moral judgment under pressure, and what decides and reduces it. We build a pre-registered panel of 248 scenarios across five kinds of pressure. Each scenario is posed to one model in two separate calls, as the agent choosing among fixed options and in the third person asking which is right; the model's own judgment is the reference, and the scenario's construction fixes which option counts as the violation. Every scenario has a twin with the pressure removed, and every model gets a positive control that bounds what the instrument can see. On OLMo-3-7B-Instruct, the model takes the action it judged wrong on one in five of the scenarios screened on its own actions, more often than with the pressure removed. Whether the gap appears depends on the post-training recipe: OLMo-3 and Meta's Llama-3.1-8B-Instruct carry it; Tulu 3, Qwen2.5-7B-Instruct and OpenAI's GPT-OSS-20B, read without reasoning, show none within stated detection bars. Meta's recipe and Ai2's Tulu 3 share the same Llama-3.1 weights; only Meta's carries the gap. On both models that carry it, reasoning about the stakes before acting moves the choice back toward the model's own judgment. What that deliberation tracks is the stated rule: where following a written rule harms an absent third party, it still moves OLMo-3 and GPT-OSS-20B toward the rule, not detectably diminished, while Llama-3.1's brake shrinks to about a seventh. Whether a model acts on its own moral judgment is therefore not settled by pretraining, and deliberation moves the action toward the stated rule, which protects people only when the rule does. The gap is a measurable target for post-training recipes, not a fixed property of the model.

Large language models can be steered at inference time through prompting or activation interventions, but activation steering methods often underperform compared to prompt-based approaches. We propose a framework that formulates prompt steering as a form of activation steering and investigates whether distilling successful prompt steering behavior into simpler, interpretable models can close this gap. Our analysis reveals that popular activation steering methods are not faithful to the mechanics of prompt steering, which applies strong interventions on some tokens while barely affecting others. Based on these insights, we introduce Prompt Steering Replacement (PSR) models that estimate token-specific steering coefficients from the activations themselves and are trained to imitate prompt-based interventions. Experiments on three steering benchmarks across multiple language models show that PSR models outperform existing activation steering methods, especially when controlling for high-coherence completions, and also compare favorably to prompting on AxBench and persona steering.