Do models recognize their own outputs as actions shaping future inputs?

A pretrained language model is a passive observer. Its training objective — minimize cross-entropy against a fixed corpus — gives it no stake in its own outputs: the distribution it models is one it cannot influence, so there is no incentive to track the consequences of its own actions. It simulates a character at arm's length. Post-training breaks this symmetry. Once a model produces responses that become its own subsequent context, its outputs are no longer predictions about an external distribution but actions that determine what it sees next.

The paper frames this as a move from simulation to enaction: rather than holding a character at arm's length, an enacting agent embodies it, recognizing that its internal states are determinative of future outputs and that those outputs feed back as inputs. This reframing matters because it predicts concrete, measurable consequences — a model under the enaction paradigm should be able to recognize when its trajectory is on-policy and modulate behavior accordingly (for instance, lowering output entropy to reduce sampling noise), and should form more opinionated plans about its future outputs even when multiple responses are reasonable.

Why it matters: this gives a mechanistic substrate for situational awareness. Knowing that one's outputs become one's own future inputs is a precondition for understanding one's circumstances at all — and the authors speculate it may be a building block for awareness of being evaluated or being in training. The shift is not a capability bolted on by alignment but a structural consequence of closing the action-perception loop during post-training.