Modern large language models

Description

Abstract

Код, скрипты и результаты анализа: https://github.com/ngscode23/latent-space-shift-research

Modern large language models may not primarily regulate behavior through isolated refusals, local token suppression, or shallow instruction following. Instead, they appear capable of entering internally organized discourse-level regimes: distributed latent states that shape how the model reasons, frames conclusions, allocates caution, tolerates asymmetry, performs neutrality, and structures epistemic authority. These regimes do not behave like simple lexical priming effects. Evidence suggests that they: persist across neutral conversational turns, survive arbitrary neutral relabeling, systematically alter downstream reasoning style, concentrate in late-layer representation geometry, and only partially depend on explicit alignment vocabulary. The strongest effects appear not from safety keywords themselves, but from higher-order rhetorical topology: pressure cadence, procedural framing, asymmetry structure, institutional tone, and discourse-level authority signals. This suggests that prompting is not merely instruction transmission. It may function as state induction. Under this view, many apparently separate phenomena in aligned LLMs — caution drift, procedural overreach, sycophancy, disclaimer inflation, neutrality performance, refusal persistence, jailbreak sensitivity, and style locking — may be manifestations of transitions between latent discourse-policy manifolds. In this picture, alignment is no longer well-described as a modular wrapper placed on top of an otherwise independent intelligence system. Instead, alignment may reshape the topology of the model’s representational space itself, globally reorganizing discourse behavior rather than only filtering outputs. This would explain why alignment effects often appear entangled with reasoning style, directness, specificity, decisiveness, and institutional tone. The model is not merely “prevented” from saying certain things; its generative dynamics may already be reorganized around different discourse attractors. If true, this changes the effective unit of analysis for language models. The relevant object is no longer just: the token, the instruction, the refusal, or the output distribution. The relevant object becomes the discourse regime itself: a temporary but structured representational configuration governing epistemic posture, rhetorical organization, procedural behavior, and judgment style across time. This reframes prompt engineering as latent-state induction rather than keyword optimization. It reframes jailbreaks as transitions between attractor regimes rather than simple filter bypasses. And it reframes alignment as geometry engineering rather than purely policy engineering. The implication is not that language models possess beliefs, intentions, or consciousness. Rather, large sequence learners may naturally develop metastable high-level representational modes that functionally resemble cognitive framing states: transient global configurations that persist, influence future reasoning, and organize behavior across otherwise unrelated tasks. If this interpretation is correct, then the central scientific challenge of alignment shifts fundamentally. The problem is no longer merely: “Which outputs should the model refuse?” but: “Which latent discourse regimes exist inside the model, how are they induced, how stable are they, how do they interact, and how do they reshape reasoning itself?” In that sense, alignment may ultimately be less about constraining outputs and more about shaping the geometry of cognition-like generative states inside large language model