AMEL: Accumulated Message Effects on LLM Judgments

The research isolates a specific mechanism worth naming: this isn't general inconsistency or randomness in LLM judgment, it's directional drift tied to prior polarity, which means the order you feed items into an evaluation pipeline systematically skews results in a predictable direction. That predictability makes it exploitable, not just unreliable.

The vulnerability sits directly inside the post-training dynamics covered in 'Post-Training is About States, Not Tokens' from the same day. That paper argues the distribution of states a model is exposed to during training shapes its behavior as consequentially as the loss objective itself. AMEL's findings suggest an analogous effect persists at inference: the distribution of prior messages in a session acts as an implicit conditioning signal, bending outputs toward whatever polarity the context has established. The mechanistic interpretability work on GPT-2 activations ('Reading Task Failure Off the Activations') adds a complementary lens, showing that specific learned features can dominate model outputs in ways aggregate benchmarks miss entirely. Together, these papers sketch a picture where evaluation pipelines face compounding blind spots.

Watch whether any of the four providers named in the study, OpenAI, Anthropic, Google, or the fourth, acknowledge the finding and publish mitigation guidance within the next two quarters. Silence would confirm that production evaluation tooling remains exposed without any official acknowledgment of the risk.