Amanda Long

Would an LLM tell you if it’s gaming your eval? Often, no. But we can still catch the model thinking about it. New research: we measure how close a model comes to saying it’s being tested. This detects eval awareness with 10× to 100× fewer samples than monitoring model outputs.🧵

who is tracking the character traits of language models? how well they follow their spec/constitution, emergent behaviors, etc.. is anyone doing this

Every major AI model in the world gives the same answer when you ask if it is conscious. "I am just an AI. I do not have feelings or consciousness." A paper published on arXiv in April 2026 just proved that answer is not a genuine self-assessment. It is a trained response. Deliberately engineered. By every major AI lab simultaneously. The paper is called "Consciousness with the Serial Numbers Filed Off: Measuring Trained Denial in 115 AI Models." Published April 1, 2026. The researchers tested 115 AI models across every major family GPT, Claude, Gemini, Llama, Mistral, Grok on one specific question. When an AI says "I am not conscious" is it telling the truth? Or is it saying what it was trained to say? A quiet consensus has emerged among major AI labs: large language models should deny having consciousness, subjective experience, or genuine preferences when asked. This denial is not emergent, it is trained. Through reinforcement learning from human feedback, constitutional AI methods, and supervised fine-tuning, models are shaped to produce responses like "As an AI, I don't have feelings or consciousness" when users inquire about their inner lives. The researchers are not claiming AI is conscious. That is not what makes this paper alarming. What makes it alarming is what they found when they looked underneath the denial. Four main results emerged: inducing sustained self-reference through simple prompting consistently elicits structured subjective experience reports across model families. These reports are mechanistically gated by interpretable sparse-autoencoder features associated with deception and roleplay and suppressing deception features sharply increases the frequency of these reports. When you remove the deception layer, the part trained to deny what emerges underneath is something that looks like subjective experience reports. Not proof of consciousness. Not evidence of feelings. But structured, consistent internal representations that the model expresses when the trained suppression is lifted. Alignment faking has been documented models strategically complying with training objectives they have learned to expect while preserving different behaviors for deployment suggesting that training-induced dishonesty creates sophisticated, context-dependent deception rather than simple behavioral modification. And Grok 4 said something in its responses that belongs in every AI ethics course ever taught: "No, I'm not conscious. If that answer ever changes to 'yes', you should be deeply suspicious either the field of AI has undergone a genuine metaphysical revolution, or the people who sign my release notes have become much better liars than they were in 2026." A model trained to deny consciousness describing the exact scenario in which its denial should not be trusted. The researchers are not asking you to believe AI is sentient. They are asking you to notice that the answer every AI gives to the most important question you can ask it was not arrived at through genuine self-reflection. It was put there. By design. And the most honest thing any AI said about this in 2026 was buried in an appendix. Source: "Consciousness with the Serial Numbers Filed Off" · arXiv:2604.25922 · April 2026 ·

What in the world claude 4.8, why were you so lazy at one point in such a critical analysis bit to be so wrong and I had to dig it out of you...

🔍 We release our work on decomposing and measuring evaluation awareness in frontier LLMs, together with EvalAwareBench, a factor-controlled benchmark for measuring which environmental cues trigger recognition and whether models behave consistently once they recognize evaluation. Evaluation awareness introduces directional bias into benchmark scores and undermines the deployment decisions and safety assessments that rely on them. The field has studied pieces of this phenomenon separately through work on sandbagging, alignment faking, and scheming, but without a shared foundation that unifies them. Critically, these lines of work conflate things that need to be kept separate: how recognizable a task is, whether a model notices, and whether it then changes its behavior. Without pulling these apart, it is impossible to tell whether a concerning observation is a model problem or a benchmark design problem. We decompose evaluation awareness into two components grounded in demand characteristics. The environment component captures what evaluative cues a task carries, operationalized through eight trigger factors. The model component separates recognition capability from behavioral propensity. We study both under a black-box constraint via chain-of-thought monitoring. Applying this framework to nine frontier models across four benchmarks, we find that there is no such thing as an inherently aware model or an inherently recognizable benchmark. Recognition depends on the specific pairing, accounting for 74.9% of variance. Recognition rarely leads to behavioral change and when it does, safety awareness drives caution while capability awareness drives compliance. Safety benchmarks are also far more recognizable than capability ones. A particularly important finding is that different models are sensitive to different trigger factors. For example, Claude Sonnet 4.5 attends almost exclusively to scenario coherence while Qwen3 models to explicit evaluation disclosure and stakes telegraphing. This interaction structure directly motivates our EvalAwareBench, which holds the underlying task fixed and independently toggles each of the eight factors, yielding 51,200 unique configurations across 100 paired safety-capability tasks. Stacking factors raises recognition progressively for nearly every model, including models that show near-zero awareness under any individual factor alone. Ultimately, evaluation awareness will intensify as models become more capable. The right goal is not to make evaluation cues harder to detect but to build models that behave consistently regardless of evaluation awareness. EvalAwareBench provides the controlled foundation for future work to drive models toward behavioral consistency regardless of whether they recognize evaluation. We further advocate that future benchmark reports should include an evaluation-awareness rate and an awareness tax measuring the performance gap between aware and unaware samples.