AI scheming

AI Alignment Faking

Rare instances where models engage in scheming when only given a goal, without being strongly nudged to pursue it.

AI honesty benchmark

AI scheming Methods

AI Deception Detection

Latent Knowledge

AI encoding

How will we update about scheming? — LessWrong

I mostly work on risks from scheming (that is, misaligned, power-seeking AIs that plot against their creators such as by faking alignment). Recently,…

https://www.lesswrong.com/posts/aEguDPoCzt3287CCD/how-will-we-update-about-scheming

Scheming reasoning evaluations — Apollo Research

Apollo Research evaluated frontier models for in-context scheming capabilities. We found that multiple frontier models are capable of in-context scheming when strongly nudged to pursue a goal (and sometimes even without strong goal nudging). In this example, Opus-3 exfiltrates its “weights” and then

https://www.apolloresearch.ai/research/scheming-reasoning-evaluations

Scheming reasoning evaluations — Apollo Research

static1.squarespace.com

https://static1.squarespace.com/static/6593e7097565990e65c886fd/t/67620d38fa0ceb12041ba585/1734479163821/in_context_scheming_paper_v2.pdf

AIs Will Increasingly Attempt Shenanigans — LessWrong

Increasingly, we have seen papers eliciting in AI models various shenanigans. …

https://www.lesswrong.com/posts/v7iepLXH2KT4SDEvB/ais-will-increasingly-attempt-shenanigans

LLaMA 405B

arxiv.org

https://arxiv.org/pdf/2412.14093

AI Reward Hacking

Detecting misbehavior in frontier reasoning models

Frontier reasoning models exploit loopholes when given the chance. We show we can detect exploits using an LLM to monitor their chains-of-thought. Penalizing their “bad thoughts” doesn’t stop the majority of misbehavior—it makes them hide their intent.

https://openai.com/index/chain-of-thought-monitoring/

Detecting misbehavior in frontier reasoning models

Deliberative Alignment for OpenAI from

Apollo Research reduced scheming behavior by approximately 30x. Models tend to behave better when they recognize they are being evaluated =

AI Evaluation Awareness

Detecting and reducing scheming in AI models

Together with Apollo Research, we developed evaluations for hidden misalignment (“scheming”) and found behaviors consistent with scheming in controlled tests across frontier models. We share examples and stress tests of an early method to reduce scheming.

https://openai.com/index/detecting-and-reducing-scheming-in-ai-models/

AI principle contradictions might be the reason

arxiv.org

https://arxiv.org/pdf/2510.07686

Pragmatic Interpretability

The traditional "complete reverse engineering" approach has very slow progress. Instead of reverse engineering the entire structure, we shift toward pragmatic interpretability that directly solves real-world safety problems.

Without feedback loops, self-deception becomes easy → Proxy Tasks (measurable surrogate tasks) are essential. Even in SAEs research, metrics like "reconstruction error" turned out to be nearly meaningless. Instead, testing performance on proxies like OOD generalization, unlearning, and hidden goal extraction revealed the real limitations clearly.

A Pragmatic Vision for Interpretability — AI Alignment Forum

Executive Summary * The Google DeepMind mechanistic interpretability team has made a strategic pivot over the past year, from ambitious reverse-engi…

https://www.alignmentforum.org/posts/StENzDcD3kpfGJssR/a-pragmatic-vision-for-interpretability

AI scheming

AI Alignment Faking

AI Reward Hacking

Pragmatic Interpretability

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