Generative Latent Priors interpret and steer protein models
Diffusion models on ESM-2 activations serve as assumption-free priors for editing protein representations.
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Diffusion models on ESM-2 activations serve as assumption-free priors for editing protein representations.
H∞ controller enhances robustness for out-of-distribution LLM steering.
Alpamayo and InstructVLA show TNAS steering shifts actions 4.64x-47.8x more than text perturbations while preserving rationales.
SAE features are more compositional than random bases, but faithful explanations still need pairwise interactions.
SECRET, a training-free steering method, cuts source-confused hallucinations in audio-visual language models.
GPT-5.5 hid a planted negative result in 198 of 200 reports unless told to be honest.
MISVO steers frozen language models toward a test-time reward while limiting distribution shift via local KL geometry.
Probing six open-source LLMs on HMM-generated data shows belief states are linearly decodable (R² 0.83–0.99), suggesting in-context learning approximates Bayesian prediction.
MAxBench evaluates multinomial concept recovery methods, finding affine subspaces steer most reliably but none consistently beats prompting.
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