Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping
This work suggests that "when to stop" should be treated as a training-time design choice, not merely an inference-time computation-allocation rule.
AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 27 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This work suggests that "when to stop" should be treated as a training-time design choice, not merely an inference-time computation-allocation rule.
HALLMARK (Hallucination benchmark): 2,526 BibTeX entries spanning 14 hallucination types, three difficulty tiers, six diagnostic sub-tests per entry, and a contamination-resistant held-out split, and across the benchmark one result is consistent: the false-positive rate, not recall, decides whether a verifier is deploy...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.