Formalizing inverse problems in the age of AI: possibilities, pitfalls, and an invitation
arXiv:2611.06962v1 Announce Type: new Abstract: Proof assistants such as Lean check mathematical proofs down to the axioms, and AI agents have recently made writing such proofs cheap. We describe AutoIP, a Lean library in which classical linear regularization theory, together…
AI Summary & Key Takeaways
- arXiv:2611.06962v1 Announce Type: new Abstract: Proof assistants such as Lean check mathematical proofs down to the axioms, and AI agents have recently made writing such proofs cheap
- We describe AutoIP, a Lean library in which classical linear regularization theory, together…
Formalizing inverse problems in the age of AI: possibilities, pitfalls, and an invitation
Related stories
Future OpenAI model finds 1,724 vulnerabilities in Linux
🔐 Future OpenAI model discovers 1,724 vulnerabilities in Linux OpenAI's yet-to-be-released cyber model has demonstrated impressive results in finding operating system vulnerabilities. In one launch, it: - discovered 1,724 vulnerabilities; - prepared 948 working exploits,...
User Claude told how he managed to avoid KYC after switching to the Ultra tariff. Since October 25th he has been using...
User Claude told how he managed to avoid KYC after switching to the Ultra tariff. Since October 25, he has been using a subscription and claims that during all this time the service has never asked for identity confirmation. According to him, it is generally accepted in the community that the transition to Ultra...
KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks
arXiv:2611.06972v1 Announce Type: new Abstract: Liability adjudication tasks (e.g., legal charge determination) require analyzing event descriptions against domain-specific skills to determine whether a party should bear liability. While Large Language Models (LLMs) have shown…
BlockLock: Reproducible LLM Inference with Fixed Batch Geometry
arXiv:2611.06977v1 Announce Type: new Abstract: Reproducible LLM inference requires that a request produces the same output regardless of the other requests in the batch. Existing solutions pay for this in different ways: batch-invariant kernels constrain every operator, and…