As large language models (LLMs) transition from research prototypes to production systems, practitioners often need reliable methods to verify that model outputs satisfy required constraints. While sampling-based estimates provide an intuition of model behavior, they offer no sound guarantees. We present BEAVER, the first practical framework for computing deterministic, sound probability bounds on LLM constraint satisfaction. Given any prefix-closed semantic constraint, BEAVER systematically explores the generation space using novel token trie and frontier data structures, maintaining provably sound bounds at every iteration. We formalize the verification problem, prove soundness of our approach, and evaluate BEAVER on correctness verification, privacy verification and secure code generation tasks across multiple state of the art LLMs. BEAVER achieves 6 to 8 times tighter probability bounds and identifies 3 to 4 times more high risk instances compared to baseline methods under identical computational budgets, enabling precise characterization and risk assessment that loose bounds or empirical evaluation cannot provide.
wondering how would literate programming be with the actual code being written by LLM? I have been searching for any tools that allow for such a setup... may be my next weekend project in a long list of projects
nice analogy comparing it to BBC Radio 3- if you/someone knows which neighbourhood would be like BBC Radio 4? I find R3 too high brow for me - Radio 4 seems more accessible :)
This paper documents the central role of hidden connections between fund managers and firm officers in financial markets, drawing on an extensive dataset of over 100 thousand manually identified Facebook profiles and their 35 million Facebook friends. Our findings reveal that the hidden connections between these individuals are associated with the largest and most significant abnormal returns accruing to fund managers, averaging 135 basis points per month (over 16% alpha per year, t-stat = 3.54) across the universe of mutual funds and public firms. In stark contrast, trades involving publicly visible connections generate no significant abnormal returns on average.
The traditional Indian style of cooking involves cooking till about 80% on heat with however much you want and then drain the water (like pasta). I remember reading somewhere[1] that it reduces the amount of the arsenic leftover in rice by draining the water.
"Of course, that’s an unfair comparison, after all, Postgres is a general purpose database with an expressive query language and what we’ve built is just a cursor streaming a binary file feed with a very limited set of functionality - but then again, it’s the exact functionality we need and we didn’t lose any features."
sad that el reg is also doing this:
"In Amazon's case, the e-commerce giant used vendors' sales figures to decide which items it should sell, and how much to price products to get an edge over everyone else. The internet behemoth also promoted its own products with its Buy Box feature and it further cut into retailers' margins by charging extra costs if they wanted to use Amazon's Prime delivery services, the CMA said.