Easy Trip Planners Has Announced Its Integration With ChatGPT - TradingView
Easy Trip Planners Has Announced Its Integration With ChatGPT TradingView
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Probabilistic AVL Trees (p-AVL): Relaxing Deterministic Balancing
arXiv:2604.02223v1 Announce Type: new Abstract: This paper studies the empirical behaviour of the p-AVL tree, a probabilistic variant of the AVL tree in which each imbalance is repaired with probability $p$. This gives an exact continuous interpolation from $p = 0$, which recovers the BST endpoint, to $p = 1$, which recovers the standard AVL tree. Across random-order insertion experiments, we track rotations per node, total imbalance events, average depth, average height, and a global imbalance statistic $\sigma$. The main empirical result is that even small nonzero p already causes a strong structural change. The goal here is empirical rather than fully theoretical: to document the behaviour of the p-AVL family clearly and identify the main patterns.
A Constant-Approximation Distance Labeling Scheme under Polynomially Many Edge Failures
arXiv:2604.01829v1 Announce Type: new Abstract: A fault-tolerant distance labeling scheme assigns a label to each vertex and edge of an undirected weighted graph $G$ with $n$ vertices so that, for any edge set $F$ of size $|F| \leq f$, one can approximate the distance between $p$ and $q$ in $G \setminus F$ by reading only the labels of $F \cup \{p,q\}$. For any $k$, we present a deterministic polynomial-time scheme with $O(k^{4})$ approximation and $\tilde{O}(f^{4}n^{1/k})$ label size. This is the first scheme to achieve a constant approximation while handling any number of edge faults $f$, resolving the open problem posed by Dory and Parter [DP21]. All previous schemes provided only a linear-in-$f$ approximation [DP21, LPS25]. Our labeling scheme directly improves the state of the art in
Multi-Objective Agentic Rewrites for Unstructured Data Processing
arXiv:2512.02289v4 Announce Type: replace Abstract: One year ago, we open-sourced DocETL, a declarative system for LLM-powered data processing that, as of March 2026, has 3.7K GitHub stars and users across domains (e.g., journalism, law, medicine, policy, finance, and urban planning). In DocETL, users build pipelines by composing operators described in natural language, also known as semantic operators, with an LLM executing each operator's logic. However, due to complexity in the operator or the data it operates on, LLMs often give inaccurate results. To address this challenge, DocETL introduced rewrite directives, or abstract rules that guide LLM agents in rewriting pipelines by decomposing operators or data. For example, decomposing a single filter("is this email sent from an executive
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![trunk/34b6e17d1a24014822e71d2f0726adafc230ed0b: [Native DSLs] DSL Registry, base tests rework (#178381)](https://avatars.githubusercontent.com/u/4992598?s=60&v=4)
trunk/34b6e17d1a24014822e71d2f0726adafc230ed0b: [Native DSLs] DSL Registry, base tests rework (#178381)
Summary: Note: Due to git-related shenanigans, this has subsumed #178518 Tests cleaning based on more explicit instructions to claude - should be better aligned with other torch tests. Add a separate registry for DSLs (alongside the existing registry for overrides). This allows a) a centralized place to query the availability of different DSLs, and b) a cleaner way to test / test for multiple DSLs without requiring manually adding each new DSL. Add Test skip decorators for current DSL list Test Plan: pytest -sv test/python_native/ Signed-off-by: Simon Layton [email protected] Pull Request resolved: #178381 Approved by: https://github.com/drisspg , https://github.com/albanD ghstack dependencies: #178637
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