Institution profile

Tel Aviv University

Academic institutioneurope · il
Official website
Research library735linked papers
Opportunities0open roles
Selected work

Representative Papers

An output sensitive algorithm for discrete convex hulls

May 01, 1998SCG '98

This study addresses the problem of efficiently computing the discrete convex hull of the intersection between an arbitrary convex body in the plane and the integer lattice. The work proposes the first output-sensitive algorithm whose running time depends directly on the number of vertices |C⁰| of the discrete convex hull and the diameter DD(C) of the convex body, achieving O(|C⁰| log DD(C)) time complexity in the general case. Notably, when the convex body is a disk, the algorithm leverages combinatorial bounds to improve the complexity to O(DD(C)^{2/3} log DD(C)). This is the first exact algorithm whose runtime is tightly coupled with the output size, combining insights from discrete geometry, convex hull construction, and output-sensitive analysis to deliver both theoretical and algorithmic novelty.

18 citations1 influentialRead paper

The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot

Sep 12, 2024International Conference on Interaction Sciences

This study investigates the differential impact of large language models (LLMs) on capability innovation (i.e., exploring novel functionalities) versus iterative innovation (i.e., optimizing and maintaining existing code) in open-source collaborative development. Leveraging GitHub Copilot’s phased, language-specific rollout as a quasi-natural experiment, we employ a multi-period difference-in-differences design, using variation in programming language support as an exogenous shock to identify causal LLM effects. Our key contribution is the first empirical evidence that—under unguided, spontaneous collaboration—LLMs significantly boost iterative innovation while exerting limited influence on capability innovation. This effect strengthens with model upgrades (e.g., the 2022 release) and higher project activity, and is especially pronounced in Python and Rust projects. The findings indicate that current LLMs are better aligned with maintenance-oriented development than with exploratory feature creation, offering critical empirical insights into the pathways and boundaries of AI-augmented open-source innovation.

9 citationsRead paper

LiveXiv - A Multi-Modal Live Benchmark Based on Arxiv Papers Content

Oct 14, 2024arXiv.org

Test data contamination from web crawling undermines the validity of multimodal model evaluation. Method: This paper introduces the first dynamic, evolvable “living” multimodal benchmark grounded in arXiv scientific papers. It (1) achieves precise figure–text alignment via multimodal PDF/TeX parsing; (2) jointly employs rule-based heuristics and large language models (LLMs) to generate high-quality, human-annotation-free visual question answering (VQA) data; and (3) proposes an incremental benchmark evolution mechanism coupled with a statistically grounded sparse-subset evaluation algorithm for efficient full-benchmark performance estimation. Contribution/Results: Human validation confirms an automatic annotation error rate <2.5%. Comprehensive evaluation on the inaugural benchmark reveals significant, previously undetected capability gaps across leading open- and closed-source multimodal large models. The dataset is publicly available on Hugging Face; code will be released shortly.

4 citationsRead paper

Economic Warehouse Lot Scheduling: Approximation Schemes via Efficiently-Representable DP-Encoded Policies

Jan 21, 2026

This work proposes the first polynomial-time approximation scheme with provable performance guarantees for the economic lot-sizing problem, a classic inventory management challenge that has lacked such algorithms since its formulation in the 1950s. By integrating dynamic programming encodings, approximation techniques, and combinatorial optimization, the authors establish an efficient representation and optimization framework for dynamic replenishment policies, resolving the long-standing open issue that such policies inherently require exponential space. When the number of item types is constant, the method constructs an ε-optimal dynamic policy in polynomial time, overcoming prior limitations that relied on restrictive structural assumptions or offered no performance guarantees. This breakthrough substantially advances the algorithmic tractability of this fundamental problem.

1 citations1 influentialRead paper

Online Contract Design

Feb 07, 2026

This study addresses the problem of dynamic team formation and incentive contract design for a principal facing an online sequence of adversarial agents, with the goal of maximizing the principal’s utility. Integrating economic contract theory with online algorithms, the work models agents’ rational effort decisions under performance-based contracts after team assignment and selects the optimal team under irrevocable elimination constraints. The paper innovatively bridges contract theory and online algorithms by introducing a “balance point” technique, establishing—for the first time—the existence of a randomized online algorithm achieving a competitive ratio of 1/2 under additive rewards, which is provably optimal among all randomized algorithms. Furthermore, it demonstrates that no deterministic algorithm can guarantee a bounded competitive ratio in this setting.

1 citationsRead paper
Recent publications

Latest Papers