44 citations · 50 across the 13 of their papers we have counts for
17 papers
LLM Evolutionary Search Strategies Should be Compared by Tokens
Tal Oved, Roi Pony, Oshri Naparstek +1
LLM evolutionary search is currently evaluated by counting proposals (candidate programs). We demonstrate this metric hides the true cost of search, and redo the comparison using t…
Spotlights: Discovering Improvement Opportunities in Software Repositories
Udi Barzelay, Ophir Azulai, Idan Friedman +9
Coding agents and evolutionary code-search systems can improve implementations once a target and evaluation criterion have been specified. Applying these methods to an existing sof…
Evolution or Illusion? Rethinking Evaluation in LLM Evolutionary Search
Tal Oved, Roi Pony, Oshri Naparstek +1
LLM-driven evolutionary search finds programs by launching seeds and iterating each one. Papers report a single budget setting, usually one seed run for a fixed number of iteration…
Optimize Cheap, Deploy Strong: Cost-Aware Cross-Tier Transfer for Evolutionary Optimization
Tal Oved, Roi Pony, Oshri Naparstek +1
Evolutionary optimization of LLM prompts and agentic programs (e.g., GEPA) is dominated by fitness evaluation: scoring each candidate runs an answering LLM over a validation set, s…
FLASH-MAXSIM: IO-Aware Fused Kernels for Late-Interaction Retrieval
Roi Pony, Daniel Ezer, Adi Raz Goldfarb +3
Late-interaction retrieval (ColBERT, ColPali) scores a query against a document via the MaxSim operator. The standard PyTorch implementation materialises the full query-token $\tim…
Is the Modality Gap a Bug or a Feature? A Robustness Perspective
Rhea Chowers, Oshri Naparstek, Udi Barzelay +1
Many modern multi-modal models (e.g. CLIP) seek an embedding space in which the two modalities are aligned. Somewhat surprisingly, almost all existing models show a strong modality…
Col-Bandit: Query-Time Top- Estimation for Late-Interaction Retrieval
Roi Pony, Adi Raz Goldfarb, Oshri Naparstek +3
Multi-vector late-interaction retrievers such as ColBERT achieve state-of-the-art quality, but their query-time cost is dominated by exhaustively computing token-level MaxSim inter…
Projected Autoregression: Autoregressive Language Generation in Continuous State Space
Oshri Naparstek
Standard autoregressive language models generate text by repeatedly selecting a discrete next token, coupling prediction with irreversible commitment at every step. We show that to…
Complexity as Advantage: A Regret-Based Perspective on Emergent Structure
Oshri Naparstek
We introduce Complexity as Advantage (CAA), a framework that defines the complexity of a system relative to a family of observers. Instead of measuring complexity as an intrinsic p…
REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark
Navve Wasserman, Roi Pony, Oshri Naparstek +4
Accurate multi-modal document retrieval is crucial for Retrieval-Augmented Generation (RAG), yet existing benchmarks do not fully capture real-world challenges with their current d…
Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence
Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60
We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document un…
KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents
Oshri Naparstek, Roi Pony, Inbar Shapira +15
In recent years, the challenge of extracting information from business documents has emerged as a critical task, finding applications across numerous domains. This effort has attra…
BusiNet -- a Light and Fast Text Detection Network for Business Documents
Oshri Naparstek, Ophir Azulai, Daniel Rotman +3
For digitizing or indexing physical documents, Optical Character Recognition (OCR), the process of extracting textual information from scanned documents, is a vital technology. Whe…
Deep Multi-User Reinforcement Learning for Distributed Dynamic Spectrum Access
Oshri Naparstek, Kobi Cohen
We consider the problem of dynamic spectrum access for network utility maximization in multichannel wireless networks. The shared bandwidth is divided into K orthogonal channels. I…
Distributed Energy Efficient Channel Allocation
Oshri Naparstek, S. M. Zafaruddin, Amir Leshem +1
Design of energy efficient protocols for modern wireless systems has become an important area of research. In this paper, we propose a distributed optimization algorithm for the ch…
Expected time complexity of the auction algorithm and the push relabel algorithm for maximal bipartite matching on random graphs
Oshri Naparstek, Amir Leshem
In this paper we analyze the expected time complexity of the auction algorithm for the matching problem on random bipartite graphs. We prove that the expected time complexity of th…
Fully distributed optimal channel assignment for open spectrum access
Oshri Naparstek, Amir Leshem
In this paper we address the problem of fully distributed assignment of users to sub-bands such that the sum-rate of the system is maximized. We introduce a modified auction algori…