4 citations · 4 across the 7 of their papers we have counts for
10 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…
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…
Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization
Omri Uzan, Asaf Yehudai, Roi pony +2
Multimodal encoders have pushed the boundaries of visual document retrieval, matching textual query tokens directly to image patches and achieving state-of-the-art performance on 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…
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…
Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks
Roi Pony, Itay Naeh, Shie Mannor
Deep neural networks for video classification, just like image classification networks, may be subjected to adversarial manipulation. The main difference between image classifiers…