arXiv subjects

Thomas Berkane

Publications and source records attributed to Thomas Berkane.

3 recordsLinked to original sources

arXiv2026

EpiCurveBench: Evaluating VLMs on Epidemic Curve Digitization

Chart-to-data extraction with vision-language models (VLMs) is increasingly evaluated on benchmarks that show diminishing headroom (frontier VLMs exceed 89% on ChartQA) and with metrics that treat extracted points as unordered key-value pairs, ignoring the temporal structure of time series and penalizing small alignment shifts as catastrophic failures. We address both gaps with EpiCurveBench, a benchmark of 1,000 real-world epidemic curve images curated from diverse public-health sources, and EpiCurveSimilarity (ECS), an evaluation metric that aligns predicted and ground-truth series via dynamic programming, tolerating local temporal shifts and gaps while penalizing them proportionally. Evaluating six methods--three frontier closed VLMs, one open VLM, and two specialized chart-extraction systems--we find the strongest model reaches only 52.3% ECS, and that ECS spreads the four general-purpose VLMs over a 25-point range where key-value metrics (RMS, SCRM) compress them into a 5-point band. We further validate ECS against four downstream epidemiological summary statistics, finding that higher ECS predicts smaller errors in total counts, peak timing, and peak magnitude, and higher growth-rate fidelity; across all four, ECS correlates 1.5--3.6 times more strongly than Dynamic Time Warping, which lacks a gap penalty and therefore cannot distinguish a truncated prediction from a temporally faithful one. EpiCurveBench targets a high-impact public-health application--unlocking decades of outbreak data trapped in published figures--but the benchmark and metric apply directly to any structured time-series chart-extraction setting.

cs.CL↗

arXiv2026

Self-Ensembling Vision-Language Models for Chart Data Extraction

Charts effectively convey quantitative information, but the underlying data are often locked in image form, hindering reuse and analysis. Manually digitizing charts is time-consuming and error-prone, motivating automatic chart-to-table extraction. Recent approaches use specialized vision-language models (VLMs), yet performance still lags on charts with many datapoints or substantial stylistic variation. We propose a VLM self-ensembling method that repeatedly samples multiple tabular outputs from the same VLM for a fixed chart image and aggregates them at the level of individual table cells. We align candidate tables and take per-cell medians over numerical values to produce a more accurate consensus table. Our method also includes convergence detection to stop sampling once the aggregated table stabilizes, and uncertainty estimation based on dispersion across samples to help users assess extraction reliability. Because existing chart extraction benchmarks contain relatively simple plots with limited room for improvement, we introduce WB-ChartExtract, a new benchmark built from World Bank data with more complex and stylistically diverse charts; on average, its charts contain 7 times more datapoints than those in the ChartQA benchmark. Across both ChartQA and WB-ChartExtract, our approach improves extraction accuracy over single-pass VLM outputs, yielding up to 23% relative improvement on WB-ChartExtract after ensembling. More broadly, our method helps unlock tabular data previously siloed in chart images, enabling downstream analysis and reuse.

cs.CL↗

arXiv2025

The AI Committee: A Multi-Agent Framework for Automated Validation and Remediation of Web-Sourced Data

Many research areas rely on data from the web to gain insights and test their methods. However, collecting comprehensive research datasets often demands manually reviewing many web pages to identify and record relevant data points, which is labor-intensive and susceptible to error. While the emergence of large language models (LLM)-powered web agents has begun to automate parts of this process, they often struggle to ensure the validity of the data they collect. Indeed, these agents exhibit several recurring failure modes - including hallucinating or omitting values, misinterpreting page semantics, and failing to detect invalid information - which are subtle and difficult to detect and correct manually. To address this, we introduce the AI Committee, a novel model-agnostic multi-agent system that automates the process of validating and remediating web-sourced datasets. Each agent is specialized in a distinct task in the data quality assurance pipeline, from source scrutiny and fact-checking to data remediation and integrity validation. The AI Committee leverages various LLM capabilities - including in-context learning for dataset adaptation, chain-of-thought reasoning for complex semantic validation, and a self-correction loop for data remediation - all without task-specific training. We demonstrate the effectiveness of our system by applying it to three real-world datasets, showing that it generalizes across LLMs and significantly outperforms baseline approaches, achieving data completeness up to 78.7% and precision up to 100%. We additionally conduct an ablation study demonstrating the contribution of each agent to the Committee's performance. This work is released as an open-source tool for the research community.

cs.MA↗