activity
20242026
collaborators

9 papers

cs.AI2026

How to Train Your LLM Web Agent: A Statistical Diagnosis

Dheeraj Vattikonda, Santhoshi Ravichandran, Emiliano Penaloza +13

LLM-based web agents have recently made significant progress, but much of it has occurred in closed-source systems, widening the gap with open-source alternatives. Progress has bee…

cs.CL2025

ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval

Ahmed Masry, Megh Thakkar, Patrice Bechard +9

Retrieval-augmented generation has proven practical when models require specialized knowledge or access to the latest data. However, existing methods for multimodal document retrie…

cs.CL2025

FocusAgent: Simple Yet Effective Ways of Trimming the Large Context of Web Agents

Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar +7

Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens of thousands of tokens. This sat…

cs.CL2025

BigCharts-R1: Enhanced Chart Reasoning with Visual Reinforcement Finetuning

Ahmed Masry, Abhay Puri, Masoud Hashemi +13

Charts are essential to data analysis, transforming raw data into clear visual representations that support human decision-making. Although current vision-language models (VLMs) ha…

cs.CL2025

LineRetriever: Planning-Aware Observation Reduction for Web Agents

Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar +6

While large language models have demonstrated impressive capabilities in web navigation tasks, the extensive context of web pages, often represented as DOM or Accessibility Tree (A…

cs.AI2025

Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs

Megh Thakkar, Quentin Fournier, Matthew Riemer +4

There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruction-tuned counterparts. However, these…