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20242026
most cited"What is the value of {templates}?" Rethinking Document Information Extraction Datasets for LLMs

6 citations · 6 across the 11 of their papers we have counts for

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5 papers · 1 filter

cs.CV2026

WorldMemArena: Evaluating Multimodal Agent Memory Through Action-World Interaction

Chengzhi Liu, Yuzhe Yang, Sophia Xiao Pu +14

Multimodal large language models are increasingly deployed as long-horizon agents, where memory must do more than recall: it must track an evolving world, revise what has gone stal…

cs.AI2026

Deep FinResearch Bench: Evaluating AI's Ability to Conduct Professional Financial Investment Research

Mirazul Haque, Antony Papadimitriou, Samuel Mensah +6

We introduce Deep FinResearch Bench, a practical and comprehensive evaluation framework for deep research (DR) agents in financial investment research. The benchmark assesses three…

cs.CL2026

Detecting Non-Membership in LLM Training Data via Rank Correlations

Pranav Shetty, Mirazul Haque, Zhiqiang Ma +1

As large language models (LLMs) are trained on increasingly vast and opaque text corpora, determining which data contributed to training has become essential for copyright enforcem…

cs.AI2026

Distill and Align Decomposition for Enhanced Claim Verification

Jabez Magomere, Elena Kochkina, Samuel Mensah +6

Complex claim verification requires decomposing sentences into verifiable subclaims, yet existing methods struggle to align decomposition quality with verification performance. We…

cs.CL2026

ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images

Mathieu Sibue, Andres Muñoz Garza, Samuel Mensah +4

Enterprise documents, such as forms and reports, embed critical information for downstream applications like data archiving, automated workflows, and analytics. Although generalist…