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20242026
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cs.CL2026

CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data

Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett +94

Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heter…

cs.CL2026

Momento: Evaluating Persistent Memory and Reasoning with Multi-Session Agentic Conversations

Adril Putra Merin, David Anugraha, Ayu Purwarianti +1

Recent advances in agentic AI have enabled agents to complete complex tasks through tool use, reasoning, and multi-step planning. Yet existing benchmarks evaluate agents within a s…

cs.CL2026

Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures

Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377

To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…

cs.CL2026

M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAG

David Anugraha, Patrick Amadeus Irawan, Anshul Singh +2

Vision-language models (VLMs) have achieved strong performance in visual question answering (VQA), yet they remain constrained by static training data. Retrieval-Augmented Generati…

cs.CL2026

mR3: Multilingual Rubric-Agnostic Reward Reasoning Models

David Anugraha, Shou-Yi Hung, Zilu Tang +3

Evaluation using Large Language Model (LLM) judges has been widely adopted in English and shown to be effective for automatic evaluation. However, their performance does not genera…

cs.CL2026

Can Large Language Models Understand, Reason About, and Generate Code-Switched Text?

Genta Indra Winata, David Anugraha, Patrick Amadeus Irawan +15

Code-switching is a pervasive phenomenon in multilingual communication, yet the robustness of large language models (LLMs) in mixed-language settings remains insufficiently underst…