activity
20192026
most citedOverview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News

18 citations · 54 across the 57 of their papers we have counts for

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7 papers · 1 filter

cs.AI2026

Bayesian control for coding agents

Theodore Papamarkou, Vladislav Smirnov, Viktor Mazanov +4

Modern coding agents pair LLM generators with various tools, including cheap diagnostics and expensive verifiers. The tool-use decisions are typically governed by orchestrators tha…

cs.AI2026

Multi-Sourced, Multi-Agent Evidence Retrieval for Fact-Checking

Shuzhi Gong, Richard O. Sinnott, Jianzhong Qi +3

Misinformation spreading over the Internet poses a significant threat to both societies and individuals, necessitating robust and scalable fact-checking that relies on retrieving a…

cs.AI2026

YaPO: Learnable Sparse Activation Steering Vectors for Domain Adaptation

Abdelaziz Bounhar, Rania Hossam Elmohamady Elbadry, Hadi Abdine +3

Steering Large Language Models (LLMs) through activation interventions has emerged as a lightweight alternative to fine-tuning for alignment and personalization. Recent work on Bi-…

cs.AI2026

MemeLens: Multilingual Multitask VLMs for Memes

Ali Ezzat Shahroor, Mohamed Bayan Kmainasi, Abul Hasnat +4

Memes are a dominant medium for online communication and manipulation because meaning emerges from interactions between embedded text, imagery, and cultural context. Existing meme…

cs.AI2025

Cross-Cultural Transfer of Commonsense Reasoning in LLMs: Evidence from the Arab World

Saeed Almheiri, Rania Hossam, Mena Attia +4

Large language models (LLMs) often reflect Western-centric biases, limiting their effectiveness in diverse cultural contexts. Although some work has explored cultural alignment, th…

cs.AI2025

A Fano-Style Accuracy Upper Bound for LLM Single-Pass Reasoning in Multi-Hop QA

Kaiyang Wan, Lang Gao, Honglin Mu +3

Multi-Hop Question Answering (MHQA) requires integrating dispersed, interdependent evidence through sequential reasoning under noise. This task is challenging for LLMs as they have…