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20102026
most citedCan AI-Generated Text be Reliably Detected?

163 citations · 1.4k across the 139 of their papers we have counts for

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Showing 2025Show all

23 papers · 1 filter

cs.CV2025

AgentComp: From Agentic Reasoning to Compositional Mastery in Text-to-Image Models

Arman Zarei, Jiacheng Pan, Matthew Gwilliam +2

Text-to-image generative models have achieved remarkable visual quality but still struggle with compositionalityaccurately capturing object relationships, attribute bindings, an…

cs.CL2025

Schoenfeld's Anatomy of Mathematical Reasoning by Language Models

Ming Li, Chenrui Fan, Yize Cheng +2

Large language models increasingly expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify and analyze beyond surface-level statist…

cs.CV2025

SliderEdit: Continuous Image Editing with Fine-Grained Instruction Control

Arman Zarei, Samyadeep Basu, Mobina Pournemat +3

Instruction-based image editing models have recently achieved impressive performance, enabling complex edits to an input image from a multi-instruction prompt. However, these model…

cs.CL2025

Decomposition-Enhanced Training for Post-Hoc Attributions In Language Models

Sriram Balasubramanian, Samyadeep Basu, Koustava Goswami +6

Large language models (LLMs) are increasingly used for long-document question answering, where reliable attribution to sources is critical for trust. Existing post-hoc attribution…

cs.CL2025

Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception

Yize Cheng, Arshia Soltani Moakhar, Chenrui Fan +5

Large language model (LLM) agents are increasingly used to interact with and execute tasks in dynamic environments. However, a critical yet overlooked limitation of these agents is…

cs.CL2025

Reasoning Under Uncertainty: Exploring Probabilistic Reasoning Capabilities of LLMs

Mobina Pournemat, Keivan Rezaei, Gaurang Sriramanan +5

Despite widespread success in language understanding and generation, large language models (LLMs) exhibit unclear and often inconsistent behavior when faced with tasks that require…