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From the 1 of 46 linked papers with an AI index.

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cs.CL2026

Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge

Arda Uzunoglu, Benjamin Van Durme, Benjamin van Durme +1

Large language models are increasingly trained and deployed with long contexts that span documents, code repositories, and interaction histories. This scaling reflects the implicit…

cs.CL2026

Better, Faster, Stronger: Programmatic Skill Learning Best Reduces Agent Cost

Zixi Huang, Xiheng Wang, Andrew Wang +4

Recently, the practice of augmenting LLM agent capability with skills has gained prevalence. We explore the cost effective adaptation of agents to novel domains by means of learnin…

cs.CL2026

Self-Compacting Language Model Agents

Tianjian Li, Jingyu Zhang, William Jurayj +5

Long agent traces composed of chains of thought and tool calls accumulate stale content that anchor subsequent generations, and eventually outgrow the context window. Existing scaf…

cs.CL2026

CreativityPrism: A Cross-Domain Evaluation Framework for Large Language Model Creativity

Zhaoyi Joey Hou, Bowei Alvin Zhang, Yining Lu +9

Creativity is often seen as a hallmark of human intelligence. While large language models(LLMs) are increasingly perceived as generating creative text, there is still no cross-doma…

cs.CL2026

Linguistic Nepotism: Trading-off Quality for Language Preference in Multilingual RAG

Dayeon Ki, Marine Carpuat, Paul McNamee +4

Multilingual Retrieval-Augmented Generation (mRAG) systems enable language models to answer knowledge-intensive queries with citation-supported responses across languages. Despite…

cs.CL2026

AgentOdyssey: Open-Ended Long-Horizon Text Game Generation for Test-Time Continual Learning Agents

Zheyuan Zhang, Zehao Wen, Alvin Zhang +4

For agents to learn continuously from interaction with the world at test time, they must be able to explore effectively, acquire new world knowledge and skills, retain relevant epi…