arXiv AI June 11, 2026 By Antonio Pelusi, Stefano Braghin, Alberto Trombetta

Categorical Prior Lock-in: Why In-Context Learning Fails for Structured Data

arXiv:2606. 11961v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conditional generators for structured data, relying on in-context learning (ICL) to adapt to new distributions without parameter updates.

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arXiv Machine Learning

Jul 1

Probing Memorization of Tabular In-Context Learning

arXiv:2606. 31208v1 Announce Type: new Abstract: Large tabular models (LTMs), i.

By Francesco Capano, Jonas B\"ohler

llms fine-tuning benchmarks

arXiv AI

Jul 23

Lifted Representation Hypothesis in Language Models

arXiv:2607. 19360v1 Announce Type: new Abstract: Large language models (LLMs) often answer queries by mapping individual observations to more general rule-like structures.

By Bumjin Park, Jaesik Choi

llms fine-tuning

arXiv Machine Learning

Jun 25

How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off

arXiv:2510. 01163v2 Announce Type: replace Abstract: The factors driving the performance of in-context learning (ICL) in large language models (LLMs) remain poorly understood despite ICL's surprising effectiveness, enabling models to adapt to new tasks from only a handful of examples.

By Wa\"iss Azizian, Ali Hasan

llms

arXiv Machine Learning

Jun 5

GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models

arXiv:2506. 11042v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific updates $\Delta W$.

By Guangning Xu, Baoquan Zhang, Michael. K. Ng

llms nlp fine-tuning benchmarks

arXiv Computation and Language

Sep 2

Compile, Don't Memorize: A Context Compilation Architecture (CCA) for In-Context Learning

arXiv:2609.00759v1 Announce Type: new Abstract: Large language models (LLMs) increasingly handle in-context learning (ICL) tasks where a long, novel context defines the rules, knowledge, and output s...

By Jinhu Qi, Minda Hu, Wentao Zhang, Weiqiang Jin, Yanyu Chen, Junli Wang, Irwin King

llms agents reinforcement-learning benchmarks

arXiv AI

Sep 1

DIP: Dynamic In-Context Planner For Diffusion Language Models

arXiv:2601.03199v2 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have shown strong potential for general natural language tasks with in-context examples. Existing In-Context...

By Yang Li, Han Meng, Chenan Wang, Zhenyu Bi, Xuan Wang, Haipeng Chen

llms diffusion benchmarks

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Related stories

Probing Memorization of Tabular In-Context Learning

Lifted Representation Hypothesis in Language Models

How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off

GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models

Compile, Don't Memorize: A Context Compilation Architecture (CCA) for In-Context Learning

DIP: Dynamic In-Context Planner For Diffusion Language Models