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Understand new AI research with readable explanations and links to the original papers.
Large Language Models (LLMs) are highly effective when fine-tuned on domain-specific data, but gathering high-quality data for specialized fields is difficul...
VLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing Vision-Language-Action (VLA) models are becoming the standard...
Automating scientific computing requires more than just writing code; it requires autonomous systems to select the right strategies and ensure those strategi...
RAISE: RAG Design as an Architecture Search Problem Retrieval-augmented generation (RAG) systems rely on a complex web of design choices, including how to re...
Temporal Stability and Few-Shot Prompting in Math Task Assessment As AI tools become increasingly common in classrooms, educators and researchers need to kno...
Anchorless Diversification for Parallel LLM Ideation Large Language Models (LLMs) are frequently used to generate pools of creative ideas, such as story prem...
Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance This paper addresses the risks associated with using standard AI chatbots i...
Conformal Certification of Reasoning Trace Prefixes Modern language models often solve complex problems by generating a step-by-step "chain-of-thought." Whil...
MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection Modern Large Language Models (LLMs) often undergo a "mid-training" phase—a critical stage...
Locally Coherent, Globally Incoherent: Bounding Compositional Incoherence in Multi-Component LLM Agents This paper investigates a fundamental reliability iss...
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