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Towards Understanding Pause Token Fine-Tuning Dynamics: A Mode Retention Perspective
arXiv:2609.04489v1 Announce Type: cross Abstract: Pause-token methods improve LLM reasoning by inserting special tokens into sequences. Prior work…
Patterns of Priming in Production: Lexical, Semantic and Structural Alignment in Language Model Generation
arXiv:2609.04484v1 Announce Type: cross Abstract: This paper investigates structural priming in language model (LM) production, examining how preceding…
Shared circuits predict whether LLMs generalize across formats in arithmetic reasoning
arXiv:2609.04463v1 Announce Type: cross Abstract: In many forms of reasoning, including arithmetic reasoning, generalizing across superficial changes in…
Cultural Misalignment in Large Language Models: Detection, Measurement, and Mitigation Through Targeted Fine-Tuning
arXiv:2609.04485v1 Announce Type: cross Abstract: We evaluate three open-weight LLMs (Gemma3-12B from the USA, Bielik-11B-v3 from Poland, and Qwen3-4B…
Hakken: Predicting future discoveries to fill the gaps in today’s knowledge
arXiv:2609.04494v1 Announce Type: cross Abstract: We present Hakken, a domain-agnostic prediction and explanation system performing knowledge prediction,…
A Systematic Evaluation of Cross-Lingual Consistency Enhancement Methods in Multilingual Language Models
arXiv:2609.04409v1 Announce Type: cross Abstract: Multilingual language models often produce inconsistent answers to semantically equivalent questions…
GRACE: Graph-Grounded Reflective Agent Copilot Engine for Expert-in-the-Loop Knowledge Expansion
arXiv:2609.04442v1 Announce Type: cross Abstract: Large language models deployed in high-stakes settings frequently generate plausible but ungrounded…
REFINE: LLM Refinement over Budgeted Text-Attributed Graphs for Personalized Medical Concept Representation
arXiv:2609.04415v1 Announce Type: cross Abstract: Learning rich medical concept representations is essential for EHR prediction. Text-attributed knowledge…
When Load-Balancing Goes Too Far: Expert Pruning in Over-Dispersed Mixture-of-Experts Models
arXiv:2609.04453v1 Announce Type: cross Abstract: Expert pruning reduces the memory and serving cost of Mixture-of-Experts (MoE) models by removing…