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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,738 papers · 148 categories

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3 results for “Context-scaling”

Context-Scaling versus Task-Scaling in In-Context Learningcs.LG

Transformers can scale both context and task, but MLPs can only scale task.

problem Understanding and scaling In-Context Learning in transformers.

method Simplified transformer architecture, feature map, and MLP combination.

result Simplified transformer can perform ICL and context-scaling but not task-scaling.

In-Context Learning Transformers Multi-Layer Perceptrons Context-scaling Task-scaling

2024-10-16abs ↗pdf ↗

Scale-invariant Attentioncs.CL

Scales attention for long contexts in LLMs.

problem Development of attention mechanisms for long context inference.

method Scale-invariant total attention and sparsity conditions, with a position-dependent transformation of logits.

result Scale-invariant attention scheme improves validation loss and long-context retrieval.

attention mechanisms long context scale-invariant Gaussian assumption zero-shot generalization

2025-05-20abs ↗pdf ↗

Multi-User Contextual Cascading Bandits for Personalized Recommendationcs.LG

New model for personalized online advertising with multi-user interaction.

problem Realistic online advertising scenarios with multiple users interacting simultaneously.

method Introduces Multi-User Contextual Cascading Bandit (MCCB) model and proposes UCBBP and AUCBBP algorithms.

result Proves UCBBP and AUCBBP achieve optimal regret bounds for multi-user context.

Bandit Algorithms Personalized Recommendation Multi-User Interaction Cascading Feedback Contextual Bandits

2025-08-19abs ↗pdf ↗