Inference + Optimization + Llm

20.0

Create an inference-time scheduler that dynamically allocates token budgets based on task complexity. This is the key to solving the 'overthinking' problem in modern chain-of-thought models.

emergingimplementation gap

cachingreasoningoptimizationllmspeculative-decodingcrfnlpinference

Signals (5)

arXiv5h ago

Continuous Semantic Caching for Low-Cost LLM Serving

arXiv5h ago

Avoiding Overthinking and Underthinking: Curriculum-Aware Budget Scheduling for LLMs

arXiv5h ago

Accelerating PayPal's Commerce Agent with Speculative Decoding: An Empirical Study on EAGLE3 with Fine-Tuned Nemotron Models

arXiv1d ago

Streaming Structured Inference with Flash-SemiCRF

arXiv1d ago

Inference + Optimization + Llm

Signals (5)

Continuous Semantic Caching for Low-Cost LLM Serving

Avoiding Overthinking and Underthinking: Curriculum-Aware Budget Scheduling for LLMs

Accelerating PayPal's Commerce Agent with Speculative Decoding: An Empirical Study on EAGLE3 with Fine-Tuned Nemotron Models

Streaming Structured Inference with Flash-SemiCRF

Two-dimensional early exit optimisation of LLM inference