Papers by Wenyao Li
ConMA : Confidence-Guided Kernel Sampling with Multi-Stage Aggregation for LLM Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to test-time scaling rely on external verifiers and one-shot independent sampling. |
| Approach: | They propose a test-time scaling framework that reallocates a fixed inference budget into iterative sample–filter–diversify–select cycles. |
| Outcome: | ConMA outperforms baselines on multiple benchmarks while converging early with only 18 samples on average, substantially reducing inference cost. |
SPEAK: Spiking Neurons as an Entropy-Aware Tokenizer for Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing tokenizers fail to explicitly leverage historical tokenization results . large language models (LLMs) have demonstrated remarkable effectiveness across NLP tasks . |
| Approach: | They propose a tokenizer that integrates spiking neurons to explicitly leverage historical tokenization results. |
| Outcome: | The proposed tokenizer leverages historical tokenization results, but does not selectively leverage history based on contextual relevance. |