Papers by Abdellah Ghassel
Entropy-Gated Branching for Efficient Test-Time Reasoning (2026.eacl-long)
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| Challenge: | Empirical results show that branching at low uncertainty points can improve reasoning capabilities of large language models . however, these methods require substantially more computational resources, causing errors in high-stakes domains . |
| Approach: | They propose an inference technique that selectively expands prediction sequences at points of high uncertainty. |
| Outcome: | Empirical results show that the proposed method improves accuracy by 22.6% over standard inference while operating 31%-75% faster across math benchmarks. |