Can LLMs Reliably Grade Olympiad Proofs? A Controlled Study of Mathematical Verification with LLMs

Azim Ospanov, Zijin Feng, Ding Ding, Chengwu Liu and 4 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Do LLMs Bind Episodes? Probing Cross-Episode Parametric Retrieval Through Shared Cues

Francesco Mantovani, Alberto Eusebio, Alexis Huet, Giulio Franzese and 3 more

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

HiFloat4 Format for Language Model Pre-training on Ascend NPUs

HiFloat4 enables stable FP4 LLM pretraining without stabilization stacks, achieving 1.55% relative loss versus 1.79% for MXFP4 and 2.00% for NVFP4 on Ascend NPUs.

Mehran Taghian Jazi, Yunke Peng, Xing Huang, Yao Wang and 21 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models

HiFloat4 enables end-to-end FP4 reinforcement learning by fixing rollout activation underflow with Rollout-ResQ, cutting accuracy gaps to 1.1% versus BF16.

Hei Yi Mak, Shadan Golestan, Hoang Le, Mehran Taghian Jazi and 9 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026