9 papers
Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation
Changcheng Li, Jiancan Wu, Hengheng Zhang +5
Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing methods are predominantly answer-first, producing confidence only after gener…
Towards Verifiable and Self-Correcting AI Physicists for Quantum Many-Body Simulations
Ken Deng, Xiangfei Wang, Guijing Duan +7
While large language models (LLMs) promise to revolutionize automated scientific discovery, their application in rigorous real-world physical research is stalled by two critical ba…
TianJi:An autonomous AI meteorologist for discovering physical mechanisms in atmospheric science
Kaikai Zhang, Xiang Wang, Haoluo Zhao +4
Artificial intelligence (AI) has achieved breakthroughs comparable to traditional numerical models in data-driven weather forecasting, yet it remains essentially statistical fittin…
Not Search, But Scan: Benchmarking MLLMs on Scan-Oriented Academic Paper Reasoning
Rongjin Li, Zichen Tang, Xianghe Wang +9
With the rapid progress of multimodal large language models (MLLMs), AI already performs well at literature retrieval and certain reasoning tasks, serving as a capable assistant to…
When Perplexity Lies: Generation-Focused Distillation of Hybrid Sequence Models
Juan Gabriel Kostelec, Xiang Wang, Axel Laborieux +2
Converting a pretrained Transformer into a more efficient hybrid model through distillation offers a promising approach to reducing inference costs. However, achieving high-quality…
Beyond Where to Look: Trajectory-Guided Reinforcement Learning for Multimodal RLVR
Jinda Lu, Junkang Wu, Jinghan Li +6
Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) for multimodal large language models (MLLMs) have mainly focused on improving final answer correctness and…