30 citations · 59 across the 9 of their papers we have counts for
9 papers
RoPE Distinguishes Neither Positions Nor Tokens in Long Contexts, Provably
Yufeng Du, Phillip Harris, Minyang Tian +5
We identify intrinsic limitations of Rotary Positional Embeddings (RoPE) in Transformer-based long-context language models. Our theoretical analysis abstracts away from the specifi…
A Practical Study of Lightweight Neural Gravitational-Wave Detection in Real LIGO Noise: Models, Ablations, and Open Software
Victoria Tiki, Eliu Huerta
Machine-learning methods offer a computationally efficient complement to conventional gravitational-wave searches, but their performance depends strongly on training data, preproce…
Context Length Alone Hurts LLM Performance Despite Perfect Retrieval
Yufeng Du, Minyang Tian, Srikanth Ronanki +7
Large language models (LLMs) often fail to scale their performance on long-context tasks performance in line with the context lengths they support. This gap is commonly attributed…
Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark
Minhui Zhu, Minyang Tian, Xiaocheng Yang +61
While large language models (LLMs) with reasoning capabilities are progressing rapidly on high-school math competitions and coding, can they reason effectively through complex, ope…
SciCode: A Research Coding Benchmark Curated by Scientists
Minyang Tian, Luyu Gao, Shizhuo Dylan Zhang +27
Since language models (LMs) now outperform average humans on many challenging tasks, it has become increasingly difficult to develop challenging, high-quality, and realistic evalua…
AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers
Minyang Tian, E. A. Huerta, Huihuo Zheng
We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with…
Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers
Minyang Tian, E. A. Huerta, Huihuo Zheng +1
We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational w…
Inference-optimized AI and high performance computing for gravitational wave detection at scale
Pranshu Chaturvedi, Asad Khan, Minyang Tian +2
We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V10…
Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers
Asad Khan, E. A. Huerta, Huihuo Zheng
We present a deep-learning artificial intelligence model that is capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms that d…