Papers
Research papers from arXiv and related sources
Quantum Random Forest for the Regression Problem
The Random Forest model is one of the popular models of Machine learning. We present a quantum algorithm for testing (forecasting) process of the Random Forest machine learning model for the Regres...
Kamil Khadiev, Liliya Safina
Understanding Bugs in Quantum Simulators: An Empirical Study
Quantum simulators are a foundational component of the quantum software ecosystem. They are widely used to develop and debug quantum programs, validate compiler transformations, and support empiric...
Krishna Upadhyay, Moshood Fakorede, Umar Farooq
Exposure-Normalized Bed and Chair Fall Rates via Continuous AI Monitoring
This retrospective cohort study used continuous AI monitoring to estimate fall rates by exposure time rather than occupied bed-days. From August 2024 to December 2025, 3,980 eligible monitoring uni...
Paolo Gabriel, Peter Rehani, Zack Drumm, Tyler Troy, Tiffany Wyatt, Narinder Singh
Caterpillar of Thoughts: The Optimal Test-Time Algorithm for Large Language Models
Large language models (LLMs) can often produce substantially better outputs when allowed to use additional test-time computation, such as sampling, chain of thought, backtracking, or revising parti...
Amir Azarmehr, Soheil Behnezhad, Alma Ghafari
MVPBench: A Multi-Video Perception Evaluation Benchmark for Multi-Modal Video Understanding
The rapid progress of Large Language Models (LLMs) has spurred growing interest in Multi-modal LLMs (MLLMs) and motivated the development of benchmarks to evaluate their perceptual and comprehensio...
Purui Bai, Tao Wu, Jiayang Sun, Xinyue Liu, Huaibo Huang, Ran He
PRISM: A Dual View of LLM Reasoning through Semantic Flow and Latent Computation
Large language models (LLMs) solve complex problems by generating multi-step reasoning traces. Yet these traces are typically analyzed from only one of two perspectives: the sequence of tokens acro...
Ruidi Chang, Jiawei Zhou, Hanjie Chen
CLiGNet: Clinical Label-Interaction Graph Network for Medical Specialty Classification from Clinical Transcriptions
Automated classification of clinical transcriptions into medical specialties is essential for routing, coding, and clinical decision support, yet prior work on the widely used MTSamples benchmark s...
Pronob Kumar Barman, Pronoy Kumar Barman
Experimental investigation of magnetic properties of MnFeCo$_{4}$Si$_{2}$ discovered by GNoME
AI-driven inorganic materials research has garnered significant attention due to its ability to reduce the time, labor, and cost associated with experiments. An AI model known as GNoME, recently de...
Shuhei Naganuma, Jiro Kitagawa
Pre Seismic Quiescence and Dynamical Regime Transitions in the Japan and Chile Earthquake Catalogs Evidence from KR Critical Slowing Down Indicators
We present the KR excitation regulation framework, a coupled ordinary differential equation system that produces Critical Slowing Down (CSD) indicators from rolling earthquake magnitude windows, an...
Ramakrishna Pasupuleti
Beyond Binary Correctness: Scaling Evaluation of Long-Horizon Agents on Subjective Enterprise Tasks
Large language models excel on objectively verifiable tasks such as math and programming, where evaluation reduces to unit tests or a single correct answer. In contrast, real-world enterprise work ...
Abhishek Chandwani, Ishan Gupta
Explanation Generation for Contradiction Reconciliation with LLMs
Existing NLP work commonly treats contradictions as errors to be resolved by choosing which statements to accept or discard. Yet a key aspect of human reasoning in social interactions and professio...
Jason Chan, Zhixue Zhao, Robert Gaizauskas
How Utilitarian Are OpenAI's Models Really? Replicating and Reinterpreting Pfeffer, Krügel, and Uhl (2025)
Pfeffer, Krügel, and Uhl (2025) report that OpenAI's reasoning model o1-mini produces more utilitarian responses to the trolley problem and footbridge dilemma than the non-reasoning model GPT-4o. I...
Johannes Himmelreich
Beyond Explanation: Evidentiary Rights for Algorithmic Accountability
Algorithmic accountability scholarship has focused heavily on explanation, helping affected parties understand why decisions were made. We argue this focus is insufficient. Explanation without evid...
Matthew Stewart
Detecting Non-Membership in LLM Training Data via Rank Correlations
As large language models (LLMs) are trained on increasingly vast and opaque text corpora, determining which data contributed to training has become essential for copyright enforcement, compliance a...
Pranav Shetty, Mirazul Haque, Zhiqiang Ma, Xiaomo Liu
Testing Properties of Edge Distributions
We initiate the study of distribution testing for probability distributions over the edges of a graph, motivated by the closely related question of ``edge-distribution-free'' graph property testing...
Yumou Fei
A Clinically Anchored Radiomics Dictionary for Explainable TI-RADS-Based Thyroid Nodule Classification in Ultrasound; Dictionary Version TU1.0
Artificial intelligence based radiomics models for thyroid ultrasound (US) often achieve strong diagnostic performance but remain difficult to interpret, limiting clinical trust and adoption. We de...
Mohammad Salmanpour, Shahram Taeb, Ali Fathi Jouzdani, Mohammad Ayazi, Siavash Hosseinpour Saffar...
BlindMarket: Enabling Verifiable, Confidential, and Traceable IP Core Distribution in Zero-Trust Settings
We present BlindMarket, an end-to-end zero-trust distribution framework for hardware IP cores. BlindMarket allows two parties, the IP user and the IP vendor, to complete an IP trading process with ...
Zhaoxiang Liu, Samuel Judson, Raj Dutta, Mark Santolucito, Xiaolong Guo, Ning Luo
Fixed-level calibration of the Cauchy combination test
The Cauchy combination test (CCT) is widely used because it gives a closed-form combined $p$-value and is known to be asymptotically valid as the nominal level $α\downarrow0$ under broad dependence...
Hirofumi Ota
Variable-Resolution Virtual Maps for Autonomous Exploration with Unmanned Surface Vehicles (USVs)
Autonomous exploration by unmanned surface vehicles (USVs) in near-shore waters requires reliable localisation and consistent mapping over extended areas, but this is challenged by GNSS degradation...
Ye Li, Yewei Huang, Wenlong GaoZhang, Alberto Quattrini Li, Brendan Englot, Yuanchang Liu
Toward Faithful Segmentation Attribution via Benchmarking and Dual-Evidence Fusion
Attribution maps for semantic segmentation are almost always judged by visual plausibility. Yet looking convincing does not guarantee that the highlighted pixels actually drive the model's predicti...
Abu Noman Md Sakib, OFM Riaz Rahman Aranya, Kevin Desai, Zijie Zhang