Papers
Research papers from arXiv and related sources
Learning Adaptive Force Control for Contact-Rich Sample Scraping with Heterogeneous Materials
The increasing demand for accelerated scientific discovery, driven by global challenges, highlights the need for advanced AI-driven robotics. Deploying robotic chemists in human-centric labs is key...
Cenk Cetin, Shreyas Pouli, Gabriella Pizzuto
GroundCount: Grounding Vision-Language Models with Object Detection for Mitigating Counting Hallucinations
Vision Language Models (VLMs) exhibit persistent hallucinations in counting tasks, with accuracy substantially lower than other visual reasoning tasks (excluding sentiment). This phenomenon persist...
Boyuan Chen, Minghao Shao, Siddharth Garg, Ramesh Karri, Muhammad Shafique
Report for NSF Workshop on Algorithm-Hardware Co-design for Medical Applications
This report summarizes the discussions and recommendations from the NSF Workshop on Algorithm-Hardware Co-design for Medical Applications, held on September 26-27, 2024, in Pittsburgh, PA. The work...
Peipei Zhou, Zheng Dong, Insup Lee, Aidong Zhang, Robert Dick, Majid Sarrafzadeh, Xiaodong Wu, We...
TOSSS: a CVE-based Software Security Benchmark for Large Language Models
With their increasing capabilities, Large Language Models (LLMs) are now used across many industries. They have become useful tools for software engineers and support a wide range of development ta...
Marc Damie, Murat Bilgehan Ertan, Domenico Essoussi, Angela Makhanu, Gaëtan Peter, Roos Wensveen
Ranking Reasoning LLMs under Test-Time Scaling
Test-time scaling evaluates reasoning LLMs by sampling multiple outputs per prompt, but ranking models in this regime remains underexplored. We formalize dense benchmark ranking under test-time sca...
Mohsen Hariri, Michael Hinczewski, Jing Ma, Vipin Chaudhary
Bridging the Skill Gap in Clinical CBCT Interpretation with CBCTRepD
Generative AI has advanced rapidly in medical report generation; however, its application to oral and maxillofacial CBCT reporting remains limited, largely because of the scarcity of high-quality p...
Qinxin Wu, Fucheng Niu, Hengchuan Zhu, Yifan Sun, Ye Shen, Xu Li, Han Wu, Leqi Liu, Zhiwen Pan, Z...
LLM2Vec-Gen: Generative Embeddings from Large Language Models
LLM-based text embedders typically encode the semantic content of their input. However, embedding tasks require mapping diverse inputs to similar outputs. Typically, this input-output is addressed ...
Parishad BehnamGhader, Vaibhav Adlakha, Fabian David Schmidt, Nicolas Chapados, Marius Mosbach, S...
When Fine-Tuning Fails and when it Generalises: Role of Data Diversity and Mixed Training in LLM-based TTS
Large language models are increasingly adopted as semantic backbones for neural text-to-speech systems. However, frozen LLM representations are insufficient for modeling speaker specific acoustic a...
Anupam Purwar, Aditya Choudhary
LookaheadKV: Fast and Accurate KV Cache Eviction by Glimpsing into the Future without Generation
Transformer-based large language models (LLMs) rely on key-value (KV) caching to avoid redundant computation during autoregressive inference. While this mechanism greatly improves efficiency, the c...
Jinwoo Ahn, Ingyu Seong, Akhil Kedia, Junhan Kim, Hyemi Jang, Kangwook Lee, Yongkweon Jeon
A Hybrid Knowledge-Grounded Framework for Safety and Traceability in Prescription Verification
Medication errors pose a significant threat to patient safety, making pharmacist verification (PV) a critical, yet heavily burdened, final safeguard. The direct application of Large Language Models...
Yichi Zhu, Kan Ling, Xu Liu, Hengrun Zhang, Huiqun Yu, Guisheng Fan
Dynamics-Predictive Sampling for Active RL Finetuning of Large Reasoning Models
Reinforcement learning (RL) finetuning has become a key technique for enhancing the reasoning abilities of large language models (LLMs). However, its effectiveness critically depends on the selecti...
Yixiu Mao, Yun Qu, Qi Wang, Heming Zou, Xiangyang Ji
An Extreme Multi-label Text Classification (XMTC) Library Dataset: What if we took "Use of Practical AI in Digital Libraries" seriously?
Subject indexing is vital for discovery but hard to sustain at scale and across languages. We release a large bilingual (English/German) corpus of catalog records annotated with the Integrated Auth...
Jennifer D'Souza, Sameer Sadruddin, Maximilian Kähler, Andrea Salfinger, Luca Zaccagna, Francesca...
OSUM-Pangu: An Open-Source Multidimension Speech Understanding Foundation Model Built upon OpenPangu on Ascend NPUs
Recent advancements in Speech Large Language Models have significantly enhanced multi-dimensional speech understanding. However, the majority of high-performance frameworks are predominantly optimi...
Yujie Liao, Xuelong Geng, Hongfei Xue, Shuiyuan Wang, Lei Xie
SiDiaC-v.2.0: Sinhala Diachronic Corpus Version 2.0
SiDiaC-v.2.0 is the largest comprehensive Sinhala Diachronic Corpus to date, covering a period from 1800 CE to 1955 CE in terms of publication dates, and a historical span from the 5th to the 20th ...
Nevidu Jayatilleke, Nisansa de Silva, Uthpala Nimanthi, Gagani Kulathilaka, Azra Safrullah, Johan...
Numerical analysis for leaky-integrate-fire networks under Euler--Maruyama
Leaky integrate-and-fire (LIF) networks are canonical models in computational neuroscience and a standard substrate for neuromorphic AI. We study Euler--Maruyama simulation of current-based LIF net...
Xu'an Dou, Frank Chen, Kevin K Lin, Zhuo-Cheng Xiao
BALD-SAM: Disagreement-based Active Prompting in Interactive Segmentation
The Segment Anything Model (SAM) has revolutionized interactive segmentation through spatial prompting. While existing work primarily focuses on automating prompts in various settings, real-world a...
Prithwijit Chowdhury, Mohit Prabhushankar, Ghassan AlRegib
Speaker Verification with Speech-Aware LLMs: Evaluation and Augmentation
Speech-aware large language models (LLMs) can accept speech inputs, yet their training objectives largely emphasize linguistic content or specific fields such as emotions or the speaker's gender, l...
Thomas Thebaud, Yuzhe Wang, Laureano Moro-Velazquez, Jesus Villalba-Lopez, Najim Dehak
A dataset of medication images with instance segmentation masks for preventing adverse drug events
Medication errors and adverse drug events (ADEs) pose significant risks to patient safety, often arising from difficulties in reliably identifying pharmaceuticals in real-world settings. AI-based p...
W. I. Chu, S. Hirani, G. Tarroni, L. Li
HanMoVLM: Large Vision-Language Models for Professional Artistic Painting Evaluation
While Large Vision-Language Models (VLMs) demonstrate impressive general visual capabilities, they remain artistically blind and unable to offer professional evaluation of artworks within specific ...
Hongji Yang, Yucheng Zhou, Wencheng Han, Songlian Li, Xiaotong Zhao, Jianbing Shen
Nurture-First Agent Development: Building Domain-Expert AI Agents Through Conversational Knowledge Crystallization
The emergence of large language model (LLM)-based agent frameworks has shifted the primary challenge in building domain-expert AI agents from raw capability to effective encoding of domain expertis...
Linghao Zhang