Papers
Research papers from arXiv and related sources
Fractal universe and quantum gravity made simple
Quantum field theory (QFT) on fractal spacetimes is a program aiming at quantizing the gravitational interaction consistently at all energy scales thanks to an intrinsically or dynamically induced ...
Fabio Briscese, Gianluca Calcagni
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
While large language models have accelerated software development through "vibe coding", prototyping intelligent Extended Reality (XR) experiences remains inaccessible due to the friction of comple...
Ruofei Du, Benjamin Hersh, David Li, Nels Numan, Xun Qian, Yanhe Chen, Zhongyi Zhou, Xingyue Chen...
Comparing Developer and LLM Biases in Code Evaluation
As LLMs are increasingly used as judges in code applications, they should be evaluated in realistic interactive settings that capture partial context and ambiguous intent. We present TRACE (Tool fo...
Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu, Chris Donahue, Ameet Talwalkar,...
The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence
Agentic artificial intelligence (AI) in organizations is a sequential decision problem constrained by reliability and oversight cost. When deterministic workflows are replaced by stochastic policie...
Biplab Pal, Santanu Bhattacharya
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characte...
Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, Tunazzina Islam
MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
Hallucination remains a critical bottleneck for large language models (LLMs), undermining their reliability in real-world applications, especially in Retrieval-Augmented Generation (RAG) systems. W...
Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie Hu, Yu Qin, Erchao ...
Anti-I2V: Safeguarding your photos from malicious image-to-video generation
Advances in diffusion-based video generation models, while significantly improving human animation, poses threats of misuse through the creation of fake videos from a specific person's photo and te...
Duc Vu, Anh Nguyen, Chi Tran, Anh Tran
POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan
Multimodal speaker identification systems typically assume the availability of complete and homogeneous audio-visual modalities during both training and testing. However, in real-world applications...
Marta Moscati, Muhammad Saad Saeed, Marina Zanoni, Mubashir Noman, Rohan Kumar Das, Monorama Swai...
Boosting LLMs for Mutation Generation
LLM-based mutation testing is a promising testing technology, but existing approaches typically rely on a fixed set of mutations as few-shot examples or none at all. This can result in generic low-...
Bo Wang, Ming Deng, Mingda Chen, Chengran Yang, Youfang Lin, Mark Harman, Mike Papadakis, Jie M. ...
LensWalk: Agentic Video Understanding by Planning How You See in Videos
The dense, temporal nature of video presents a profound challenge for automated analysis. Despite the use of powerful Vision-Language Models, prevailing methods for video understanding are limited ...
Keliang Li, Yansong Li, Hongze Shen, Mengdi Liu, Hong Chang, Shiguang Shan
Evaluating Chunking Strategies For Retrieval-Augmented Generation in Oil and Gas Enterprise Documents
Retrieval-Augmented Generation (RAG) has emerged as a framework to address the constraints of Large Language Models (LLMs). Yet, its effectiveness fundamentally hinges on document chunking - an oft...
Samuel Taiwo, Mohd Amaluddin Yusoff
Orientation Reconstruction of Proteins using Coulomb Explosions
We solve the orientation recovery of a tumbling protein in the gas phase from single-event measurements of the spatial positions of its ions after an X-ray laser induced explosion. We simulate diff...
Tomas André, Alfredo Bellisario, Nicusor Timneanu, Carl Caleman
The role of spatial context and multitask learning in the detection of organic and conventional farming systems based on Sentinel-2 time series
Organic farming is a key element in achieving more sustainable agriculture. For a better understanding of the development and impact of organic farming, comprehensive, spatially explicit informatio...
Jan Hemmerling, Marcel Schwieder, Philippe Rufin, Leon-Friedrich Thomas, Mirela Tulbure, Patrick ...
Detection of local geometry in random graphs: information-theoretic and computational limits
We study the problem of detecting local geometry in random graphs. We introduce a model $\mathcal{G}(n, p, d, k)$, where a hidden community of average size $k$ has edges drawn as a random geometric...
Jinho Bok, Shuangping Li, Sophie H. Yu
Analysing the Safety Pitfalls of Steering Vectors
Activation steering has emerged as a powerful tool to shape LLM behavior without the need for weight updates. While its inherent brittleness and unreliability are well-documented, its safety implic...
Yuxiao Li, Alina Fastowski, Efstratios Zaradoukas, Bardh Prenkaj, Gjergji Kasneci
Radial Distribution Function in a Two Dimensional Core-Shoulder Particle System
An important quantity in liquid state theory is the radial distribution function $g(r)$. It can be calculated within the framework of classical density functional theory in two very distinct ways. ...
Michael Wassermair, Gerhard Kahl, Andrew J Archer, Roland Roth
Robust Multilingual Text-to-Pictogram Mapping for Scalable Reading Rehabilitation
Reading comprehension presents a significant challenge for children with Special Educational Needs and Disabilities (SEND), often requiring intensive one-on-one reading support. To assist therapist...
Soufiane Jhilal, Martina Galletti
No Single Metric Tells the Whole Story: A Multi-Dimensional Evaluation Framework for Uncertainty Attributions
Research on explainable AI (XAI) has frequently focused on explaining model predictions. More recently, methods have been proposed to explain prediction uncertainty by attributing it to input featu...
Emily Schiller, Teodor Chiaburu, Marco Zullich, Luca Longo
TuneShift-KD: Knowledge Distillation and Transfer for Fine-tuned Models
To embed domain-specific or specialized knowledge into pre-trained foundation models, fine-tuning using techniques such as parameter efficient fine-tuning (e.g. LoRA) is a common practice. However,...
Yushi Guan, Jeanine Ohene-Agyei, Daniel Kwan, Jean Sebastien Dandurand, Yifei Zhang, Nandita Vija...
AVO: Agentic Variation Operators for Autonomous Evolutionary Search
Agentic Variation Operators (AVO) are a new family of evolutionary variation operators that replace the fixed mutation, crossover, and hand-designed heuristics of classical evolutionary search with...
Terry Chen, Zhifan Ye, Bing Xu, Zihao Ye, Timmy Liu, Ali Hassani, Tianqi Chen, Andrew Kerr, Haich...