In recent years, Orthogonal Recurrent Neural Networks (ORNNs) have gained popularity due to their ability to manage tasks involving long-term dependencies, such as the Copy-task, and their linear complexity. However, existing ORNNs utilize full precision weights and activations, which prevents their deployment on compa...
We present DeepOpen, a decision-Oriented intent-classification framework with dataset- specific systems for BANKING77 and CLINC150. On BANKING77, a single English classi- fier continued with sharpness-aware minimization (SAM) achieves 94.1234% test accuracy (2,899/3,080). This is the best observed result among three fi...
Shuang Song, Muhammad Luqman Naseem, Zhongqiao Yuan· 0 citations
Artificial intelligence has advanced through scale, data, and computation, yet remains bounded by its representational framework. We ask what makes intelligence open-ended. Every inferential system selects meaningful distinctions, limiting what it can learn. Within a fixed purpose, performance reaches a ceiling set by...
Simon Hegelich, Bardh Prenkaj, Habiba Sarhan et al.· 0 citations
Long-text image-text alignment is essential for fine-grained scene understanding, yet existing methods mainly focus on extending context length or improving local alignment. We identify an overlooked failure mode, saliency collapse, where models over-rely on text that features visual prominence while underutilizing non...
Qiuyu Kong, Zanxi Ruan, Marco Cristani et al.· 0 citations
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Coding agents are rapidly advancing from single-file generation to long-horizon repository-scale engineering, yet the evaluation of their capabilities to author interactive extended reality (XR) applications remains an open challenge. Unlike traditional software, XR applications operate within dynamic 3D physical envir...
William Huang, David Li, Xiang 'Anthony' Chen et al.· 0 citations
EvoSkill v2 is a three-agent system that edits the instructions (skills) an agent is given, leaving its model and harness intact. The user starts a run by describing a benchmark and supplying a grader, which a benchmark adaptor turns into a benchmark-specific plugin. A solver attempts the tasks, and an evolver reads th...
Da-Wei Huang, Baran Nama, Salaheddin Alzubi· 0 citations
ID-TAP is presented, a scalable, IDS-inspired neural dueling-bandit framework for large discrete persona spaces that combines a query-conditioned neural utility model with a lightweight Bradley-Terry explorer, and transfers comparison evidence across related queries through similarity-weighted warm starts.
Jie Mao, Ping-Chen Lu, Xiang-Yi Wang et al.· 0 citations
The central idea is to regard algorithm execution trajectories as computational experience: by learning how algorithmic states evolve over iterations, a model can internalize the knowledge embodied in existing algorithms, reproduce their computational behaviors, and further evolve beyond them through training.
Jian Yao, Zhi-Yao Zhang, Yi-Chen Li et al.· 0 citations
SHACL shapes enable data graph validation, making automatic shape learning essential for knowledge graph applications. We investigate the well-known fitting approach to this task: given sets P and N of positive and negative example nodes from an input graph, compute a shape expression C, possibly using shape names defi...
Bente Gortworst, Cem Okulmus, Magdalena Ortiz et al.· 0 citations
Disagreement between language models is often read as missing knowledge. Questions that need the same missing knowledge should then be right or wrong together. We test for this structure with the eigenvalues of the questions' correlation matrix. Within one model, independently drawn answers show no detectable structure...
Agent safety is moving from content moderation toward preventing operational failures before tool-using agents act. We propose Janus, a foresight-oriented framework for long-horizon agent safety that trains guards to anticipate delayed risks from partial trajectories. Janus synthesizes diverse agent trajectories via mu...
Yuan Xiong, Linji Hao, Shizhu He et al.· 0 citations
