Prépublication
· 2026 arXiv
EN
Interpreting machine learning model predictions is a central challenge, particularly for black-box vision models. Concept-based attribution methods interpret models using human-understandable concepts. Existing concept-based methods can only interpret classifi...
Bhanu Tokas, Rahul Nair et al.
#cs.CV
Prépublication
· 2026 arXiv
EN
Recursive self-improvement (RSI) aims to transform interaction experience into persistent improvements, yet external feedback is costly and world-model predictions may become unreliable as policies evolve. We introduce RSI-Anything, a domain-general framework...
Taoyong Cui, Pinxian Zeng et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Vision-Language Models (VLMs) increasingly analyze and reason about images, yet it remains unclear whether their internal representations faithfully encode the structured relations required for such reasoning. We introduce EICS (Evaluation-Invariant Concept St...
Shubh Garg, Chen Shani
#cs.CL
Prépublication
· 2026 arXiv
EN
While deep learning models for tabular data have recently received significant research attention, the choice of objective functions for regression tasks remains an understudied degree of freedom. Several lines of research offer alternatives to the standard Me...
Danil Rudenko, Georgii Bulgakov et al.
#cs.LG
Prépublication
· 2026 arXiv
EN
Neural semantic parsers generate meaning representations token by token, yet well-formedness depends on references, scope, and graph relations that unfold during generation. Constrained decoding prevents locally invalid tokens but does not guarantee that a val...
Xiao Zhang
#cs.CL
Prépublication
· 2026 arXiv
EN
Large language models reuse the same attention heads across tasks, but identifying these shared heads does not explain what they do differently in each task. A natural source of inspiration is task-dependent interactions among shared brain regions, which motiv...
Sovesh Mohapatra, Dani S Bassett
#cs.CL
Prépublication
· 2026 arXiv
EN
As AI agents increasingly rely on external tools, accurate retrieval is essential for reliable behavior. Existing methods optimize semantic relevance but largely ignore tool availability during execution. To address this gap, we propose RATED (Reliability-Awar...
Weiliang Chen, Song Liao
#cs.AI
Prépublication
· 2026 arXiv
EN
Personal agents increasingly connect to services across the web and applications by using tools. As the effects of their actions accumulate and interact across services, unintended or harmful outcomes can arise when agents fulfill benign user requests. Proacti...
Yeonsung Jung, Zhiyuan Wang et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Comics are one of the great visual records of the twentieth century: hundreds of thousands of pages in which a mass readership saw its leaders, its wars and its heroes. Most of this record has never been studied as a whole; comics scholarship proceeds one book...
Emilio Ferrara
#cs.AI
Prépublication
· 2026 arXiv
EN
When solving complex spatial reasoning tasks, ordinary humans naturally revisit the scenes from task-relevant viewpoints and mentally reconstruct the spatial layouts from those viewpoints. But can Large Vision-Language Models (LVLMs) proactively think in the s...
Yuhong Liu, Shenxi Wu et al.
#cs.CV
Prépublication
· 2026 arXiv
EN
A central challenge in embodied recursive self-improvement is enabling agents to convert interaction history into capabilities that persist beyond one world and transfer to later tasks. A saved memory or successful final action does not by itself establish tha...
Jingwei Song, Zhisheng Chen et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Recent advances in automatic pronunciation assessment (APA) have driven a shift from scripted to free-form assessment. However, existing methods often rely on multi-stage pipelines with forced alignment and separate feature extractors, limiting their practical...
Fu-An Chao, Bi-Cheng Yan et al.
#eess.AS