When Intelligence Fails: An Empirical Study on Why LLMs Struggle with Password Cracking
arXiv:2510.17884v1 Announce Type: cross Abstract: The remarkable capabilities of Large Language Models (LLMs) in natural language understanding and generation have…
Learning from N-Tuple Data with M Positive Instances: Unbiased Risk Estimation and Theoretical Guarantees
arXiv:2510.18406v1 Announce Type: cross Abstract: Weakly supervised learning often operates with coarse aggregate signals rather than instance labels. We study…
C-SEO Bench: Does Conversational SEO Work?
arXiv:2506.11097v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are transforming search engines into Conversational Search Engines (CSE). Consequently, Search…
GACO-CAD: Geometry-Augmented and Conciseness-Optimized CAD Model Generation from Single Image
arXiv:2510.17157v1 Announce Type: cross Abstract: Generating editable, parametric CAD models from a single image holds great potential to lower the…
Online automatic code generation for robot swarms: LLMs and self-organizing hierarchy
arXiv:2510.04774v2 Announce Type: replace-cross Abstract: Our recently introduced self-organizing nervous system (SoNS) provides robot swarms with 1) ease of behavior…
VimoRAG: Video-based Retrieval-augmented 3D Motion Generation for Motion Language Models
arXiv:2508.12081v2 Announce Type: replace-cross Abstract: This paper introduces VimoRAG, a novel video-based retrieval-augmented motion generation framework for motion large language…
Curiosity-driven RL for symbolic equation solving
arXiv:2510.17022v1 Announce Type: cross Abstract: We explore if RL can be useful for symbolic mathematics. Previous work showed contrastive learning…
Does Capital Dream of Artificial Labour?
arXiv:2510.16042v1 Announce Type: cross Abstract: This paper investigates the concept of Labour as an expression of `timenergy’ – a fusion…
Mixture of Experts Approaches in Dense Retrieval Tasks
arXiv:2510.15683v1 Announce Type: cross Abstract: Dense Retrieval Models (DRMs) are a prominent development in Information Retrieval (IR). A key challenge…
CoUn: Empowering Machine Unlearning via Contrastive Learning
arXiv:2509.16391v2 Announce Type: replace-cross Abstract: Machine unlearning (MU) aims to remove the influence of specific “forget” data from a trained…