arXiv:2509.21886v1 Announce Type: new Abstract: Learning to compute, the ability to model the functional behavior of a computational graph, is a fundamental challenge for graph representation learning. Yet, the dominant paradigm is architecturally mismatched for this task. This flawed assumption,…
arXiv:2509.21896v1 Announce Type: new Abstract: We present GenesisGeo, an automated theorem prover in Euclidean geometry. We have open-sourced a large-scale geometry dataset of 21.8 million geometric problems, over 3 million of which contain auxiliary constructions. Specially, we significantly accelerate the…
arXiv:2410.07145v3 Announce Type: replace-cross Abstract: Recent advancements in recurrent architectures, such as Mamba and RWKV, have showcased strong language capabilities. Unlike transformer-based models, these architectures encode all contextual information into a fixed-size state, leading to great inference efficiency. However, this…
arXiv:2509.21902v1 Announce Type: new Abstract: Dynamic job shop scheduling, a fundamental combinatorial optimisation problem in various industrial sectors, poses substantial challenges for effective scheduling due to frequent disruptions caused by the arrival of new jobs. State-of-the-art methods employ machine learning…
arXiv:2502.21263v2 Announce Type: replace-cross Abstract: This study investigates the feasibility of automating clinical coding in Russian, a language with limited biomedical resources. We present a new dataset for ICD coding, which includes diagnosis fields from electronic health records (EHRs) annotated…
arXiv:2509.21943v1 Announce Type: new Abstract: Plantar pressure mapping is essential in clinical diagnostics and sports science, yet large heterogeneous datasets often contain outliers from technical errors or procedural inconsistencies. Statistical Parametric Mapping (SPM) provides interpretable analyses but is sensitive to…
arXiv:2505.10426v2 Announce Type: replace-cross Abstract: We use the notion of oracle machines and reductions from computability theory to formalise different Human-in-the-loop (HITL) setups for AI systems, distinguishing between trivial human monitoring (i.e., total functions), single endpoint human action (i.e., many-one…
arXiv:2509.21981v1 Announce Type: new Abstract: Effective real-world multi-agent collaboration requires not only accurate planning but also the ability to reason about collaborators’ intents — a crucial capability for avoiding miscoordination and redundant communication under partial observable environments. Due to their…
arXiv:2505.15674v2 Announce Type: replace-cross Abstract: Large language models (LLMs) require iterative updates to address the outdated information problem, where LLM unlearning offers an approach for selective removal. However, mainstream unlearning methods primarily rely on fine-tuning techniques, which often lack precision…
arXiv:2509.21982v1 Announce Type: new Abstract: E-commerce risk management requires aggregating diverse, deeply embedded web data through multi-step, stateful interactions, which traditional scraping methods and most existing Graphical User Interface (GUI) agents cannot handle. These agents are typically limited to single-step…
