arXiv:2512.18991v1 Announce Type: cross Abstract: Dominant paradigms for 4D LiDAR panoptic segmentation are usually required to train deep neural networks with large superimposed point clouds or design dedicated modules for instance association. However, these approaches perform redundant point processing and consequently become computationally expensive, yet still overlook the rich geometric priors inherently provided by raw […]
The Universe Learning Itself: On the Evolution of Dynamics from the Big Bang to Machine Intelligence
arXiv:2512.16515v2 Announce Type: replace-cross Abstract: We develop a unified, dynamical-systems narrative of the universe that traces a continuous chain of structure formation from the Big Bang to contemporary human societies and their artificial learning systems. Rather than treating cosmology, astrophysics, geophysics, biology, cognition, and machine intelligence as disjoint domains, we view each as successive regimes […]
Evaluating the Challenges of LLMs in Real-world Medical Follow-up: A Comparative Study and An Optimized Framework
arXiv:2512.18999v1 Announce Type: cross Abstract: When applied directly in an end-to-end manner to medical follow-up tasks, Large Language Models (LLMs) often suffer from uncontrolled dialog flow and inaccurate information extraction due to the complexity of follow-up forms. To address this limitation, we designed and compared two follow-up chatbot systems: an end-to-end LLM-based system (control group) […]
Exploration vs Exploitation: Rethinking RLVR through Clipping, Entropy, and Spurious Reward
arXiv:2512.16912v2 Announce Type: replace-cross Abstract: This paper examines the exploration-exploitation trade-off in reinforcement learning with verifiable rewards (RLVR), a framework for improving the reasoning of Large Language Models (LLMs). Recent studies suggest that RLVR can elicit strong mathematical reasoning in LLMs through two seemingly paradoxical mechanisms: spurious rewards, which suppress exploitation by rewarding outcomes unrelated […]
Assessing High-Risk AI Systems under the EU AI Act: From Legal Requirements to Technical Verification
arXiv:2512.13907v2 Announce Type: replace-cross Abstract: The implementation of the AI Act requires practical mechanisms to verify compliance with legal obligations, yet concrete and operational mappings from high-level requirements to verifiable assessment activities remain limited, contributing to uneven readiness across Member States. This paper presents a structured mapping that translates high-level AI Act requirements into concrete, […]
TakeAD: Preference-based Post-optimization for End-to-end Autonomous Driving with Expert Takeover Data
arXiv:2512.17370v2 Announce Type: replace-cross Abstract: Existing end-to-end autonomous driving methods typically rely on imitation learning (IL) but face a key challenge: the misalignment between open-loop training and closed-loop deployment. This misalignment often triggers driver-initiated takeovers and system disengagements during closed-loop execution. How to leverage those expert takeover data from disengagement scenarios and effectively expand the […]
The 6th International Verification of Neural Networks Competition (VNN-COMP 2025): Summary and Results
arXiv:2512.19007v1 Announce Type: cross Abstract: This report summarizes the 6th International Verification of Neural Networks Competition (VNN-COMP 2025), held as a part of the 8th International Symposium on AI Verification (SAIV), that was collocated with the 37th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of […]
Efficient Jailbreak Mitigation Using Semantic Linear Classification in a Multi-Staged Pipeline
arXiv:2512.19011v1 Announce Type: cross Abstract: Prompt injection and jailbreaking attacks pose persistent security challenges to large language model (LLM)-based systems. We present an efficient and systematically evaluated defense architecture that mitigates these threats through a lightweight, multi-stage pipeline. Its core component is a semantic filter based on text normalization, TF-IDF representations, and a Linear SVM […]
SCOPE: Sequential Causal Optimization of Process Interventions
arXiv:2512.17629v2 Announce Type: replace-cross Abstract: Prescriptive Process Monitoring (PresPM) recommends interventions during business processes to optimize key performance indicators (KPIs). In realistic settings, interventions are rarely isolated: organizations need to align sequences of interventions to jointly steer the outcome of a case. Existing PresPM approaches fall short in this respect. Many focus on a single […]
CytoDINO: Risk-Aware and Biologically-Informed Adaptation of DINOv3 for Bone Marrow Cytomorphology
arXiv:2512.17930v1 Announce Type: new Abstract: Bone marrow cell cytomorphology analysis is critical for the diagnosis of hematological malignancies but remains a labor-intensive process subject to significant inter-observer variability. While recent foundation models have shown promise in computational pathology, they often require extensive computational resources and fail to account for the asymmetric risks associated with clinical […]