arXiv:2506.11045v2 Announce Type: replace Abstract: Although the power of LLM tool-use agents has ignited a flurry of recent research in this area, the curation of tool-use training data remains an open problem$-$especially for online RL training. Existing approaches to synthetic…
arXiv:2509.02407v2 Announce Type: replace Abstract: The estimation of continuous parameters from measured data plays a central role in many fields of physics. A key tool in understanding and improving such estimation processes is the concept of Fisher information, which quantifies…
arXiv:2509.20315v1 Announce Type: cross Abstract: Hope speech language that fosters encouragement and optimism plays a vital role in promoting positive discourse online. However, its detection remains challenging, especially in multilingual and low-resource settings. This paper presents a multilingual framework for…
arXiv:2502.01375v2 Announce Type: replace Abstract: Rule-based models are essential for high-stakes decision-making due to their transparency and interpretability, but their discrete nature creates challenges for optimization and scalability. In this work, we present the Fuzzy Rule-based Reasoner (FRR), a novel…
arXiv:2509.17918v2 Announce Type: replace-cross Abstract: Recommender systems (RS) greatly influence users’ consumption decisions, making them attractive targets for malicious shilling attacks that inject fake user profiles to manipulate recommendations. Existing shilling methods can generate effective and stealthy fake profiles when…
arXiv:2509.20049v1 Announce Type: cross Abstract: Kolmogorov-Arnold Networks (KANs) relocate learnable nonlinearities from nodes to edges, demonstrating remarkable capabilities in scientific machine learning and interpretable modeling. However, current KAN implementations suffer from fundamental inefficiencies due to redundancy in high-dimensional spline parameter…
arXiv:2509.19376v1 Announce Type: new Abstract: We address temporal failures in RAG systems using two methods on cybersecurity data. A simple recency prior achieved an accuracy of 1.00 on freshness tasks. In contrast, a clustering heuristic for topic evolution failed (0.08…
arXiv:2509.19379v1 Announce Type: new Abstract: Imitation Learning (IL) algorithms offer an efficient way to train an agent by mimicking an expert’s behavior without requiring a reward function. IL algorithms often necessitate access to state and action information from expert demonstrations.…
arXiv:2509.19372v1 Announce Type: new Abstract: We critically assess the efficacy of the current SOTA in hallucination detection and find that its performance on the RAGTruth dataset is largely driven by a spurious correlation with data. Controlling for this effect, state-of-the-art…
arXiv:2509.19375v1 Announce Type: new Abstract: Despite their widespread applications, Large Language Models (LLMs) often struggle to express uncertainty, posing a challenge for reliable deployment in high stakes and safety critical domains like clinical diagnostics. Existing standard baseline methods such as…
