A Neuro-Symbolic Multi-Agent Approach to Legal-Cybersecurity Knowledge Integration

Anomaly detection is essential in fields like cybersecurity and finance, particularly with large-scale tabular data. Traditional unsupervised methods struggle due to their reliance on a single global distribution, which does not account for the diverse contexts present in real-world data. This paper introduces a contextual learning framework that models normal behavior variations across different contexts, focusing on conditional data distributions instead of a global joint distribution, enhancing anomaly detection effectiveness.

MalRAG is a novel retrieval-augmented framework designed for the fine-grained identification of open-set malicious traffic in cybersecurity. As cyber threats continuously evolve, the ability to detect both known and new types of malicious traffic is paramount. This framework utilizes a frozen large language model (LLM) to construct a comprehensive traffic knowledge database, employing adaptive retrieval and prompt engineering techniques to enhance identification capabilities.

The study investigates the ability of transformer models to predict long steps in the Collatz sequence, a complex arithmetic function that maps odd integers to their successors. The accuracy of the models varies significantly depending on the base used for encoding, achieving up to 99.7% accuracy for bases 24 and 32, while dropping to 37% and 25% for bases 11 and 3. Despite these variations, all models exhibit a common learning pattern, accurately predicting inputs with similar residuals modulo 2^p.

Vision-Language Models (VLMs) are emerging models that integrate visual content with natural language. Current methods typically fuse data either early in the encoding process or late through pooled embeddings. This paper introduces a lightweight fusion module utilizing cross-only, bidirectional attention layers to align hidden states from both modalities, enhancing understanding while keeping encoders non-causal. The proposed method aims to improve the performance of VLMs by leveraging the inherent structure of visual and textual data.

The Professional Reasoning Bench (PRBench) is introduced as a new benchmark for evaluating high-stakes professional reasoning in the fields of Finance and Law. It comprises 1,100 expert-authored tasks and 19,356 expert-curated criteria, making it the largest public, rubric-based benchmark in these domains. The project involved 182 qualified professionals from 114 countries and 47 US jurisdictions, aiming to address the limitations of existing evaluations that often overlook open-ended, economically significant tasks.

Higher-order Neural Additive Models (HONAMs) have been introduced as an advancement over Neural Additive Models (NAMs), which are known for their predictive performance and interpretability. HONAMs address the limitation of NAMs by effectively capturing feature interactions of arbitrary orders, enhancing predictive accuracy while maintaining interpretability, crucial for high-stakes applications. The source code for HONAM is publicly available on GitHub.

The paper titled 'Bias-Restrained Prefix Representation Finetuning for Mathematical Reasoning' introduces a new method called Bias-REstrained Prefix Representation FineTuning (BREP ReFT). This approach aims to enhance the mathematical reasoning capabilities of models by addressing the limitations of existing Representation finetuning (ReFT) methods, which struggle with mathematical tasks. The study demonstrates that BREP ReFT outperforms both standard ReFT and weight-based Parameter-Efficient finetuning (PEFT) methods through extensive experiments.