arXiv:2603.15848v2 Announce Type: replace Abstract: The report presents with the development and optimisation of an enhanced algorithmic trading strategy through the use of historical S&P 500 market data and earnings call sentiment analysis. The proposed strategy integrates various technical indicators such as moving averages, momentum, volatility, and FinBERT-based sentiment analysis to improve overall trades being […]
AEGIS: From Clues to Verdicts — Graph-Guided Deep Vulnerability Reasoning via Dialectics and Meta-Auditing
arXiv:2603.20637v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly adopted for vulnerability detection, yet their reasoning remains fundamentally unsound. We identify a root cause shared by both major mitigation paradigms (agent-based debate and retrieval augmentation): reasoning in an ungrounded deliberative space that lacks a bounded, hypothesis-specific evidence base. Without such grounding, agents fabricate […]
Leveraging Natural Language Processing and Machine Learning for Evidence-Based Food Security Policy Decision-Making in Data-Scarce Making
arXiv:2603.20425v1 Announce Type: new Abstract: Food security policy formulation in data-scarce regions remains a critical challenge due to limited structured datasets, fragmented textual reports, and demographic bias in decision-making systems. This study proposes ZeroHungerAI, an integrated Natural Language Processing (NLP) and Machine Learning (ML) framework designed for evidence-based food security policy modeling under extreme data […]
SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection
arXiv:2603.20686v1 Announce Type: cross Abstract: Recent advancements in text-to-speech technologies enable generating high-fidelity synthetic speech nearly indistinguishable from real human voices. While recent studies show the efficacy of self-supervised learning-based speech encoders for deepfake detection, these models struggle to generalize across unseen speakers. Our quantitative analysis suggests these encoder representations are substantially influenced by speaker […]
Uniform Loss vs. Specialized Optimization: A Comparative Analysis in Multi-Task Learning
arXiv:2505.10347v3 Announce Type: replace-cross Abstract: Specialized Multi-Task Optimizers (SMTOs) balance task learning in Multi-Task Learning by addressing issues like conflicting gradients and differing gradient norms, which hinder equal-weighted task training. However, recent critiques suggest that equally weighted tasks can achieve competitive results compared to SMTOs, arguing that previous SMTO results were influenced by poor hyperparameter […]
Memory-Efficient Fine-Tuning Diffusion Transformers via Dynamic Patch Sampling and Block Skipping
arXiv:2603.20755v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have significantly enhanced text-to-image (T2I) generation quality, enabling high-quality personalized content creation. However, fine-tuning these models requires substantial computational complexity and memory, limiting practical deployment under resource constraints. To tackle these challenges, we propose a memory-efficient fine-tuning framework called DiT-BlockSkip, integrating timestep-aware dynamic patch sampling and block […]
Deep reflective reasoning in interdependence constrained structured data extraction from clinical notes for digital health
arXiv:2603.20435v1 Announce Type: new Abstract: Extracting structured information from clinical notes requires navigating a dense web of interdependent variables where the value of one attribute logically constrains others. Existing Large Language Model (LLM)-based extraction pipelines often struggle to capture these dependencies, leading to clinically inconsistent outputs. We propose deep reflective reasoning, a large language model […]
Can ChatGPT Really Understand Modern Chinese Poetry?
arXiv:2603.20851v1 Announce Type: cross Abstract: ChatGPT has demonstrated remarkable capabilities on both poetry generation and translation, yet its ability to truly understand poetry remains unexplored. Previous poetry-related work merely analyzed experimental outcomes without addressing fundamental issues of comprehension. This paper introduces a comprehensive framework for evaluating ChatGPT’s understanding of modern poetry. We collaborated with professional […]
Learning to Generate Rigid Body Interactions with Video Diffusion Models
arXiv:2510.02284v3 Announce Type: replace-cross Abstract: Recent video generation models have achieved remarkable progress and are now deployed in film, social media production, and advertising. Beyond their creative potential, such models also hold promise as world simulators for robotics and embodied decision making. Despite strong advances, current approaches still struggle to generate physically plausible object interactions […]
Natural Gradient Descent for Online Continual Learning
arXiv:2603.20898v1 Announce Type: cross Abstract: Online Continual Learning (OCL) for image classification represents a challenging subset of Continual Learning, focusing on classifying images from a stream without assuming data independence and identical distribution (i.i.d). The primary challenge in this context is to prevent catastrophic forgetting, where the model’s performance on previous tasks deteriorates as it […]
Entropy and Information is Transferred from Peripherical Sites to the Catalytic Sites of Enzymes
arXiv:2603.20469v1 Announce Type: new Abstract: This research reports the entropy and information transfer throughout seven different enzymatic systems, namely, TIM-Barrel, Human Lysozyme, Ribonuclease A1, Pepsin , b-lactamase, Human Glucokinase and Carbonic anhydrase II. A general trend is detected: entropy and information is transported form the peripherical regions towards the catalytic site of the analyzed enzymatic […]
Beyond Expression Similarity: Contrastive Learning Recovers Functional Gene Associations from Protein Interaction Structure
arXiv:2603.20955v1 Announce Type: cross Abstract: The Predictive Associative Memory (PAM) framework posits that useful relationships often connect items that co-occur in shared contexts rather than items that appear similar in embedding space. A contrastive MLP trained on co-occurrence annotations–Contrastive Association Learning (CAL)–has improved multi-hop passage retrieval and discovered narrative function at corpus scale in text. […]