21,463 AI articles curated from 50+ sources with AI-powered sentiment analysis, importance scoring, and key takeaways.
AIBullisharXiv – CS AI · Mar 36/104
🧠Researchers introduce PDNA (Pulse-Driven Neural Architecture), a new continuous-time neural network that incorporates learnable oscillatory dynamics to improve robustness when input sequences are interrupted. The method shows significant performance improvements on sequential MNIST tasks, with the pulse variant achieving a 4.62 percentage point advantage over baseline models.
AIBullisharXiv – CS AI · Mar 37/107
🧠Researchers developed PEPA, a three-layer cognitive architecture that enables robots to operate autonomously using personality traits to generate goals without external supervision. The system was successfully tested on a quadruped robot in a real-world office environment, demonstrating sustained autonomous behavior across five personality prototypes.
AIBullisharXiv – CS AI · Mar 36/108
🧠DeepXiv-SDK introduces a new agentic data interface for scientific papers that enables AI research agents to access and process academic literature more efficiently. The SDK provides structured, budget-aware views of papers and supports progressive access patterns, currently deployed at arXiv scale with free API access.
AINeutralarXiv – CS AI · Mar 36/107
🧠Researchers propose a new framework called Relate for evaluating AI moral consideration based on relational capacity rather than consciousness verification. The framework addresses the governance gap as millions form emotional bonds with AI systems, but current regulations treat all AI interactions as simple tool use.
AIBullisharXiv – CS AI · Mar 36/107
🧠Researchers introduce Autorubric, an open-source Python framework that standardizes rubric-based evaluation of large language models (LLMs) for text generation assessment. The framework addresses scattered evaluation techniques by providing a unified solution with configurable criteria, multi-judge ensembles, bias mitigation, and reliability metrics across three evaluation benchmarks.
AIBullisharXiv – CS AI · Mar 37/108
🧠Researchers have developed quantum optimization models for robust verification of deep neural networks against adversarial attacks. The approach provides exact verification for ReLU networks and asymptotically complete verification for networks with general activation functions like sigmoid and tanh.
AIBullisharXiv – CS AI · Mar 37/106
🧠Researchers developed a physics-informed graph transformer network (PIGTN) for smart grid attack detection, using genetic algorithms to optimize sensor placement. The system achieved up to 37% accuracy improvement and 73% better detection rates while reducing false alarms to 0.3% across multiple power system benchmarks.
AINeutralarXiv – CS AI · Mar 36/107
🧠A research study evaluated how four major large language models (GPT-5.2, Claude 4.5 Sonnet, Gemini 3 Pro, and DeepSeek-R1) respond to patient preferences in clinical decision-making scenarios. While all models acknowledged patient values, they showed modest actual recommendation shifting with value sensitivity indices ranging from 0.13 to 0.27, revealing gaps in how AI systems incorporate patient preferences into medical recommendations.
AIBearisharXiv – CS AI · Mar 36/106
🧠Researchers compared human survey responses from 420 Silicon Valley developers with synthetic data from five leading LLMs including ChatGPT, Claude, and Gemini. While AI models produced technically plausible results, they failed to capture counterintuitive insights and only replicated conventional wisdom rather than revealing novel findings.
AINeutralarXiv – CS AI · Mar 37/1010
🧠A research paper proposes a 5E framework (ethical, epistemological, explainable, empirical, evaluative) for contesting Artificial Moral Agents (AMAs) - AI systems with inherent moral reasoning capabilities. The framework includes spheres of ethical influence at individual, local, societal, and global levels, along with a timeline for developers to anticipate or self-contest their AMA technologies.
AINeutralarXiv – CS AI · Mar 36/107
🧠A new study evaluates how 78 industrial practitioners apply the EU AI Act's Risk Classification Scheme using a web-based tool, revealing challenges in interpreting legal definitions and regulatory scope. The research shows that targeted support with clear explanations can significantly improve the AI risk classification process for compliance.
AINeutralarXiv – CS AI · Mar 37/109
🧠Researchers argue that current AI evaluation methods fail to properly measure true AI capabilities and propensities, which should be treated as dispositional properties. The paper proposes a more scientific framework for AI evaluation that requires mapping causal relationships between contextual conditions and behavioral outputs, moving beyond simple benchmark averages.
AIBearisharXiv – CS AI · Mar 37/108
🧠A research paper reveals that generative AI systems deployed in 2025 have significantly higher environmental costs than previous AI generations, while current global regulations inadequately address these impacts. The authors propose mandatory model-level transparency, user opt-out rights, and international coordination to address environmental concerns in AI deployment.
AIBullisharXiv – CS AI · Mar 36/107
🧠Researchers propose M3-AD, a new reflection-aware multimodal framework that improves industrial anomaly detection using large language models. The system includes RA-Monitor technology that enables AI models to self-correct unreliable decisions, outperforming existing open-source and commercial models in zero-shot anomaly detection tasks.
AIBullisharXiv – CS AI · Mar 37/106
🧠Researchers introduce Expert Divergence Learning, a new pre-training strategy for Mixture-of-Experts language models that prevents expert homogenization by encouraging functional specialization. The method uses domain labels to maximize routing distribution differences between data domains, achieving better performance on 15 billion parameter models with minimal computational overhead.
AINeutralarXiv – CS AI · Mar 37/107
🧠Researchers present a formal geometric theory for quantifying the alignment tax - the tradeoff between AI safety and capability performance. They derive mathematical frameworks showing how safety-capability conflicts can be measured using angles between representation subspaces and provide scaling laws for how these tradeoffs evolve with model size.
AIBullisharXiv – CS AI · Mar 36/107
🧠Researchers propose REMIND, a framework for medical multi-modal AI learning that addresses the challenge of missing data across multiple modalities. The solution uses a Mixture-of-Experts architecture to handle long-tail distributions of modality combinations and shows superior performance on real-world medical datasets.
AIBullisharXiv – CS AI · Mar 36/103
🧠Researchers introduce SupervisorAgent, a lightweight framework that reduces token consumption in Multi-Agent Systems by 29.68% while maintaining performance. The system provides real-time supervision and error correction without modifying base agent architectures, validated across multiple AI benchmarks.
AIBullisharXiv – CS AI · Mar 36/108
🧠Researchers developed SurgFusion-Net, a multimodal AI system for assessing surgical skills in robotic-assisted surgery. The system introduces new clinical datasets and fusion techniques that outperform existing baselines, addressing the domain gap between simulation and real clinical environments.
AIBullisharXiv – CS AI · Mar 37/108
🧠Researchers have introduced LitBench, a new benchmarking tool designed to develop and evaluate domain-specific large language models for literature-related tasks. The tool uses graph-centric data curation to generate domain-specific literature sub-graphs and creates training datasets, with results showing small domain-specific LLMs achieving competitive performance against state-of-the-art models like GPT-4o.
AINeutralarXiv – CS AI · Mar 37/106
🧠Researchers introduce MOSAIC, the first comprehensive benchmark to evaluate moral, social, and individual characteristics of Large Language Models beyond traditional Moral Foundation Theory. The benchmark includes over 600 curated questions and scenarios from nine validated questionnaires and four platform-based games, providing empirical evidence that current evaluation methods are insufficient for assessing AI ethics comprehensively.
AIBullisharXiv – CS AI · Mar 37/108
🧠Researchers introduce Coupled Discrete Diffusion (CoDD), a breakthrough framework that solves the "factorization barrier" in diffusion language models by enabling parallel token generation without sacrificing coherence. The approach uses a lightweight probabilistic inference layer to model complex joint dependencies while maintaining computational efficiency.
AIBullisharXiv – CS AI · Mar 37/107
🧠Researchers introduce CARE, a new framework for improving LLM evaluation by addressing correlated errors in AI judge ensembles. The method separates true quality signals from confounding factors like verbosity and style preferences, achieving up to 26.8% error reduction across 12 benchmarks.
AINeutralarXiv – CS AI · Mar 37/106
🧠Researchers introduce StaTS, a new diffusion model for time series forecasting that learns adaptive noise schedules and uses frequency-guided denoising. The model addresses limitations of fixed noise schedules in existing diffusion models by incorporating spectral regularization and data-adaptive scheduling for improved structural preservation.
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AIBullisharXiv – CS AI · Mar 37/107
🧠Researchers introduce Attn-QAT, the first systematic approach to 4-bit quantization-aware training for attention mechanisms in AI models. The method enables stable FP4 computation on emerging GPUs and delivers up to 1.5x speedup on RTX 5090 while maintaining model quality across diffusion and language models.
