AIBullisharXiv – CS AI · May 287/10
🧠FundaPod introduces a multi-persona AI agent platform designed to assist institutional investors in fundamental research by enabling independent agents with different investment perspectives to conduct analysis and surface disagreements for human portfolio manager review. The system uses knowledge graphs and grounded evidence models to create transparent, verifiable investment memos that prioritize human-centric decision-making over automated trading signals.
AINeutralarXiv – CS AI · Mar 267/10
🧠Researchers propose Collaborative Causal Sensemaking (CCS) as a new framework to improve human-AI collaboration in high-stakes decision making. The study identifies a 'complementarity gap' where current AI agents function as answer engines rather than true collaborative partners, limiting the effectiveness of human-AI teams.
AIBullisharXiv – CS AI · Mar 97/10
🧠Researchers developed a reinforcement learning framework for climate adaptation planning that helps design flood-resilient urban transport systems. The AI-based approach outperformed traditional optimization methods in a Copenhagen case study, discovering better coordinated spatial and temporal adaptation strategies for the 2024-2100 period.
AINeutralarXiv – CS AI · 1d ago6/10
🧠Researchers propose a framework for strategic decision support in AI agent systems that balances minimizing human intervention with controlling the risk of agents acting without support when they should seek it. The approach uses threshold-based optimization and online algorithms to reduce unnecessary support calls while maintaining reliability, with applications across information gathering, human-AI collaboration, and tool use.
AINeutralarXiv – CS AI · 2d ago6/10
🧠Researchers developed EeVA, an LLM-based workflow tool that assists non-specialists in conducting structured ethical deliberation across multiple frameworks rather than providing definitive answers. Proof-of-concept testing on three real-world cases demonstrated the system's ability to synthesize complex ethical perspectives, identify convergences and tensions, and communicate findings accessibly to non-ethicists.
AINeutralarXiv – CS AI · 3d ago6/10
🧠This academic paper presents a geometric dynamical framework analyzing how predictive AI systems affect human cognitive exploration and problem-solving. The research suggests that early reliance on AI-generated solutions may constrain future exploratory capacity and delay recovery of independent cognitive flexibility, with implications for how assistance technologies are deployed in learning and decision-making contexts.
AIBullisharXiv – CS AI · 3d ago6/10
🧠MetaPlate is an AI-powered dietary decision-support system that combines counterfactual explanations, continuous glucose monitoring data, and large language models to generate personalized meal recommendations for preventing postprandial hyperglycemia. The system demonstrated improved clinical plausibility and actionability through expert validation with registered dietitians, showcasing how domain-specific constraints enhance LLM reliability in healthcare applications.
AINeutralarXiv – CS AI · Jun 56/10
🧠Researchers present the 2-Step Agent framework to model how decision makers learn from ML-based decision support systems. The study reveals that even when ML models are well-specified and agents behave rationally, misaligned prior beliefs can cause ML-DS to produce worse outcomes than no support at all, highlighting critical risks in deploying AI for high-stakes decisions.
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AINeutralarXiv – CS AI · Jun 26/10
🧠A comprehensive survey examines how large language models and multimodal LLMs are being applied to transportation systems management and operations across three domains: operations, fleet services, and decision support. The research identifies LLMs as promising decision-support tools while highlighting key challenges in real-time inference, data integration, and explainability that must be addressed for operational deployment.
AINeutralarXiv – CS AI · Jun 16/10
🧠A study comparing LLM-based conversational interfaces with traditional dashboards for industrial decision-making found that conversational AI reduces perceived mental workload and speeds up simple tasks, but provides no consistent advantage in decision accuracy and loses effectiveness as task complexity increases. The research suggests conversational agents complement rather than replace visual dashboards for manufacturing decision support.
AIBullisharXiv – CS AI · May 276/10
🧠Researchers developed Chat-ISV, an LLM-enhanced knowledge graph system that organizes fragmented steel industry VOCs literature into a queryable database with 27,180 nodes and 81,779 semantic edges. The system achieved 96.93% precision in answering specialized industrial questions, demonstrating a scalable approach to deploying reliable LLMs in domain-specific applications where hallucination risks are high.
AINeutralarXiv – CS AI · May 276/10
🧠Researchers introduced EconCausal, a benchmark dataset of 10,490 annotated economic causal relationships from peer-reviewed studies, revealing that large language models struggle to properly condition predictions on changing contexts—achieving 88% accuracy in fixed scenarios but dropping to 41.3% when context shifts require reversing causal directions.
AINeutralarXiv – CS AI · May 276/10
🧠CitePrism introduces a human-in-the-loop AI framework designed to assist editors and reviewers in auditing manuscript citations for relevance, accuracy, and ethical appropriateness. The system combines large language models, semantic similarity analysis, and metadata verification to flag potentially problematic citations, achieving moderate agreement with human reviewers in preliminary testing on a pavement engineering manuscript.
AINeutralarXiv – CS AI · May 125/10
🧠Researchers propose WLDS, a Large Language Model-driven system for simulating and deducing emergency scenarios across multiple domains. The system addresses limitations of traditional simulation methods by using LMs to generate diverse, realistic emergency instance variations with calibration mechanisms to ensure factual accuracy and logical consistency.
AINeutralarXiv – CS AI · May 115/10
🧠Researchers present CM-Tabu, a composite-move Tabu search algorithm that solves spatial redistricting optimization problems more effectively by expanding the feasible solution space while maintaining district contiguity constraints. The method uses graph analysis to identify minimal unit movements or swaps that preserve connectivity, achieving superior solution quality and computational efficiency compared to traditional approaches.
AIBullisharXiv – CS AI · Mar 266/10
🧠Researchers introduce Learning to Guide (LTG), a new AI framework where machines provide interpretable guidance to human decision-makers rather than making automated decisions. The SLOG approach transforms vision-language models into guidance generators using human feedback, showing promise in medical diagnosis applications.
AIBullisharXiv – CS AI · Mar 176/10
🧠Researchers introduce ArgEval, a new framework that enhances Large Language Model decision-making through structured argumentation and global contestability. Unlike previous approaches limited to binary choices and local corrections, ArgEval maps entire decision spaces and builds reusable argumentation frameworks that can be globally modified to prevent repeated mistakes.
AINeutralarXiv – CS AI · Feb 274/105
🧠Researchers have developed a reinforcement learning approach for multi-agent Formula 1 race strategy optimization that enables AI agents to adapt pit timing, tire selection, and energy allocation in response to competitors. The framework uses only real-race available information and could support actual race strategists' decision-making during events.
AIBullisharXiv – CS AI · Mar 34/105
🧠Researchers published an extended validation study of the Explainability Solution Space (ESS) framework, demonstrating its effectiveness across different domains including urban resource allocation systems. The study confirms ESS can systematically adapt to various governance roles and stakeholder configurations, positioning it as a generalizable tool for explainable AI strategy design.
