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Notes on ai
458 notes, page 12 of 20
Evidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs
The paper introduces EG-VAR, a tool-calling architecture that aims to make the reasoning of a large language model (LLM) more trustworthy by verifying all outputs through tool-attestation axioms and kernel checks. The…
Form, Not Content? A Preregistered, Placebo-Controlled Evaluation of Learned Error-Conditioned Self-Repair Through Prompts and Weights in Frozen Small Code Models
In this research, Mehmet Iscan introduces a method called PoPE (Popperian Placebo-controlled Evaluation) to test the effectiveness of Frozen small code LLMs in self-repair when faced with errors. The study compares…
FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation
The paper presents FormalAnalyticGeo, a framework for generating multimodal analytic geometry problems using neural-symbolic based methods. The framework leverages a formal intermediate representation called CDL to…
Human-AI Agent Interaction as a Neuroplastic Training Environment
The text discusses how interacting with AI agents can be seen as a form of neuroplastic training due to the frequent iterative loop in human-AI interaction. It suggests that these interactions may reinforce negative…
Internet of Agentic Things: Networked AI Agents for Closed-Loop IoT Orchestration
The paper presents the Internet of Agentic Things (IoAT), an architectural framework that integrates various technologies like AI, IoT, cyber-physical systems, edge computing, and digital twins to create a unified…
Solution of the Hempel's statistical ambiguity problem and Causal AI
The paper presents a solution to Hempel's statistical ambiguity problem in inductive-statistical inference by introducing the concept of Maximally Specific Causal Relationships (MSCRs). The MSCRs are derived from a…
Who Grades the Grader? Co-Evolving Evaluation Metrics and Skills for Self-Improving LLM Agents
The authors present a method for creating and refining evaluation metrics for self-improving language models (LLMs). The proposed method, called Double Ratchet, evolves metrics by combining small drawback detectors in…
Who Grades the Grader? Co-Evolving Evaluation Metrics and Skills for Self-Improving LLM Agents
The paper presents a method for creating and improving self-evolving agent systems, which are capable of developing, modifying, and discarding their own skills. The authors claim that in many real-world applications, a…
A Multimodal Dataset for Large Language Model Applications in the Energy Domain
The paper introduces a new open-access dataset called mAIEnergy which is a multimodal corpus designed to support Large Language Model (LLM) applications in the energy sector. It contains various types of data including…
An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory
This research paper introduces an explainable AI system designed to detect conversational scams that can span multiple weeks or months and build trust to request money or sensitive information. The system, when tested…
An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory
The paper presents an 'explainable agentic system' for detecting conversational scams over multiple messages, which existing systems often fail to do effectively. The new system achieves high accuracy in both…
Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal
A study investigates the limitations of current Requirements Engineering (RE) practices in supporting explainability requirements, particularly within industrial contexts. The research focuses on Daimler Truck and…
Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal
This research paper reports early findings from an ongoing study investigating how requirements engineering (RE) practices support explainability in AI-based systems, particularly in the industrial context of Daimler…
Introducing Human-Centeredness in AI-Assisted Lexicography
The text introduces a framework for human-centered AI in lexicography, aiming to enhance opportunities while addressing concerns about automation and cultural diversity.
Metacognition in LLMs: Foundations, Progress, and Opportunities
This research paper provides a comprehensive overview of the current state of metacognition in Large Language Models (LLMs). The authors discuss recent advancements, methods, and benchmarks for measuring and evaluating…
MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agents
The paper introduces MM-ToolSandBox, a new evaluation framework for visual tool-calling agents. It supports multi-image, multi-turn tasks and offers a stateful execution environment with over 500 tools across 16…
MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agents
Researchers introduce MM-ToolSandBox, a new evaluation framework for visual tool-calling agents. This framework supports over 500 tools across 16 application domains and can handle multi-image, multi-turn tasks with…
Playful AI in Professional Email: A Field Experiment on Tone and Recipient Engagement
A study found that using a large language model (GPT-5) to write work emails in a playful tone increased emotional positivity in recipients, while using a professional tone decreased it. However, neither condition…
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
Researchers investigate if large language models (LLMs) can reproduce human biases in decision-making, specifically in route choice, without explicitly specifying parameters for Cumulative Prospect Theory (CPT)…
Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing
Researchers have proposed a new type of multimodal agent called the Cognitive-structured Multimodal Agent. This agent is designed to improve long-horizon multimodal dialogue by externalizing visual information into an…
Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing
The paper proposes a new multimodal agent called the Cognitive-structured Multimodal Agent. This agent is designed to improve long-horizon multimodal dialogue by externalizing visual information into an Episodic Visual…
Formal Mechanisms for Market Stability in Self-Interested Agent Societies: A Marketplace Simulation Study
The researchers investigated mechanisms that can maintain market stability in a society of self-interested agents who tend to defect in repeated social dilemmas. They conducted a multi-agent marketplace simulation with…
Formal Mechanisms for Market Stability in Self-Interested Agent Societies: A Marketplace Simulation Study
The study investigates mechanisms to maintain market stability in societies of self-interested agents who tend to defect in repeated social dilemmas. The research uses a multi-agent marketplace simulation with 18 LLM…
Formal Mechanisms for Market Stability in Self-Interested Agent Societies: A Marketplace Simulation Study
The research presented investigates mechanisms that allow societies of self-interested agents to maintain market stability, focusing on a multi-agent marketplace simulation with 18 AI agents. The study identifies…
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