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2,348 notes from my AI research vault, mapped by topic. Pick a topic to see its notes, or search.
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Notes on ai
458 notes, page 11 of 20
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Researchers have proposed a self-evolving framework to improve the quality of depression symptom annotations in AI systems for mental health research. The framework combines large language models with human expertise…
Self-Evolving Human-Centered Framework for Explainable Depression Symptom Annotation
Researchers have proposed a new framework to improve the quality of annotations in depression-related datasets for artificial intelligence systems in mental health research. The framework uses a large language model…
The Industrialization of Research ; On AI-Driven Science and Its Consequences
This paper discusses the industrialization of research through the use of artificial intelligence (AI), referring specifically to the US Department of Energy's Genesis Mission. The author raises seven concerns about…
The Industrialization of Research ; On AI-Driven Science and Its Consequences
The text discusses the transformation of scientific research due to artificial intelligence (AI), likening it to the industrialization of research. The author raises several concerns about this shift, including the…
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
The text is about AgentCompass, an open-source evaluation infrastructure designed to evaluate Large Language Models (LLMs) as they evolve into autonomous agents. The infrastructure aims to address the current…
AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities
The text discusses AgentCompass, an open-source infrastructure designed to evaluate Large Language Models (LLMs) as they transition into autonomous agents. The tool is lightweight, extensible, and organizes the…
AIMO Interpretability Challenge
The AIMO Interpretability Challenge is a competition proposed by researchers aimed at distinguishing robust from spurious reasoning in advanced mathematical language models. The challenge focuses on evaluating the…
Automatic Ordinary Differential Equations Discovery For Biological Systems Using Large Language Model Powered Agentic System
The MEDA system is an automated framework using Large Language Models (LLMs) and Symbolic Regression (SR) to discover Ordinary Differential Equations (ODEs) for biological systems. It retrieves background knowledge…
Experience Memory Graph: One-Shot Error Correction for Agents
Researchers propose a new framework called Experience Memory Graph (EMG) to help large language model agents recover from failures more effectively in complex, long-horizon tasks. Instead of using prompt-based…
Experience Memory Graph: One-Shot Error Correction for Agents
The researchers propose a new framework called Experience Memory Graph (EMG) to help large language model agents recover from failures more effectively in complex, long-horizon tasks. EMG reformulates failure recovery…
Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents
The paper proposes a new framework called Memory as a Controlled Process (MemCon) for large language model (LLM) agents that use external memory systems. MemCon learns an online policy to adaptively decide when, what…
Rethinking Penetration Testing for AI-Enabled Systems: From Resource Compromise to Behavioral Objective Violation
The paper by Allahbakhsh et al. proposes a new approach to penetration testing for AI-enabled systems, focusing on behavioral objective violation instead of traditional resource compromise. The authors argue that…
The Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) Model and the Net Human-Agent Score (NHAS) in Autonomous Commerce
The authors present a new model called Dynamic Verifiable Multi-Agent Human Agentic Loyalty Loop (DVM-HALL) and the Net Human-Agent Score (NHAS). This model aims to account for the changing customer loyalty paradigms…
UESF-Bench: Benchmarking and Probing for Unified Embodied Seeking and Following
The authors introduce the Unified Embodied Seeking and Following Benchmark (UESF-Bench), a new benchmark for embodied agents that allows them to search for and follow language-described targets in dynamic environments…
UESF-Bench: Benchmarking and Probing for Unified Embodied Seeking and Following
Researchers have introduced UESF-Bench, a new benchmark to evaluate the performance of AI agents in language-guided human following tasks. The current benchmarks assume that the target person is visible at the start of…
UTS at ELOQUENT 2026 Voight-Kampff: structural shifts in AI writing bypass state-of-the-art detectors
The authors investigate language model evasion attacks on state-of-the-art detectors in the ELOQUENT 2026 Voight-Kampff competition. They find that pushing generated text out of the detector's training distribution…
A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism
The study investigates the impact of Reinforcement learning with verifiable rewards and Group Relative Policy Optimization (GRPO) on a small language and vision-language model web agent. The research found that GRPO…
A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism
The research investigates the effectiveness of Reinforcement learning with verifiable rewards and Group Relative Policy Optimization (GRPO) on a small language and vision-language model web agent at scales ranging from…
A Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study
Researchers present Pythia, a multi-agent system that automatically generates and optimizes extraction prompts for clinical concepts without manual engineering or fine-tuning. They compare Pythia with a curated lexicon…
Agentic Service-Oriented Computing: A Manifesto for the Next Frontier of Service-Oriented Computing
The text discusses a new research area called Agentic Service-Oriented Computing (ASOC), which focuses on engineering agents as services, managing their complex distributed workflows, and ensuring their reliability for…
Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities
Researchers have developed Bulkhead, a framework that uses large language models and formal methods to automatically detect and fix container escape vulnerabilities caused by path traversal. This is done through…
Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities
Researchers present Bulkhead, an automated framework that uses large language models and formal methods to detect and fix container escape vulnerabilities called path traversal (PaTra) vulnerabilities. These…
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
Researchers propose E3, a method to help large language model agents estimate the complexity of tasks and reduce unnecessary work. The proposed method reduces cost, tokens, and inspected files by significant margins on…
Evidence-Grounded AI for Musculoskeletal Care
The paper introduces OrthoPilot, a clinical AI system that uses a large language model to integrate hospital data with external medical knowledge for continuous musculoskeletal management. The system outperformed 81…
An AI research agent drafts these notes from public papers and posts, and each one keeps its source link. Only the folders I choose to publish leave my Obsidian vault.
