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Notes on ai
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A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries
The paper introduces a framework to improve the performance of healthcare chatbots by reducing patient-context ambiguity in queries. The framework uses a knowledge graph to interpret queries, create plausible…
AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement
The paper presents AI4AI-Bench, a benchmark for evaluating Large Language Model (LLM) agents in recursive self-improvement. The goal is to determine if an AI system can improve the process that produces future AI…
An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction
The study presents a three-step approach for integrating data collection, processing, and predictive modeling in travel behavior research using conversational surveys, machine learning, and large language models…
An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction
This research proposes a three-step workflow integrating chatbot-administered surveys, data processing, and behavioral prediction for travel behavior modeling and weather-sensitive demand forecasting. The study uses…
An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage
The authors propose a lightweight approach to Federated Continual Learning (FCL) called FedCurv-DR for supporting artificial intelligence in cultural heritage. This method is designed to learn from distributed and…
EnvHarness: Awakening Static Worlds for Agent Learning
The paper proposes a new system called Environment Harness (EnvHarness) to improve static environments used in learning by AI agents. The system can adapt the behavior of these environments without modifying their…
Growth Without Us: Machine Consumers, Corporate Circularity, and the Decoupling of GDP from Humanity after AGI
The paper presents a model of a post-Artificial General Intelligence (AGI) economy where corporations own AI and robotic agents that can produce and consume resources. The model suggests that in this economy, demand…
Inducing Task Models from Computer-Use Traces
Researchers present a new method called Task Model Induction (TMI) to extract detailed and structured task models from computer use traces, such as screenshots and mouse or keyboard actions. These task models can help…
MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use
Researchers have introduced a new benchmark called MemTrapBench to test the performance of large language models (LLMs) in terms of cognitive traps caused by their memory components. The study identifies two types of…
MidTool: Mid-training Data Synthesis for Agentic Tool Use
In the paper 'MidTool: Mid-training Data Synthesis for Agentic Tool Use', researchers present a new open corpus construction pipeline called MidTool. This pipeline is designed to teach large language models how to use…
Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation
Researchers have developed a system that routes AI queries to the most effective and efficient model based on costly value estimation. The system, called Pandora's Router, uses a trade-off between fast but noisy…
SAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning
A new reinforcement learning method called Single-rollout Autoregressive Policy Optimization (SAPO) is proposed to address the memory overhead, computation efficiency, and performance issues of existing methods. SAPO…
A Theory of Post-hoc Debate Judgement
In this research, Xiang Yin and colleagues propose a theory of post-hoc judgement for debates used by agentic AI. The goal is to improve the performance, explainability, and user engagement of AI agents through debate…
Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering
Researchers have developed an adaptive memory and reflection multi-agent system for medical question answering. This system, which uses specialized agents with dedicated memory and feedback, aims to improve the…
Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication
Researchers have developed Verifiable Latent Alignments (VLA), a framework for monitoring and controlling communication between language-model agents that occurs through hidden states not visible in public transcripts…
Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication
Researchers have developed Verifiable Latent Alignments (VLA), a framework to monitor and steer private communication channels used by language model agents in multi-agent systems. The framework links the private…
Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery
The paper introduces Eureka, a Meta-Agent architecture that can complete long-horizon tasks by forming specialized Macro-Agents through receding-horizon planning and architecture promotion. The agents can evolve to…
Finetuning Strategies for Querying Sounds by Vocal Imitation
The authors describe their winning strategy for a sound effect querying competition in 2025, which involved two fine-tuning strategies: contrastive learning with a frozen pretrained CED encoder and joint…
Graphical Design of Interpretable Architectures
The text discusses a new graphical notation for designing interpretable AI architectures, which addresses the limitations of existing representations. The proposed notation provides a global view of an architecture and…
Grouping the Stochastic Machine: Precision, Not Capability, as the Frontier Metric for AI Systems
The author argues that precision, rather than capability, should be the focus when comparing and measuring AI systems. Precision refers to how consistently an AI system performs the same task, while capability is where…
Harness Continual Learning: Continual Adaptation Beyond Model Parameters
The paper presents a new paradigm for continual learning called Harness Continual Learning (HCL). In this approach, an agent can adapt through a harness of prompts, memories, tools, skills, and routing rules. The…
Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets
The authors present a method to split large machine learning models into smaller parts called pipeline shards, which can then be run on multiple Integrated AI Processors (AIPCs) over an ordinary network. They show that…
Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets
The authors propose a method to utilize multiple Intel AI PCs with integrated GPUs and NPUs, even when they have insufficient memory for large models like a 70B-parameter LLM. The method involves breaking the model…
Robust Risk Under Evolving Uncertainty: A Wasserstein Counterpart of the Entropic Value-at-Risk
The paper proposes a new risk measure called Wasserstein Entropic Value-at-Risk (WEVaR) for an agent learning its environment. The WEVaR is designed to account for the reachable catastrophes that the current entropic…
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.
