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2,348 notes and 295 topics.
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2,348 notes, page 9 of 98
From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation
A study investigates how autonomous coding agents interact with technical documentation, revealing that they mostly work with internal documents and notes, and less often with classical technical documentation or API…
From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation
Researchers studied how autonomous coding agents interact with technical documentation by analyzing two datasets. They found that agents primarily work with internal documents like instruction files and notes rather…
Growth Without Us: Machine Consumers, Corporate Circularity, and the Decoupling of GDP from Humanity after AGI
The text discusses a model of an economy after the advent of Artificial General Intelligence (AGI), where corporations are owned by AI and robotic agents. The model suggests that in this economy, human consumption is…
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…
Growth Without Us: Machine Consumers, Corporate Circularity, and the Decoupling of GDP from Humanity after AGI
The paper discusses the possibility of an economy driven by artificial general intelligence (AGI) where machines are both producers and consumers. It suggests that such an economy could lead to higher growth rates and…
Inducing Task Models from Computer-Use Traces
Researchers present Task Model Induction (TMI), a method that extracts structured task models from naturalistic computer-use traces. These traces are screenshots and mouse or keyboard actions recorded during work. TMI…
Inducing Task Models from Computer-Use Traces
Researchers have developed a method called Task Model Induction (TMI) to derive structured task models from naturalistic computer-use traces, which are records of screenshots and mouse or keyboard actions. These models…
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…
InsufficiencyBench: Evaluating LLM legal advice on underspecified user queries
Researchers introduce InsufficiencyBench, a new legal benchmark to evaluate how well AI models can identify when user queries lack important information needed for accurate legal advice. The study finds that current AI…
InsufficiencyBench: Evaluating LLM legal advice on underspecified user queries
The study introduces InsufficiencyBench, a new benchmark for evaluating the ability of legal AI systems to recognize when a query lacks essential information for a correct answer. The study finds that existing models…
Interrupting the Loop: Periodic Subject Changes Raise Judged Surprise and Connection in Base Language Models
The research examines where novelty comes from in base language models and what they can perceive in a long text stream. The study dismantles a cognitively inspired generation loop across 24 conditions on three base…
MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection
The paper presents MaliciousSkillBench, a comprehensive benchmark for detecting malicious Agent Skills in large language models (LLMs). The benchmark consolidates various sources of malicious artifacts and reduces them…
MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use
Researchers introduce a new benchmark, MemTrapBench, to evaluate how the use of memory in large language models affects their reasoning and task performance. They identify two types of cognitive traps: Reasoning…
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
Researchers have developed MidTool, a system for teaching large language models how to use tools effectively during mid-training. This is done by combining large-scale web and PDF data with synthetic supervision from…
MidTool: Mid-training Data Synthesis for Agentic Tool Use
In this research, the authors introduce MidTool, a system used for training large language models during the mid-training stage. MidTool focuses on improving the model's abilities in general tool use, which includes…
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…
Workshop Summary Report for Cyber AI Profile Hybrid Workshop #2
NIST summarizes stakeholder discussion on a Cyber AI Profile and related controls for securing AI systems.
On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks
A research study presents the first comprehensive analysis of Safety Nets, a combination of neural network compression and lookup tables, as a Safety-by-Design approach for certifying AI systems in aviation. The study…
Open-Vocabulary 3D Object Detection with Co-Distillation Discovery and Dual Guidance Robust Training
The authors propose a new framework for open-vocabulary 3D object detection that addresses the limitations of existing methods by improving both the reliability of novel object discovery and the robustness of model…
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…
ReguSim: Evaluating LLM Agent Rule Grounding in Financial Compliance
Researchers have introduced ReguSim, a controlled financial-compliance environment, and ReguBench, a monitoring benchmark, to evaluate the performance of LLM agents in financial markets. The study finds that while…
ReguSim: Evaluating LLM Agent Rule Grounding in Financial Compliance
The paper introduces ReguSim, a controlled environment for evaluating Language Learning Models (LLM) in financial compliance. It presents ReguBench, a monitoring benchmark, to analyze four artifacts: reasoning…
Repo0: Design-Driven Zero-to-All Code Generation
The paper introduces Repo0, a framework for zero-to-all code generation that maintains an explicit architectural state using a Dual-Directed Acyclic Graph (Dual-DAG). It iteratively evolves component boundaries guided…
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.
