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Notes on artificial intelligence
262 notes, page 4 of 11
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution
SkillRise is a new reinforcement learning framework that helps large language model agents solve related yet distinct tasks more efficiently. It organizes tasks into sequences and uses a single policy to alternate…
UrbanDS: A Graph-Guided LLM Multi-Agent System for Data-Intensive Urban Tasks
The paper presents UrbanDS, a multi-agent system that uses a graph to organize and analyze large-scale and heterogeneous urban data. The system includes a Data Profiling Agent, a Relation Agent, a Planner Agent…
What Does It Take to Detect an AI Agent? Minimal Feature Sets for Behavioral Detection under Browser Automation
Researchers present a new AI detection framework that can distinguish between humans, bots, and AI agents. The study finds that existing binary classifiers struggle to identify AI agents as they browse the web through…
Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents
Researchers propose APPA, an IFC framework to address usability issues in LLM agents handling mixed-confidentiality data. The proposed solution involves engine-managed context branching and prospective acquisition…
DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data
The paper proposes a framework called DataOrchestra that learns to curate pre-training data example-by-example rather than applying a uniform strategy across many examples. The framework decides whether to drop, clean…
Evaluating RAG for French immigration law: a benchmark and baseline study
The paper presents a new benchmark for evaluating AI models used in French immigration law, focusing on permit-type recommendation, required document retrieval, and legal citation coverage. The study compares a…
Evaluating RAG for French immigration law: a benchmark and baseline study
The authors present a new benchmark for evaluating AI models in the context of French immigration law. They compare a parameterized language model baseline with dense retrieval augmentation at two scales (Qwen3.5-9B…
The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation
The paper focuses on improving the multi-turn long-horizon planning for foundation model agents. The authors introduce a controlled environment to study the acquisition, shaping, and integration of planning abilities…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Researchers propose a new method called Agent-Guided Concept Discovery to help deep learning models assess surgical margins more effectively, particularly in noisy and unlabeled data acquired during surgery. The…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Researchers propose Agent-Guided Concept Discovery, a framework that helps deep learning models interpret REIMS data for surgical margin assessment by learning meaningful concepts directly from data without predefined…
Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks
Researchers introduced an open-source framework to evaluate the effectiveness of AI agents powered by open-weight large language models (LLMs) in data preparation tasks for longitudinal population studies. The study…
Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
The paper discusses Agentic Context Management (ACM), a new approach to managing the context in which AI agents operate. It argues that current methods for managing an agent's context, such as storing and retrieving…
AI Assistants Overassist
Researchers introduce Int-Bench, a tool to evaluate how AI language models intervene during problem-solving tasks across code debugging, mathematics, and brain teasers. The study finds that AI models provide more…
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
The paper introduces Euclid-MCP, an open-source MCP server designed to provide deterministic logical reasoning using SWI-Prolog. It presents a new engine-agnostic intermediate representation for Horn-clause logic…
From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs
The authors present a methodology using Petri nets to generate tests for concurrent stateful Rust APIs, aiming to bridge the gap between formal scenario design and low-cost test concretization.
From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs
The authors propose a methodology for generating executable tests for concurrent stateful Rust APIs using Petri nets. This approach aims to bridge the gap between formal scenario design and low-cost test…
GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG
The authors introduce GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating improvements across multiple agents in the pipeline, rather than optimizing components in isolation…
GraphVid: Interactive Graph-Controllable Video Generation
The researchers introduce GraphVid, a model that generates interactive videos using structured interaction graphs, allowing for flexible and precise control. The model shows strong performance compared to Motion-I2V in…
ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders
The paper introduces a new benchmark called ICAE-Bench for evaluating coding agents in interactive project building settings. This benchmark aims to test the ability of agents to transform incomplete product intent…
OpenForgeRL: Train Harness-native Agents in Any Environment
OpenForgeRL is an open-source framework for training AI agents using various inference harnesses like Claude Code, Codex, OpenClaw, etc., in diverse environments. The framework allows end-to-end training with standard…
Regulating autonomous and agentic AI
This research paper discusses the difficulties in regulating activities that use autonomous and agentic AI due to changes in traditional regulatory assumptions about knowledge and control. The authors analyze four…
Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation
A study tests two scenarios: direct exposure of a dangerous objective to an AI model and the same objective being transformed and relayed by other agents. The results show that the AI model's advice is opposed to the…
The Boundaries of Automation: A Theory of Persistent Human Participation
This research paper questions the assumption that as AI becomes more capable, human participation in tasks will decrease. Instead, it argues that there are three reasons why human participation may persist even with…
Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry
The paper discusses a reliability challenge in AI agents created by non-engineering users within organizations, due to multiple dependencies that can cause silent degradation after deployment. The authors propose a…
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