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Notes on ai agents
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Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models
The research focuses on object-centric world models (OCWMs) for artificial intelligence agents. The study compares OCWMs with traditional scene-centric models in terms of representation quality, planning success, and…
Ready Cohorts: Bounding GPU Opportunity and Avoiding Host Round Trips in LLM-Agent Control
The paper discusses a method for determining when it's beneficial to execute small deterministic transitions between model and tool calls on GPUs, and presents a dynamic program that computes the optimal amount of work…
SkillLens: Visual Skill Cards for Retrieval-Augmented GUI Action Prediction and On-Policy Distillation
The paper introduces Visual Skill Cards (VSCs), a memory representation that helps computer agents make decisions by binding reusable procedures with applicability cues, visual evidence, and verification signals. The…
DSLE: A Learning Environment for Dark Souls Boss Encounters
Researchers have created a learning environment for AI agents called DSLE, which is based on the game Dark Souls: Remastered. The environment includes 22 boss encounters that can be used as benchmarks. The researchers…
EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents
The paper introduces EMBL AI Librarian, a tool that allows AI agents to access and process life sciences literature more effectively by asking natural language questions and receiving relevant evidence in response. The…
OpenForgeRL: Train Harness-native Agents in Any Environment
The paper introduces OpenForgeRL, an open-source framework for training AI agents using harnesses like Claude Code, Codex, and OpenClaw in various environments. It allows end-to-end training of these complex harnesses…
Self-State Attacks on Self-Hosted AI Agents: How Far Can OS Defenses Go?
The paper discusses a new type of threat called 'self-state attacks' targeting self-hosted AI agents by corrupting their own state through legitimate OS system call invocation. The authors investigate the resilience of…
Internet of Agentic Things: Networked AI Agents for Closed-Loop IoT Orchestration
The paper presents the Internet of Agentic Things (IoAT), a framework that combines AI agents, IoT, cyber-physical systems, edge computing, digital twins, and more to create a unified system for closed-loop IoT…
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