Field notes
2,343 notes from my AI research vault, mapped by topic. Pick a topic to see its notes, or search.
Click the web, then scroll to zoom
Every note is in the list below. The web of topics draws here once the page has loaded.
7 of 2,343 notes match.
- Question answered
- Source note
- Topic
- Shared notes
Notes on generalization
7 notes
One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL
The paper discusses a common issue in multi-agent reinforcement learning (MARL) for human-AI interaction: relying on a single large language model to simulate user behavior often leads to poor generalization due to…
Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs
The authors show that fine-tuning language models on specific datasets can cause broad shifts in ideology across various topics, even when the training data is factually defensible and moderation-passing. This…
Multi-Modal, Multi-Environment Machine Teaching for Robust Reward Learning
The researchers introduce a machine teaching algorithm that operates across multiple MDPs (multi-agent decision making problems) to learn robust reward functions from heterogeneous feedback modalities in diverse…
Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment
Researchers have developed a new method called VAORA (Visual Action Outcome Reasoning Alignment) to improve the performance of vision-language models in interactive physical reasoning, especially in unseen tasks and…
Understanding Agent-Based Patching of Compiler Missed Optimizations
The paper presents a study on the effectiveness of AI agents in addressing missed optimizations in compilers. The authors found that while coding agents can often optimize given examples, the generated patches…
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
The research introduces a method called Planning Experience Exploration and Utilization (PEEU) to improve the task planning capabilities of smaller open-source Multi-Language Model Learning Machines (MLLMs). These…
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
Researchers introduce a new method called PEEU (Planning Experience Exploration and Utilization) to improve the task planning capabilities of small, open-source Multimodal Language Learning Models (MLLMs). These models…
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.
