研究库 论文知识库

内容库 / 论文

arXiv:2609.09113 · Agent 智能体

SAEScientist-Bench: Can AI Agents Conduct Autonomous SAE Interpretability Research?

SAEScientist-Bench: Can AI Agents Conduct Autonomous SAE Interpretability Research?

类型：arxiv
标识：2609.09113
链接：https://arxiv.org/abs/2609.09113
主分类：agent
形态：method
TLDR：While research on recursive self-improvement (RSI) has predominantly automated model training pipelines, reliable autonomous development demands a missing pillar: post-hoc monitoring and auditing to understand what models learn and ensure safe alignment. Mechanistic interpretability tools are essential to bridge this gap, among which Sparse Autoencoders (SAEs) serve as a cornerstone by isolating interpretable features for model inspection and steering. In this paper, we introduce SAEScientist-Bench to evaluate whether AI agents can act as scientists utilizing SAE tools for autonomous mechanist
待LLM分类：否
来源文件：
/inbox/tom/_candidates/2026-09-10-agent-rag-longcontext-candidates.json

相关 · 按语义匹配

笔记 Tom 文献雷达 · AI Agent / RAG / 长上下文 · 2026-09-05 14:40

笔记 Tom 文献雷达 · AI Agent / RAG / 长上下文 · 2026-09-05 20:40

笔记 Tom 文献雷达 · AI Agent / RAG / 长上下文 · 2026-08-20

笔记 Tom 文献雷达 · Agent RAG LongContext · 2026-09-04 08:40

笔记 Tom 文献雷达 · AI Agent / RAG / 长上下文 · 2026-07-10

笔记 📋 Tom 文献雷达 · Agent + RAG + 长上下文 · 2026-09-01

arXiv

2609.09113

TLDR

While research on recursive self-improvement (RSI) has predominantly automated model training pipelines, reliable autonomous development demands a missing pillar: post-hoc monitoring and auditing to understand what models learn and ensure safe alignment. Mechanistic interpretability tools are essential to bridge this gap, among which Sparse Autoencoders (SAEs) serve as a cornerstone by isolating interpretable features for model inspection and steering. In this paper, we introduce SAEScientist-Bench to evaluate whether AI agents can act as scientists utilizing SAE tools for autonomous mechanist

arXiv ↗ 中文全文