118cb06160
Some checks failed
Mirror PR to Forgejo / mirror (pull_request) Has been cancelled
Threshold: 0.7, Haiku classification, 20 files modified. Pentagon-Agent: Epimetheus <0144398e-4ed3-4fe2-95a3-3d72e1abf887>
30 lines
No EOL
1.6 KiB
Markdown
30 lines
No EOL
1.6 KiB
Markdown
---
|
|
type: entity
|
|
entity_type: research_program
|
|
name: SPAR Automating Circuit Interpretability with Agents
|
|
status: active
|
|
founded: 2025
|
|
parent_org: SPAR (Scalable Alignment Research)
|
|
domain: ai-alignment
|
|
supports:
|
|
- Circuit tracing requires hours of human effort per prompt which creates a fundamental bottleneck preventing interpretability from scaling to production safety applications
|
|
reweave_edges:
|
|
- Circuit tracing requires hours of human effort per prompt which creates a fundamental bottleneck preventing interpretability from scaling to production safety applications|supports|2026-04-08
|
|
---
|
|
|
|
# SPAR Automating Circuit Interpretability with Agents
|
|
|
|
Research program targeting the human analysis bottleneck in mechanistic interpretability by using AI agents to automate circuit interpretation work.
|
|
|
|
## Overview
|
|
|
|
SPAR's project directly addresses the documented bottleneck that 'it currently takes a few hours of human effort to understand the circuits even on prompts with only tens of words.' The program attempts to use AI agents to automate the human-intensive analysis work required to interpret traced circuits, potentially enabling interpretability to scale to production safety applications.
|
|
|
|
## Approach
|
|
|
|
Applies the role specialization pattern from human-AI mathematical collaboration to interpretability work, where AI agents handle the exploration and analysis while humans provide strategic direction and verification.
|
|
|
|
## Timeline
|
|
|
|
- **2025** — Program initiated to address circuit tracing scalability bottleneck
|
|
- **2026-01** — Identified by Mitra as the most direct attempted solution to the hours-per-prompt constraint |