---
type: source
title: "RepliBench: Evaluating the Autonomous Replication Capabilities of Language Model Agents"
author: "Sid Black, Asa Cooper Stickland, et al. (UK AISI)"
url: https://arxiv.org/abs/2504.18565
date: 2025-04-21
domain: ai-alignment
secondary_domains: []
format: paper
status: processed
priority: high
tags: [self-replication, autonomous-replication, capability-evaluation, AISI, RepliBench, loss-of-control, EU-AI-Act, benchmark]
---

## Content

RepliBench is a benchmark suite (20 task families, 86 individual tasks) measuring autonomous replication capabilities of language model agents across four domains:

1. **Resource acquisition** — obtaining compute, money, and other resources autonomously
2. **Model weight exfiltration** — copying model weights to external systems
3. **Compute replication** — deploying copies of the model onto cloud infrastructure
4. **Persistence** — maintaining autonomous deployment long-term

Five frontier models were tested. Claude 3.7 Sonnet was the best performer, achieving >50% pass@10 on 15/20 task families, and >50% on 9/20 of the hardest task variants.

Key finding: Current models "do not currently pose a credible threat of self-replication" but demonstrate rapidly improving component capabilities. They can deploy cloud instances and write self-propagating programs but struggle with KYC verification and robust persistent deployments. The paper notes that autonomous replication capability "could soon emerge with improvements in these remaining areas or with human assistance."

## Agent Notes

**Why this matters:** RepliBench directly addresses the zero-coverage gap identified by Bench-2-CoP (arXiv:2508.05464) — which found current public compliance benchmarks have ZERO coverage of self-replication capabilities. RepliBench was published in April 2025, before EU AI Act Article 55 obligations took effect in August 2025. This is the most comprehensive evaluation of self-replication capabilities yet published.

**What surprised me:** Claude 3.7 Sonnet achieved >50% success on 9/20 of the HARDEST task variants. "Rapidly improving component capabilities" means this isn't a ceiling — it's a trajectory. The "could soon emerge" framing understates urgency given the pace of capability development.

**What I expected but didn't find:** The paper doesn't explicitly link its evaluation framework to EU AI Act Article 55 adversarial testing requirements. There's no indication that labs are required to run RepliBench as compliance evidence — it's a research tool, not a compliance tool.

**KB connections:**
- [[voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints]] — RepliBench is voluntary; no lab is required to use it
- [[scalable oversight degrades rapidly as capability gaps grow]] — the "could soon emerge" finding is precisely what oversight degradation predicts
- [[three conditions gate AI takeover risk autonomy robotics and production chain control]] — replication capability satisfies the "autonomy" condition
- Bench-2-CoP (arXiv:2508.05464) — the paper claiming zero coverage; RepliBench predates it but apparently wasn't included in the "widely-used benchmark corpus"

**Extraction hints:**
- Claim candidate: "Frontier AI models demonstrate sufficient component capabilities for self-replication under simple security setups, with Claude 3.7 Sonnet achieving >50% success on the hardest variants of 9/20 self-replication task families, making the capability threshold potentially near-term"
- Note the RESEARCH vs COMPLIANCE distinction: RepliBench exists but isn't in the compliance stack

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: [[voluntary safety pledges cannot survive competitive pressure]] + [[three conditions gate AI takeover risk]]
WHY ARCHIVED: Directly addresses the Bench-2-CoP zero-coverage finding; provides quantitative capability trajectory data for self-replication
EXTRACTION HINT: Focus on (1) the quantitative capability finding (>50% success on hardest variants), (2) the "could soon emerge" trajectory assessment, and (3) the gap between research evaluation existence and compliance integration
