AGI Advance: Weekly AI & AGI Insights (May 6, 2025)

We’ve been evaluating how LLM-based agents can move from suggesting edits to owning decisions in the code review process. After analyzing thousands of real-world PRs across production repositories, we surfaced a few key insights:

• Precision is learnable; trust is harder: Our internal agent achieved 100% precision and good recall on small, self-contained PRs. But developer trust doesn’t come from metrics alone—it depends on explainability, consistency, and when the agent knows to defer.
• Context is the unlock: Incorporating repo schema, CI status, commit metadata, and prior reviewer comments dramatically improved model reasoning. Shallow diffs weren’t enough—agents needed structure to interpret intent.
• Evaluation isn’t one-size-fits-all: We benchmarked agents from Gemini, Claude, Copilot, Greptile, and internal models, revealing significant tradeoffs in speed, verbosity, and error boundaries depending on the repo and PR type.

Code review isn’t just a classification task; it’s a reasoning task. Getting agents to participate responsibly means going beyond correctness to model intent, ambiguity, and the subtle social contract of engineering work. We’re still early, but the pathway is becoming clearer.

📑 The Article: AI writing is shaping human language, making it more uniform—sparking concerns about lost nuance and a possible pushback toward more personal, human expression.

🗣️Sam Ho, Product Leader:
"One stat from The Atlantic really stuck with me: after reading AI-generated drafts, people nearly tripled their word count—from 32.7 to 87 words. Instead of making us more concise, AI might be training us to ramble even more.

But that’s not the whole story. What makes LLMs truly valuable isn’t just that they generate content—it’s that they can structure it. AI can turn scattered thoughts into clean TL;DRs, bullet points, and digestible insights. That’s not just a knowledge multiplier—it’s a wisdom multiplier.

And that’s exactly what we focus on at Turing—training our models not just to reason step-by-step, but to communicate complex ideas in a way that’s structured, clear, and easy to understand. Because clarity isn’t optional—it’s how we unlock insight at scale."

• Defeating Prompt Injections by Design
  CaMeL is a security framework developed by Google and ETH Zurich that defends LLM agents against prompt injection attacks. Rather than modifying the model, it surrounds it with a secure system layer that controls data and execution flow using capability-based policies. By isolating trusted planning logic from untrusted data handling, CaMeL prevents malicious instructions from triggering unintended actions, achieving strong results on the AgentDojo benchmark without sacrificing much utility.
• Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
  This study benchmarks LLMs on causal graph construction, counterfactuals, and attribution. GPT-4 achieves state-of-the-art accuracy in generating causal graphs from metadata, answering counterfactuals, and identifying necessary/sufficient causes across medical, scientific, and ethical vignettes, showing potential to augment or even automate causal workflows, while highlighting failure risks that demand human oversight.
• SuperARC: An Agnostic Test for Narrow, General, and Super Intelligence
  SuperARC introduces a novel benchmark grounded in algorithmic probability and recursive compression, designed to evaluate LLMs and other AI systems on abstraction, prediction, and generalization. Unlike conventional tests, SuperARC quantifies intelligence via the ability to compress and simulate sequences, avoiding benchmark contamination through open-ended, generative tasks.

Turing will be at two major AI conferences in the coming months—join us to discuss the future of AGI:

• MLSys 2025 [Santa Clara, CA | May 12 – 15]
  A major event focused on the intersection of machine learning and systems, discussing efficient AI model training, distributed learning, and AI hardware innovations.
• ICML 2025 [Vancouver Convention Center, Canada | July 13 – 19]
  The International Conference on Machine Learning (ICML) is a leading international conference focused on the advancements in machine learning and its applications.

If you’re attending, reach out—we’d love to connect and exchange insights!

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