AGI Advance: Weekly AI & AGI Insights (July 29, 2025)

This week, we’ve been focused on advanced reasoning and how to build the data and evaluation scaffolding required to stress-test and improve frontier models across knowledge, STEM, and code.

Here’s what’s coming up across our work:

• Benchmarks aren’t enough, reasoning needs structure: Real-world reasoning is rarely clean, closed, or single-step. We’re designing datasets that go beyond final-answer accuracy—grounding questions in verifiable logic, structured error patterns, and programmatic evaluation.
• CoT is moving from output format to alignment signal: We’re scoring reasoning traces step-by-step, not just to flag incorrect answers, but to identify brittle steps even when the final answer is right. This shift enables better reward modeling, targeted fine-tuning, and model introspection.
• Taxonomy matters: We’re organizing reasoning difficulty by domain and task type, not just subject area. This lets labs isolate capability gaps, structure learning curricula, and track improvements over time.

As models race ahead in breadth, the next breakthroughs will come from depth: high-signal data, verifiable reasoning, and structured evaluation built for the questions benchmarks can’t yet answer.

🗣️Krishna Vinod, Delivery Manager:
“Like any LLM, your brain is only as good as its training data, and the prompts you feed it.”

In a recent post, Krishna draws a striking parallel between prompt engineering and cognitive alignment. From automatic negative thoughts (ANTs) to goal-directed reasoning, he shows how reframing internal prompts can reshape our thinking patterns the same way fine-tuning and evaluation improve language models. If LLMs can be audited, steered, and improved—so can we.

→ Read the full post

• Hierarchical Reasoning Model
  This paper introduces HRM, a 27M parameter model that uses dual recurrent loops to mimic high-level planning and low-level execution, enabling efficient, generalizable reasoning without CoT prompting or intermediate supervision. Trained from scratch on just 1,000 examples, HRM outperforms GPT-4-class models on reasoning benchmarks: 40.3% on ARC-AGI-2 (vs. Claude 3.7’s 21.2%), 74.5% on Sudoku-Extreme, and 55.0% on Maze-Hard where baselines score 0%. By leveraging adaptive computation and shallow updates, HRM challenges the assumption that scale is required for cognition-like reasoning.
• Cursor’s New Bugbot Is Designed to Save Vibe Coders From Themselves
  Cursor, the popular AI coding platform, has launched Bugbot, a tool that integrates with GitHub to automatically flag errors introduced by humans or AI agents. Designed for modern “vibe coders,” Bugbot targets hard-to-catch issues: logic bugs, edge cases, and security flaws, as AI-generated code now accounts for 30–40% of total output on many teams. The tool recently proved itself by predicting a code change that would shut itself down, only to be ignored and proven right. As velocity increases, Bugbot reflects a growing shift: from AI writing code to AI safeguarding it.
• LingBench++: A Linguistically-Informed Benchmark and Reasoning Framework for Multi-Step and Cross-Cultural Inference with LLMs
  LingBench++ introduces a linguistically grounded benchmark for evaluating LLMs on complex, IOL-style problems across 90+ typologically diverse languages. Unlike typical benchmarks that focus on final answers, LingBench++ includes expert-verified reasoning traces and step-by-step evaluations to test interpretability, logic, and cross-cultural inference. A multi-agent framework with external grammar lookups and iterative hypothesis testing boosts Gemini 2.5-Pro’s performance from 0.381 to 0.459, highlighting the power of structured reasoning and agentic collaboration for language-based tasks.

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

• KDD 2025 [Toronto, ON, Canada | Aug 3 – 7]
  The ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) focuses on innovative research in data mining, knowledge discovery, and large-scale data analytics.
• COLM 2025 [Montreal, Canada | Oct 7 – 10]
  The Conference on Language Modeling (COLM) aims to create a community of researchers with expertise in different disciplines, focused on understanding, improving, and critiquing the development of LM technology.

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

Turing is leading the charge in bridging AI research with real-world applications. Subscribe to AGI Advance for weekly insights into breakthroughs, research, and industry shifts that matter.

[Subscribe & Read More]