AGI Advance: Weekly AI & AGI Insights (Feb 3, 2026)

This week, we’re spotlighting how Turing built a dataset of 10,000+ annotated GUI interaction tasks to support pretraining and alignment of general-purpose computer-use agents. Each task captures a real application workflow with prompts, timestamped actions, screenshots, and structured metadata across operating systems and task types.

Here’s what we delivered:

• 10,000+ GUI tasks with full action logs, metadata, and dual screenshots per step
• OS- and app-level diversity: Office, daily use, professional tools, and system tasks across Windows, macOS, and Linux
• 5–100 step trajectories reviewed through automated QA, rubric-based scoring, and audit calibration

💡 The future of agent intelligence starts with grounded data, and this dataset teaches models how people actually use software in the real world.

→ Read the full case study

🧠 Benchmarks Are Dead. Here’s Why.

In a new post, Turing CEO Jonathan Siddharth lays out why public leaderboards are no longer meaningful signals for AI progress, and why real deployments are.

“Models don’t fail on leaderboards. They fail on real workflows: PDF tables, messy data, implicit logic, unwritten norms. None of that shows up on a chart.”

Jonathan makes the case for forward-deployed engineers, private evals, and system-specific tuning as the way forward, and explains why deployment, not scoring, is how models actually improve.

→ Read the full post

• The Adolescence of Technology
  In this essay, Dario Amodei argues that humanity is entering a decisive “technological adolescence,” as AI approaches the level of a country of geniuses in a datacenter with the power to transform or destabilize civilization. He lays out five major risk categories: loss of AI autonomy and control, misuse by individuals for mass destruction (especially biological weapons), misuse by states for authoritarian power, extreme economic disruption and inequality, and unpredictable indirect effects on human purpose and society. Amodei rejects both doomerism and complacency, advocating instead for pragmatic, evidence-based interventions such as constitutional AI, mechanistic interpretability, transparency requirements, targeted regulation, export controls on chips, and strong norms against AI-enabled totalitarianism. He warns that labor displacement and wealth concentration could arrive faster and more broadly than past technological shocks, potentially breaking the social contract of democracy. The essay concludes that while the challenge is severe and imminent, coordinated action, honest disclosure, and moral resolve give humanity a real chance to pass this civilizational test.
• THINKSAFE: Self-Generated Safety Alignment for Reasoning Models
  This paper addresses the “safety tax” in large reasoning models, where reinforcement learning for long chain-of-thought improves problem-solving but degrades safety, making models more vulnerable to harmful prompts. The authors propose THINKSAFE, a self-generated alignment method that uses lightweight refusal steering to elicit a model’s own latent safety reasoning, avoiding the distribution shift caused by external teacher distillation. By fine-tuning on these in-distribution, self-generated safety traces, THINKSAFE restores robust refusal behavior while preserving native reasoning ability. Across Qwen3 and DeepSeek-R1-Distill (0.6B–8B), THINKSAFE reduces harmful responses by 2–5× (for example, HarmBench drops from 38.2 → 9.6 on Qwen3-4B) while maintaining or improving reasoning accuracy. It also outperforms online RL (GRPO) in safety with ~8× lower training cost, showing that safety can be recovered without sacrificing reasoning or efficiency.
• From Abstract to Contextual: What LLMs Still Cannot Do in Mathematics
  This paper studies why strong benchmark performance in mathematics fails to transfer to real-world settings, focusing on contextual mathematical reasoning, where the math must first be formulated from narrative descriptions. The authors introduce ContextMATH, which transforms AIME and MATH-500 problems into two controlled variants: Scenario Grounding (SG), embedding math into realistic narratives, and Complexity Scaling (CS), hiding explicit constraints inside sub-problems. Evaluating 61 open-source and proprietary models, they find large accuracy drops: open-source models fall 13 points on SG and 34 on CS, and proprietary models 13 and 20, with errors dominated (≈80%) by incorrect problem formulation rather than calculation. Analysis shows formulation is a necessary but not sufficient condition for success, even for top models like GPT-5, revealing a second bottleneck in downstream reasoning. Fine-tuning on scenario data improves performance but does not close the gap, establishing contextual math reasoning as a central unsolved challenge for LLMs.

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