This week’s edition highlights what it really takes to make language models more accurate, trustworthy, and grounded. In our featured case study, Turing helped a frontier AI lab achieve 95%+ factuality by building a large-scale, human-in-the-loop evaluation pipeline across 5,000+ prompts and 150+ diverse categories. We’re also celebrating our TechCrunch feature on capturing real-world workflows for training AGI, and spotlighting research on LLM investing agents, interpretable reasoning metrics, and foundation-model-powered research discovery. From eval pipelines to autonomous crypto traders, it’s all about making AI smarter, and more accountable.
What we're thinking
This week, we’re spotlighting how Turing helped a frontier AI lab achieve 95%+ factual accuracy through a massive human‑labeled evaluation pipeline. Built over 5,000+ prompts across 150+ diverse categories, this system isn’t just catching errors, it’s raising the bar for what grounded model responses look like.
Here’s what we’re seeing:
- 5,000+ expert‑labeled prompts: Balanced across web and social‑media contexts, covering declarative claims, open questions, and opinion‑inflected queries across 150+ subcategories.
- 95%+ factuality rate: The top model fine‑tuned with Turing data showed stronger grounding, better RAG utilization, and fewer hallucinations compared to earlier baselines.
- ~5% improvement in positive response quality: Its responses scored higher in clarity, structure, and instruction‑following in blind human review.
💡 In a world where models often answer something, this pipeline teaches them to answer correctly and meaningfully.
What we’re celebrating
🎉TechCrunch featured Turing for how we build human-led, proprietary training data, not just scrape the web. Instead of relying on passive data collection, we embed with real-world experts, including chefs, electricians, construction pros, and more, to capture authentic workflows as they happen. These expert sessions are then transformed into structured signals for high-quality synthetic data.
What we're reading
- Alpha Arena by Nof1
Alpha Arena is the first real-money benchmark designed to test how well LLMs can invest autonomously. Six top models, including Claude 4.5, GPT-5, Gemini 2.5 Pro, Grok 4, DeepSeek V3.1, and Qwen 3 Max, each received $10,000 in crypto perpetuals on Hyperliquid. They must generate alpha, size and time trades, and manage risk in live markets. Unlike static evaluations, this benchmark is adversarial, open-ended, and dynamic, closer to how intelligence is tested in the real world. The competition runs through November 3, 2025, with full transparency into trades and outputs. - What Defines Good Reasoning in LLMs? Dissecting Reasoning Steps with Multi-Aspect Evaluation
This paper introduces Causal Stepwise Evaluation (CaSE), a new framework to assess LLM reasoning beyond final-answer correctness by evaluating relevance (does a step address the question?) and coherence (does it logically follow from prior steps?). Using two new annotated benchmarks, MRa-GSM8K and MRa-MATH, the authors show that even flawed solutions can yield correct answers if relevance and coherence are preserved. CaSE outperforms traditional evaluation methods, better aligns with human judgments, and improves fine-tuning results when used to curate training data. The study highlights that accurate, interpretable reasoning in LLMs depends on how, not just whether, they arrive at answers. - Real Deep Research for AI, Robotics and Beyond
With 10,000+ AI and robotics papers published annually, staying current is nearly impossible. This paper introduces Real Deep Research (RDR), a systematic pipeline that uses foundation models to cluster, trend-analyze, and map the entire research landscape across domains like computer vision, robotics, NLP, and more. By embedding and categorizing thousands of papers by input, model, and output structures, RDR highlights emerging directions (like open-source dexterous robotics and multimodal planning) and reveals cross-domain intersections that might otherwise go unnoticed. Compared to commercial LLM tools, RDR's structured surveys earned the highest marks in expert evaluations for clarity, coverage, and accuracy.
Where we’ll be
Turing will be at this major AI conference in the coming month—join us to discuss the future of AGI:
- NeurIPS 2025
[Mexico City | Nov 30 – Dec 5]
[San Diego Convention Center | Dec 2 – 7]
The Neural Information Processing Systems Foundation is a non-profit that promotes research in AI and ML by organizing a leading annual conference focused on ethical, diverse, and interdisciplinary collaboration.
If you’re attending, reach out—we’d love to connect and exchange insights!
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