This week, we’re focusing on why multimodal AI is moving from capability to precision, and how frontier labs are rethinking deployment benchmarks, domain-specific reliability, and real-world evaluation. Additionally, we take a closer look at reasoning collapse, visual conceptualization gaps, and the emerging shift toward agent-guided human collaboration.

What we're thinking

This week, we’ve been reflecting on how multimodal AI is evolving, not just across modalities, but into deeper, more specialized domains. The shift isn’t about coverage; it’s about precision.

Here’s what we’re seeing across recent and upcoming work:

Handling modalities isn’t the same as domain expertise: Leading AI labs are moving beyond generic tasks, developing systems that interpret financial diagrams, annotate medical scans, or analyze phonetic detail with contextual rigor.
Benchmarks don’t reflect deployment readiness: Public leaderboards tell one story, but our private VLM benchmark lets clients evaluate unreleased models on high-stakes, real-world tasks.
Multimodal needs are emerging from new frontiers: Recent conversations span autonomous driving, robotics, and on-device reasoning—domains where perception meets control, and performance has to translate to action.

Multimodal is becoming mission-critical. The next wave of adoption won’t be driven by capability alone—it’ll be driven by how well we tailor AI to the complexity of specific verticals.

What we're saying

🗣️James Raybould, SVP & GM:
“Agent in the Loop: When AI Guides Humans

We often talk about “human in the loop”—but the next shift will reverse the flow. In domains like healthcare, finance, and legal strategy, AI will increasingly guide us, determining where human input adds the most value. This isn’t about replacement; it’s about reorientation. Legacy regulation, trust, and verification will keep humans in critical roles, but when we step in will be increasingly up to the agent.”

What we're reading

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
The Illusion of Thinking challenges assumptions about reasoning models by evaluating them in controlled puzzle environments. The study finds that models like Claude 3.7 and DeepSeek-R1 show gains on medium-complexity tasks—but collapse at higher complexity, often using fewer tokens as difficulty increases. Even with explicit algorithms, models failed to execute correct steps—suggesting a fundamental limit in reasoning generalization and compute scaling.
How Much Do Language Models Memorize?
This paper introduces a method to precisely measure how much a language model memorizes, estimating that GPT-family models retain around 3.6 bits per parameter. It distinguishes unintended memorization (specific data recall) from generalization, showing that models memorize up to capacity, then begin to generalize—mirroring the double descent effect. The authors demonstrate that membership inference becomes ineffective when dataset size exceeds capacity, confirming that most large LLMs can't reliably reveal individual training points. Notably, rare or non-English tokens are among the most memorized, even in fully deduplicated training sets.
Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models
This research introduces a benchmark to test whether vision and multimodal LLMs can recognize the same concept across different visual representations. Despite near-perfect human accuracy, models like GPT-4o and Claude 3.5 Sonnet failed basic tasks like graph isomorphism and cycle detection when layout changed. The study highlights a critical gap: today’s models match patterns, but still lack true visual conceptualization.

Where we’ll be

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

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.
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.

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

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