AGI Advance: Weekly AI & AGI Insights (Dec 16, 2025)

This week, we’re spotlighting our collaboration with NVIDIA on CVDP (Comprehensive Verilog Design Problems), a benchmark-grade dataset for evaluating LLMs in real-world RTL design workflows. Built around 783 Verilog tasks, CVDP spans everything from single-file prompts to Git-style agentic challenges involving tool invocation, bug fixing, and architectural comprehension.

Here’s what we’re seeing:

• Hardware-grade difficulty: Tasks span 13 categories across design generation, testbenching, assertion creation, and RTL-to-spec mapping, built for pass/fail simulation under commercial and open-source EDA tools.
• Agentic evaluation unlocks deeper insights: Trajectory-level logging and toolchain integration expose failure modes missed by synthetic benchmarks.
• Frontier model limits revealed: GPT-4o and Claude 3.7 Sonnet drop to 29% and 34% pass@1 on CVDP, with further declines on agentic tasks, highlighting reasoning and tool interaction gaps.

💡 Standard code generation benchmarks can’t capture the complexity of RTL design. With agentic simulations and real-world failure triggers, CVDP redefines what it means to benchmark hardware AI.

 → Read the full case study

🎉 Our Computational STEM QA dataset just hit #2 trending on Hugging Face.

This curated set of high-difficulty reasoning tasks spans physics, math, biology, and chemistry; each one crafted to require multi-step logic, symbolic manipulation, or numerical accuracy that LLMs can’t fake. Designed by PhD-level SMEs and validated through multi-phase expert review, these problems go beyond text prediction to stress-test real computation and reasoning.

→ Explore the dataset

• Introducing GPT-5.2
  OpenAI launched GPT-5.2, a major leap in professional-grade intelligence, designed for agentic workflows, tool use, and deeply contextual tasks. The model sets new records across SWE-Bench Pro (55.6%), GPQA Diamond (92.4%), FrontierMath (40.3%), and ARC-AGI-2 (52.9%), while outperforming industry experts in 70.9% of GDPval knowledge tasks. It delivers near-perfect long-context resolution at 256k tokens and achieves 98.7% tool use accuracy on Tau2-bench Telecom. GPT-5.2 also halves hallucination rates from 5.1 and introduces a refined API with multi-level reasoning control.
• Does Less Hallucination Mean Less Creativity? An Empirical Investigation in LLMs
  This paper investigates how hallucination-reduction techniques impact the creative output of LLMs. Using two creativity benchmarks (NeoCoder for structured code generation and CS4 for open-ended storytelling), the authors test three methods: Chain of Verification (CoVe), Decoding by Contrasting Layers (DoLa), and Retrieval-Augmented Generation (RAG). They find CoVe enhances divergent creativity, DoLa dampens it, and RAG has minimal impact. Surprisingly, convergent thinking remains largely unchanged. The study suggests that some forms of hallucination may mimic the associative leaps essential for creative breakthroughs, and that blindly suppressing them might hinder discovery.
• The AI Productivity Index: APEX-v1-extended
  Mercor introduces APEX-v1-extended, a rigorous benchmark evaluating whether LLMs can complete economically valuable tasks in law, medicine, consulting, and finance. With 400 closed-set prompts and detailed grading rubrics, APEX tests full-task outputs grounded in source documents (e.g., spreadsheets, contracts, case notes). GPT‑5 (Thinking) leads the leaderboard at 67.0%, but even top models fall short on complex roles like investment banking. Unlike narrow benchmarks, APEX evaluates end-to-end deliverables with rubric-based LM judging, setting a new standard for benchmarking productivity, not just intelligence.

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]