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reacted to SeaWolf-AI's post with 🔥 9 days ago
ALL Bench — Global AI Model Unified Leaderboard https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard If you've ever tried to compare GPT-5.2 and Claude Opus 4.6 side by side, you've probably hit the same wall: the official Hugging Face leaderboard only tracks open-source models, so the most widely used AI systems simply aren't there. ALL Bench fixes that by bringing closed-source models, open-weight models, and — uniquely — all four teams under South Korea's national sovereign AI program into a single leaderboard. Thirty-one frontier models, one consistent scoring scale. Scoring works differently here too. Most leaderboards skip benchmarks a model hasn't submitted, which lets models game their ranking by withholding results. ALL Bench treats every missing entry as zero and divides by ten, so there's no advantage in hiding your weak spots. The ten core benchmarks span reasoning (GPQA Diamond, AIME 2025, HLE, ARC-AGI-2), coding (SWE-bench Verified, LiveCodeBench), and instruction-following (IFEval, BFCL). The standout is FINAL Bench — the world's only benchmark measuring whether a model can catch and correct its own mistakes. It reached rank five in global dataset popularity on Hugging Face in February 2026 and has been covered by Seoul Shinmun, Asia Economy, IT Chosun, and Behind. Nine interactive charts let you explore everything from composite score rankings and a full heatmap to an open-vs-closed scatter plot. Operational metrics like context window, output speed, and pricing are included alongside benchmark scores. All data is sourced from Artificial Analysis Intelligence Index v4.0, arXiv technical reports, Chatbot Arena ELO ratings, and the Korean Ministry of Science and ICT's official evaluation results. Updates monthly.
reacted to SeaWolf-AI's post with 🔥 17 days ago
FINAL Bench Released: The Real Bottleneck to AGI Is Self-Correction We release FINAL Bench, the first benchmark for measuring functional metacognition in LLMs — the ability to detect and correct one's own reasoning errors. Every existing benchmark measures final-answer accuracy. None measures whether AI knows it is wrong. Dataset: [FINAL-Bench/Metacognitive](https://huggingface.co/datasets/FINAL-Bench/Metacognitive) | 100 Tasks | 15 Domains | 8 TICOS Types | Apache 2.0 Leaderboard: https://huggingface.co/spaces/FINAL-Bench/Leaderboard Article: https://huggingface.co/blog/FINAL-Bench/metacognitive Core Innovation Our 5-axis rubric separates what no prior benchmark could: MA (Metacognitive Accuracy) — the ability to say "I might be wrong", and ER (Error Recovery) — the ability to actually fix it. This maps directly to the monitoring-control model of Nelson & Narens (1990) in cognitive psychology. Three Findings Across 9 SOTA Models We evaluated GPT-5.2, Claude Opus 4.6, Gemini 3 Pro, DeepSeek-V3.2, Kimi K2.5, and others across 100 expert-level tasks: 1. ER Dominance. 94.8% of MetaCog gain comes from Error Recovery alone. The bottleneck to AGI is not knowledge or reasoning — it is self-correction. 2. Declarative-Procedural Gap. All 9 models can verbalize uncertainty (MA = 0.694) but cannot act on it (ER = 0.302). They sound humble but fail to self-correct — the most dangerous AI safety profile. 3. Difficulty Effect. Harder tasks benefit dramatically more from metacognition (Pearson r = -0.777, p < 0.001). ```python from datasets import load_dataset dataset = load_dataset("FINAL-Bench/Metacognitive", split="train") ``` Paper: FINAL Bench: Measuring Functional Metacognitive Reasoning in LLMs FINAL Bench is the first tool to tell apart what AI truly knows from what it merely pretends to know.
reacted to hassenhamdi's post with 🔥 about 2 months ago
Google published the paper. I shipped the code. 🚀 DeepMind just released PACEvolve (Progress-Aware Consistent Evolution), a massive overhaul of the AlphaEvolve framework. It solves the critical issues of "Context Pollution" and "Mode Collapse" that have historically crippled evolutionary coding agents. But there was no public implementation. So I built one. Introducing OpenPACEvolve: A fully open-source, production-grade implementation of the PACEvolve framework. 🛠 I engineered this framework solo, but I wasn't working alone. I orchestrated a custom coding agents powered by Claude Opus 4.5 as Engineer and Gemini Pro 3 Preview ensuring fiedelity and quallty. By leveraging these SOTA models, I was able to translate complex theoretical research into functional, modular Python architecture in record time. This is what the future of AI engineering looks like: Human architectural oversight + AI velocity. 🧠 What OpenPACEvolve Solves: Unlike standard agents that get "stuck" in loops, this framework implements the paper's full recipe for long-horizon stability: ✅ Hierarchical Context Management (HCM): Bi-level pruning to keep the agent's memory clean. ✅ Momentum-Based Backtracking (MBB): Uses "power-law backtracking" to detect stagnation and force pivots. ✅ Self-Adaptive Crossover: Intelligent code-sharing between parallel "islands." 👨‍💻 This project is more than a repo; it's a demonstration of rapid research-to-production cycles using next-gen AI workflows. 📎 Link of the paper : https://arxiv.org/abs/2601.10657 The code is live. The agents are ready. Check out the repository below. 👇 https://github.com/hassenhamdi/OpenPACEvolve Star the repo 🌟.
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