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SeaWolf-AIย 
posted an update about 22 hours ago
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3779
๐Ÿš€ Introducing MARL โ€” Runtime Middleware That Reduces LLM Hallucination Without Fine-Tuning

Now available on PyPI ยท GitHub ยท ClawHub ยท HuggingFace
AI models sense they could be wrong, but they can't actually fix what's broken.

๐Ÿค— Live A/B test: VIDraft/MARL

We evaluated 9 SOTA models (GPT-5.2, Claude Opus 4.6, Gemini 3 Pro, etc.) across 1,800 assessments in FINAL Bench and found a 39.2%p gap between "recognizing potential errors (MA=0.694)" and "actually finding and fixing them (ER=0.302)."

MARL (Model-Agnostic Runtime Middleware for LLMs) was built to close this metacognitive gap. It decomposes a single LLM call into a 5-stage expert pipeline (Hypothesis โ†’ Solver โ†’ Auditor โ†’ Adversarial Verifier โ†’ Synthesizer), transforming "answer in one shot" into "think, doubt, correct, and rewrite."

No weight modification โ€” works instantly with GPT-5.4, Claude, Gemini, Llama, or any OpenAI API-compatible LLM by changing one line: base_url. Ships with 9 domain-specific emergence engines (invention, pharma, genomics, chemistry, ecology, law, and more โ€” 5,538 expert data items) activated by a simple tag like model="gpt-5.4::pharma".

pip install marl-middleware

MARL is also officially registered on ClawHub, the skill marketplace of OpenClaw โ€” an AI agent platform with 260K+ developers and 3,200+ skills. It's the first middleware in the Reasoning Enhancement category. One command โ€” clawhub install marl-middleware โ€” gives your AI agent a metacognition upgrade.

๐Ÿ“ Technical deep dive: https://huggingface.co/blog/FINAL-Bench/marl-middleware
๐Ÿ“ฆ PyPI: https://pypi.org/project/marl-middleware/
๐Ÿ™ GitHub: https://github.com/Vidraft/MARL
๐Ÿฆ€ ClawHub: https://clawhub.ai/Cutechicken99/marl-middleware

#MARL #LLM #Hallucination #Metacognition #MultiAgent #AIMiddleware #FINALBench #OpenClaw #ClawHub #PyPI #AGI #HuggingFace #ReasoningAI #SelfCorrection #GlassBoxAI
Lyteย 
posted an update 2 days ago
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5244
Introducing Nanochat Moroccan

Nanochat Moroccan is the first language model family built specifically for Moroccan Darija.

This project brings together a small family of models and datasets centered on Darija, with the goal of building something genuinely useful for a language that is still underserved in AI.

1. Models

- KandirResearch/Nanochat-Moroccan-Base-0.7B
- KandirResearch/Nanochat-Moroccan-Instruct-0.7B-pt-raw
- KandirResearch/Nanochat-Moroccan-Instruct-0.7B

2. Data

- Lyte/darija-pretraining-corpus
- Lyte/darija-pretraining-corpus-nanochat
- Lyte/Moroccan-Darija-Instruct-573K
- GemMaroc/TULU-3-50k-darija-english

3. Collection

- https://huggingface.co/collections/KandirResearch/nanochat-the-first-moroccan-darija-language-model-family

Moroccan Darija is spoken by millions of people, yet it remains underrepresented in language technology. Nanochat Moroccan is a step toward building tools that take the language seriously.

You are welcome to try it and chat with it here:
Lyte/Nanochat-Moroccco-Instruct
SeaWolf-AIย 
posted an update 2 days ago
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5845
ALL Bench Leaderboard โ€” Structural Problems in AI Benchmarking and the Case for Unified Evaluation

FINAL-Bench/all-bench-leaderboard

The AI benchmark ecosystem has three structural problems. Major benchmarks like MMLU have surpassed 90%, losing discriminative power. Most leaderboards publish unverified self-reported scores โ€” our cross-verification found Claude Opus 4.6's ARC-AGI-2 listed as 37.6% (actual: 68.8%), Gemini 3.1 Pro as 88.1% (actual: 77.1%). OpenAI's own audit confirmed 59.4% of SWE-bench Verified tasks are defective, yet it remains widely used.

ALL Bench addresses this by comparing 91 models across 6 modalities (LLM ยท VLM ยท Agent ยท Image ยท Video ยท Music) with 3-tier confidence badges (โœ“โœ“ cross-verified ยท โœ“ single-source ยท ~ self-reported). Composite scoring uses a 5-Axis Framework and replaces SWE-Verified with contamination-resistant LiveCodeBench.

Key finding: metacognition is the largest blind spot. FINAL Bench shows Error Recovery explains 94.8% of self-correction variance, yet only 9 of 42 models are even measured. The 9.2-point spread (Kimi K2.5: 68.71 โ†’ rank 9: 59.5) is 3ร— the GPQA top-model spread, suggesting metacognition may be the single biggest differentiator among frontier models today.

VLM cross-verification revealed rank reversals โ€” Claude Opus 4.6 leads MMMU-Pro (85.1%) while Gemini 3 Flash leads MMMU (87.6%), producing contradictory rankings between the two benchmarks.

๐Ÿ“Š Article: https://huggingface.co/blog/FINAL-Bench/all-bench
๐Ÿ“ฆ Dataset: FINAL-Bench/ALL-Bench-Leaderboard
โšก GitHub: https://github.com/final-bench/ALL-Bench-Leaderboard
๐Ÿ† Leaderboard: FINAL-Bench/all-bench-leaderboard
๐Ÿงฌ FINAL Bench: FINAL-Bench/Metacognitive
prithivMLmodsย 
posted an update 3 days ago
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3987
The Qwen3.5 Multimodal Understanding Demo, powered by Qwen3.5-2B, is now available on HF Spaces! It is a lightweight model designed for fast image and video reasoning. Built with Gradio, the demo showcases Image QA, Video QA, object detection, and 2D point tracking, along with real-time token streaming.

๐Ÿค— Demo: prithivMLmods/Qwen-3.5-HF-Demo
โœ… Collection: https://huggingface.co/collections/prithivMLmods/multimodal-implementations
๐Ÿ”— Qwen3.5-2B: Qwen/Qwen3.5-2B

To learn more, visit the app page or the respective model pages.
Reubencfย 
posted an update 3 days ago
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2525
๐Ÿš€ I am thrilled to announce the release of a new Konkani LLM!

We've seen some fantastic results for both translation and transliteration tasks, and I'm excited to share this progress with the community.

๐Ÿ“– Read the launch article and see the results: https://huggingface.co/blog/Reubencf/konkani-llm
๐Ÿค– Explore the model and collection:
konkani


I would love to hear your feedback or see what you build with it! #Konkani #LLM #NLP #HuggingFace #IndicNLP #Konkani
codelionย 
posted an update 1 day ago
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259
Scaling Pedagogical Pre-training to 10 Billion Tokens

New blog post exploring what happens when you take optimal data mixing insights and scale up the data generation itself.

We built Sutra, a multi-stage framework for generating pedagogical pre-training data guided by a knowledge graph of ~2,000 concepts across 9 domains. The pipeline includes structured content generation, six-dimension quality evaluation, diversity management across 20 content styles, and a cleaning stage to prevent collapse.

The result is codelion/sutra-10B, a 10.2 billion token pedagogical dataset with rich metadata (domain, complexity, prerequisites, quality scores) on every entry.

We trained codelion/SmolLM2-70M on it for 3 full epochs (30.6B tokens) on a single A10 GPU in ~78 hours.

Key finding: perplexity kept improving across epochs, but benchmark gains plateaued fast. At 70M parameters, the model hits a representational ceiling that more data alone can't break through.

Full writeup with comparisons against 7 other datasets, detailed benchmark breakdowns, and connections to recent work on synthetic data scaling, curriculum learning, and data mixing laws: https://huggingface.co/blog/codelion/scaling-pedagogical-pretraining-10-billion-tokens

All datasets at multiple scales (10M, 100M, 1B, 10B) plus seed concepts and an SFT variant are in the Sutra Pedagogical Datasets collection.
DavidAUย 
posted an update 10 days ago
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4692
Gemma 3 27B - The record breaker (Heretic'ed (uncensored) ; then training on Unsloth):

arc_challenge,arc_easy,boolq,hellaswag,openbookqa,piqa, winogrande
0.661 ,0.816 ,0.878,0.763 ,0.464 ,0.808 ,0.762

For comparison:
Qwen3.5-27B-Text
qx86-hi 0.443,0.498,0.857,0.701,0.372,0.770,0.752

Trained on a HERETIC uncensored base too ;

DavidAU/Gemma3-27B-it-vl-Polaris-HI16-Heretic-Uncensored-INSTRUCT
GVA21q2ย 
posted an update 12 days ago
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1504
# ฯ€.Guy.AI โ€” AI-Powered Neuropedagogy Math Lessons

Students with math anxiety, ADHD, dyslexia, or low working memory need different learning experiences โ€” but teachers can't create individualized materials for every student.

**ฯ€.Guy.AI** generates interactive HTML math lessons adapted to 7 cognitive profiles, using a multi-agent AI pipeline:

1. **Neuro-Interpreter** โ€” enriches prompts with profile-specific adaptations
2. **Creative Agent** โ€” generates a 12-slide lesson with SVG visualizations
3. **Quality Control** โ€” validates against 8 neuropedagogy principles

Each lesson is a standalone HTML file with inline CSS/JS/SVG โ€” works offline, no dependencies.

## The Model

Fine-tuned **Qwen2.5-7B-Instruct** with LoRA on 313 curated Hebrew math lessons.

- Model: [GVA21q2/piguyai-lessons-v2-enhanced](https://huggingface.co/GVA21q2/piguyai-lessons-v2-enhanced)
- Dataset: [GVA21q2/pi-guy-ai-lessons](https://huggingface.co/datasets/GVA21q2/pi-guy-ai-lessons)
- Demo: [GVA21q2/pi-guy-ai-demo]( GVA21q2/pi-guy-ai-demo)
- Web app: [gva21q2.github.io/pi.guy.ai](https://gva21q2.github.io/pi.guy.ai/)

7 profiles: math anxiety, ADHD, dyslexia, dysgraphia, low working memory, visual processing, weak inhibition.

Built by [Guy Assal](https://www.guyassal.education)
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darkc0deย 
posted an update 15 days ago
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8323
1440GB of VRAM is incredibly satisfying ๐Ÿ˜
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AINovice2005ย 
posted an update about 12 hours ago