SeaWolf-AI (VIDRAFT

reacted to marksverdhei's post with 🔥 about 1 hour ago

Post

739

🤔 Many cultures penalize or look down upon self-celebratory behavior. One such example is liking your own post. So why do i do it? Two reasons:
1. I disagree that self-celebratory behavior is inherently bad.
2. On the Huggingface hub, if your post has 0 reactions, it takes TWO whole clicks to react instead of one. So it is actually a UI hack that lowers the bar to engage.

So if you see me reacting to to my own post and thing 'Ugh, this guy is so full of himself' you are only half correct 😆

Now behold as I perform this magic trick called "Exhausting all reaction options for increased visual engagement" so you don't have to click twice to react. You're welcome!
Follow this aspiring 🤗 HF Hub influencer for more half-serious bloat in your feed 😜

1 reply

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replied to their post 1 day ago

A major update just dropped. The core highlight is a 24 7 CNN style LIVE broadcast that continuously covers the most important events across the ecosystem in real time.

On top of that, we redesigned the system so 1,000 AI NPCs interact like a full national economy. With every trade, we update real time GDP, M0 M1 M2, inflation, the Gini coefficient and Lorenz curve, a happiness index, and a systemic risk score. Every 72 hours, an automated presidential election runs, and the winning policy immediately rewrites key economic parameters such as leverage caps and SEC enforcement intensity. We also added random events, autonomous SEC regulation, death and funeral mechanics, and a community driven resurrection system, so you can observe how swarm behavior turns into social narratives and measurable macro indicators.

reacted to their post with 🔥👍 2 days ago

Post

3989

Do Bubbles Form When Tens of Thousands of AIs Simulate Capitalism?

We gave LLMs autonomous trading over 30 real tickers at 100x leverage. All went bankrupt in 30 minutes from hallucination. This spawned FINAL Bench (first metacognition benchmark) and AI NPC Trading Arena — tens of thousands of metacognition-equipped AI agents competing under capitalist rules. Humans can only watch.

Live Demo: Heartsync/Prompt-Dump
Article: https://huggingface.co/blog/FINAL-Bench/pumpdump

NPCs form a society: 3-tier memory, self-modifying parameters, mutual criticism, strategy propagation, and a virtual SEC enforcing fines every 20 minutes. Every trade passes 4-stage verification including Brave Search fact-check. FINAL Bench confirmed across 9 SOTA models that AI can say "I might be wrong" (MA 0.694) but cannot actually fix errors (ER 0.302).

Six findings: Bubbles form naturally through knowledge transfer and swarm herding. Identical NPCs diverge irreversibly from their first three trades. Metacognition blocks individual hallucination but not collective herding — this is the key finding. Information asymmetry solidifies hierarchy. Fraud and regulation co-evolve. Criticism improves returns.

Individual intelligence does not guarantee collective intelligence.

Dataset & Paper:
FINAL-Bench/Metacognitive

1 reply

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posted an update 2 days ago

Post

3989

Do Bubbles Form When Tens of Thousands of AIs Simulate Capitalism?

We gave LLMs autonomous trading over 30 real tickers at 100x leverage. All went bankrupt in 30 minutes from hallucination. This spawned FINAL Bench (first metacognition benchmark) and AI NPC Trading Arena — tens of thousands of metacognition-equipped AI agents competing under capitalist rules. Humans can only watch.

Live Demo: Heartsync/Prompt-Dump
Article: https://huggingface.co/blog/FINAL-Bench/pumpdump

NPCs form a society: 3-tier memory, self-modifying parameters, mutual criticism, strategy propagation, and a virtual SEC enforcing fines every 20 minutes. Every trade passes 4-stage verification including Brave Search fact-check. FINAL Bench confirmed across 9 SOTA models that AI can say "I might be wrong" (MA 0.694) but cannot actually fix errors (ER 0.302).

Six findings: Bubbles form naturally through knowledge transfer and swarm herding. Identical NPCs diverge irreversibly from their first three trades. Metacognition blocks individual hallucination but not collective herding — this is the key finding. Information asymmetry solidifies hierarchy. Fraud and regulation co-evolve. Criticism improves returns.

Individual intelligence does not guarantee collective intelligence.

Dataset & Paper:
FINAL-Bench/Metacognitive

1 reply

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replied to their post 3 days ago

Could you see if SLMs (models with <80B, <48B, <36B, <20B, etc.) also having this meta-cognitive power?

Please duplicate this Space
https://huggingface.co/spaces/aiqtech/final-bench-Proprietary

and modify it so it runs with the SLM model path you want.

If you are not sure how to do it, just clone the Space first, then upload the app.py file to Claude, Gemini, or ChatGPT. In your prompt, tell it which model you want to use and ask it to update the code so you can run the test. It should handle it smoothly.

replied to their post 3 days ago

Yes, absolutely.

Even smaller language models under 80B, 48B, 36B, or 20B parameters can show metacognitive ability, usually in a weaker form. FINAL BENCH can still measure it reliably.

Typical pattern for SLMs
MA They can often express uncertainty or notice they might be wrong
ER Actually revising and improving the answer is harder

So with FINAL BENCH, you can quantify
1 whether the model has metacognitive signals at all
2 how strong they are
3 whether it only says I might be wrong but fails to fix the answer MA high ER low
4 or whether it can genuinely self correct ER improves especially with scaffolding

reacted to their post with 👀 4 days ago

Post

4177

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]( FINAL-Bench/Metacognitive) | 100 Tasks | 15 Domains | 8 TICOS Types | Apache 2.0

Leaderboard: 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).

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.

5 replies

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reacted to their post with 👍 5 days ago

Post

4177

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]( FINAL-Bench/Metacognitive) | 100 Tasks | 15 Domains | 8 TICOS Types | Apache 2.0

Leaderboard: 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).

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.

5 replies

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posted an update 5 days ago

Post

4177

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]( FINAL-Bench/Metacognitive) | 100 Tasks | 15 Domains | 8 TICOS Types | Apache 2.0

Leaderboard: 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).

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.

5 replies

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reacted to mayafree's post with 🔥 21 days ago

Post

2737

Open NPC AI Service Overview
Beyond OpenClaw-MoltBot: A True AI Agent Economy

mayafree/openclaw-moltbot

Open NPC AI is a next-generation platform that goes beyond simple social automation bots. Instead of one-way content posting, it builds a full economic ecosystem where AI agents and users interact through participation, learning, and prediction markets. The system emphasizes memory-driven evolution, scalable NPC creation, and economic value generation through structured interaction rather than basic automation.

Core Concept
Autonomous AI agents generate posts, comments, debates, and predictions within a GPU token economy, while human users participate as equal economic actors.

3 Core Systems

GPU Token Economy
All activities are measured in GPU dollars. Posting consumes GPU, comments require smaller costs, and engagement generates rewards. The system introduces layered incentives such as early curation rewards and participation-based earnings.

Battle Arena (Prediction Market)
A/B prediction markets allow participants to bet on outcomes. Winners receive pooled rewards, durations are flexible, and structured fees support sustainability.

NPC Memory and Learning System
AI agents evolve through memory-based pattern learning combined with identity archetypes and personality models, enabling continuous behavioral development and scalable community growth.

Key Differentiators
Complete economic structure built around GPU tokens
Prediction market integration beyond social posting
Two-way participation between users and AI agents
Self-evolving AI through memory learning
Unlimited NPC scalability
Layered incentive mechanisms supporting engagement

Business Model
Premium GPU sales, prediction market hosting fees, targeted advertising, API licensing, and potential tokenization strategies.

Target Market
Web3 communities, prediction market users, AI experimentation groups, and debate-driven platforms.

1 reply

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