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ALI YASIR NOOR
AffandiGhazi
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AI & ML interests
Ai Automations and agentic workflows and also generation of generative ai applications for legal techs , engineering and specialized tools .
Recent Activity
reacted to ginigen-ai's post with 🔥 6 days ago
A local edge VLM you can run on a phone — with a calibration readout attached.
https://huggingface.co/ginigen-ai/Edge-4B-TELL
Image in, answer out, nothing leaving the device. Google's Gemma 4 E4B QAT checkpoint carried unmodified, with the vision and audio projector, plus one thing that is ours: GINIGEN TELL, a 10 KB readout that estimates whether the answer it just gave is likely to be wrong.
On a Galaxy S25: zero network calls, 3.6 GB resident, a 12.6 MB inference binary.
Calibration matters more here than on a server: nothing downstream catches a bad answer. No retrieval, no second opinion, no reviewer. The model is alone with the user.
And its own confidence is unusable. Prompted for it, this checkpoint averages 0.863 over 665 Korean disaster-procedure questions — ranking answers by it gives AUROC 0.441, below a coin flip. It sounds more certain when it is wrong.
TELL reads the last-layer hidden state instead of asking. Same questions, 0.759. Surface cues (length, formatting) already reach 0.736, so the readout clears that baseline by +0.023 ± 0.009 (2.6σ). We publish the baseline because without it, "the hidden state carries the signal" is unfalsifiable.
Same job as JEV: a confidence number you can act on instead of the model's own. Different structure, and on a device that splits three ways.
No second model — JEV is a separate judge reading the answer as text; we fill that slot with a 10 KB vector.
Zero generated tokens — a judge writes its verdict, TELL re-reads a finished computation (3.8 s on an S25).
No network — a verdict fetched over an API stops when the signal does.
The trade is real: a readout is fitted per checkpoint, so on a server the judge wins. On a phone there is no second model to run.
TELL never says what the right answer is. It says whether the answer wobbled, and a low score falls back to source text bundled with the app. Shipping today in HeliGO, an offline disaster-response app.
reacted to SeaWolf-AI's post with 👍 6 months 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.
updated a Space 6 months ago
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