Why is cosine similarity failing in modern embedding systems (RAG, LLMs, search)?

[ahsanshaokat]

Why is cosine similarity failing in modern embedding systems (RAG, LLMs, search)?

Cosine similarity has been the default similarity metric for almost 20 years.

And yes — it worked beautifully back when embeddings were:

• small (300 dimensions)
• from clean text
• from one domain
• short and consistent in length

But 2025 embeddings are totally different.
They are:

multi-domain
noisy
multi-scale (10 words → 400 words)
multi-modal
uneven in magnitude
generated by different models
Cosine similarity was never designed for this world.

❌ The Core Problem

Cosine similarity ignores magnitude completely.

It throws away information about:

whether a chunk is long or short
whether a vector is confident or noisy
whether a document is rich or empty
Cosine only cares about the direction of vectors.

This is why cosine behaves badly in Retrieval-Augmented Generation (RAG):

Example

Query: “What is the meaning of balance in the Quran?”

Chunk A (long, meaningful paragraph):
“The Quran emphasizes balance (Mizan) as a universal moral principle…”

Chunk B (short, noisy):
“Mizan = balance.”

Cosine says: “Both point the same way → same similarity!”

So the system often picks the noisy chunk and ignores the good one.
This leads to hallucination, unstable retrieval, and wrong context selection.

❌ Multi-Scale Collapse

Real embedding magnitudes vary widely:

short chunk → 3.1
long paragraph → 16.4
OCR text → 0.8
technical explanation → 22.7
Cosine erases this information.

The result:

short noisy text wins over long meaningful text
RAG quality drops
retrieval becomes unstable
cross-domain systems fail
This is the hidden crisis of similarity in modern AI.

✔ The Solution: The Mizan Balance Function

Instead of asking:

“Do these vectors point in the same direction?”

Mizan asks:

“Are these vectors balanced relative to their scale?”

It measures:

direction
proportional magnitude
relative confidence
balance
Mizan fixes cosine's biggest blind spot.

Short noisy vectors no longer outrank long informative vectors.

✔ Real Example

Let magnitudes be:

A = 10
B = 10
C = 2
Cosine:

cos(A, B) = 0.98
cos(A, C) = 0.97 → almost identical
Mizan:

M(A, B) ≈ 0.97
M(A, C) ≈ 0.61 → correctly penalized
This is exactly what RAG systems need.

✔ When to switch to Mizan

Use Mizan if your system contains:

✔ variable-length text
✔ OCR / noisy data
✔ multi-domain mixed corpora
✔ multi-model embeddings
✔ lengthy documents
✔ paragraph + sentence mixtures
✔ hallucination issues in RAG

Cosine is fine only for academic datasets and clean single-domain text.

✔ Final takeaway

Cosine was the right tool for 2015.
It is the wrong tool for 2025.

Mizan restores:

scale awareness
proportional balance
retrieval stability
semantic fairness
Cosine measures direction.
Mizan measures meaning.

This shift is essential for next-generation AI search and retrieval.

[xiaofan-luan]

Don't think this really works becasue pretrained model already handled the difference.
but glad to see more results on evalaute in real world RAG systems