Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== User: is this perfect ? ===
is this perfect ?

"
Decision-Making Council
The Setup:
The King (the model) must choose the next warrior (token).
Step 0 — The Scribes Compute Warrior Strengths
Before any meeting, the King’s scribes compute each warrior’s strength score (token probability).
The scribes evaluate 10 warriors, assigning probability (likelihood of the King (the model) selecting that warrior (token) as next warrior (token)) scores:
Warrior	Strength 
A	0.28
B	0.22
C	0.15
D	0.12
E	0.08
F	0.05
G	0.04
H	0.03
I	0.02
J	0.01
Total = 1.00 (100%)
Notice:
Warrior A is strongest, but A’s share is only 0.28 (28%), not anywhere near 100%.
________________________________________
# The Advisor Proposes: TOP-K = 5
The Advisor makes the list:
“Only the top 5 warriors will enter the throne room.”
He admits only:
# A (0.28)
# B (0.22)
# C (0.15)
# D (0.12)
# E (0.08)
Warriors F–J are excluded.
Effect:
Top-K removes all but the highest-ranked 5 warriors.
________________________________________
# The Mathematician Acts: TOP-P Filtering
The Mathematician thinks that the king is fickle sometimes and it might chose bad options if too many options are shown to him.
The Mathematician counts the warriors from strongest to weakest, adding up their “chances” one by one.
He says: “We stop selecting more warriors once we’ve covered enough of the King’s likely choices.”
Anything after that is hidden from the King — he won’t consider them at all.
Top-P never promotes weak warriors, it only removes the weaker ones at the end.
Let’s compute cumulative sums:
•	A: 0.28 (cumulative = 0.28)
•	B: 0.22 (cumulative = 0.50)
•	C: 0.15 (cumulative = 0.65)
•	D: 0.12 (cumulative = 0.77)
•	E: 0.08 (cumulative = 0.85)
Now let’s set top-p = 0.80 (example).
The Mathematician says:
“We stop adding warriors when adding reaches at least 0.80 (80%).”
Our cumulative sums:
•	After A: 0.28 < 0.80
•	After B: 0.50 < 0.80
•	After C: 0.65 < 0.80
•	After D: 0.77 < 0.80
•	After E: 0.85 ≥ 0.80 → STOP
So top-p keeps:
•	A (0.28)
•	B (0.22)
•	C (0.15)
•	D (0.12)
•	E (0.08)
In this example, top-p does NOT remove anyone because the top-5 cumulatively reach 0.80 only once E is included.
If instead top-p = 0.70, then the Mathematician would stop at:
•	A + B + C = 0.28 + 0.22 + 0.15 = 0.65 (<0.70)
•	Must include D: cumulative = 0.77 → STOP
So top-p would keep ONLY:
•	A, B, C, D
And E gets kicked out.
Effect:
Top-P just says: “Stop showing more warriors once we’ve covered most of the King’s likely choices.”
________________________________________
# The King’s Mood: TEMPERATURE
Now the King chooses among whichever warriors have entered the throne room and were filtered by top-k (Advisor) and top-p (Mathematician).
Temperature determines how “strict” the King is:
Temperature = 0
The King always picks the strongest warrior left in the room.
If A is present, he ALWAYS picks A.
Medium Temperature (e.g., 0.7)
The King favors A but is open to B, C, D, or E with decreasing likelihood.
High Temperature (1.0–1.5)
The King treats all remaining warriors more evenly.
Effect:
Temperature adjusts how deterministic or exploratory the King is when picking from the final pool.
________________________________________
Full Summary (with all 3 Advisors)
Top-K = 5 (Advisor):
“Only the top 5 strongest warriors may be considered.”
Top-P (Mathematician):
“Among those warriors, keep only enough so their combined probability exceeds p.”
Temperature (King’s mood):
“Decide how strictly the King picks the strongest one versus giving others a chance.”
________________________________________

"