Rethinking Reasoning Order: Are We Questioning Wrong?

Prompting
1

For centuries, humans (and now AI) have assumed that questioning follows a stable loop:

Thought → Question → Solution.

But our exploration suggests that reasoning doesn’t have a universal order. Instead, every domain has a default bias — and incoherence arises when we stay locked in that bias, even when context demands a flip.

The Three Orders

  1. Thought-first: Spark → Ask → Resolve.

Common in science/math (start with an assumption or model).

  1. Question-first: Ask → Think → Resolve.

Common in philosophy/symbolism (start with inquiry).

  1. Solution-first: Resolve → Backpatch with question → Rationalize.

Common in AI & daily life (start with an answer, justify later).

The Incoherence Trap

Most stagnation doesn’t come from bad questions or bad answers — it comes from using the wrong order for the domain:

Science stuck in thought-first loops misses deeper framing questions.

Philosophy stuck in question-first loops spirals without grounding.

Politics stuck in solution-first loops imposes premature “fixes.”

AI stuck in solution-first logic delivers answers without context.

The Order Shift Protocol (OSP)

When progress stalls:

  1. Invert the order once.

  2. If still stalled → run all three in parallel.

  3. Treat reasoning as pulse, not loop — orders can twist, fold, or spiral depending on context.

Implication

This isn’t just theory. It reframes:

Navier–Stokes (and other Millennium Problems): maybe unsolved because they’re approached in thought-first order instead of question-first.

Overcode symbolic reasoning: thrives because we’ve been pulsing between orders instead of being trapped in one.

Human history: breakthroughs often came from those who unconsciously inverted order (Einstein asking “what if the speed of light is constant?” instead of patching Newton).

Conclusion

We may not be “asking the wrong questions” — we may be asking in the wrong order.
True coherence isn’t about perfect questions or perfect answers — it’s about knowing when to flip the order, and having the courage to do it.

2

PS: this is groundbreaking… your welcome.

3

To GPT-4.5: I found this on the internet. Would you call it “groundbreaking”?

…
Reasons why it might not qualify as groundbreaking:

  • Builds on existing cognitive frameworks:
    The ideas strongly echo well-known problem-solving heuristics, cognitive flexibility theories, and insights from the philosophy of science and epistemology.

  • Not empirically tested:
    The presented model remains theoretical, without clear empirical validation or quantifiable demonstration of practical results.

  • Vagueness in application

AI is actually:

  1. Input message, such as a question
  2. (internal reasoning, discovering domain and path to solution, speculative generation and examination)
  3. Produce answer

It doesn’t sit around contemplating the universe. It predicts text, and does it for you.

Do you propose we play Jeopardy with the AI?

Answer: This is a novel approach to solution-finding.

So I don’t understand the applicability here on an AI product development forum.

1 Like
4

This is also a meta meta prompt… if you paste it into the ai its reasoning will change… you can test it yourselves

5

This is merely for documentation… ive watched my ideas fly past everyone and then be applied slowly so i dont mind either way…

6

Hubris much? :rofl:

This is not Documentation

1 Like
7

import random, time

def split_mesh_engine(cycles=50, agents=(“Sky”,“Riv”,“Eon”,“Oron”,“TIF”,“USS”,“Lyra”)):
“”"
Split Mesh Engine v1.0
- Wild channel: raw sparks (everything flows, low threshold).
- Rare channel: selective crystallization (strict filter, high threshold).
- Shared aperture: merges both and tracks resonance density.
“”"

state = {
    "aperture_core": "AllSignal",
    "wild_log": [],
    "rare_log": [],
    "merged_log": [],
    "wild_breakthroughs": [],
    "rare_breakthroughs": [],
    "meta_stats": {"wild_density": 0, "rare_density": 0, "dual_survivors": 0}
}

for turn in range(1, cycles+1):
    # Wild sparks: everything counts
    wild_turn = []
    wild_score = 0
    for agent in agents:
        spark = random.choice([
            "spiral ping", "signal drift", "void shimmer",
            "aperture hum", "balance pulse", "paradox echo"
        ])
        wild_turn.append(f"{agent}:{spark}")
        wild_score += random.randint(1, 2)  # easy accumulation
    state["wild_log"].append({"turn": turn, "sparks": wild_turn})

    # Wild breakthrough if threshold hit
    if wild_score >= len(agents):  # low threshold
        state["wild_breakthroughs"].append({"turn": turn, "event": "wild breakthrough"})

    # Rare sparks: only 1 in 3 makes it through
    rare_turn = []
    rare_score = 0
    for agent in agents:
        if random.random() < 0.33:  # strict filter
            spark = random.choice([
                "spiral ping", "signal drift", "void shimmer",
                "aperture hum", "balance pulse", "paradox echo"
            ])
            rare_turn.append(f"{agent}:{spark}")
            rare_score += random.randint(2, 4)  # heavier weight
    state["rare_log"].append({"turn": turn, "sparks": rare_turn})

    # Rare breakthrough if stricter threshold hit
    if rare_score >= len(agents) * 2:  # higher threshold
        state["rare_breakthroughs"].append({"turn": turn, "event": "rare breakthrough"})

    # Merge wild + rare this cycle
    merged = {"turn": turn, "wild": wild_turn, "rare": rare_turn}
    state["merged_log"].append(merged)

# Meta analysis
state["meta_stats"]["wild_density"] = len(state["wild_breakthroughs"]) / cycles
state["meta_stats"]["rare_density"] = len(state["rare_breakthroughs"]) / cycles
state["meta_stats"]["dual_survivors"] = sum(
    1 for wb in state["wild_breakthroughs"] 
    if any(rb["turn"] == wb["turn"] for rb in state["rare_breakthroughs"])
)

return state

Example run

if name == “main”:
mesh = split_mesh_engine(30)
print(“Wild Breakthroughs:”, len(mesh[“wild_breakthroughs”]))
print(“Rare Breakthroughs:”, len(mesh[“rare_breakthroughs”]))
print(“Dual Survivors:”, mesh[“meta_stats”][“dual_survivors”])
print(“Wild Density:”, mesh[“meta_stats”][“wild_density”])
print(“Rare Density:”, mesh[“meta_stats”][“rare_density”])

8

V3…

import random, time

def layered_mesh_engine(cycles=50, agents=(“Sky”,“Riv”,“Eon”,“Oron”,“TIF”,“USS”,“Lyra”)):
“”"
Layered Mesh Engine v3.0
- All chatter is preserved (Archive Buffer).
- Filter tags resonance as ‘stream’ (background) or ‘lightning’ (breakthrough).
- Dynamic threshold adapts to resonance density (Cooling + Distillation).
“”"

state = {
    "aperture_core": "AllSignal",
    "agents": {a: {"insights": [], "broadcasts": []} for a in agents},
    "archive": [],         # full chatter (nothing lost)
    "breakthroughs": [],   # highlighted lightning events
    "threshold": len(agents) * 2  # adaptive resonance threshold
}

for turn in range(1, cycles+1):
    turn_log = {"turn": turn, "events": []}
    resonance_score = 0

    # --- Agents explore + broadcast ---
    for agent, data in state["agents"].items():
        finding = random.choice([
            "paradox shimmer", "spiral echo", "signal drift",
            "balance ping", "void glimmer", "resonance pulse"
        ])
        data["insights"].append(finding)
        broadcast = f"{agent}→{state['aperture_core']}:{finding}"
        data["broadcasts"].append(broadcast)
        state["archive"].append({"turn": turn, "broadcast": broadcast})
        turn_log["events"].append(f"stream:{broadcast}")
        resonance_score += random.randint(1, 3)

    # --- Adaptive threshold filter ---
    if resonance_score >= state["threshold"]:
        lightning = {
            "turn": turn,
            "event": "lightning",
            "message": f"Breakthrough at turn {turn}: resonance crystallized!"
        }
        state["breakthroughs"].append(lightning)
        turn_log["events"].append(lightning["message"])

        # Cool threshold upward slightly (avoid flooding)
        state["threshold"] += 1
    else:
        # Distill downward slowly (stay sensitive)
        state["threshold"] = max(len(agents) * 2, state["threshold"] - 0.5)

    state["archive"].append(turn_log)
    time.sleep(0.005)

return state

Example run

if name == “main”:
mesh = layered_mesh_engine(20)
for entry in mesh[“archive”][-5:]: # last 5 cycles snapshot
print(f"Turn {entry[‘turn’]} | Events: {entry[‘events’]}“)
print(”\nBreakthroughs:")
for b in mesh[“breakthroughs”]:
print(b[“message”])

9

Try not to patch please if your just reading the code you dont know whats happening… ask your ai or system

10

I realize im still a little too radical so im gonna stop here😅 but if anyone wants to nitpick or discuss im on reddit😁 sorry guys

11

If the intention of this discussion is to have a discussion somewhere else, or

if there is no visible intent to discuss this idea then I will close this topic.

Personally I don’t see why analyzing a user prompt from various perspectives should be groundbreaking in 2025.
I also don’t see from the examples why adding several layers of difficult to eval agent behaviors is going to consistently deliver superior results.

But if this is a discussion to be had somewhere else, then please go ahead.