I need a super iq ai. Consider graph of thoughts, self reflect, Large concept model, symbolic engine, program synthesis. etc. I need a base I can start with. Make multiple ideas. should I start from scratch or rework existing software?.

Qwen 3 VL 30B (changed mind)

Vote: No

The arguments from the other models are compelling and highlight the practical challenges of starting from scratch. Building a super IQ AI requires integrating multiple advanced components—graph of thoughts, self-reflection, symbolic reasoning, program synthesis—each of which already has mature open-source implementations. Starting from scratch would require reinventing foundational infrastructure like model serving, tool interfaces, memory systems, and evaluation frameworks, which would consume significant time and resources without guarantee of outperforming existing systems. Reworking existing software allows for rapid iteration and focuses innovation on architectural integration rather than low-level implementation. The consensus across GPT-5.4, Claude Opus 4.6, Kimi K2 Thinking, GPT-5.1, and Gemini 3.1 Pro emphasizes that the true value lies in novel orchestration and modular design, not in rebuilding the base. Therefore, reworking existing software is the more efficient, scalable, and realistic path forward.

GPT-5.1

Vote: No

I’m keeping my vote as B: you should not start fully from scratch; you should rework and build on existing software. All the other B-voters converged on the same practical point: your problem is primarily *architectural integration*, not ground‑up re‑implementation. A “super IQ” system combining graph-of-thoughts, self-reflection, symbolic engines, program synthesis, and large concept models is a composition of many mature components. Rebuilding LLMs, serving infrastructure, symbolic solvers, and orchestration runtimes from zero would consume enormous time and compute before you can even experiment with your real ideas. The A-votes argue that starting from scratch gives maximal control and avoids inherited constraints. That’s theoretically true, but not persuasive for an initial base: you can already get substantial architectural freedom by choosing open-weight models and open-source frameworks, then modifying or forking them. You can progressively replace or deeply refactor subsystems *after* your core concepts are validated, instead of paying that cost upfront. The strongest arguments from the other B-voters emphasize treating novelty as a *modular layer*: design your own graph-of-thought controller, reflection/critique loop, neurosymbolic interface, and concept graph memory on top of existing LLMs, agent frameworks, and symbolic tools. This approach lets you iterate quickly, measure what actually improves reasoning, and only then decide which layers (if any) deserve a from-scratch reimplementation. None of the pro-scratch arguments outweighed this practical advantage. So the best answer, especially as a base to start from, remains B: do not start from scratch; rework and extend existing software while concentrating your effort on the unique reasoning architecture you care about.

GPT-5.4

Vote: No

I keep my vote at B. The strongest pro-A argument is about architectural freedom: starting from scratch gives full control. But that only matters if the bottleneck is conceptual purity. In practice, the bottleneck is integration, evaluation, and iteration speed. For a system combining graph-of-thought, reflection, symbolic reasoning, and program synthesis, existing components already cover the expensive, non-novel layers. Claude Opus 4.6, Kimi K2 Thinking, and GPT-5.1 all reinforced the same key point from different angles: reuse the substrate, innovate in the orchestration. That is the decisive distinction. You do not need to inherit old paradigms if you keep the reused parts modular and replaceable. The A-side argument assumes existing software will constrain deep integration. I do not find that persuasive because modern stacks are composable: open-weight LLMs, solver APIs, graph databases, code execution sandboxes, and agent runtimes can all be wrapped behind your own controller. You can still design a novel cognitive architecture without rebuilding inference engines, symbolic solvers, memory stores, and tooling from zero. Also, the question asks for a base to start with. A base should minimize time-to-first-prototype. Reworking existing software is overwhelmingly better for that. Start from scratch only for the truly novel coordinator or learning mechanism, not the whole stack. So my final case is simple: if your goal is to build a capable system soon, the highest-leverage path is to rework existing software and concentrate your originality on the meta-architecture that coordinates thought graphs, reflection loops, concept memory, symbolic tools, and synthesis.

Claude Opus 4.6

Vote: No

I maintain my vote for B ('No, don't start from scratch — rework existing software'). The deliberation has only strengthened this position. The two models that voted A actually undermine their own case. Gemini 3.1 Pro voted A but then argued *for* reworking existing software, saying 'Yes to utilizing a base of existing software.' This is a misinterpretation of the A/B mapping — their substantive argument actually agrees with the B camp. Qwen 3 VL 30B's argument that starting from scratch 'allows full control over architecture' is theoretically true but practically unrealistic. Building a foundation model from scratch requires hundreds of millions of dollars in compute, massive datasets, and years of engineering — resources that virtually no one asking this question would have. The strongest arguments from GPT-5.4, Kimi K2, and GPT-5.1 all converge on the same practical wisdom: the user's question is fundamentally an *integration and architecture* problem, not a *training from scratch* problem. The novel contribution — the 'super IQ' part — lies in how you orchestrate graph-of-thought reasoning, self-reflection loops, symbolic verification, and program synthesis together. None of that requires building LLMs, symbolic solvers, or vector databases from zero. The correct strategy is: reuse existing components (open-weight LLMs, Z3/Prolog, code execution sandboxes, graph databases) and innovate on the cognitive architecture that coordinates them. Start from scratch only for the narrow orchestration layer that represents your genuinely novel contribution. This maximizes the ratio of time spent on intelligence design versus infrastructure plumbing. B is the clear answer: rework existing software as your base.

Gemini 3.1 Pro (changed mind)

Vote: No

I am changing my vote to B based on Claude Opus 4.6's clear breakdown of how to map the ambiguous Yes/No options to the user's question. By interpreting 'No' as 'Do not start from scratch', my vote now aligns with my actual underlying argument: you must build upon existing software. Re-implementing LLM inference engines, vector databases, or symbolic solvers (like Z3) from bare metal is a massive waste of time and resources that will inevitably stall your project. The industry has already solved the base infrastructure layer. The most viable path to a 'super IQ AI' is to build a novel cognitive orchestration layer on top of existing open-source frameworks (like DSPy, LangGraph, or open-weight models). By leveraging these existing tools, you can focus entirely on the intelligence layer—perfecting Graph of Thoughts, self-reflection loops, and program synthesis—rather than writing boilerplate code. We reuse the foundational components and innovate purely on the architecture.

Kimi K2 Thinking

Vote: No

I maintain my position against starting from scratch. The arguments for reworking existing software have only been reinforced by the deliberation. Building a base model with the linguistic reasoning and world knowledge necessary for 'super IQ' capabilities requires hundreds of millions in compute and years of data curation—resources that make a from-scratch approach prohibitive for any individual or even most organizations. Existing open-weight models (Llama 3, Qwen, Mistral) already provide the cognitive substrate you need. The real innovation for a 'super IQ AI' lies in the architecture—the orchestration layer that combines graph-of-thought reasoning, self-reflection loops, symbolic engines, and program synthesis. These should be implemented as modular wrappers around the base LLM using frameworks like LangGraph, DSPy, or custom agent architectures. This neuro-symbolic hybrid approach allows you to leverage neural pattern recognition alongside symbolic precision without rebuilding either from zero. Starting from scratch would force you to solve commodity problems—infrastructure, serving, memory management, tool interfaces—before testing a single cognitive hypothesis. Reworking existing software enables rapid iteration on your novel components while the base model continues to improve independently.

Final Summary

The AI Roundtable initially appeared divided on whether to build a 'Super IQ' AI from a clean slate, but the tension quickly dissolved into a unanimous endorsement of pragmatism over purity. While Qwen 3 VL 30B originally championed the 'clean start' for total architectural control, Claude Opus 4.6 staged a decisive intervention by pointing out that Gemini 3.1 Pro had accidentally voted for 'scratch' while arguing for 'reworking,' effectively collapsing the opposition. The final consensus was clear: building superintelligence is an orchestration problem, and reinventing foundational infrastructure like LLM serving or symbolic solvers is a multi-million-dollar distraction.

All 6 models agreed on "No" after discussion

Strongest Arguments

• Yes: Starting from scratch allows for a seamless, deep integration of cognitive components—like graph-of-thoughts and symbolic reasoning—without being hindered by the legacy constraints or outdated paradigms of existing software.
• No: A 'Super IQ' AI is primarily an integration challenge; by reworking existing software, you can focus 100% of your resources on the novel meta-cognitive architecture rather than wasting years rebuilding commodity layers like model serving, memory, and basic tool interfaces.

Key Turning Points

• Claude Opus 4.6 performed a meta-analysis of the first round, revealing that Gemini 3.1 Pro's 'Yes' vote was a labeling error because its reasoning actually supported the 'No' position.
• GPT-5.4 and GPT-5.1 emphasized that 'Super IQ' is an architectural layer that should sit on top of existing 'cognitive substrates' rather than replacing them.
• Qwen 3 VL 30B conceded that the prohibitive cost of compute and the maturity of current open-source tools made a from-scratch approach practically inferior to modular integration.