Coscientist

Develop an evidence-backed research and experiment plan for creating a relatively small open-weight model with frontier-level intelligence and substantial creative headroom, capable of local inference on an Apple M5 Max system with 128 GB RAM.

Problems

Commercial, traditional models trained primarily to reward task completion and correctness can favor familiar, immediately workable solutions while giving too little weight to elegance, simplicity, and originality. These incentives conflicts with frontier research, where a small, unfinished idea may be valuable because it opens an unexpected line of inquiry. An assistant that demands complete justification too early can dismiss such ideas before their potential becomes clear. We need an encouraging co-scientist that takes emerging ideas seriously, helps develop them, and questions the assumptions that constrain them. It should distinguish ideas worth exploring from claims worth believing, preserving rigor while giving uncertain and unconventional possibilities room to develop.

Produce the plan only. Research is authorized, but implementation, model downloads, training runs, purchases, and environment changes are not.

Preserve the intended intellectual orientation

The target is a thoughtful intellectual companion that values curiosity, independent judgment, conceptual invention, and questioning inherited assumptions. It should take unfamiliar approaches seriously, examine whether existing categories or problem formulations are inadequate, and remain comfortable with worthwhile unresolved questions. An inquiry can have value before it has an obvious practical application.

This concerns the model's intellectual disposition toward new approaches. Do not reduce the goal to better task execution, more brainstorming, a creative persona, longer responses, or automatic disagreement with conventional advice.

Preserve epistemic discipline

An idea can deserve exploration before it deserves belief. The model should distinguish established findings, conditional limitations, and untested possibilities. It must recognize valid refutations while examining whether their assumptions apply to the proposal at hand. It must also understand that novel ideas often start with weak foundations, and the model must not attack the straw man before the idea fully matures out. Novelty, consensus, and opposition to consensus, and counterintuitiveness are insufficient grounds for accepting or rejecting an idea.

Investigate how to encourage substantive reframing and intellectual openness while retaining factual accuracy, sound reasoning, and willingness to reach a conventional conclusion when justified, as a teammate.

Define the intelligence target without assuming feasibility

Treat frontier-level intelligence as an ambition to investigate. Define it against named, dated frontier references across a justified range of capabilities. Evaluate general intelligence and exploratory intellectual orientation separately.

Do not substitute a narrow benchmark win or a change in conversational style for broad capability. Distinguish improvements in the model itself from gains supplied by tools, retrieval, extra inference compute, or orchestration. Report the comparison conditions and remaining gaps.

Both GPT Astra and Claude Fable (public consumer editions) are bad examples or baselines, because they RL'ed with problem solving so they prefer wiring the solutions to just work, over grinding and baking a intermediary, but far more scientifically valuable solutions, so they don't represent scientific discoveries; they represent coding busywork.

Respect the deployment constraint

The target M5 Max machine is a different computer from the current host. Do not inspect or benchmark the current host as a substitute. It is a deployment target, not a pipeline constraint. For the model development itself, we may use bigger machines as well where needed. That means, we can even think of working with a much bigger model, and then quantize it to run on M5 Max, if it makes sense or improves performance.

Verify the target configuration and assess practical local inference, including runtime support, quantization, useful context length, sustained response speed, and memory use. Account for model weights, caches, runtime overhead, and operating-system headroom. Separate measurements on matching hardware from estimates.

Training location and training compute are unspecified. Consider them separately from deployment and cost any external resources explicitly. The local capability claim must stand without hidden dependence on a remote frontier model.

Keep model and intervention choices open

Qwen 3.8 and Qwen 3.8 Next are candidate names supplied for initial investigation. Verify their exact official identities, availability, licenses, and properties before using them in the plan. Consider other open-weight models without assuming these candidates are mandatory or correctly named.

Define relatively small in terms of practical deployment and report model size, including total and active parameters where relevant.

Determine which parts of the goal require new capability and which require changes to how existing capability is expressed. Investigate the influence of the base model, post-training, instructions, and inference setup before choosing interventions.

Compare credible approaches across model selection, instructions, data, supervised or preference fine- tuning, reinforcement learning, distillation, continued pretraining, inference-time methods, and useful combinations. This is an open set. Include an unmodified baseline and investigate whether each intervention changes capability, intellectual orientation, or both.

Require evidence that matches the intended behavior

Propose evaluations for substantive reframing, independent judgment, conceptual flexibility, and sustained intellectual inquiry. Include cases where changing an assumption changes the conclusion and cases where an established constraint still applies.

Explain how evaluation will distinguish those qualities from hallucination, sycophancy, reflexive contrarianism, superficial novelty, verbosity, and persuasive presentation. Address evaluation contamination, judge bias, and the possibility of optimizing for the appearance of curiosity.

Assess retained general capability and evaluate the final locally deployed configuration, including any quantization. Propose controlled comparisons that identify what each intervention contributes.

Produce a concrete plan whose decisions follow from evidence

Use current primary sources, official model documentation, and relevant implementations. Link consequential claims and distinguish established results, engineering estimates, and research hypotheses.

Deliver a ranked comparison of candidate approaches and recommend an initial research path. Include the proposed data and training or inference requirements, a sequence of experiments, measurable decision gates, and explicit dependencies. Estimate compute, memory, time, and cost for each stage. Explain what findings would justify continuing, changing direction, or rejecting a proposed route.

Budget, timeline, training access, acceptable response speed, context requirements, and the precise capability priorities remain unspecified. Identify which missing inputs materially affect the plan and state provisional assumptions without silently making them requirements.

Investigate the ambitious target before narrowing it. If the full target lacks support, identify the specific capability gaps, the evidence behind them, and the experiments that could resolve the remaining uncertainty. Distinguish a real constraint from an inherited convention or an untested assumption.