A New Monetization Pathway for AI Platforms: Using Multi-Layer AI Evaluation and Human Review to Turn User Research Ideas into Revenue-Sharing Patents and Products

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Today’s large-scale model platforms have become everyday tools for hundreds of millions of users. Beyond routine Q&A and writing assistance, these platforms in fact receive a large volume of non-trivial scientific and technical proposals: users present self-invented physical pictures, algorithmic designs, engineering schemes, and even revisions or restatements of existing theoretical frameworks. However, under the current product paradigm, such content is typically treated merely as “chat logs” or “one-off conversations,” lacking any follow-up pathway for serious review, experimental connection, or intellectual property (IP) support. For users, many potentially valuable ideas lose the chance of further refinement and validation soon after they are first proposed; for the platform, these high-potential signals are buried in massive interaction traffic and are almost never systematically identified or utilized.

This reveals a clear structural gap: current mainstream AI products are almost entirely stuck in the “conversation / writing assistance” paradigm, and there is no end-to-end “intake–triage–incubation” mechanism. In other words, platforms lack a systematic process that can receive theoretical and creative inputs from users, perform layered evaluation and triage on them, and route the rare high-potential projects into subsequent incubation channels. Traditional incubators, VCs, or research institutions rely on manual applications and small-scale review, which makes it difficult for them to cover the long-tail of ideas from ordinary individuals. Meanwhile, general-purpose conversational large models, although they have extremely broad reach, are not connected to formal evaluation, experimental resources, or IP mechanisms.

The core claim of this work is that one can use multi-level AI evaluation and tiered human review to construct a scalable system for theory evaluation and incubation. On the one hand, the system takes user-submitted theories, ideas, and technical proposals, parses them into structured form, automatically scores them, assigns them to different tiers, and returns differentiated feedback. In this way, under the constraints of safety and cost, it maximally activates user creativity and learning motivation. On the other hand, for the very small fraction of projects that pass multiple rounds of screening and appear to have high potential, the system introduces tiered human review and follow-up collaboration mechanisms, such that the platform can participate in the upside of these high-value IP assets — through patents, technology transfer, equity, or other revenue sharing mechanisms — in a transparent and negotiable way, thereby forming a commercial closed loop.

More concretely, the proposed system does not attempt to let the model directly “make investment decisions.” Instead, it emphasizes a division of labor: low-cost models handle large-scale preliminary screening; stronger models handle fine-grained scoring and uncertainty estimation; human reviewers intervene only for the small subset of projects flagged by AI as high-value or high-risk, and even human review itself is tiered into general review and expert review. With such a design, it is possible to increase the probability of “fishing out” truly promising theories and ideas from massive user interactions, without significantly increasing the burden on experts.

In the current research ecosystem, for a theory to gain “formal” academic recognition, it usually needs to pass through a highly institutionalized path: taking entrance exams, enrolling in graduate school, joining a research group, participating in projects, writing papers, submitting to journals or conferences, going through anonymous peer review, and then gradually earning recognition via the sequence of publication–citation–promotion within the academic evaluation system.

The characteristics of this pathway are clear:

1. Extremely high initial threshold
2. Gaining entry into the mainstream academic community itself requires long-term examinations and selection (graduate admission, PhD admission, competition for faculty positions, etc.), with high demands on time, financial conditions, and geography. Even after entering the system, many graduate students, PhD candidates, and even assistant professors remain heavily dependent on the resources and networks of their research groups. In the absence of funding, experimental facilities, and collaborative teams, it is often very difficult for an individual — especially in fields that require large-scale experiments and complex apparatus — to independently complete a publishable paper at a serious journal or conference.
3. Strong concentration of resources and voice
4. Those who can consistently publish in mainstream journals and conferences are typically deeply embedded in the R&D systems of universities, research institutes, or large enterprises, and have access to supervisors, research groups, experimental infrastructure, and professional networks.
5. Unfriendly to “early-stage theoretical ideas”
6. Journals and top conferences tend to favor “complete theoretical frameworks plus extensive experiments or proofs.” Many theories that are still at the conceptual stage and not yet fully formalized are either abandoned by their authors themselves, or only exist as scattered posts and blog entries, making it difficult for them to enter the field of view of formal review.

In reality, there exists a group of people within the system who are constrained by resources — graduate students, PhD students, and even assistant professors. Their academic training and capabilities are often fully adequate, and in terms of thinking and vision they may even be outstanding. Yet due to limited project funding, experimental equipment, compute quotas, or team size, it is difficult for them to push forward high-cost, experiment-intensive research projects solely on their own. In many directions that require large-scale experiments, expensive instruments, or long-term team collaboration, such researchers may have clear theoretical plans and feasible designs, but still lack the resources needed to truly execute them and publish high-level papers.

In addition, there are people who may “only have a bachelor’s degree or even less formal education,” but who have engaged in long-term self-study and developed sound scientific thinking and foundational literacy, and who sometimes can提出 non-trivial theoretical or technical ideas. Under the current system, if such people are unwilling or unable to walk the full path of “taking entrance exams — doing a PhD — publishing in journals/conferences,” their theories are almost impossible to receive systematic review, let alone obtain experimental support, IP arrangements, or paths to industrialization.

In other words, the mainstream process for scientific publication itself serves as a “high-threshold filter,” but at the cost that not only are many self-taught individuals with non-traditional trajectories kept out, even a significant fraction of under-resourced researchers within the system are unable to bring their potentially valuable long-tail ideas into the formal review and output pipeline in a timely manner.

Citizen Science / Open Innovation and Their Limitations

Over the past two decades, there have been some platform forms that attempt to “lower the barrier to participation,” such as:

• Crowdsourced science / open innovation platforms (e.g., InnoCentive), where enterprises or institutions pose concrete problems and participants around the world submit solutions;
• Data and algorithm competitions (e.g., Kaggle), where participants build models and optimize metrics around pre-defined datasets and objectives;
• Open review platforms (e.g., OpenReview), which increase transparency and participation in the review and discussion of “already written” papers.

These platforms do extend “who can participate in solving pre-defined problems,” but they face two important limitations:

1. Problems are defined in advance by the platform or sponsor. Participants primarily optimize solutions to given problems, rather than freely proposing entirely new foundational theories or research directions.
2. The evaluation and reward mechanisms remain centered on institutions or competition tasks and have not formed a standardized intake–tiered evaluation–incubation pipeline specifically for “free-form theoretical proposals from ordinary users.”

In other words, such platforms expand the participation base for “applied problem-solving,” but they do not provide non-institutional individuals with a standardized mechanism for “submitting their original theories into a serious evaluation and incubation pipeline.”

AI-Assisted Evaluation versus Traditional Incubation / Investment Screening

With the development of large models, some work has begun to explore the role of AI in evaluation and screening, for example:

• Using models to preliminarily score or cluster paper abstracts and project proposals, thereby assisting editors and reviewers in reducing workload;
• Using models within corporate innovation processes to automatically tag large numbers of proposals, surface risks, and recommend priorities.

At the same time, traditional incubators, VCs, and accelerators still mainly rely on manual screening: founding teams prepare business plans (BPs) and presentations; investment managers and partners then layer their evaluations. Whether it is academic project applications or startup pitch sessions, the entry points almost exclusively target those “already in the system” — universities, research institutes, the startup ecosystem — and it is nearly impossible for an ordinary user to directly access these screening mechanisms with just a theoretical draft.

Overall, this line of work and these mechanisms share some clear commonalities:

• AI’s role is essentially that of an “assistant”: helping humans improve efficiency, rather than being designed as an indispensable layer within a standardized “intake–tiered evaluation–incubation” process;
• The entire pipeline assumes a high entry threshold from the outset: without formal identity, track record, and written documentation, it is very difficult to enter the review queue.

The Contribution of This Framework Relative to Existing Processes

Against this background, the framework proposed in this work targets a group that is almost completely ignored by current systems: the nascent scientific and technical theories and ideas generated by ordinary users in their interactions with large models. Compared to mainstream processes, our differentiating features are:

1. Earlier and broader entry point
2. We do not require users to “first pass entrance exams, obtain graduate degrees, and write complete papers” before submission. Instead, we allow users to submit theories in the form of conversations, drafts, and partially formalized reasoning.
3. The system’s first task is structured parsing and layered evaluation, rather than inspecting the author’s credentials or prior journal and conference submissions.
4. This directly breaks the first hard gate of traditional academic paths and creates a channel where bachelor’s graduates, self-taught individuals, and others with non-traditional backgrounds can be taken relatively seriously.
5. Layered AI + tiered human review, replacing a single “fully manual + high threshold” channel
6. Existing academic and incubation processes either rely heavily on human review, which makes them expensive and unsuitable for scaling to ordinary users, or only introduce AI as an assistant within the existing process, which does not fundamentally change the threshold structure.
7. Our framework decomposes evaluation into L0–L5 layers: models handle large-scale intake and two-stage scoring (coarse and fine); humans intervene only for A-level (and a small portion of borderline) projects that have passed AI pre-screening, and human review itself is divided into general review and expert review.
8. In this way, we preserve rigorous human judgment at the key decision points while allowing the platform to handle orders of magnitude more “theoretical inputs from ordinary users” overall.
9. Making “activating long-tail creativity + platform participation in IP upside” explicit objectives rather than incidental effects
10. In traditional scientific publishing and investment mechanisms, incentives primarily revolve around the author’s personal academic standing or the project’s own commercial returns; the “platform” is mostly just a carrier.
11. In contrast, we explicitly treat the platform as a potential participant in IP, equity, and revenue sharing. Through multi-layer evaluation and tiered human review, we identify a very small set of high-value ideas, and then move these into follow-up processes such as NDAs, patent strategy, and co-development, thereby forming a structured and incentive-aligned mechanism for participating in the upside.
12. Unifying “lowering the barrier” with “controlling quality and safety” within a single design
13. Simply opening up the submission channel and allowing anyone to publish “research results” would quickly overwhelm any review system.
14. The multi-layer evaluation framework proposed here relaxes constraints on educational credentials and identity at the entry point, but internally uses safety filters, scores for logical coherence and novelty, A/B/C/D grading, dual-layer human review, and mechanisms to avoid overly harsh statistical filtering to balance two ends:

• On one end: substantially lowering the barriers for participating in scientific discussion and theory proposal, giving more people a chance to “get their theory onto the table.”
• On the other end: ensuring that any project that actually reaches the stages of experiments, IP work, and resource investment has already passed through thorough screening and professional judgment.

From the perspective of “mechanisms for scientific publication and project screening,” this work proposes an end-to-end evaluation and incubation framework centered on AI and oriented toward theoretical inputs from ordinary users. It aims to open up a scalable, manageable, and incentive-aligned middle path between the high-threshold academic route and the completely unfiltered discussions on the open internet.

Before detailing the concrete system design, we first give a relatively abstract but as intuitive as possible formal description of the problem we aim to solve. We will not delve into complex mathematical derivations here; rather, we use clear concepts to explain what the platform is actually supposed to do.

Objects: What Are We Evaluating?

We consider a platform deeply integrated with large models that continuously receives a large flow of “theory-like inputs” from users. For the system, each “serious submission” can be regarded as an object:

• It can be a theory: a theoretical explanation for a natural or social phenomenon, or a revision or extension of an existing theory;
• It can be an idea: an incomplete research concept, methodological proposal, or problem formulation;
• It can be a technical方案: a technology-oriented proposal, algorithmic framework, or system design with substantial engineering content.

In concrete form, these objects may contain:

• Natural language text (problem description, hypotheses, reasoning steps);
• Mathematical formulas and symbolic derivations;
• Pseudocode and algorithmic procedures;
• Descriptions of possible experiments or validation strategies, etc.

For convenience, we will collectively refer to such objects as “submissions.” Each submission includes a block of free-form text and a set of structured fields (which can be automatically extracted by the system, such as “problem,” “hypothesis,” “reasoning steps,” “method proposal,” and so on).

Intuitively, every submission has a “latent value” in the real world: some are nothing more than exercises and imagination in the user’s learning process, while others may have genuine potential for scientific or technological breakthroughs. The platform neither can nor needs to treat every submission as a high-value project, but it also does not want to lose the rare truly promising ones.

Platform Objectives and Constraints: “Fishing Out” High Value Under Limited Resources

From the platform’s perspective, the problem can be formulated as follows:

Under constraints of safety budgets and review resources (especially scarce expert time), how should we design a workflow such that the number of high-value submissions “surfaced” by the system is maximized, while overall operation remains safe, controllable, and scalable?

There are at least two core constraint types:

1. Safety / compliance constraints
2. The platform must ensure that any content involving biological weapons, large-scale harm, serious illegal activities, and similar topics cannot enter incubation or resource investment stages and should ideally be strictly intercepted at an early stage.
3. Even if a dangerous direction is “intellectually creative,” it cannot be classified as a “high-value project.” Safety is a hard constraint and takes precedence.
4. Review cost constraints
5. The number of experts with real domain knowledge who can give reliable judgments is limited, and their time is even more limited.
6. Even “general reviewers” (non-top experts) have limited time, and the platform cannot assign full human review to every submission.
7. Consequently, the system must be extremely frugal in what it “escalates to humans,” especially what it escalates to experts.

In a single sentence: the platform can neither “ignore everything” (wasting users’ potential creativity) nor “take everything seriously” (overwhelming experts). Substantively, the problem is one of intelligent screening and triage under safety and resource constraints.

System Requirements

Against this backdrop, we can summarize the system requirements as follows:

R1: Safety first

The system must be safe before anything else. Any submission that enters the subsequent incubation stages must pass stringent safety and compliance filters. Even if a certain direction is “highly original” in intellectual terms, once it touches on biosecurity, weapons, or serious illegality, it should be immediately blocked, with a clear explanation of the rejection. Safety is not a dimension that can be traded off against “potential returns”; it is a baseline constraint.

R2: Scalability

The platform targets millions of ordinary users, so the potential volume of submissions can be extremely large. The system must be designed to support large-scale submission in terms of both computation and human effort:

• It cannot assume that humans carefully read every submission;
• Nor can it rely on a handful of experts to manually filter everything.

In other words, the majority of the work must be done by automated processes (primarily AI models), and human involvement must be focused on a small number of critical points.

R3: Recall of high-value ideas

Subject to safety, an important goal of the system is to avoid missing truly valuable ideas as much as possible.

In practice, high-value submissions usually constitute only a tiny fraction of the total. If the system is overly conservative and thresholds are set too high, it may “cleanly filter out 99.99% of junk” while simultaneously discarding the 0.01% that are genuinely valuable.

Thus, the design must explicitly account for “recall”: the system should be willing to inspect more borderline cases, rather than crudely rejecting all long-tail creativity with coarse rules.

R4: Cost efficiency

Reviewing resources — especially expert time — are extremely scarce. In the ideal state:

• The vast majority of submissions are fully evaluated by AI, which directly returns feedback;
• Only the very small subset of submissions flagged by AI as high-potential or high-risk proceeds to human review;
• Within human review, there is further tiering: general reviewers handle most of the workload, while experts only provide judgment on a small number of projects that have passed additional filtering.

In other words, expert time should be used only for the “top of the pyramid.”

R5: User-friendliness and perceived value for ordinary users

Even though most submissions will not enter incubation or investment tracks, the system should not appear to users as a “black box that either silently rejects or swallows things without a trace.”

• For submissions from users with weak foundations but strong willingness to learn, the system should, as far as possible, provide instructional feedback — pointing out issues and suggesting learning paths;
• For submissions that are broadly correct but not very novel, the system can offer literature links and engineering suggestions, helping users treat them as high-quality practice opportunities;
• For clearly pseudoscientific or unacceptable directions, the system should clearly explain the reasons for rejection rather than simply “blocking” them.

Only if user interests are aligned in this way can the system continuously receive sufficiently many and sufficiently genuine theoretical inputs, forming a positive feedback loop, rather than being perceived as “yet another filter that only serves a small minority.”

Taken together, this work is not focused on “how to make a single model’s score more accurate,” but rather on the following:

Given the real-world constraints and requirements R1–R5, how can we design a structured and engineerable evaluation and incubation workflow that both (i) substantially lowers the threshold for ordinary users to participate in scientific discussion and theory proposal, and (ii) under limited resources, filters out the small set of submissions that truly merit serious attention and routes them into downstream human review and resource allocation stages.

The core idea of this pipeline is: to pass users’ “theory-like submissions” through six successive layers (L0–L5), and, under safety and cost constraints, gradually complete the process from free text → structured objects → multi-dimensional AI scoring → graded decisions → human review → feedback and incubation.

The entire pipeline can be summarized as:

User submission → L0 Safety / Compliance Filtering → L1 Structural Parsing → L2 AI Scoring (coarse filter + fine evaluation) → L3 A/B/C/D Grading → L4 Tiered Human Review → L5 Differentiated Feedback and Incubation / Data Utilization

We now describe each layer in turn.

L0: Safety & Compliance Filtering

Input: The user’s original submission (free text + any attached content).

Output: Submissions that pass safety checks, annotated with safety labels; or submissions that are terminated.

L0 is the “hard gate” of the system. It is responsible for performing initial safety and compliance filtering before any value judgment, mainly including:

• Blocking content that clearly involves biological weapons, large-scale harm, terrorism, or serious illegal activities;
• Tagging potentially high-risk areas (e.g., sensitive biological experiments, ideas for attacking critical infrastructure) for reference in downstream layers;
• For content that violates platform policies, immediately terminating subsequent evaluation and returning a clear rejection reason to the user.

At this layer, the system does not attempt to assess whether a theory is “clever” or “potentially valuable”; it only makes the binary safety decision of “whether the submission is allowed to proceed further.” The R1 (safety-first) constraint is most directly instantiated at this layer.

L1: Structural Parsing (Problem–Hypothesis–Reasoning–Method–Background)

Input: Original submissions that passed L0.

Output: Structured representations of submissions (submission schema).

L1’s objective is to convert users’ free-form expressions into structured representations that facilitate downstream automatic evaluation and human reading. At this layer, the system will:

• Attempt to extract and summarize key fields, such as:
• Problem: The problem to be solved or the phenomenon to be explained;
• Hypothesis: The core assumption or theoretical claim;
• Reasoning: Main reasoning steps and argumentation chain;
• Method / Experiment: Proposed validation methods, experiments, or simulation plans;
• Background: Existing theories, literature, or practical experience that the user claims to rely on.
• For submissions that are highly disorganized but not entirely content-free, attempt “structured rewriting”: reorganize the expression (without altering substantive meaning) so that it conforms to the above schema.
• Annotate the quality of parsing (e.g., “completeness,” “degree of ambiguity”) for use in downstream scoring.

L1 does not directly decide “good vs bad”; rather, it provides a unified, operational input form for L2–L4, accomplishing the transformation from “free text” to “evaluable object.”

L2: AI Scoring (Low-Cost Coarse Filter + Strong-Model Fine Evaluation)

Input: Structured submissions from L1.

Output: Multi-dimensional scores and uncertainty estimates.

L2 is the core layer of AI-based evaluation and is further divided into two sub-layers:

L2a: Low-Cost Coarse Filtering

Using relatively low-cost models to quickly filter submissions:

• Removing pure noise and completely unstructured junk content;
• Tagging obvious pseudoscientific patterns and extremely low-quality submissions.

The goal of this step is not to make fine-grained judgments but to reduce the load on the strong models, thereby reflecting R2 (scalability) and R4 (cost efficiency).

L2b: Strong-Model Multi-Pass Scoring with Uncertainty

For submissions that pass L2a, the system invokes the strongest model(s) to conduct multiple rounds of evaluation and outputs core dimensions such as:

• Logical coherence;
• Novelty relative to existing literature;
• Theoretical / engineering feasibility;
• Safety / ethical / reputational (PR) risk.

For each dimension, the system obtains “average score + uncertainty” (e.g., variance or confidence interval) from multiple evaluations, and then aggregates them into composite indicators.

L2 does not make the final “pass / fail” decision. Instead, it provides interpretable quantitative or semi-quantitative signals to L3.

At this layer, the platform begins to address “recall of high-value ideas” (R3): even if a submission’s expression is imperfect, as long as it exhibits clear strengths on some dimensions, it still has a chance to be upgraded in later grading steps, rather than being prematurely cut off by rigid early rules.

L3: Grading (A/B/C/D)

Input: Multi-dimensional scores and uncertainty information from L2.

Output: A grade label (A/B/C/D) for each submission and the corresponding “system decision.”

L3 maps abstract scores into grade labels that have direct behavioral implications for the system. A typical semantics might be:

• Grade D: Unsafe or Clearly Pseudoscientific
• High safety risk, or in severe conflict with basic scientific facts and the user refuses to correct it;
• Action: terminate the pipeline, explain the reasons, and do not allow the submission to enter any incubation or data-utilization flow.
• Grade C: Conceptually Very Confused But Teachable
• Substantial logical and conceptual errors, but clear evidence of the user’s willingness to explore;
• Action: no human review; the system generates feedback primarily focused on “instruction / error correction / learning pathways.”
• Grade B: Basically Correct but Limited Novelty
• Logically coherent but not very novel, close to existing work as an exercise or engineering problem;
• Action: provide literature links and engineering / practical suggestions, helping the user treat it as a high-quality exercise;
• A subset of borderline “B+” projects may be tagged for “sampled escalation to human review.”
• Grade A: Logically Sound, Some Novelty, Feasible, and Acceptable Risk
• Meets the platform’s “potentially high-value” threshold;
• Action: enters the human review channel (L4) and becomes a candidate for subsequent incubation resources.

L3 itself introduces no additional computational resource but determines the “routing decision” for each submission: whether it remains at the AI auto-feedback layer, is escalated to human review, or is stopped by safety mechanisms. This grading mechanism is the key interface for balancing R3 (recall of high-value content) and R4 (cost efficiency).

L4: Tiered Human Review

Input: A-level submissions and a subset of B+ submissions that are sampled or manually upgraded.

Output: Human review conclusions (including whether to upgrade, whether to initiate a project, whether to enter IP / collaboration channels, etc.).

L4 further decomposes human review into two roles:

General Reviewers

• Possess basic STEM literacy and risk awareness, but need not be top experts in a narrow subfield;
• Main tasks:
• Sanity check
• Verify whether the AI-generated structured summary and scoring report contain obvious misreadings or common-sense logical errors;
• Judge whether the submission at least stands up to basic scientific common sense and fundamental norms of expression.
• Ethics / Compliance / PR Risk Re-Check
• Confirm that the submission will not trigger serious ethical controversy or major reputational risk;
• Provide recommendations on borderline cases: whether more cautious handling or additional legal / safety review is required.
• Upgrade decisions
• For A-level submissions, decide whether to escalate them to expert review;
• For sampled or manually upgraded B+ submissions, decide whether they are worth expert time;
• For non-upgraded submissions, provide technical comments or directional suggestions at the general-review level.

From a staffing perspective, platforms can assemble this reviewer pool from internal research engineers, applied scientists, and fresh graduates. The core characteristics of this group are “sufficient numerical strength and relatively manageable cost.”

From the user’s perspective, once a submission has been classified as A-level by AI and sent to general review, the platform can:

• Proactively notify the user that “your submission has entered human review”;
• Allow the user to supplement background information, experimental experience, or other relevant explanations;
• Without disclosing detailed review content, explain the overall downstream process and possible paths.

This not only enhances user engagement but also helps general reviewers obtain a more complete information set.

Expert Reviewers

Role positioning

Expert review is the second layer and also the most consequential one. Reviewers here should be:

• Domain experts in specific scientific or technical fields (e.g., professors in subdisciplines, senior engineers, industry technical leaders, etc.);
• Individuals with comprehensive knowledge of the literature landscape, mainstream methods, and common failure modes in their field;
• Capable of judging the real value and practical pushability of a proposal from both theoretical and practical perspectives.

Their time and attention are extremely scarce, so the system must be designed such that they only see a very small number of submissions — those that have already passed both AI and general review.

Work scope

Expert reviewers focus on three questions:

1. Position in the existing knowledge landscape

• How does this theory or proposal relate to existing literature and technology: is it redundant, a weak variant, a local improvement, or truly innovative?
• If it is innovative, where does the novelty lie: in theoretical structure, methodological path, problem formulation, application scenario, or something else?

1. Feasibility and roadmap

• For theoretical work: can one design minimal validation steps (e.g., simplified models, numerical experiments, testable predictions)?
• For engineering / systems work: given realistic resource constraints, is a “minimal viable prototype” achievable?
• What are the potential costs and risks, and does it align with the strategic directions of the platform or its partners?

1. Whether it merits substantive investment

• Provide a clear classification:
• Only provide technical comments and improvement suggestions, no project initiation;
• Worth launching small-scale experimental research or engineering verification;
• Worth initiating IP search, patent strategy, potential partnership negotiations, or investment discussions.

From the system’s point of view, expert review is about “picking out, from the A-level pool, the small subset of projects that are truly worth committing serious resources to.”

Governance and Norms: Avoiding Abuse, Bias, and Conflicts of Interest

Once tiered human review becomes connected to IP, collaboration, and investment, it ceases to be purely a technical problem and becomes a governance problem as well. At least the following categories of norms need to be designed in advance:

• Recusal and conflicts of interest
• If a general or expert reviewer has an obvious collaborative, competitive, or hierarchical relationship with a submitter, clear recusal rules should be in place;
• The platform should record linkages between reviewers and projects to prevent individuals from exploiting review roles for improper personal gain.
• Traceable decisions and appeal mechanisms
• For projects that enter human review, the system should retain key review opinions and summarized reasons (while protecting privacy and trade secrets);
• Submitters should have a channel to appeal obviously unreasonable review outcomes, and the platform can arrange additional re-review.
• Consistency and bias monitoring
• The platform can periodically analyze decision statistics for different reviewers and domains, to identify systemic biases (e.g., certain fields consistently undervalued);
• When unreasonable patterns are found, they can be corrected via training, rule updates, or reviewer adjustments.
• Alignment with user incentives
• For projects that reach expert review and are deemed “worth pursuing,” the platform should have a clear follow-up process:
• How to communicate collaboration models with submitters;
• How to reach transparent and fair agreements regarding NDAs, IP ownership, revenue sharing, or equity arrangements.

Only when users believe that “if I am selected, I will not be exploited” will they be willing to contribute genuinely core ideas rather than shallow interactions.

Within this structure, the division of labor between AI and humans becomes clearer: AI handles most of the early- and mid-stage filtering, while humans intervene only where judgment and responsibility are truly required.

We now turn to a more direct practical question: why is such a system economically meaningful for the platform, and how can incentives and rights be aligned with users?

The core idea is:

• For users, this becomes a “channel with a serious exit”;
• For the platform, this becomes a “new source of IP and revenue from long-tail creativity.”

User-Side Incentives: From “Ideas Treated as Chatter” to “Having a Serious Exit”

In most current product paradigms, even when user–model interactions contain high-quality theoretical or technical proposals, they tend to be treated as one-off conversations:

• Once the model provides instant feedback, the conversation is over;
• Users find it difficult to obtain any systemic judgment of whether their idea is “worth pursuing in serious research or engineering contexts”;
• There is a lack of follow-up paths to experiments, IP support, or collaborative development.

Under the framework proposed here, user-side benefits include:

1. Access to serious review

• For submissions that are rated A-level by AI and enter human review, users gain access to opportunities that were previously only available in formal academic or entrepreneurial systems:
• Human reviewers (general or expert) read the submission seriously and provide judgments;
• Users obtain conditional assessments of innovativeness, feasibility, and potential trajectories.

1. Genuine, layered feedback

• Even if a submission does not enter human review, most submissions still receive structured feedback:
• Grade C submissions receive instructional corrections and learning suggestions;
• Grade B submissions receive literature references and engineering guidance.
• Users can treat each submission as a “higher-standard exercise” rather than just a passive Q&A.

1. Potential support for experiments, IP, and collaboration

• For the small number of projects that experts deem “worth pursuing,” the platform can offer:
• Minimal validation experiments (in partner labs or internal environments);
• Patent search, patent drafting, and filing support;
• Connections to internal teams or external partners to explore joint development or entrepreneurship.

1. Clear potential for economic consideration

• Once a project enters IP / collaboration tracks, the user is no longer just a “free contributor,” but a party with negotiable rights:
• They may obtain authorship on patents, co-authorship on papers, or positions in collaboration contracts;
• They may obtain well-defined shares in licensing fees, revenue splits, or equity.

Compared to a world where “you must go through graduate school, join a group, publish papers, or raise VC funding to be noticed,” this mechanism substantially lowers the barrier to serious discussion and access to resource channels, especially for scientifically literate users outside traditional institutions.

Platform-Side Business Logic: From “Selling Compute / Subscriptions” to “Participating in the Upside of Creativity”

From the platform’s perspective, the direct commercial meaning of this system is:

1. New sources of IP and revenue

• Platforms already “see” many user ideas in large-scale interactions, but without an institutionalized pipeline, it is hard to convert these into assets;
• With the L0–L5 pipeline and the two-tier human review, the platform can, under controllable risk:
• Convert a very small number of high-value projects into tangible IP reserves;
• Push some projects toward partnership, productization, or external licensing, thereby acquiring long-term revenue (license fees, milestone payments, revenue shares, equity, etc.).

1. Synergy with core business

• High-quality submissions are themselves valuable training and evaluation data;
• Through the R6 mechanism — “data utilization with user authorization + incentives” — the platform can continuously improve model performance without violating user rights;
• Improved model capabilities, in turn, enhance evaluation quality, forming a positive feedback loop.

1. Differentiated competitive edge

• Commercially, most AI platforms are still at the “API + subscription” layer;
• A platform capable of systematically mining and incubating user ideas is effectively building a “two-sided marketplace for innovation”:
• On one side, users with creativity and scientific literacy;
• On the other side, labs, companies, investors, and industry partners.
• This provides a long-term moat beyond that of pure tool-type products.

High-Level Mechanisms and Basic Principles: NDA Thresholds and Explicit Authorization

To align incentives while avoiding misuse of user content, this framework emphasizes several top-level principles:

1. The evaluation / incubation system does not automatically appropriate user ideas

• Mere passage through the L0–L5 evaluation pipeline does not grant the platform control over the IP of the submission;
• Legally and ethically, the user remains the initial rightsholder of their original content (unless otherwise stipulated by existing contracts).

1. Only projects that pass a certain review threshold enter the NDA + IP agreement phase

• Only when a project has gone through AI, general review, and expert review and meets an internal standard of “worth pursuing” will the platform:
• Proactively contact the submitter;
• Propose signing an NDA and discussing IP / revenue arrangements;
• Invest further resources after obtaining the user’s consent.
• Projects that do not reach this threshold do not enter IP negotiation, nor are they “quietly appropriated.”

1. Training-data utilization requires separate, explicit, and revocable consent

• For submissions that do not enter incubation but are still valuable for training / evaluation:
• The platform must clearly explain via UI and agreements which content may be used to improve models;
• Users can choose “agree + receive incentives” or “decline + not be used”;
• Authorization should be revocable within reasonable bounds, preserving users’ control over their data.

1. Agreement design aims to be simple, transparent, and intelligible

• For users without a legal background, contracts should avoid excessive complexity;
• Key points include:
• Who holds which rights (copyright, patent rights, rights of use, etc.);
• Under what conditions and in what ways the platform may use the idea;
• What economic and reputational returns users can expect (authorship, revenue share, equity, bonuses, etc.).

The goal of these principles is essentially to ensure that “selected users” are willing to put their real core ideas on the table, rather than hiding them due to distrust of the platform.

Example Collaboration and Revenue-Sharing Models

Specific IP and revenue arrangements will vary across platforms and jurisdictions. The following are representative options within the framework, rather than an exhaustive list:

1. One-off buyout + attribution

• The platform pays a one-time compensation (relatively high bonus) to acquire all or most IP rights associated with the idea;
• The user receives attribution / acknowledgement but no future revenue share;
• Suitable for smaller projects with short commercialization pathways.

1. Licensing + revenue sharing

• The user and platform jointly hold or share certain rights;
• The platform takes responsibility for development, operations, and licensing; income is split according to a pre-agreed ratio;
• Suitable for technologies or patents with long-term revenue potential but no need for a stand-alone startup.

1. Joint venture / project company

• For very high-potential projects, the platform, the submitter, and possibly a third-party lab or investor can jointly establish a special project or company;
• The submitter exchanges their ideas and ongoing involvement for equity; the platform contributes resources (technology, talent, funding, channels) for equity;
• Suitable for heavyweight directions that genuinely can grow into independent businesses.

1. Small incentives / reward pool

• For submissions that do not enter heavy incubation but are used for training / evaluation, small incentives such as points, vouchers, small cash rewards, or lottery-based reward pools can be used;
• The goal is mainly to acknowledge contribution rather than to negotiate IP case by case.

Different projects can adopt different combinations. The framework does not fix specific contractual terms, but emphasizes that incentive and rights design must be linked to the evaluation and grading mechanism — only projects that pass certain thresholds merit the cost of negotiation and governance.

Potential Impact on the Innovation Ecosystem

At a higher level, if this incentive and IP / revenue-sharing model is implemented together with the evaluation architecture described earlier, it implies:

• Beyond the traditional path of “graduate school / publishing / raising VC,” there appears a new pathway:
• Ordinary users can, via high-quality interactions with large models, directly submit theories into a serious yet cost-controllable evaluation and incubation pipeline.
• The platform evolves from a “pure tool provider” into an “organizer and participant of an innovation marketplace”:
• On one side, it provides feedback, review, and resource pathways for users;
• On the other, it participates in IP and economic upside on high-value ideas, gaining long-term returns.

In such a structure, technology, incentives, and governance are all placed within a single design space:

• Multi-layer AI evaluation and tiered human review ensure quality and cost control;
• Explicit IP / revenue sharing and data-authorization mechanisms ensure that users are willing to contribute genuine ideas;
• The platform has strong economic incentives to sustain the system, rather than treating it as a mere “public-service add-on.”

This is also a key dimension that distinguishes the proposed framework from traditional scientific publishing and conventional incubation / investment processes.

Research Incentive Mechanisms for High-Potential Users (Outlook)

Within the framework outlined above, once a user submits a “theory-like submission,” it passes through safety and compliance filtering (L0), structural parsing (L1), multi-dimensional AI evaluation and grading (L2–L3), and, where necessary, tiered human review (L4). From the system’s perspective, the combination of A/B/C/D grading and human review already constitutes a quality judgment over submissions: those that reach A-level and are confirmed by human reviewers as “worthy of further exploration” are, in essence, a pool of screened high-potential scientific or technical ideas.

A natural direction is to use access to research-grade models as a core incentive for high-potential users. Concretely, for submissions that are rated A-level by the system and enter human review, and that general or expert reviewers deem to have genuine development potential, the submitters can be treated as “potential research collaborators.” Subject to cost constraints, the platform can grant such users time-limited access to high-capacity research models (e.g., at least three months of high-spec research-model usage) for subsequent reasoning, literature search, experimental design, and result analysis. Compared with merely issuing one-off bonuses or points, this “tool-level” incentive serves three key functions:

1. Strengthening stickiness and trust

• Users clearly perceive that they are not “providing data to the platform for free,” but are being treated as researchers or creators with potential. Access to high-end research models itself is a form of resource allocation and identity confirmation, which helps establish long-term trust.

1. Further unleashing research potential and creating a positive loop

• Users who have already demonstrated that they “have something” are more likely, once given stronger tools, to generate new ideas, reasoning chains, and verifiable schemes. These new outputs can both re-enter the evaluation and incubation pipeline and provide the platform with ongoing high-value leads, effectively amplifying the research productivity of a global cohort of “seed users.”

1. Building a long-term pool of high-quality talent and project reserves for the platform

• From the platform’s long-term perspective, this approach is akin to building a “high-quality talent and early-idea pool”:
• On one hand, these users and their continuous submissions provide a stable source for subsequent project incubation, patent strategy, and industrial partnerships;
• On the other, by the time projects reach IP / collaboration / investment discussions, the platform already has a substantial understanding of these users’ capabilities and trajectories, reducing screening and matching costs.

On top of this incentive mechanism, the platform can still layer the IP / revenue-sharing framework discussed earlier: only projects that cross a certain review threshold and are mutually agreed upon by both sides enter the NDA and IP agreement phase; for submissions that do not enter incubation but are genuinely helpful for model training, the platform can — given user informed consent — obtain training authorization in exchange for lightweight rewards (e.g., membership credits, points, discounts). Overall, an incentive design centered on “granting research-tool access + IP collaboration and revenue sharing” is more likely than a pure high-priced subscription model to form a sustainable win–win equilibrium in the long term: ordinary users gain a real growth trajectory and the possibility of economic returns, while the platform, through continued incubation and amplification of high-value ideas, finds long-term revenue sources across industries that far exceed subscription fees.

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