APOLLO: Autonomous Predictive On-Chain Learning Orchestrator for AI-Driven Blockchain Governance

Abstract

Decentralized Autonomous Organizations (DAOs) suffer from critical governance challenges, such as low voter participation, large token holders’ dominance, and inefficient proposal analysis by manual processes. We propose APOLLO (Autonomous Predictive On-Chain Learning Orchestrator), an AI-powered approach that automates the governance lifecycle in order to address these problems. The gemma-3-4b Large Language Model (LLM) in conjunction with Retrieval-Augmented Generation (RAG) powers APOLLO’s multi-agent system, which enhances contextual comprehension of proposals. The system enhances governance by merging real-time on-chain and off-chain data, ensuring adaptive decision-making. Automated proposal writing, logistic regression-based approval probability prediction, and real-time vote outcome analysis with contextual feature-based confidence scores are some of the major advancements. LLM is used to draft proposals and a feedback loop to enrich its knowledge base, reducing whale dominance and voter apathy with a transparent, bias-resistant system. This work demonstrates the revolutionary potential of AI in promoting decentralized governance, paving the way for more effective, inclusive, and dynamic DAO systems.

1. Introduction

DAO is an organizational structure that functions independently of a central authority. It is made up of people who are all committed to furthering the interests of the community and was made popular by blockchain enthusiasts. It provides accountability by being completely open and recording all decisions and acts on a distributed ledger. DAOs use a bottom–up management style in which members vote to make decisions collectively, giving everyone a say in the organization’s course [1]. This methodology involves token-weighted voting on ideas that are encoded as smart contract transactions, with approved measures running independently over blockchain infrastructure. Despite offering previously unheard-of openness, real-world DAO governance encounters structural challenges. Token-based voting creates new vulnerabilities not found in traditional corporate governance, such as power disparities and inflexible procedures [2].

The efficacy of DAO governance is threatened by a number of serious problems as we summarized in Figure 1. When a small number of token holders acquire an excessive amount of power over decision-making procedures, voter apathy is a serious problem, as poor involvement is sometimes caused by proposals’ complexity. If the voting rate is less than 15% in DAOs, it indicates a lack of interest and participation [3]. Inflexible tooling on platforms such as Tally confines proposal generation to pre-established templates, which reduces flexibility [4]. Decentralized aspirations become oligarchic realities as a result of these inefficiencies’ creation of bottlenecks [5].

LLMs have become powerful tools for automating governance duties at the same time. Their ability to think contextually enables them to comprehend proposal semantics in a nuanced way [6]. Additionally, multi-step task automation enables processes to be executed sequentially starting with data retrieval, followed by analysis, and concluding with drafting proposals [7]. LLMs achieve high accuracy in classifying DAO proposals and success in synthesizing executable transactions [8], outperforming traditional rule-based systems.

To harness this potential, we introduce an end-to-end framework for AI-driven governance. This work aims to autonomously analyze, predict, and draft proposals. It also focuses on advancing transaction-centric automated governance systems such as AgentDAO [8]. The contributions of this work are as follows:

• Introduces an end-to-end LLM-based system for automating DAO governance with a multi-agent setup powered by Retrieval-Augmented Generation (RAG);
• Combines on-chain and off-chain data (transactions and forum discussions) for context-aware governance decisions;
• Addresses DAO governance issues such as whale dominance and voter apathy through neutral, data-driven analysis and proposal drafting;
• Implements a continuous feedback loop, enhancing future decisions by enriching the knowledge base with each executed proposal.

The remainder of this paper outlines preliminaries (Section 2), related work (Section 3), and motivation (Section 4). Then, the proposed methodology (Section 5), and evaluation (Section 6) are discussed. Finally, we conclude (Section 7) with future work.

2. Preliminaries

Blockchain Governance Mechanics: On-chain governance operates through token-weighted voting systems, where stakeholders’ influence corresponds to their token holdings as seen in protocols like Uniswap [9]. The governance lifecycle unfolds in three phases: (i) the drafting phase, where proposals are encoded as technical smart contract payloads; (ii) the snapshot phase, which is an off-chain signaling period used to gauge sentiment; and (iii) the execution phase, where approved proposals deploy autonomously [10].

The voting power distribution can be quantified using the Gini Coefficient, which measures the inequality of voting power among token holders. The Gini coefficient is calculated using the following equation:

$$G={\displaystyle \frac{{\sum }_{i=1}^n{\sum }_{j=1}^n|v_i-v_j|}{2n{\sum }_{i=1}^n{v}_i}}$$

where $v_i$ is the voting power of token holder i, and n is the total number of voters. A higher Gini coefficient indicates more centralized voting power, often leading to whale dominance. To address such challenges, Quadratic Voting is proposed as an alternative to traditional voting systems [11].

LLM Integration: LLMs improve DAO governance via the self-attention mechanism by analyzing proposal details, where RAG combines real-time data and past discussions for informed decisions [8,12]. Finally, chain-of-thought prompting breaks tasks into steps for clear reasoning [13]. These features make LLMs valuable tools for enhancing decision-making in DAOs [14]. Specialized LLMs (agents) act as collaborative agents, such as analysts and forecasters, performing roles typically seen in organizational structures [15]. These agents are coordinated by orchestration systems such as LangChain, which manages task transitions and error recovery during operations [5]. Safety mechanisms, including constraint prompting, ensure responses adhere to specific rules. Additionally, transaction emulation in sandboxed environments, like Ganache, validates payloads before blockchain deployment [16].

Relevant Terminologies: Vote Escrow involves locking tokens to increase governance power, as seen in Curve’s veCRV model, promoting long-term holding and governance stability [17]. Proposal Payload is the celldata specifying on-chain actions, which is crucial for smart contract execution once a proposal is approved [18]. Vector DB stores semantic search indexes and governance documents to enable the efficient querying of complex data in decentralized governance systems, with tools like Weaviate and Pinecone providing enhanced search functionality [19].

3. Related Work

Recent work has leveraged LLMs for automating tasks within DAO governance. At early-stage tooling in DAOs was focused on static rule-based automation, which has since evolved into more advanced AI-powered systems [20]. It can achieve over 90% accuracy in classifying DAO proposals, a significant leap over traditional rule-based systems [7]. Additionally, Ref. [6] explored multi-agent systems to automate crypto management, emphasizing the role of specialized agents in governance automation. Furthermore, Ref. [21] introduced a novel approach using Graph Attention Networks for forecasting DAO vote outcomes, enhancing predictive models by analyzing relational data between voters and proposals.

In addition to LLMs, multi-agent systems have become integral to governance automation. For instance, Ref. [22] introduced an LLM-powered framework for automated proposal analysis, significantly streamlining proposal triage by autonomously categorizing and flagging proposals for further review. Similarly, a multi-modal RAG system for financial decision support underscores the versatility of RAG in dynamic, real-time environments [23]. As blockchain ecosystems expand, the need for compliance and legal reasoning becomes critical. Using LLMs for smart contract explainability is a technique that could be integrated to provide transparent legal interpretations of governance [24]. The use of RAG for dynamic compliance highlights how this approach automates legal checks and ensures that proposals adhere to governance and regulatory standards [25].

Table 1. Comparison of related work with proposed APOLLO.

Work	Data Sources	Core Methods	Automation	Key Limitations
(2024) [7]	Off-chain proposal text	LLM prompts; iterative category refinement	Analysis only	No drafting/execution; small labeled set; offline accuracy only
(2025) [26]	NL specs → on-chain payload	Multi-agent LLM; DAOLang DSL; Label-Centric Retrieval	Drafting → executable payload	Requires DSL; no vote prediction; preliminary evaluation
(2020) [27]	Platform events/rules	Policy functions (procedural governance)	Execution of procedures	Not blockchain; no ML adaptivity or forecasting
(2021) [21]	Historical official results	Voter model with stubborn nodes	Forecasting only	Not DAO; relies on prior election series
(2023) [22]	Forum/Snapshot text	Single-agent LLM drafting	Drafting only	Blog-scale demo; no quantitative evaluation; no execution
APOLLO (2025)	On-chain, and Off-chain (forums/news/chat)	LLM, and RAG multi-agent; logistic regression predictor; continuous feedback	Analysis, Draft, Predict, Submit, and Track	Scalability under high load; legal reasoning out of scope

presents a comparison between the proposed work and existing related studies, highlighting the improvements achieved by our approach.

4. Problem Statement and Motivation

One of the governance issues that DAOs deal with is whale dominance, which occurs when a small number of token owners have an excessive level of voting power. Sybil attacks use fictitious identities to influence the governance. When proposal intricacy reduces voter turnout and gives whales the upper hand, rational ignorance takes place. It is challenging to match preferences in DAOs with several blockchains due to cross-chain fragmentation, which complicates governance. The manual vetting process involves core teams spending hours verifying proposals, causing delays in decision making [26]. Tooling rigidity on platforms limits proposals to predefined templates, reducing innovation and flexibility in governance [3]. Additionally, there is a legitimacy and efficiency tradeoff, where prioritizing speed can undermine inclusivity, while thoroughness may slow down proposal execution and decision-making [27].

In order to tackle the issues mentioned, the proposed system (APOLLO) has encouraging prospects for additional improvement. By incorporating Reinforcement Learning (RL), the system can learn from previous governance outcomes to improve forecast accuracy. More coherence and smoother governance across different blockchain networks can result from support for multi-chain DAOs in the crucial domain of cross-chain governance [3,26]. Using a multi-agent system with RAG-powered decision-making to automate the whole proposal lifecycle represents a major advance in AI-driven decentralized governance [6,22]. APOLLO combines continuous learning, predictive modeling, and real-time data to improve decisions while addressing rigid tooling, voter apathy, and whale dominance. The comparative analysis in Table 1 exposes key limitations in the existing approaches, thereby justifying the problem formulation and motivating the proposed solution.

5. Proposed Method

The system is a modular framework for decentralized governance, integrating on-chain and off-chain data collection from social media, news, and blockchain nodes using specialized collectors. The data is processed into context through RAG, using LLMs (gemma-3-4b) for summarization and sentiment extraction. Cosine Similarity and embeddings are applied for context relevance. Predictions are made using the outcome forecaster agent, which utilizes statistical models such as logistic regression or a linear heuristic based on contextual data, including sentiment and historical voting trends. This model is fine-tuned on collected governance data to ensure accurate predictions aligned with community sentiment and trends. Governance proposals are generated using LLMs and broadcast across participants. The execution status is tracked, ensuring transparent and immutable outcomes. The system maintains a continuous feedback loop, improving decisions with updated data to stay responsive to community needs. The successful implementation of this work is presented in the corresponding GitHub repository [28].

5.1. APOLLO Layers

APOLLO consists of three interconnected layers as shown in Figure 2: (i) data layer, (ii) agents layer (LLM-based), and (iii) execution layer.

Data Layer: Off-chain data, on-chain data, and the knowledge base are the three main parts of the data layer. Off-chain data, which is driven by BeautifulSoup, gathers sentiment and trends in the market by extracting and preprocessing textual data from social media, news websites, and other sources. Web3.py and RPC indexer APIs are used for On-chain data such as transactions, smart contract interactions, voting patterns, and governance operations. The knowledge base acts as a central hub, using vector databases for semantic search and integrating data from APIs. It also incorporates stakeholder feedback and continuously updates insights from the agents layer to provide accurate, context-driven analytics.

Agents Layer (LLM-Based): The agents layer comprises AI-powered agents for automation and governance prediction. On-chain and off-chain data sources are gathered by the Data Collector Agent, and stakeholder sentiment is assessed by the Sentiment-Analysis Agent. Data and sentiment are combined by the Context Generator Agent, while governance outcomes are predicted by the Prediction Analysis Agent. Both the Proposal-Generator Agent and the Proposal Submission Agent generate governance proposals and automate proposal submissions.

Execution Layer: Through connections to community platforms and blockchain infrastructure, the Execution Layer makes it possible for APOLLO to automate governance. By automating governance execution, the governor smart contract guarantees that recommendations are implemented in a safe, transparent, and predictable manner. Integrating with apps like Telegram or Discord links community involvement with governance, encouraging openness, confidence, and participation.

In the proposed system, we normalize the voting weight of each token holder. The normalized voting weight $w_i$ for voter i is calculated using the following formula.

$$w_i={\displaystyle \frac{t_i}{{\sum }_{j=1}^n{t}_j}}$$

where $w_i$ is the normalized voting weight of token holder i, $t_i$ is the number of tokens held by voter i, and n is the total number of voters. This ensures that the voting power of each token holder is proportional to the number of tokens they hold within the system, maintaining a fair distribution of voting rights.

5.2. RAG Loop for Knowledge Base Enrichment

To ensure ongoing improvement and accuracy, APOLLO integrates a feedback loop using RAG as shown in Figure 3. Contextual insights generated by the LLM-based Context-Generator Agent are continuously fed back into the knowledge base, enhancing context precision. Each submitted proposal, along with metadata and community feedback, enriches the knowledge base, creating a growing historical context of governance decisions and their outcomes. Additionally, post-execution results from governance actions performed by the governor smart contract are systematically stored and analyzed, facilitating continuous learning and iterative improvement of predictive and proposal-generation models.

In the Continuous RAG Loop, we first calculate the Cosine Similarity for retrieval. The cosine similarity between two vectors $v_i$ and $v_j$ is given by the following formula.

$$\mathrm{sim}(v_i,v_j)={\displaystyle \frac{v_i·v_j}{\parallel v_i\parallel \parallel v_j\parallel }}$$

where $v_i$ and $v_j$ are the vectors, and $\parallel v_i\parallel$ and $\parallel v_j\parallel$ are the magnitudes of the vectors. Next, we apply Reciprocal Rank Fusion (RRF) [29], which combines the results of multiple retrieval systems. The RRF for document d is calculated as follows.

$$\mathrm{RRF}\left(d\right)=\sum _{r\in R}{\displaystyle \frac{1}{k+r\left(d\right)}}$$

where $r\left(d\right)$ is the rank of document d, and k is a constant. This formula helps in combining ranked lists from multiple sources by applying diminishing returns to the ranks. Finally, we use Mean Pooling for Embeddings. The mean pooling of the embeddings is calculated as the following equation:

$$e_d={\displaystyle \frac{1}{n}}\sum _{i=1}^n{h}_i^{\left(L\right)}$$

where $h_i^{\left(L\right)}$ represents the hidden state of token i at layer L, and n is the total number of tokens. This approach aggregates the token representations into a single vector, representing the overall context.

5.3. Proof of Concept (PoC) Implementation

The MVP has been successfully implemented and validated across all layers. The data layer handles data efficiently and the agents layer generates accurate proposals. The execution layer automates governance with a smart contract. A continuous RAG Loop improves decision-making. In the proposed system, we first normalize the embedding for each text chunk $t_i$, where each chunk consists of a maximum of 512 tokens. The normalized vector $v_i$ is computed using the following formula.

$$v_i={\displaystyle \frac{f_0\left(t_i\right)}{\parallel f_0\left(t_i\right){\parallel }_2}}$$

Here, $t_i$ represents the i-th text chunk, which is a sequence of tokens (words or subwords) with a maximum length of 512 tokens. Each chunk is a small portion of the input text, processed to maintain consistency and manageable size. $f_0\left(t_i\right)$ denotes the embedding vector generated by the MiniLM-L6 encoder for the text chunk $t_i$. This encoder transforms the raw text into a dense vector representation in a high-dimensional space. The term $\parallel f_0\left(t_i\right){\parallel }_2$ represents the L2 norm (Euclidean norm) of the vector $f_0\left(t_i\right)$, which normalizes the embedding to unit length, ensuring consistent scale for comparison. The normalized vector $v_i$ has a dimensionality of $d=384$, capturing the meaning and context of the chunk.

For retrieval, we use Cosine Similarity in conjunction with IVFFlat (Inverted File with Flat Index) and HNSW (Hierarchical Navigable Small World) acceleration in Pinecone [30,31]. Empirically, recall@10 achieves 0.94 on a 5k query benchmark, indicating that the top 10 retrieved results capture 94% of the relevant data. For any artifact a (such as a tweet, transaction log, or proposal), we compute and store its embedding $e_a$ as follows:

$$e_a=f_0\left(\mathrm{norm}\left(a\right)\right)\in {\mathbb{R}}^d$$

Here, a represents the artifact, which is preprocessed using the function $\mathrm{norm}\left(a\right)$. This preprocessing step strips URLs, tags, and hex blobs to clean the text before embedding. The encoder $f_0$ refers to the text embedding model, where the parameters $\theta$ are frozen after fine-tuning. The resulting embedding vector $e_a$ has a dimensionality of $d=3072$, representing the artifact in a high-dimensional space and capturing rich semantic information.

For retrieval in the RAG process, we use Cosine Similarity to retrieve the top-$k=8$ chunks for RAG prompts. Additionally, for any tokenized text fragment $d=\{t_1,\dots ,t_n\}$, where each $t_i$ corresponds to a token, the transformer encoder $f_0$ maps each token to a hidden state $h_i$, which represents the model’s learned features for each token. The final embedding $e_d$ for the entire fragment is computed by mean-pooling the hidden states over the last layer of the transformer as follows.

$$e_d={\displaystyle \frac{1}{n}}\sum _{i=1}^n{h}_i^{\left(L\right)}$$

The resulting dimensionality of the final embedding $e_d$ is $\parallel e_d\parallel =1768$, representing the tokenized fragment as a 1768-dimensional vector. For enhanced retrieval, Cosine Similarity is initially applied, followed by Reciprocal Rank Fusion (RRF) for re-ranking the top-200 hits. RRF combines the strengths of sparse BM25, which is a classical retrieval model, and dense scores from the embeddings, improving retrieval quality. This technique enhances the Hits@1 score by approximately 4% in offline tests, ensuring that more relevant and contextually grounded information is retrieved.

Data Collection and Normalization: Algorithm 1 outlines the process of collecting and normalizing on-chain and off-chain data. First, blockchain RPC events are polled to capture on-chain data (RAW_ON), followed by scraping forum and social media HTML, as well as gathering off-chain API feeds (RAW_OFF). The collected data is then combined and normalized to create a canonical governance dataset (NORM_DS). Next, the current block timestamp (t) is captured to record when the data was ingested. A timestamped log of the data sources (SRC_LOG) is stored, which logs the source IDs for both on-chain and off-chain data. Finally, the normalized governance dataset (NORM_DS) is returned, ready for further processing and analysis.

Algorithm 1 Data ingestion and normalization.

1: Inputs:  RAW_ON: Blockchain RPC events.  2: RAW_OFF:  Forum & social-media HTML, Off-chain API feeds.  3: Outputs:  NORM_DS: Canonical dataset (JSON-Lines).  4: SRC_LOG:  Timestamped log of data sources, Error/latency metrics.  5: procedure Data_Layer_Ingest  6:     Step 1: Poll blockchain RPC events:  7:        RAW_ON ← PollRPC()  8:     Step 2: Scrape forum and gather off-chain feeds:  9:        RAW_OFF ← ScrapeFeeds() 10:     Step 3: Normalize on-chain and off-chain data: 11:        NORM_DS ← Normalize(RAW_ON ∪ RAW_OFF) 12:     Step 4: Capture current block timestamp: 13:        t ← block.timestamp 14:     Step 5: Store timestamped log of sources: 15:        SRC_LOG[t] ← {sources: ids(RAW_ON, RAW_OFF)} 16:     Step 6: Return normalized dataset: 17:        return NORM_DS 18: end procedure

Contextual Analysis and Prediction: Algorithm 2 processes raw data into structured context windows, enabling the extraction of insights. It calculates community sentiment and predicts voter behavior for the next 24 h, supporting informed decision-making. It derives sentiment scores from the normalized governance dataset (NORM_DS), creating a context packet (CTX_PKT), and predicting voter turnout (PRED). Using this context, the system generates a draft proposal (DRAFT) via a language model (LLM), signs it with the agent’s wallet (SIG), and returns the signed draft for community discussion and on-chain voting.

Algorithm 2 Agents layer pipeline for governance proposal generation.

1: Inputs: Normalized governance dataset (NORM_DS)  2: Outputs: CTX_PKT (≤ 8 kB), Historical turnout archive, Vector-DB embeddings, Sentiment scores, Forecast (turnout and sentiment-drift)  3: procedure Agents_Layer_Pipeline  4:     Step 1: Calculate sentiment scores:  5:        sentiment ← SentimentScore(NORM_DS)  6:     Step 2: Construct context packet (CTX_PKT) using sentiment:  7:        CTX_PKT ← ContextBuild(NORM_DS, sentiment)  8:     Step 3: Predict voter turnout:  9:        PRED ← PredictTurnout(CTX_PKT) 10:     Step 4: Generate draft proposal (DRAFT): 11:        DRAFT ← LLM_Draft(CTX_PKT, PRED) 12:     Step 5: Sign the draft proposal (DRAFT): 13:        SIG ← Wallet.sign(DRAFT.calldata) 14:     Step 6: Return draft proposal and signature: 15:        return $\{DRAFT,SIG\}$ 16: end procedure

Proposal Submission, Tracking, and Execution Process: Algorithm 3 outlines the process of submitting and executing a proposal in a decentralized governance system. The draft proposal (DRAFT) is first posted for community feedback, then signed (SIG) and sent to the governor for approval. After the voting period ends, the algorithm checks if the vote threshold is met. If successful, the proposal is executed, and an execution receipt (RECEIPT) is generated. If the threshold is not met or the proposal expires, the receipt is marked as “Failed or Expired”. The final status of the proposal is returned through the execution receipt (RECEIPT).

Algorithm 3 Proposal submission and execution.

1: Inputs: Context packet, Prediction report, DAO parameters  2: Outputs: Draft markdown, EIP-712 calldata, Governor propose() transaction hash  3: procedure Execution_Layer_ProposeExecute  4:     Step 1: Post proposal draft for discussion:  5:        PostForum(DRAFT.markdown)  6:     Step 2: Submit signed draft to governor:  7:        TX_HASH $\leftarrow$ Governor.propose(SIG)  8:     Step 3: Retrieve proposal ID:  9:        PID$\leftarrow$GetProposalID(TX_HASH) 10:     Step 4: Wait until voting deadline: 11:        Wait Until(block.timestamp > deadline(PID)) 12:     Step 5: Check if proposal meets vote threshold: 13:        if forVotes(PID) ≥ threshold 14:     if forVotes(PID) ≥ threshold then 15:         Step 6: Execute proposal: 16:            RECEIPT $\leftarrow$ Governor.execute(PID) 17:     else 18:         Step 7: Mark proposal as failed: 19:            RECEIPT.status $\leftarrow$ “Failed/Expired” 20:     end if 21:     Step 8: Return execution receipt: 22:        return RECEIPT 23: end procedure

Knowledge Base Update and Enrichment Process: Algorithm 4 describes how the APOLLO system updates its knowledge base (KB). It combines the context packet (CTX_PKT), draft proposal (DRAFT), and execution receipt (RECEIPT) into a single record (REC), then generates embeddings (EMB) for structured storage. These embeddings are added to the knowledge base (KB), and retrieval metrics are recalculated for accuracy. The updated KB is returned, enriched for future use in governance decisions and predictions.

Algorithm 4 RAG_Loop_Update (REC, EMB, KB).

1: Inputs: Proposal-ID / Tx hash, Execution receipt, Community feedback  2: Outputs: Indexed embeddings, Updated retrieval metrics  3: procedure RAG_Loop_Update  4:     Step 1: Combine context packet, proposal, and receipt:  5:        REC ← {CTX_PKT, DRAFT, RECEIPT}  6:     Step 2: Generate embeddings for the record:  7:        EMB ← Embed(REC)  8:     Step 3: Append embeddings to the knowledge base:  9:        AppendVectorDB(KB, EMB) 10:     Step 4: Recalculate retrieval metrics: 11:        RecalculateRetrievalMetrics(KB) 12:     Step 5: Return updated knowledge base: 13:        return KB 14: end procedure

6. Performance Evaluation and Analysis

This section evaluates APOLLO’s performance in decentralized governance, focusing on data handling, prediction accuracy, and processing efficiency.

Table 2. Data fetch and analysis.

Component	Tool/Library	Description
Off-Chain Data Fetch	BeautifulSoup, Requests	Scraping blogs, forums, and community data.
Sentiment Analysis	gemma-3-4b, Python Scripts	Real-time sentiment classification.
Community Discussion Monitoring	Custom Python Scripts	Monitor discussions on platforms: Discord, Telegram etc.
Prediction Analysis	Simple Average Method	Derives baseline predictions based on historical averages.
Forecasting	Naive Forecasting Method	Estimates future outcomes using historical means.

outlines the tools and methods used for data fetching and analysis , such as BeautifulSoup (Python Software Foundation, Wilmington, DE, USA; version 4.12.2) and gemma3:4b (Google LLC, Mountain View, CA, USA; 4B parameter model) were used for sentiment analysis and forecasting.

Table 3. Simulation, storage and voting tracking.

Component	Environment	Tool/Library	Description
On-Chain Simulation	Ganache (Local EVM)/Polkadot	Web3.py	Proposal execution and validation on Polkadot network.
Local Sheet/Vector Storage	Local CPU Server, Excel	Pandas, NumPy, OpenAPI	Embedding and semantic retrieval using Pinecone or Weaviate for RAG.
Voting Result Tracking	Smart Contract	Custom Python Scripts	Track voter turnout, proposal status, and vote breakdown via smart contracts.

details the components for on-chain simulation, storage, and voting tracking, implemented using Web3.py (Ethereum Foundation, Zug, Switzerland; version 6.15.1) and custom Python scripts (Python Software Foundation, Wilmington, DE, USA; version 3.10) for proposal execution and voting outcome tracking. The local server configuration supporting APOLLO includes an Intel Core i9-12900 CPU (Intel Corporation, Santa Clara, CA, USA), 64 GB DDR4 RAM, and the Ubuntu Linux operating system (Canonical Ltd., London, UK; version 22.04 LTS).

6.1. Benchmarking and Prediction Process

The system begins by benchmarking the margin of error (MoE) and confidence in predictions related to governance outcomes. The MoE is calculated using the Wilson score interval for binomial proportion, which blends statistical methods with a context-driven heuristic. This formula is expressed as follows:

$${\mathrm{MoE}}_{\mathrm{Wilson}}={\displaystyle \frac{z}{1+\frac{z^2}{N_{\mathrm{eff}}}}}\sqrt{{\displaystyle \frac{p(1-p)}{N_{\mathrm{eff}}}}+{\displaystyle \frac{z^2}{4N_{\mathrm{eff}}^2}}}$$

where z is the Z-value for the confidence level, p is the estimated approval probability, and $N_{\mathrm{eff}}$ is the effective sample size. The final margin of error is determined by blending this Wilson score with a context-specific heuristic as the following equation:

$$\mathrm{MoE}=max\left\{0.01,min\left\{0.45,w_h{\mathrm{MoE}}_{\mathrm{heur}}+w_s{\mathrm{MoE}}_{\mathrm{Wilson}}\right\}\right\}$$

where $w_h$ and $w_s$ are weighting factors based on the effective sample size $N_{\mathrm{eff}}$, which help adjust the MoE based on the available data. Next, the confidence level is defined as the complement of the MoE as follows:

$$\mathrm{Confidence}=1-\mathrm{MoE}$$

The confidence is then adjusted for stronger sentiment signals and clipped to a range of $[0.05,0.99]$. Prediction of governance outcomes is performed using a logistic regression model as follows:  

$$\widehat{p}=\sigma \left({\beta }_0+\sum _i{\beta }_i{x}_i\right)$$

where $\sigma \left(z\right)={\displaystyle \frac{1}{1+e^{-z}}}$ is the logistic function, and $x_i$ are the features derived from context, such as sentiment scores, proposal length, historical rates, and engagement levels. The prediction model provides an estimated probability of approval, $\widehat{p}$, which is used for forecasting.

Finally, forecasting is performed by combining historical approval or turnout baselines with the predictions above, computing both MoE and confidence to estimate potential outcomes. This process provides a comprehensive set of forecasts, including approval probabilities and turnout estimates, ensuring data-driven insights into governance outcomes.

6.2. Prediction Accuracy and Data Source Evaluation

Table 4. Data sources and scraping volume.

Source Type	# Documents	Token Volume	Update Frequency
Forum	30	47,002	Daily
News Blogs	27	13,567	Hourly
Community Chat	104	4290	Real-time
Governance Docs	1985	942,069	Every Run
On-chain	19 proposals	41	6 s

outlines the different data sources, their document counts, token volumes, and update frequencies. It shows that Forum data consists of 30 documents with 47,002 tokens, updated daily, while News Blogs includes 27 documents and 13,567 tokens, updated hourly. Community Chat data has 104 documents, totaling 4290 tokens, updated in real-time, and Governance Docs comprise 1985 documents, with a significant token volume of 942,069, updated every run. On-chain data, consisting of 19 proposals, is updated every 6 s but has a low token volume of 41 tokens.

Table 5. Sentiment analysis and knowledge base embedding.

Batch ID	Source	CTX Size (KB)	Sentiment	Confidence	Embedded in KB
1	Forum	7.9	Positive	0.50	Yes
2	Chat	7.9	Positive	0.30	Yes
3	News	7.8	Mixed	0.20	No
4	OnChain	0.2	Mixed	0.00	No

summarizes sentiment analysis and knowledge base embedding for various data sources. Forum and Chat data have positive sentiment, with confidence scores of 0.50 and 0.30, respectively, and both are embedded. News and On-chain data show mixed sentiment, with confidence scores of 0.20 and 0.00, and neither are embedded.

6.3. Governance Efficiency and Proposal Execution

Table 6. Success prediction and forecast.

Source	Title	Prediction	Confidence (%)	Margin of Error
Forum	Increased Voter Education and Incentive Program for Referenda Participation	Pass	94.87%	±6.30%
Chat	Root Track Proposal: Enhanced Polkadot Community Engagement and Turnout Initiatives	Pass	94.33%	± 6.79%
News	Root Track Proposal: Addressing Low Voter Turnout and Prioritizing Ecosystem Funding	Pass	93.80%	±6.86%
Onchain	Enhanced Incentive for Referendum Participation—Addressing Low Turnout	Pass	93.81%	±6.86%

shows the predicted success of proposals across Forum, Chat, News, and On-chain data, with high confidence ranging from 93.81% to 94.87% and consistent margins of error limit ±6.30% to ±6.86%. This highlights accuracy in forecasting governance proposal outcomes. The results shows the success of decision-making in governance processes.

Figure 4 compares the prediction times for governance proposals in two aspects. Chart (i) shows success prediction times across four data sources: Forum (404 ms), Chat (424 ms), News (309 ms), and Onchain (430 ms), with News having the shortest and Onchain the longest. Chart (ii) compares the voting result prediction times for various proposals: Proposal ID 1397 (352 ms), Proposal ID 599 (368 ms), Proposal ID 1353 (365 ms), Proposal ID 666 (309 ms), and Proposal ID 38 (314 ms), with Proposal ID 666 having the fastest and Proposal ID 599 the slowest. This figure demonstrates the prediction model’s efficiency for both success and voting results.

Table 7. Voting result prediction vs. actual outcomes in Polkadot DAO’s historical referenda.

Proposal ID	Predicted	Actual	Confidence (%)	Margin of Error
1397	✓	✓	99.00%	±5.47%
599	✓	✓	99.00%	±5.50%
1353	✓	✗	94.21%	±5.79%
666	✓	✓	93.98%	±6.02%
38	✓	✓	99.00%	±5.71%

compares the predicted voting outcomes with the actual results from Polkadot DAO’s referenda, showing high accuracy (94.21% to 99.00% confidence) and low margins of error (up to ±6.02%). The predictions closely match the actual results, demonstrating APOLLO’s effectiveness in real-world scenarios.

Table 8. End-to-end timing benchmarking.

Scenario	# Proposals	Ingestion (s)	Analysis and Prediction (s)	Draft and Signing (s)	Total Time (s)
Light Load	5	190.00	600.12	2.00	792.12
Medium Load	5	470.40	1289.39	9.00	1768.80
High Load	100	9800.61	28,500.71	3803.26	42,104.58

shows APOLLO’s processing times under different loads: 792.12 s for light load, 1768.80 s for medium load, and 42,104.58 s for high load. The processing times include ingestion, analysis and prediction, and draft and signing times. The significant rise in total time under high load highlights scalability challenges particularly in the analysis and prediction phase. These results suggest the need for optimizations in handling higher loads.

6.4. Risk Mitigation and Error Handling

Table 9. Failure cases and edge conditions.

Failure Type	Cause	Detected By	Mitigation Strategy
Invalid Payload	Missing or malformed calldata in EVM tx	proposal_submission.py	Dropped before submission; validated by schema
Sentiment Ambiguity	Conflicting polarity signals from model	sentiment_analyser.py	Escalated to human verifier for manual correction
Spam Proposal	Repeated flooded titles detected	context_generator.py	Regex + deduplication + rate- limit enforcement
Rate-Limit Exceeded	API quota or flood of external requests	main.py	Backoff and exponential retry; request batching

outlines common failure cases and edge conditions. These include invalid payloads, detected by the proposal_submission.py script, which ensures only properly formatted payloads are processed. Sentiment ambiguity occurs when conflicting signals are detected, leading to manual review by a human verifier. Spam proposals are flagged by the context_generator.py script, using techniques like deduplication and rate-limit enforcement. Lastly, rate-limit exceeded failures are handled by main.py and context_generator.py, employing a backoff strategy, exponential retries, and request batching to maintain functionality. These strategies ensure APOLLO’s robustness and smooth operation under various failure conditions.

Finally, Figure 5 shows that APOLLO DAO has the potential to generate successful proposals. With a focus on enhancing security, community engagement, and developing vital infrastructure such as SubWallet and Snowbridge, the DAO approach emphasizes prioritizing key management and proposal accessibility. This proactive strategy addresses concerns over declining trends, positioning APOLLO to generate impactful and successful proposals that foster growth and sustainability.

7. Conclusions

APOLLO streamlines the whole proposal lifecycle with AI-driven automation, providing a revolutionary solution for decentralized governance. The key governance issues including voter apathy, strict tooling is addressed by the combination of LLMs and a multi-agent system augmented by RAG. It introduces an effective solution for DAOs and supports real-time, context-aware decision-making, which allows it to process governance proposals 3600 times faster than conventional techniques. This revolutionary potential demonstrates how AI can greatly enhance decentralized governance, promoting inclusion and flexibility in decision-making.

Some aspects of APOLLO could still be improved in spite of these advancements. Incorporating legal reasoning models is essential to improving compliance with complex and dynamic regulatory frameworks, particularly in cross-jurisdictional settings. The scalability and appropriateness for application in other blockchain ecosystems would be enhanced by expanding its capability to include multi-chain governance. Resolving cross-chain interoperability problems, improving prediction models, and improving decision-making algorithms should be the main goals of future research. With limited resources, we used gemma-3-4b; more powerful LLM models such as GPT-4o could outperform it in future deployments. A more robust, decentralized governance framework that can meet the needs of numerous blockchain platforms will be made possible by these initiatives.