DART: A DAG-Based Reputation and Incentive Framework via Blockchain-Enabled Governance for Trustworthy LLM Multi-Agent Collaboration

Manoj Kumala; Xinyun Liua; Ronghua Xu

cs.MASep 1, 2026

DART: A DAG-Based Reputation and Incentive Framework via Blockchain-Enabled Governance for Trustworthy LLM Multi-Agent Collaboration

Authors: Manoj Kumala, Xinyun Liua, Ronghua Xu

Organizations: Department of Applied Computing, Michigan Technological University, Houghton, MI 49931, USA

Abstract

Large language model (LLM)-based multi-agent systems (MAS) predominantly rely on centralized orchestration and lack formal verification mechanisms for agent reliability, participation, and system-level behavioral alignment. These shortcomings leave open environments severely vulnerable to uncooperative or malicious agents. This work proposes DART, a Directed Acyclic Graph (DAG)-based reputation and incentive regulation framework for trustworthy multi-agent collaboration, combining centralized operational orchestration with blockchain-enabled decentralized governance and accountability. DART unifies DAG workflow orchestration, capability and reputation-aware task allocation, dynamic behavior updates, multi-factor incentives, and smart contract accountability paired with IPFS storage. Under this paradigm, agent selection dynamically balances task alignment, historical reputation, and workload, while post-execution behavioral evidence continuously calibrates agent trust and the probability of future participation. Evaluated across four axes, DART achieves 93.6% Pass@1 on GSM8K and builds a full-stack application in 142 s using two agents, outperforming centralized baselines. Across five independent 150-round longitudinal trials, Full DART achieves a mean task success rate of 93.33 +/- 2.26%, output quality of 0.9357 +/- 0.0117, retry rate of 0.2307 +/- 0.0816, and allocation delay of 1.1153 +/- 0.0408 s, consistently outperforming its ablated configurations DART isolates persistent and intermittent malicious agents, obtaining a 99.3% output containment rate and restoring system success to 99.8%. These results demonstrate the potential of coupling reputation, incentives, DAG-based coordination, and verifiable blockchain-enabled governance to support adaptive and accountable multi-agent collaboration.

Figures & tables

Method	Decentralized Governance	Agent Behavior Tracking	Adaptive Coordination	Incentive Compatibility
CAMEL [ 20 ]	$\times$	$\times$	$\times$	$\times$
MetaGPT [ 16 ]	$\times$	✓	$\times$	$\times$
AgentVerse [ 10 ]	$\times$	$\times$	✓	$\times$
ChatDev [ 27 ]	$\times$	✓	✓	$\times$
MegaAgent [ 31 ]	$\times$	✓	✓	$\times$
DecoAgent [ 17 ]	✓	✓	$\times$	◐

TABLE I: Comparison with LLM multi-agent frameworks

Fig. 1 : System architecture of Dart for Decentralized LLM multi-agent collaboration with decentralized blockchain governance.

Framework	MBPP	HumanEval	MATH	GSM8K
MetaGPT	81.7	82.3	–	–
CAMEL	78.1	57.9	22.3	45.6
AgentVerse	82.4	89.0	54.5	81.2
AutoGen	85.3	85.9	69.5	87.8
MegaAgent	92.2	93.3	69.0	93.0
Dart (Ours)	81.0	88.0	64.0	93.6

TABLE II : Performance Comparison (

\text{Pass}@1

Accuracy, %) Across Standard Code Generation and Mathematical Reasoning Benchmarks.

Fig. 2 : Meta-prompt specification utilized for the end-to-end Gobang game development scenario.

Framework	C1	C2	C3	C4	#Agents	Time(s)	Time/Agent (s)
AutoGen	✓	✓	✗	✗	2	120	60
MetaGPT	✓	✓	✗	✗	6	480	80
CAMEL	✗	✗	✗	✗	2	1,168	584
AgentVerse	✗	✗	✗	✗	4	1,980	495
MegaAgent	✓	✓	✓	✓	7	800	114
DART (Our)	✓	✓	✓	✓	2	142	71

TABLE III : Gobang Game Development Results

Stage	Input	Output	Total	Time (s)
Planning	2,529	1,867	4,396	0-45
Task-Solving	40,307	7,580	47,887	30-140
Merging	18,723	5,656	24,379	140-160
Total	61,559	15,103	76,662	160

TABLE IV : Stage-Wise Token Consumption and Execution Latency in Dart

Tag	Description
Tag1	Object recognition and classification
Tag2	Spatial reasoning and planning
Tag3	Language understanding (instruction parsing)
Tag4	Grasping and manipulation
Tag5	Path planning and navigation
Tag6	Scene understanding (layout/context extraction)

TABLE V : Taxonomy of Agent Capability Tags for Profile Vectors (

\mathbf{w}_{i}

)

Method	Cap.	Rep.	Work.	Inc.	Anom. Det.	Success Rate (%) $\uparrow$	Quality Score $\uparrow$	Task Retry Rate (%) $\downarrow$	Allocation Delay $\downarrow$
Capability Only	✓	$\times$	$\times$	$\times$	$\times$	89.07 $\pm$ 2.14	0.9144 $\pm$ 0.0098	0.3480 $\pm$ 0.0693	1.1740 $\pm$ 0.0347
+ Reputation	✓	✓	$\times$	$\times$	$\times$	90.40 $\pm$ 1.92	0.9195 $\pm$ 0.0092	0.3120 $\pm$ 0.0357	1.1560 $\pm$ 0.0179
+ Workload	✓	✓	✓	$\times$	$\times$	90.93 $\pm$ 3.08	0.9253 $\pm$ 0.0158	0.2987 $\pm$ 0.0732	1.1493 $\pm$ 0.0366
Dart $-$ Incentive	✓	✓	✓	$\times$	✓	90.10 $\pm$ 1.53	0.9220 $\pm$ 0.0088	0.3080 $\pm$ 0.0540	1.1540 $\pm$ 0.0270
Dart $-$ Anomaly Detection	✓	✓	✓	✓	$\times$	91.60 $\pm$ 2.48	0.9257 $\pm$ 0.0135	0.2760 $\pm$ 0.0651	1.1380 $\pm$ 0.0325
Full Dart	✓	✓	✓	✓	✓	93.33 $\pm$ 2.26	0.9357 $\pm$ 0.0117	0.2307 $\pm$ 0.0816	1.1153 $\pm$ 0.0408

TABLE VI: Ablation Study of Dart Components for Task Allocation and Execution

Fig. 3 : Agent reputation across domains.

Fig. 4 : Longitudinal reputation trajectories ( $\rho_{i}^{t}$ ) of persistent and intermittent (Alpha and Gamma) adversarial agents, highlighting attack activation ( $t=30$ ) and quarantine isolation milestones.

Operational Phase	Rounds	Success Rate (%)	Mean Quality	Failure Rate (%)
Pre-Attack Baseline	1–29	100.0	0.85	0.0
Active Attack	30–64	87.6	0.74	12.4
Post-Isolation	65–150	99.8	0.84	0.2

TABLE VII : System Resiliency Across Operational Phases Under Adversarial Attack

Layer	Operation	Mean Latency (ms)	Gas Used
On-Chain (EVM)	Agent Registration	4.40	166,078
	Task Registration	6.70	168,802
	Reputation Update	5.50	47,974
	Incentive Settlement	4.90	29,235
Off-Chain (IPFS)	Artifact Upload	31.70	–
Off-Chain (IPFS)	Artifact Retrieval	2.59	–

TABLE VIII : System Latency and Gas Consumption Across On-Chain and Off-Chain Operations

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