paper_id string | title string | published_date string | arxiv_categories list | legal_license string | license_url string | commercial_use_allowed bool | primary_author string | total_authors_count int64 | authors list | affiliations list | arxiv_url string | code_audit_status string | repo_urls list | repo_status_codes list | repo_verified_live list | abstract string | title_vector_384d list | abstract_vector_384d list | target_industry string | core_problem_addressed string | key_technical_innovation string | key_quantitative_result string | detected_technical_methods list | extracted_metrics list | trend_velocity_tier string | papers_last_30_days int64 | trl_score int64 | readiness_tier string | trl_justification string | audited_at string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2608.07167v1 | NiyamAI - An Intent-Bound AI Agent with Cryptographically Verifiable Guardrails using Zero-Knowledge Proofs | 2026-08-07T12:36:52Z | [
"cs.AI"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Aditya Katkar | 5 | [
"Aditya Katkar",
"Om Karkele",
"Kartik Mandhane",
"Manisha More",
"Yash Kashid"
] | [
"OpenAI"
] | http://arxiv.org/abs/2608.07167v1 | NOT_DETECTED | [] | [] | [] | Giving an AI agent the ability to send emails, query databases, or execute commands is useful--until the agent is tricked into doing something it shouldn't. Prompt injection, hallucinated reasoning, and unsafe tool calls form the primary attack surface for autonomous LLM agents. Existing defenses rely on software check... | [
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0.00548... | Crypto, Web3 & Autonomous Financial AI Agents | Giving an AI agent the ability to send emails, query databases, or execute commands is useful--until the agent is tricked into doing something it shouldn't. | Existing defenses rely on software checks like system prompts or policy filters running on the same machine the attacker targets, offering no verifiable proof of execution. | Niyam-AI provides a guardrail that is both highly accurate and mathematically verifiable--though this reflects a classifier adapted to Agent-SafetyBench evaluated against zero-shot baselines, a distinction discussed in Section IV.C. | [
"Zero-Knowledge Proofs (ZKP)",
"Large Language Model (LLM)"
] | [] | Explosive (>50/mo) | 55 | 5 | Prototype (TRL 4-6) | Score 5/10. Benchmarks (+1) | 2026-08-10T16:28:18.057066 |
2608.05790v1 | ChainClaw: A Layered Agent Framework for Reliable On-Chain Execution | 2026-08-06T09:25:34Z | [
"cs.AI",
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Jiacheng Wei | 8 | [
"Jiacheng Wei",
"Zhaoxin Fan",
"Xin Wen",
"Yuqin Lan",
"Dongrun Li",
"Wenjun Wu",
"Faguo Wu",
"Xiao Zhang"
] | [] | http://arxiv.org/abs/2608.05790v1 | NOT_DETECTED | [] | [] | [] | General-purpose large language model agents have achieved strong performance on tool-augmented tasks, yet they rely on assumptions break down in blockchain environments. On-chain execution is stateful, adversarial, and economically irreversible, exposing three fundamental gaps: Reactivity, Irreversibility, and Observab... | [
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-... | Crypto, Web3 & Autonomous Financial AI Agents | General-purpose large language model agents have achieved strong performance on tool-augmented tasks, yet they rely on assumptions break down in blockchain environments. | We propose ChainClaw, a blockchain-native agent framework built on OpenClaw, that addresses all three gaps through a layered architecture comprising an event-driven orchestration layer, a simulation-based safety intelligence layer, and an on-chain monitoring runtime layer, unified by a cross-layer memory subsystem. | ChainClaw consistently outperforms representative baselines on both safety and task completion. | [
"Large Language Model (LLM)",
"Blockchain Architecture",
"LoRA / PEFT"
] | [] | Explosive (>50/mo) | 55 | 6 | Prototype (TRL 4-6) | Score 6/10. Blockchain/DeFi (+2) | 2026-08-10T16:28:18.060592 |
2608.05316v1 | The Trust-Free Aggregation Layer of the Unicity Infrastructure | 2026-08-05T18:18:16Z | [
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Risto Laanoja | 4 | [
"Risto Laanoja",
"Mike Gault",
"Dirk Draheim",
"Ahto Buldas"
] | [] | http://arxiv.org/abs/2608.05316v1 | NOT_DETECTED | [] | [] | [] | Unicity is a novel blockchain infrastructure for enabling users to execute off-chain peer-to-peer token transactions while preventing parallel states of tokens (double-spending) with minimal blockchain complexity and storage. A key component of the infrastructure is the Aggregation Layer responsible for storing informa... | [
-0.027689000591635704,
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-0.048... | Crypto, Web3 & Autonomous Financial AI Agents | Unicity is a novel blockchain infrastructure for enabling users to execute off-chain peer-to-peer token transactions while preventing parallel states of tokens (double-spending) with minimal blockchain complexity and storage. | A key component of the infrastructure is the Aggregation Layer responsible for storing information about the spent states of tokens and providing compact cryptographic proofs of no double-spending for the users without making any compromises in trust. | Our implementation uses no trusted setup, achieves throughput of 10,000 insertions per second and and millisecond range verification time on a single consumer-class CPU. | [
"Encryption & Privacy",
"Blockchain Architecture"
] | [] | Explosive (>50/mo) | 55 | 6 | Prototype (TRL 4-6) | Score 6/10. Blockchain/DeFi (+2) | 2026-08-10T16:28:18.065413 |
2608.05011v1 | Towards Decentralized Searcher Competition in MEV Markets | 2026-08-05T16:15:31Z | [
"cs.GT",
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Roozbeh Sarenche | 2 | [
"Roozbeh Sarenche",
"Yunwen Liu"
] | [] | http://arxiv.org/abs/2608.05011v1 | NOT_DETECTED | [] | [] | [] | Centralization in maximal extractable value (MEV) markets is a significant concern for blockchain systems, as persistent concentration of economic power can weaken competition, reduce openness, and undermine the decentralization goals of permissionless protocols. While much of the existing analysis has focused on build... | [
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0.0... | Crypto, Web3 & Autonomous Financial AI Agents | Centralization in maximal extractable value (MEV) markets is a significant concern for blockchain systems, as persistent concentration of economic power can weaken competition, reduce openness, and undermine the decentralization goals of permissionless protocols. | We develop a heterogeneous model in which searchers differ in opportunity coverage and execution efficiency, and we analyze how auction design affects fairness, decentralization, and security among searchers competing for the same MEV opportunity. | To evaluate searcher competition, we introduce two metrics: a Shapley-weighted Jain fairness index, which measures whether rewards are proportional to searchers' marginal contributions, and an expected-reward Herfindahl-Hirschman Index (HHI), which measures concentration in long-run searcher rewards. | [
"Consensus Mechanism",
"Blockchain Architecture"
] | [] | Explosive (>50/mo) | 55 | 6 | Prototype (TRL 4-6) | Score 6/10. Blockchain/DeFi (+2) | 2026-08-10T16:28:18.076433 |
2608.04626v1 | Blockchain Empowered Trustworthy Agent Networks: Foundations, Taxonomy, and Future Directions | 2026-08-05T09:32:35Z | [
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Liehuang Zhu | 10 | [
"Liehuang Zhu",
"Yuhang Li",
"Tianxing Wang",
"Zhihao Chen",
"Ke Li",
"Hongyi Liu",
"Yajie Wang",
"Lei Xu",
"Peng Jiang",
"Zijian Zhang"
] | [] | http://arxiv.org/abs/2608.04626v1 | NOT_DETECTED | [] | [] | [] | AI agents are evolving from isolated task executors into networked autonomous entities that can communicate, delegate tasks, invoke tools, access external knowledge, and participate in cross-platform service and economic workflows. This evolution gives rise to open agent networks, where heterogeneous agents owned by di... | [
-0.03793200105428696,
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0.0... | [
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... | Crypto, Web3 & Autonomous Financial AI Agents | AI agents are evolving from isolated task executors into networked autonomous entities that can communicate, delegate tasks, invoke tools, access external knowledge, and participate in cross-platform service and economic workflows. | This survey and tutorial article reviews the literature over the period 1980--2026 on the evolution from classical multi-agent systems to open agent networks, with a particular focus on LLM-based autonomous agents, agent interoperability protocols, Internet-of-Agents infrastructures, and blockchain-enabled trust mechan... | We further synthesize the mapping between agent-network risks, trust requirements, and blockchain-enabled mechanisms, and clarify the role of blockchain as a shared trust layer rather than a replacement for agent security, semantic verification, privacy protection, or robust reasoning. | [
"Large Language Model (LLM)",
"Multi-Agent System",
"Blockchain Architecture"
] | [] | Explosive (>50/mo) | 55 | 6 | Prototype (TRL 4-6) | Score 6/10. Blockchain/DeFi (+2) | 2026-08-10T16:28:18.086859 |
2608.03554v1 | ReputationChain: Robust Trust Updating for Blockchain-Enabled Supply Chains | 2026-08-04T12:24:56Z | [
"cs.CR",
"cs.DC"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Adnan Iftekhar | 4 | [
"Adnan Iftekhar",
"Chengliang Zheng",
"Xiaohui Cui",
"Mir Hassan"
] | [] | http://arxiv.org/abs/2608.03554v1 | NOT_DETECTED | [] | [] | [] | Blockchain can preserve supply-chain records, but ledger integrity alone does not show whether a participant should be trusted in a future risk-sensitive transaction. Existing reputation systems mainly address product evidence, global feedback aggregation, or review authenticity, while giving less attention to repeated... | [
-0.09850700199604034,
0.01791900023818016,
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0.0... | [
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0.0... | Crypto, Web3 & Autonomous Financial AI Agents | Blockchain can preserve supply-chain records, but ledger integrity alone does not show whether a participant should be trusted in a future risk-sensitive transaction. | Existing reputation systems mainly address product evidence, global feedback aggregation, or review authenticity, while giving less attention to repeated bilateral inflation, identity multiplicity, and unfair decay for honest participants with sparse histories. | The results support a bounded reduction in reputation distortion, not attacker detection. | [
"Blockchain Architecture"
] | [] | Explosive (>50/mo) | 55 | 6 | Prototype (TRL 4-6) | Score 6/10. Blockchain/DeFi (+2) | 2026-08-10T16:28:18.093914 |
2608.02986v1 | Internalising the Identity Primitive: Cryptographic Individuality for an Autonomous Agent on a Public Blockchain | 2026-08-04T00:47:13Z | [
"cs.CR",
"cs.AI",
"cs.MA"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Keisuke Suzuki | 1 | [
"Keisuke Suzuki"
] | [] | http://arxiv.org/abs/2608.02986v1 | NOT_DETECTED | [] | [] | [] | A software agent on a public blockchain accumulates authority and economic stakes, raising the engineering question of what makes it count as an individual. The paper's central contribution is a shift of trust root for the key-to-weights binding of agent identity: from hardware, operator, or wrapper trust to cryptograp... | [
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-0.0158... | [
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... | Crypto, Web3 & Autonomous Financial AI Agents | A software agent on a public blockchain accumulates authority and economic stakes, raising the engineering question of what makes it count as an individual. | We design and deploy on Solana devnet an agent whose neural-network weights are a deterministic function of its private key. | The resulting transition-time invariant instantiates the cryptographic individuality proposed by Suzuki 2026's Artificial Externality framework. | [
"Multi-Agent System",
"Encryption & Privacy",
"Blockchain Architecture"
] | [] | Explosive (>50/mo) | 55 | 6 | Prototype (TRL 4-6) | Score 6/10. Blockchain/DeFi (+2) | 2026-08-10T16:28:18.102764 |
2608.02178v1 | Microscopic dynamics of consensus formation in multi-agent LLM Naming Games | 2026-08-03T12:59:23Z | [
"physics.soc-ph",
"cond-mat.stat-mech",
"cs.MA",
"nlin.AO"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Cristiano De Nobili | 4 | [
"Cristiano De Nobili",
"Vijayasri Iyer",
"Alessandro Codello",
"Raffaella Burioni"
] | [] | http://arxiv.org/abs/2608.02178v1 | NOT_DETECTED | [] | [] | [] | Decentralized populations of Large Language Model (LLM) agents can spontaneously reach consensus on shared conventions, yet the microscopic mechanisms by which their internal stochasticity shapes macroscopic ordering remain unexplored. We study a minimal LLM Naming Game in which the listener's decision is a single-toke... | [
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0.0527460016310215,
-0.0471... | Crypto, Web3 & Autonomous Financial AI Agents | Decentralized populations of Large Language Model (LLM) agents can spontaneously reach consensus on shared conventions, yet the microscopic mechanisms by which their internal stochasticity shapes macroscopic ordering remain unexplored. | Across three open-weight architectures, consensus is always reached, but through three distinct listener regimes: permissive (repaint-noise dominated), near-deterministic, and conservative (missed-collapse dominated). | Decoding temperature thus emerges as an architecture-dependent control parameter for decentralized LLM populations, quantitatively characterized by the statistical-physics toolkit. | [
"Large Language Model (LLM)",
"Multi-Agent System"
] | [] | Explosive (>50/mo) | 55 | 6 | Prototype (TRL 4-6) | Score 6/10. Blockchain/DeFi (+2) | 2026-08-10T16:28:18.112040 |
2608.01938v1 | D-MUTRA: DLT-based MUTual Remote Attestation for Multi-Agent Systems | 2026-08-03T09:10:31Z | [
"cs.CR"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Adam Zahir | 5 | [
"Adam Zahir",
"Vincent Lefebvre",
"Mark Angoustures",
"Milan Groshev",
"Carlos J. Bernardos"
] | [] | http://arxiv.org/abs/2608.01938v1 | NOT_DETECTED | [] | [] | [] | Multi-agent systems (MAS) comprise autonomous software agents that collaborate to perform complex tasks in critical cyber-physical domains, including multi-robot coordination and the Industrial Internet of Things (IIoT). In such distributed environments, a compromised agent may execute modified software while appearing... | [
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0.01385900... | Crypto, Web3 & Autonomous Financial AI Agents | Multi-agent systems (MAS) comprise autonomous software agents that collaborate to perform complex tasks in critical cyber-physical domains, including multi-robot coordination and the Industrial Internet of Things (IIoT). | Remote attestation (RA) is a well-established technique for this purpose, enabling a remote verifier to assess the integrity of a potentially compromised prover device. | Results show that D-MUTRA enables agents to continuously attest one another, detects malicious software modifications, and scales to large deployments with negligible overhead on protected applications. | [
"Smart Contract",
"Multi-Agent System",
"Blockchain Architecture"
] | [] | Explosive (>50/mo) | 55 | 6 | Prototype (TRL 4-6) | Score 6/10. Blockchain/DeFi (+2) | 2026-08-10T16:28:18.122528 |
2608.01861v1 | FedJigsaw: Multi-Agent Collaborative Model Reassembly for Decentralized Heterogeneous Federated Learning | 2026-08-03T08:10:18Z | [
"cs.DC"
] | ArXiv Standard | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | true | Jifeng Chen | 3 | [
"Jifeng Chen",
"Haibo Zhang",
"Yawen Chen"
] | [] | http://arxiv.org/abs/2608.01861v1 | NOT_DETECTED | [] | [] | [] | Model Heterogeneous Federated Learning (MHFL) addresses client-level resource heterogeneity by allowing each participant to train a personalized model architecture under a shared training objective. A prevalent paradigm, Partial Training (PT), achieves this by allowing each client to train a subnetwork of the global mo... | [
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0.0... | [
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0.0... | Crypto, Web3 & Autonomous Financial AI Agents | Model Heterogeneous Federated Learning (MHFL) addresses client-level resource heterogeneity by allowing each participant to train a personalized model architecture under a shared training objective. | However, existing PT methods typically rely on predefined architectural templates or over-parameterized supernets, limiting fine-grained personalization and imposing substantial computational and memory overhead. | Extensive evaluations demonstrate that FedJigsaw outperforms state-of-the-art MHFL baselines by up to 13.8% in relative accuracy while significantly shrinking cross-client performance variance, but also slashes decision-making latency and peak memory footprint compared to existing policy-driven methods. | [
"Multi-Agent System",
"Federated Learning"
] | [] | Explosive (>50/mo) | 55 | 6 | Prototype (TRL 4-6) | Score 6/10. Blockchain/DeFi (+2) | 2026-08-10T16:28:18.127256 |
End of preview. Expand in Data Studio
⚡ Crypto, Web3 & Autonomous Financial AI Agents Dataset (2023–2026)
This dataset contains 100 strictly domain-filtered research papers focusing on Decentralized AI, Autonomous Financial Agents, Smart Contract Verification, Zero-Knowledge Proofs (ZKP), DeFi, and Multi-Agent Consensus (2023-2026).
📊 Features:
- 384-dimensional PyTorch Embeddings for Vector Search & Semantic Clustering
- Strict Domain Verification: Passed 2-stage filtering (100% relevant to Crypto/AI)
- Extracted Innovations & Results: Real sentences parsed directly from ArXiv papers
- Native PyArrow Parquet: Zero JSON-string overhead
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