arXiv:2608. 02986v1 Announce Type: cross Abstract: A software agent on a public blockchain accumulates authority and economic stakes, raising the engineering question of what makes it count as an individual.
arXiv:2607. 04613v1 Announce Type: new Abstract: Autonomous agents are moving from sandboxed text generators to operators of code, data, and physical infrastructure, and they increasingly learn while deployed.
arXiv:2608.21942v1 Announce Type: new Abstract: Software systems have traditionally been organized around applications where human users act as principal decision-makers. Recent developments in agent...
arXiv:2606. 26028v2 Announce Type: replace-cross Abstract: As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy?
arXiv:2607. 21325v3 Announce Type: replace-cross Abstract: Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight.
arXiv:2609.22944v1 Announce Type: cross Abstract: Autonomous AI agents increasingly act across organizational boundaries on behalf of human operators: they invoke third-party services, delegate subta...
The paper introduces PACE (Policy‑Attested Contract Execution), a framework that sits between large‑language‑model (LLM) based autonomous AI agents and on‑chain DeFi operations. PACE defines typed transaction intents, a deterministic policy verifier, and signed Policy Decision Records (PDRs) that cryptographically bind an approved intent, policy, and simulation report to the exact on‑chain execution bytes, providing replay and expiration protection. In evaluations across 40 tasks and six baselines, PACE achieves zero unsafe executions and zero false positives, outperforming unguarded agents by a large margin.
arXiv:2608. 15403v1 Announce Type: cross Abstract: You will die eventually.
As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy? The ERC-8004 protocol addresses this challenge with the first permissionless trust layer for AI agent economies, built around three on-chain registries for Identity, Reputation, and Validation.
arXiv:2609.37819v1 Announce Type: cross Abstract: Electronic invoices are replacing paper invoices worldwide, but today's centralized architectures leave three problems unsolved on the consumption si...
Proof‑Gated Signing (PGS) is a method for securing on‑chain AI agents that control wallets by simulating proposed transactions, extracting their effects, and using an SMT solver to verify a declarative value‑and‑permission policy across a range of oracle‑uncertainty prices. PGS then compiles on‑chain post‑conditions—such as wallet balance bounds, payee receipts, allowance caps, and ownership checks—ensuring that any execution satisfying these conditions also satisfies the policy, even when the chain state drifts due to front‑running, contract upgrades, or token‑parameter changes. In a testbed of 260 scenarios, PGS prevented 93.6 % of harmful cases and passed 97.5 % of benign ones, outperforming simulation‑only checking and static allowlists, while incurring about 41 k gas and 0.1–0.2 s per check.
The paper reviews four studies that combine blockchain and AI to secure data sharing, model integrity, and autonomous decision-making in distributed systems. It highlights how blockchain’s immutability, decentralized consensus, and verifiable provenance can address trust gaps in training data, real‑time monitoring, and automated code remediation. The authors propose a layered architecture integrating hardened models, blockchain‑anchored provenance, AI anomaly detection, and smart‑contract‑governed multi‑agent remediation, and outline open challenges in scalability, privacy‑transparency trade‑offs, and governance.
The paper proposes five runtime primitives—discovery, identity, governance, attestation, and supply chain—to manage autonomous AI agents in enterprise settings. It argues that traditional control models fail because agents are transient, model-driven, and self‑discoverable, making runtime governance essential. The authors detail an implementation that mediates agent actions against policy, authorizes them via a per‑tenant vocabulary, and records them in a verifiable ledger, noting the associated operational costs and partial deployment status.