The paper introduces a claim‑anchored execution contract that binds a tool‑using agent’s emitted claim to its exact source span, the ordered execution prefix that produced it, and the source version and access state observed. Each receipt contains deterministic anchors, source identifiers, offsets, hashes, quotes, and a domain‑separated execution commitment, allowing a verifier to reconstruct these bindings before semantic or task labels are joined. The contract defines seven independently testable properties and demonstrates high detection rates against cross‑object attacks, with strong performance on conflict‑aware support guard evaluations.
By Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Yina Sa, Daren Zha, Jun Xiao
The paper introduces Verifiable Agent Insurance (VAI), a framework that lets AI agent providers commit to policy compliance through smart contracts and escrow funds on a blockchain. When a user detects a policy violation, a verifier reviews execution traces logged by the agent harness; if the violation is confirmed, the escrow is paid to the user. Experiments on three agent benchmarks show VAI detects 42.2% of violations, rising to 83.0% when agents emit explicit structured records.
By Cheng Hua, Taro Tsuchiya, Nicolas Christin, Giulia Fanti
arXiv:2607. 10487v1 Announce Type: cross Abstract: LLM agents can commit durable effects from authority evidence that was valid earlier in execution: a DOM snapshot, approval epoch, version witness, branch token, or worker result.
By Igor Santos-Grueiro
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.
By Rabimba Karanjai (Larry), Yang Lu (Larry), Richard Williamson (Larry), Hemanth Hm (Larry), Prakhar Mehrotra (Larry), Lei Xu (Larry), Weidong (Larry), Shi
arXiv:2607. 25364v1 Announce Type: new Abstract: Tool-using agents expose structured calls but commonly attach free-form rationales.
By Genliang Zhu (Accentrust, Georgia Institute of Technology), Chu Wang (Accentrust, University of Illinois Urbana-Champaign)
arXiv:2608.21159v1 Announce Type: cross
Abstract: Tool-using AI agents turn delegated tasks into provider effects, yet authorization often ends at admission while provider state, delivery, retry, and...
By Yingzhe Tong, Leyu Dai, Songhui Guo
arXiv:2608. 16402v1 Announce Type: new Abstract: Large language model-based agentic frameworks primarily optimize capability: whether an agent can reason, retrieve information, call tools, delegate work, and complete a goal.
By Bhaskar Tripathi, Anurag Kumar, Ramendra Kumar, Bhavesh Gadhe
The paper introduces Soei, a structured observability framework for AI coding agents, addressing the need to track what work was done, by whom, and whether it is finished. It evaluates an existing tool, Agenttrail, revealing inconsistencies and security gaps, and presents empirical findings from 45 real coding-agent sessions, highlighting issues such as unverified code edits and credential exposure. Soei comprises an event schema, hash‑chained ledger, PROV‑aligned graph, and verification layer, achieving efficient storage, fast verification, and tamper detection on the same dataset.
By Mohammed Nayeem Khan, K S Sarun Kumar
The paper introduces a protocol for cross‑interface authorization of AI computer‑use agents, ensuring that policy decisions remain consistent across graphical interfaces, command lines, APIs, scripts, and Model Context Protocol servers. It defines a profile‑bounded system that enumerates all effect‑bearing routes, normalizes requests to canonical effects, issues prepare tickets, and re‑verifies all relevant state at commit time, requiring a sink receipt for a committed result. Extensive testing across two runtime mappings produced thousands of traces, with rigorous checks confirming no semantic regressions, mutant rejections, and correct outcome matching.
By Genliang Zhu, Chu Wang
arXiv:2609.38266v1 Announce Type: cross
Abstract: Agentic large language models (LLMs) now move money through tools, yet the record of what they did is usually a trace their own process emits beside...
By Mustafa Arslan
BekchiAI introduces a benchmark and platform for evaluating large language model agents. The benchmark comprises 13 tool‑using ReAct agents across seven task categories, totaling 2,057 deterministic test tasks with verifier‑checkable gold answers. The platform offers web‑based observability, token and latency telemetry, and remote run termination for live agents.
By Mesut Toruk
arXiv:2609.14744v2 Announce Type: replace
Abstract: By acquiring compute, credentials, accounts, services, and other agents, autonomous AI agents can introduce new authority into a task. Payment, bud...
By Genliang Zhu (Accentrust, Georgia Institute of Technology), Chu Wang (Accentrust, University of Illinois Urbana-Champaign)