arXiv AI By Ehud Shapiro

Volition Elicitation: Operational Semantics for People and Their Machines

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arXiv:2607. 14138v1 Announce Type: cross Abstract: The most prevalent distributed systems today include people and their personal machines (smartphones).

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arXiv AI
Jul 21

ETAS: An Effect-Typed Language for Agent Systems

arXiv:2607. 17780v1 Announce Type: cross Abstract: ETAS is a programming language for agent systems that treats model-backed agents, tool calls, prompts, typed memory, human approvals, policies, and execution traces as semantic program elements rather than library conventions.

By Huiri Tan, Yikun Wang, Puyang Zhang, Shangyu Li, Jiasi Shen
arXiv AI
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A Formal Analysis of Agent Payment Protocols

The paper presents a formal analysis of four agent payment protocols—x402, MPP, ACP, and AP2—using the Tamarin prover. By modeling each protocol’s roles, state, and trust assumptions, the authors verify 86 cases, reproducing 46 known results and uncovering 40 new formal-consistency findings. They further validate ten findings through implementation proofs of concept, SDK/schema witnesses, and executable traces, highlighting the importance of consistent delegated authorization across all protocol stages.

By Ke Jiang, Mohan Yu, Yuan Chang, Mohit Kumar Jangid, Jianyu Niu, Cong Wang, Yinqian Zhang
Hugging Face Trending Papers
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Agent libOS: A Library-OS-Inspired Runtime for Long-Running, Capability-Controlled LLM Agents

Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for external events, request human authority, generate tools, and perform side effects that must be resumed and audited. This paper presents Agent libOS, a library-OS-inspired runtime substrate for LLM agents.

arXiv AI
Aug 18

Specifying AI-SDLC Processes: A Protocol Language for Human-Agent Boundaries

arXiv:2606. 20615v3 Announce Type: replace Abstract: AI agents now act as first-class members of the software development lifecycle, but the instruments teams use to direct them enforce nothing: process encoded in prompts is flexible but unenforceable, while workflow formalisms are enforceable but do not model autonomous agents.

By Ylli Prifti, Pasquale De Meo, Alessandro Provetti