惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

S
SegmentFault 最新的问题
月光博客
月光博客
大猫的无限游戏
大猫的无限游戏
GbyAI
GbyAI
博客园 - 叶小钗
小众软件
小众软件
WordPress大学
WordPress大学
I
InfoQ
Last Week in AI
Last Week in AI
Vercel News
Vercel News
博客园 - Franky
Stack Overflow Blog
Stack Overflow Blog
P
Proofpoint News Feed
A
About on SuperTechFans
Engineering at Meta
Engineering at Meta
腾讯CDC
D
DataBreaches.Net
有赞技术团队
有赞技术团队
宝玉的分享
宝玉的分享
Jina AI
Jina AI
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
G
Google Developers Blog
V
Visual Studio Blog
酷 壳 – CoolShell
酷 壳 – CoolShell

cs updates on arXiv.org

Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment Agentic Proving for Program Verification MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents One Policy, Infinite NPCs: Persona-Traceable Shared RL Policies for Scalable Game Agents How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework Benchmarking Google Embeddings 2 against Open-Source Models for Multilingual Dense Retrieval and RAG Systems Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts Solving the Aircraft Disassembly Scheduling Problem Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents CP or DP? Why Not Both: A Case Study in the Partial Shop Scheduling Problem Asking For An Old Friend: Diagnosing and Mitigating Temporal Failure Modes in LLM-based Statutory Question Answering EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction Naturalistic measure of social norms alignment Articulatory strategy as a source of variation in acoustic vowel dynamics When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems EquiSumm : A Gender Bias-Aware Framework for Inclusive Tweet Summarization Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning Cultural Adaptation in Large Language Models for Political Discourse Emotion Recognition in Sign Language Conversation ClimateChat-300K: A Multi-Modal Facebook Dataset for Understanding Diverse Perspectives in Climate Communication AraHopeCorpus: Annotation Guidelines and Dataset for Hope Speech in Arabic Social Media Crisis Discourse Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning DART: Semantic Recoverability for Structured Tool Agents Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems Parallel Context Compaction for Long-Horizon LLM Agent Serving
On the Encodability of Reversible Process Calculi
[Submitted on 24 Jun 2026] · 2026-06-25 · via cs updates on arXiv.org

View PDF HTML (experimental)

Abstract:Reversibility, allowing one to execute a program not only forwards as usual, but also backwards, has emerged as a main concept in computing, with applications ranging from debugging and fault tolerance to biological and quantum systems. CCSK, a reversible extension of CCS, is a paradigmatic model of reversible concurrent computation. In this paper, we investigate the encodability of CCSK into classical forward-only concurrent models. We establish a separation theorem showing that there is no basic, success-sensitive encoding of CCSK into CCS or the {\pi}-calculus, highlighting the strong impact of reversibility on the expressive power. We then present an encoding of CCSK processes with only top-level parallel composition into the internal {\pi}-calculus, correct up to strong bisimilarity. We also identify a fundamental limitation: no parallel-preserving encoding of CCSK (with arbitrary parallel composition) into the {\pi}-calculus can be correct up to strong bisimilarity. Finally, we provide a parallel-preserving encoding correct under a weaker behavioural correspondence: weak mutual simulation. Our findings extend the literature of encodability results to reversible process calculi.

Submission history

From: Iain Phillips [view email]
[v1] Wed, 24 Jun 2026 14:59:50 UTC (145 KB)