CBCL: Safe Self-Extending Agent Communication

• URL: http://arxiv.org/abs/2604.14512v1
• Date: Thu, 16 Apr 2026 00:57:48 GMT
• Title: CBCL: Safe Self-Extending Agent Communication
• Abstract summary: We present CBCL (Common Business Communication Language), an agent communication language that constrains all messages, including runtime language extensions, to the deterministic context-free language (DCFL) class.<n> CBCL allows agents to define, transmit, and adopt domain-specific "dialect" extensions as first-class messages; three safety invariants (R1-R3), machine-checked Lean 4 and enforced in a Rust reference implementation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agent communication languages (ACLs) enable heterogeneous agents to share knowledge and coordinate across diverse domains. This diversity demands extensibility, but expressive extension mechanisms can push the input language beyond the complexity classes where full validation is tractable. We present CBCL (Common Business Communication Language), an agent communication language that constrains all messages, including runtime language extensions, to the deterministic context-free language (DCFL) class. CBCL allows agents to define, transmit, and adopt domain-specific "dialect" extensions as first-class messages; three safety invariants (R1--R3), machine-checked in Lean 4 and enforced in a Rust reference implementation, prevent unbounded expansion, applying declared resource limits, and preserving core vocabulary. We formalize the language and its safety properties in Lean 4, implement a reference parser and dialect engine in Rust with property-based and differential tests, and extract a verified parser binary. Our results demonstrate that homoiconic protocol design, where extension definitions share the same representation as ordinary messages, can be made provably safe. As autonomous agents increasingly extend their own communication capabilities, formally bounding what they can express to each other is a precondition for oversight.

Related papers

• Beyond Cross-Lingual Transfer: Benchmarking Propagation Boundaries in Multilingual LLM Unlearning [80.05116806217397]
  Large Language Model (LLM) unlearning aims to suppress target knowledge while preserving general capabilities.<n>We introduce CLLPU (Cross-Lingual and Language-Bound Protocol for LLM Unlearning), a multilingual benchmark that formulates this problem through two settings.<n>Common-goal forgetting, where target knowledge should be suppressed across all languages, and language-conditioned forgetting, where suppression should remain confined to a designated language.
  arXiv  Detail & Related papers  (2026-09-05T08:26:15Z)
• MNC: Scope-Bound Semantic Declassification for Private LLM-Agent Communication [6.029029648872896]
  Multi-agent large language model (LLM) systems can expose protected state through internal messages, tool arguments, logs, and persistent memory.<n>We introduce Minimum-Necessary Communication (MNC), a typed semantic-declassification protocol that binds a task-sufficient disclosure to explicit recipient, purpose, forwarding, lifetime, logging, and memory scopes.<n>Controlled semantic-join, memory, probing, and longitudinal experiments show that conventional defenses can preserve protocol-level utility while exposing substantial additional inference signal.
  arXiv  Detail & Related papers  (2026-08-03T05:36:49Z)
• Weave of Formal Thought [51.56484100374058]
  We introduce Weave of Formal Thought (WoFT), a paradigm uniting rigorous syntactic validation with learned structural representations.<n>We present a latent-variable fine-tuning method training the language model to interleave non-terminal grammar symbols directly into generation.<n>For Python, fine-tuning StarCoder2-3B with our RWS objective reduces per-token cross-entropy by 14.3% relative to a text-only SFT baseline.
  arXiv  Detail & Related papers  (2026-06-24T15:58:11Z)
• Beyond tokens: a unified framework for latent communication in LLM-based multi-agent systems [0.65268245109828]
  Multi-agent systems built on large language models (LLMs) have become a prevailing paradigm for tackling complex reasoning, planning, and tool-use tasks.<n>The dominant communication protocol in such systems is natural language: agents exchange messages token-by-token, verbalising their internal reasoning so that peers can read, verify, and respond.<n>A growing body of work therefore explores an alternative protocol -- latent communication, in which agents exchange continuous representations directly, bypassing the bottleneck of text generation.<n>This paper presents a unified framework for organising the rapidly expanding literature on latent communication.
  arXiv  Detail & Related papers  (2026-06-04T05:10:20Z)
• Provable Coordination for LLM Agents via Message Sequence Charts [0.0]
  We introduce a domain-specific language for specifying agent coordination based on message sequence charts (MSCs)<n>We define the syntax and semantics of the language and present a syntax-directed projection that generates deadlock-free local agent programs from global coordination specifications.<n>An open-source Python implementation of our framework is available as ZipperGen.
  arXiv  Detail & Related papers  (2026-04-19T20:54:30Z)
• Turn: A Language for Agentic Computation [0.0]
  We present textbfTurn, a compiled, actor-based programming language.<n>Turn is for agentic software: programs that reason and act autonomously by delegating inference to large language models.
  arXiv  Detail & Related papers  (2026-03-07T09:10:09Z)
• X-Blocks: Linguistic Building Blocks of Natural Language Explanations for Automated Vehicles [14.815119135668247]
  Natural language explanations play a critical role in establishing trust and acceptance of automated vehicles (AVs)<n>This paper introduces X-Blocks, a hierarchical analytical framework that identifies the linguistic building blocks of natural language explanations for AVs at three levels: context, syntax, and lexicon.<n> RACE achieves 91.45 percent accuracy and a Cohens kappa of 0.91 against cases with human annotator agreement, indicating near-human reliability for context classification.
  arXiv  Detail & Related papers  (2026-02-02T07:18:25Z)
• Language Server CLI Empowers Language Agents with Process Rewards [8.38905909723596]
  We present Lanser-CLI, a CLI-first orchestration layer that pins and mediates a Language Server Protocol (LSP) server.<n>Our position is that language servers provide not only structural information but also an actionable process reward.<n>We formalize determinism under frozen snapshots and establish a monotonicity property for the process reward.
  arXiv  Detail & Related papers  (2025-10-27T01:25:20Z)
• Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference [2.172419551358714]
  Large language models (LLMs) are increasingly applied in multilingual contexts, yet their capacity for consistent, logically grounded alignment across languages remains underexplored.<n>We present a framework for multilingual natural language inference that generates synthetic, logic-based premise-hypothesis pairs and translates them into a typologically diverse set of languages.<n>Code-switching does not degrade, and can even improve, performance, suggesting that translation-induced lexical variation may serve as a regularization signal.
  arXiv  Detail & Related papers  (2025-08-20T14:30:34Z)
• MrGuard: A Multilingual Reasoning Guardrail for Universal LLM Safety [56.77103365251923]
  Large Language Models (LLMs) are susceptible to adversarial attacks such as jailbreaking.<n>This vulnerability is exacerbated in multilingual settings, where multilingual safety-aligned data is often limited.<n>We introduce a multilingual guardrail with reasoning for prompt classification.
  arXiv  Detail & Related papers  (2025-04-21T17:15:06Z)
• CRIL: A Concurrent Reversible Intermediate Language [0.0]
  We present a reversible intermediate language with composing for translating a high-level concurrent programming language to another lower-level concurrent programming language, keeping reversibility. We propose CRIL as an extension of RIL used by Mogensen for a functional reversible language, incorporating a multi-thread process invocation and the synchronization primitives based on the P-V operations.
  arXiv  Detail & Related papers  (2023-09-13T20:52:54Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)
• VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation [77.82373082024934]
  We plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages. It can effectively avoid the degeneration of predicting masked words only conditioned on the context in its own language. The proposed cross-lingual model delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark.
  arXiv  Detail & Related papers  (2020-10-30T03:41:38Z)
• GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction [107.8262586956778]
  We introduce graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations. GCNs struggle to model words with long-range dependencies or are not directly connected in the dependency tree. We propose to utilize the self-attention mechanism to learn the dependencies between words with different syntactic distances.
  arXiv  Detail & Related papers  (2020-10-06T20:30:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.