mlsanityData to 7 Oct 2026

Protocol Compression Changes Which Party Pays: Bilateral Cost in Cross-Organization LLM Agent Communication

arXiv 2609.06129 ↗First posted 5 Sep 2026cs.CLcited by 0

On the map: LLM agents and tool use → Agent memory systems

Abstract (arXiv)

Agents that talk across organizations exchange long messages billed by the token. A shorter notation therefore looks like a saving that costs nothing but an agreement to use it. Recent work reports the saving is conditional. Compressed notation can instead raise total tokens by 8% to 11% over a JSON baseline, when parsing failures force extra model calls. That is measured for one payer. Between two organizations neither side can install a decoder at the other end, and each pays under its own tokenizer, price, and cache state. We measure both sides. A preregistered token-level study covered 198 content-matched item pairs across six vendors, for 2,376 native-usage cells. We then overlay an English baseline, runtime schema negotiation followed by compression, and injected-schema compression on a two-party procurement bargain with an exactly enumerated feasible set. The overlay covers 1,053 completed dialogues of a 1,215-cell grid across 3 model pairs, plus a 405-dialogue rerun of the negotiated condition. Compression amplifies cross-vendor cost dispersion by a factor of 1.078, with a 95% CI of [1.066, 1.091], and two vendor pairs reverse which endpoint is cheaper. Runtime negotiation succeeds as a protocol and fails as a bargain. The parties agree a schema in 121 of 135 headline dialogues, none of them the schema we would have supplied. They settle the task in only 9 of those dialogues, and they reach impasse in 106 of them. The negotiated sessions average 10.8 turns against 17.6, and cost 52% of the English total because sessions end sooner, not because the handshake is repaid. Break-even horizons run from 20 to 70 turns, the low end only under the conditional accounting, and all of them lie above every observed English session. On one cross-vendor pair both parties keep about half their cost. On the other the receiving party pays more at a high cache-hit rate.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 11 of its 19 references that are in the map. First posted on arXiv 5 Sep 2026.

Builds on

11 of 19 references.

References · 11

  1. 2024LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compressioncited by 343
  2. 2024Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Followingcited by 94
  3. 2026Don't Break the Cache: An Evaluation of Prompt Caching for Long-Horizon Agentic Taskscited by 18
  4. 2026TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Ratecited by 6
  5. 2026SEQUOR: A Multi-Turn Benchmark for Realistic Constraint Followingcited by 5
  6. 2026Repair, Not Improvement: Decomposing Constrained Decoding in Tool-Call Abstentioncited by 2
  7. 2026Empirical Comparison of Agent Communication Protocols for Task Orchestrationcited by 1
  8. 2026Talk is Cheap, Communication is Hard: Dynamic Grounding Failures and Repair in Multi-Agent Negotiationcited by 1
  9. 2026Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systemscited by 1
  10. 2026Cache-Aware Prompt Compression:A Two-Tier Cost Model for LLM API Cachingcited by 1
  11. 2026Dynamic Coalition Formation and Communication Pricing in Skill-Based Agentic AI Systemscited by 1

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  7. 2026What Does Context Compression Cost an Agent? Interaction Costs Unrevealed by Task-Completion Metrics
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