Trustworthy Multi-Agent Systems: Mitigating Semantic Drift with the Argent Signaling Protocol
Quick Answer
This paper shows that The Argent Signaling Protocol (ASP) enhances multi-agent LLM systems by providing structured quality signals, improving QA performance significantly across models.
Quick Take
For instance, on Qwen (0.8B), the pass rate increased from 11.1% to 33.3%, while ASP blocked 100% of ungrounded outputs in setups.
Key Points
- ASP introduces quality signals: certainty, grounding, stochasticity, and assumption index.
- In standalone mode, ASP improved Qwen's pass rate from 11.1% to 33.3%.
- Dobby (8B) saw a pass rate increase from 33.3% to 44.4% with ASP.
- ASP blocks 100% of ungrounded outputs in multi-agent configurations.
- Aggregate improvement in QA benchmark: passes increased from 12/81 to 21/81.
Paper Resources
📖 Reader Mode
~2 min readAbstract:When multi-agent LLM systems produce bad answers, not all failures are equal: some answers are grounded in the right material but incomplete, while others are simply ungrounded and should be stopped. Current retry strategies treat both cases identically (try again and hope for the best), leaving human supervisors unable to tell whether a retry was warranted or whether the system should have halted instead.
We introduce the Argent Signaling Protocol (ASP), a compact machine-readable header that accompanies every AI-generated response with structured quality signals: certainty (@C), grounding (@G), stochasticity (@S), and an assumption index that classifies the evidentiary basis of each claim. These signals enable a controller to distinguish repairable failures from containment failures and route each case differently.
We evaluate ASP in two modes. In standalone mode, a 27-question document-grounded QA benchmark over the Array BioPharma/Ono license agreement compares baseline prompts against ASP-instrumented controller actions across three local GGUF models. On Qwen~(0.8B), ASP improves pass rate from 11.1% to 33.3% and mean term coverage from 36.7% to 65.4%; on Dobby~(8B), ASP produces 4 fail-to-pass recoveries, raising pass rate from 33.3% to 44.4%; on SmolLM3~(3B), ASP alternates between repair and containment per question. Aggregate improvement is meaningful (12/81 to 21/81 passes). In multi-agent mode, an ASP sidecar sits between a retrieval agent and a downstream decision agent; the sidecar blocks 100% of ungrounded upstream outputs from reaching the downstream agent (24/27 blocked, 0 ungrounded propagations).
| Comments: | 17 pages |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.19356 [cs.CL] |
| (or arXiv:2606.19356v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.19356 arXiv-issued DOI via DataCite |
Submission history
From: Anantha Sharma [view email]
[v1]
Thu, 14 May 2026 15:25:02 UTC (200 KB)
— Originally published at arxiv.org
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