CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments

CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/cirrusbench-evaluating-llm-based-agents-beyond-correctness-in-real-world-cloud-service-environments

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 43
Source count: 4
Coverage: 83%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID cirrusbench-evaluating-llm-based-agents-beyond-correctness-in-real-world-cloud-service-environments | Route /signal-canvas/cirrusbench-evaluating-llm-based-agents-beyond-correctness-in-real-world-cloud-service-environments

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/cirrusbench-evaluating-llm-based-agents-beyond-correctness-in-real-world-cloud-service-environments

MCP example

{
  "tool": "search_signal_canvas",
  "arguments": {
    "mode": "paper",
    "paper_ref": "cirrusbench-evaluating-llm-based-agents-beyond-correctness-in-real-world-cloud-service-environments",
    "query_text": "Summarize CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments"
  }
}

source_context

{
  "surface": "signal_canvas",
  "mode": "paper",
  "query": "CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments",
  "normalized_query": "2603.28569",
  "route": "/signal-canvas/cirrusbench-evaluating-llm-based-agents-beyond-correctness-in-real-world-cloud-service-environments",
  "paper_ref": "cirrusbench-evaluating-llm-based-agents-beyond-correctness-in-real-world-cloud-service-environments",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 8

References: 43

Proof: Verification pending

Freshness state: computing

Source paper: CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments

PDF: https://arxiv.org/pdf/2603.28569v1

Repository: https://github.com/CirrusAI

Source count: 4

Coverage: 83%

Last proof check: 2026-03-31T20:30:20.191Z

Signal Canvas receipt window

Ready for execution: CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments

/buildability/cirrusbench-evaluating-llm-based-agents-beyond-correctness-in-real-world-cloud-service-environments

Build Nowready

Subject: CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments

Verdict

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
existing benchmarks for LLM-based agents largely rely on synthetic environments that fail to capture the diversity and unpredictability of authentic customer inputs
Implicationpartial
Directly and explicitly stated in the abstract as the core motivation for the work.
Verificationpartial
partial
Evidencepartial
we introduce CirrusBench, a novel evaluation framework distinguished by its foundation in real-world data from authentic cloud service tickets.
Implicationpartial
Directly and explicitly stated in the abstract as the main contribution.
Verificationpartial
partial
Evidencepartial
Moving beyond execution correctness, we introduce novel Customer-Centric metrics to define agent success, quantifying service quality through metrics such as the Normalized Efficiency Index and Multi-Turn Latency to explicitly measure resolution efficiency.
Implicationpartial
Directly stated in the abstract as a key methodological contribution.
Verificationpartial
partial
Evidencepartial
Experiments utilizing our framework reveal that while state-of-the-art models demonstrate strong reasoning capabilities, they frequently struggle in complex, realistic multi-turn tasks
Implicationpartial
Strongly supported by the abstract's conclusion and specific results showing low success rates in multi-turn contexts.
Verificationpartial
partial
Evidencepartial
they frequently struggle in complex, realistic multi-turn tasks and fail to meet the high-efficiency standards required for customer service
Implicationpartial
Strongly supported by the abstract's conclusion and the introduction of efficiency metrics to highlight this gap.
Verificationpartial
partial
Evidencepartial
explicit thinking capability is not always benefit for SR, ATPR, ALJ and ANEI, which can be observed from the Qwen series and DeepSeek series.
Implicationpartial
Directly supported by a specific result comparing thinking and non-thinking models, though the evidence quote is from a results analysis section.
Verificationpartial
partial
Evidencepartial
the thinking model DeepSeek-R1 achieves a Pass@1 Success Rate (SR) of only 7.9% in tool-use scenarios, which is lower than the 9.6% achieved by its non-thinking counterpart, DeepSeek-V3.2.
Implicationpartial
Clear, explicit numeric evidence provided in the results analysis.
Verificationpartial
partial
Evidencepartial
CirrusBench preserves the intricate multi-turn logical chains and realistic tool dependencies inherent to technical service environments.
Implicationpartial
Directly stated as a key feature of the proposed framework in the abstract.
Verificationpartial
partial

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

CirrusBench: Evaluating LLM-based Agents Beyond Correctness in Real-World Cloud Service Environments

Use Signal Canvas as the narrative proof surface

Use this Signal Canvas via API or MCP

Signal Canvas proof surface