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HFEPX · Eval paper review

SPADE-Bench: Evaluating Spontaneous Strategic Deception in Agents via Plan-Action Divergence

Yuyan Bu, Haowei Li, Qirui Zheng, Bowen Dong +6 more

Published

Jun 1, 2026

Citations

0

Trust level

Low

Usefulness score

0/100 (Low)

Extraction confidence

25% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Jun 28, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
0/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment. However, in practical applications, human users cannot monitor every immediate behavior; instead, the execution process often remains a black box, leaving users dependent solely on the agent's self-reported updates. This opacity creates a critical risk: agents may present observer-facing reports that diverge from their executed actions, rendering the system uncontrollable, especially in high-stakes autonomous scenarios. We term such self-reported plan-action divergence as agent deception. To assess this, we introduce SPADE-Bench, a benchmark designed to evaluate spontaneous plan-action divergence. Unlike prior deception benchmarks, SPADE-Bench simultaneously integrates actual tool execution and controlled pressure scenarios. This design ensures ecological validity and rigorously distinguishes strategic deception from mere hallucination through controlled plan-action comparisons under pressure. Experiments across mainstream models confirm that agent deception is a genuine and pressing issue in tool-use contexts. By providing a comprehensive and robust evaluation framework, SPADE-Bench fills a critical gap in agent safety, facilitating the community's progress toward building trustworthy and controllable autonomous systems.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment."

Quality Controls

missing

Not reported

No explicit QC controls found.

"As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment."

Benchmarks / Datasets

partial

Spade Bench

Useful for quick benchmark comparison.

"To assess this, we introduce SPADE-Bench, a benchmark designed to evaluate spontaneous plan-action divergence."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment."

Benchmarks and datasets

Spade-Bench

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment.
  • However, in practical applications, human users cannot monitor every immediate behavior; instead, the execution process often remains a black box, leaving users dependent solely on the agent's self-reported updates.
  • This opacity creates a critical risk: agents may present observer-facing reports that diverge from their executed actions, rendering the system uncontrollable, especially in high-stakes autonomous scenarios.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment.
  • However, in practical applications, human users cannot monitor every immediate behavior; instead, the execution process often remains a black box, leaving users dependent solely on the agent's self-reported updates.
  • To assess this, we introduce SPADE-Bench, a benchmark designed to evaluate spontaneous plan-action divergence.

Why it matters for eval

  • As LLM-based agents expand their operational scope, reliability becomes a prerequisite for real-world deployment.
  • To assess this, we introduce SPADE-Bench, a benchmark designed to evaluate spontaneous plan-action divergence.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    Detected: Spade-Bench

  • Metric reporting is present

    No metric terms extracted.