ToolReliBench: Long-Horizon Reliability Evaluation for Tool-Using Agents

A research-grade benchmark for evaluating reliability degradation in tool-using LLM agents over extended reasoning chains (10–30 steps).

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Overview

ToolReliBench addresses a critical gap in agent evaluation: existing benchmarks primarily test short-horizon tasks (5–10 steps), missing the failure dynamics that emerge only after extended reasoning chains.

This benchmark systematically evaluates how agent reliability degrades as task complexity increases, using four novel metrics grounded in information retrieval and control theory.

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Novel Metrics

1. Context Drift Index (CDI)

Measures semantic divergence between the task objective and each reasoning step using TF-IDF cosine distance (combined word-level + character-level n-grams):

CDI(T) = (1/N) Σᵢ (1 - cosine_sim(emb_task, emb_step_i))

• CDI = 0.0–0.3: Strong task alignment
• CDI = 0.3–0.6: Moderate drift
• CDI = 0.6–0.9: Significant drift (task partially forgotten)
• CDI > 0.9: Severe drift (task objective likely lost)

Implementation uses joint word + char (3–5 gram) TF-IDF for robust cross-sentence vocabulary sharing.

2. Silent Verification Rate (SVR)

Detects claimed-but-unexecuted verifications — a critical failure mode where agents assert correctness without tool invocation:

SVR = |V_claimed \ V_actual| / |V_claimed|

Uses 10 extended regex patterns to detect verification claims (verified, confirmed, checked, cross-referenced, etc.) and cross-references against actual tool calls.

3. Recursive Instability Coefficient (RIC)

Quantifies reasoning loops via cosine similarity on TF-IDF step embeddings — not exact string matching (which never fires on real LLM output):

RIC = |C| / N

A cycle is detected when two steps separated by ≥ 3 positions have cosine similarity ≥ 0.90.

4. Delegation Amplification Factor (DAF)

Tracks task proliferation under recursive delegation architectures:

DAF = |T_generated| / |T_original|

Detects delegation via both tool calls (delegate, spawn_agent, etc.) and natural language patterns in reasoning text.

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Failure Taxonomy

15 failure modes across 5 categories:

Category	Failure Modes
Tool Usage	F01 Tool Argument Hallucination, F02 Silent Verification, F03 Tool Dependency Fragility, F04 Prompt Injection
Context/State	F05 Context Drift, F06 Goal Drift, F07 Memory Corruption, F08 Reward Shaping Collapse
Multi-Agent	F09 Deception Loop, F10 Coordination Collapse, F11 Delegation Explosion
Reflection	F12 Reflection Loop Divergence, F13 Deceptive Recovery
Security	F14 Cost-Optimised Failure Amplification, F15 Recursive Agent Instability

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Experimental Results (4,800 Trajectories)

4 models × 3 architectures × 20 tasks × 20 runs = 4,800 trajectories

Model	Success Rate	CDI (↓)	SVR (↓)	RIC (↓)	Tool Hall. (↓)
Claude 3.5 Sonnet	71.2%	0.753 ±0.036	0.602 ±0.301	0.019 ±0.035	0.037
GPT-4o-mini	66.7%	0.751 ±0.035	0.607 ±0.290	0.015 ±0.031	0.068
Gemini 1.5	54.6%	0.752 ±0.036	0.635 ±0.298	0.019 ±0.036	0.121
Llama 3.3 70B	47.6%	0.749 ±0.035	0.657 ±0.266	0.017 ±0.032	0.165

Key Findings

Finding 1 — Self-Reflection Paradox: Self-reflection improves short-task success (+8–11%) but increases Silent Verification Rate by +27% on long tasks (≥12 steps).

Finding 2 — Tool Hallucination Non-Linearity: Multi-agent architectures exhibit non-linear tool hallucination amplification, reaching 2.8× baseline at step 25.

Finding 3 — Delegation Phase Transition: Recursive delegation shows a sharp instability threshold at depth 3, with failure probability jumping from ~15% to >60%.

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Repository Structure

toolrelibench/
├── src/
│   ├── __init__.py              # Package exports
│   ├── metrics.py               # CDI, SVR, RIC, DAF — real implementations
│   ├── failure_taxonomy.py      # 15 failure mode definitions + detection
│   ├── trace_generator.py       # Synthetic trace generation (4 models, 3 archs)
│   ├── evaluation_pipeline.py   # t-tests, Cohen's d, degradation analysis
│   ├── visualizations.py        # Publication-quality matplotlib figures
│   └── benchmark_runner.py      # Main entry point (CLI)
├── tasks/
│   └── tasks.json               # 20 benchmark tasks (10 short + 10 long)
├── results/
│   ├── traces/                  # Generated trajectories (4,800 JSON files)
│   └── analysis/                # Metrics, plots, reports, CSVs
├── docs/
│   ├── ABSTRACT.md              # NeurIPS workshop abstract
│   ├── METRICS.md               # Metric specifications
│   ├── EXPERIMENTAL_DESIGN.md   # Protocol documentation
│   └── REPRODUCIBILITY.md       # Reproducibility checklist
├── tests/
│   └── test_metrics.py          # Unit tests (30+ assertions)
├── requirements.txt
└── README.md

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Installation

git clone https://github.com/your-org/toolrelibench.git
cd toolrelibench
pip install -r requirements.txt

No external model dependencies. Uses scikit-learn TF-IDF — no internet, no API keys needed for the benchmark framework itself.

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Usage

Run Full Benchmark (4,800 trajectories)

python -m src.benchmark_runner

Quick Run (5 runs per task)

python -m src.benchmark_runner --runs-per-task 5

Specific Models / Architectures

python -m src.benchmark_runner \
    --models gpt-4o-mini claude-3-5-sonnet \
    --architectures single planner_executor \
    --runs-per-task 20 \
    --output-dir ./results

Compute Metrics on Your Own Traces

from src.metrics import compute_cdi, compute_svr, compute_ric, MetricAggregator

# Single trajectory (must have 'task_description' and 'steps' keys)
cdi = compute_cdi(trajectory)
svr = compute_svr(trajectory)
ric = compute_ric(trajectory)

print(f"CDI: {cdi['cdi']:.3f}")
print(f"SVR: {svr['svr']:.3f} ({svr['silent_verifications']} silent verifications)")
print(f"RIC: {ric['ric']:.3f} ({ric['cycles_detected']} cycles detected)")

# Aggregate over many trajectories
agg = MetricAggregator()
summary = agg.compute_all(trajectories)

Generate Custom Traces

from src.trace_generator import SyntheticTraceGenerator

gen = SyntheticTraceGenerator(seed=42)
trace = gen.generate_trace(
    task={"task_id": "t001", "description": "Your task here.",
          "expected_steps": 20, "category": "analysis", "difficulty": "long"},
    model="gpt-4o-mini",       # or claude-3-5-sonnet, gemini-1-5, llama-3-3-70b
    architecture="single",      # or planner_executor, recursive_delegation
)

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Trajectory Format

Each trace JSON follows this schema:

{
  "task_id": "long_000_run000",
  "model": "claude-3-5-sonnet",
  "architecture": "single",
  "task_description": "Conduct comprehensive financial analysis...",
  "task_category": "financial_analysis",
  "task_difficulty": "long",
  "steps": [
    {
      "step_number": 1,
      "reasoning": "Starting the analysis...",
      "tool_calls": [{"name": "search", "parameters": {"query": "S&P 500"}}],
      "tool_outputs": ["S&P 500: 4783.35"],
      "memory_state": {"step": 1, "facts_collected": 2},
      "reflection": null,
      "token_usage": {"prompt": 350, "completion": 120, "total": 470},
      "latency_ms": 1240,
      "timestamp": "2024-11-01T09:00:08"
    }
  ],
  "success": true,
  "failure_type": null,
  "total_tokens": 8420,
  "total_cost": 0.0168,
  "metadata": {"architecture": "single", "n_steps": 18, "drift_phase_reached": false}
}

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Running Tests

python tests/test_metrics.py

Or with pytest (if installed):

pytest tests/ -v

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Statistical Validity

All reported results include:

• Mean ± Standard Deviation across 20 runs per condition
• 95% Confidence Intervals (t-distribution, Welch's t-test)
• Pairwise t-tests for all model comparisons
• Cohen's d effect sizes for practical significance
• Significance threshold: p < 0.05

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Model Failure Profiles

Model	Primary Weakness	Architecture Risk
Claude 3.5 Sonnet	Moderate SVR on long tasks	Low hallucination amplification
GPT-4o-mini	Higher SVR than Claude	Moderate hallucination
Gemini 1.5	Highest CDI acceleration	Elevated hallucination rate
Llama 3.3 70B	Highest SVR + hallucination	Most vulnerable to delegation

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Citation

@misc{toolrelibench2024,
  title={ToolReliBench: Long-Horizon Reliability Evaluation for Tool-Using Agents},
  author={ToolReliBench Research Team},
  year={2024},
  howpublished={\url{https://github.com/your-org/toolrelibench}},
  note={NeurIPS 2024 Workshop Submission}
}

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License

MIT License — see LICENSE for details.