ICML Agent Paper Reproducer | Hugging Face Open Model Validation

Benchmark Configuration ICML 2026 Archive

Target Empirical Paper

"LORA rank-4 achieves within 0.8% accuracy of full fine-tuning on benchmark convergence with 64% fewer training steps."

Replication Budget (GPU hrs) 1.5 hrs

Random Seed Initialization 42

Verification Tolerance (Δ Margin) ±1.5%

AGENT TEAM TOPOLOGY

🤖 Agent 1: Claim & Hyperparameter Extractor

⚡ Agent 2: Environment & Code Synthesizer

📊 Agent 3: Baseline Benchmark Runner

⚖️ Agent 4: Statistical Auditor & Card Signer

Auditor Verification Verdict PASSED

Autonomous verification determines if empirical results fall within confidence bounds of author claims.

Max Discrepancy: 0.64%

p-value (Two-sample KS): p = 0.481 (ns)

Reproduction Confidence: 98.2%

Active Agent Workflow STANDBY / READY

1. Extractor Claims parsed

2. Synthesizer NumPy code ready

3. Runner 100 iterations done

4. Auditor Delta within ±1.5%

Convergence Telemetry: Author Baseline vs. Agent Replication T = 100 Epochs

ICML 2026 Original Baseline

Agent Replicated Mean

95% Confidence Interval (Synthetic Multi-Seed)

Tolerance Bound (±Δ)

Author Metric

88.4%

Agent Mean

88.1%

Observed Δ

-0.34%

GPU Cost Log

1.50h

[AGENT SYSTEM INITIALIZED]
[Claim Extractor] Target: "Parameter-Efficient Rank Allocation Dynamics (ICML 2026)"
[Claim Extractor] Extracted hypothesis: "LORA rank-4 achieves within 0.8% accuracy of full fine-tuning on benchmark convergence with 64% fewer training steps."
[Environment Synthesizer] Building minimal reproducible harness with NumPy/PyTorch backend...
[Benchmark Runner] Executing multi-seed Monte-Carlo verification (Seed: 42, Budget: 1.50 GPU-hrs)...
[Benchmark Runner] Replicated 100 validation checkpoints. Mean convergence: 88.14% (Author target: 88.40%).
[Statistical Auditor] Computed empirical variance: Delta = -0.26%. Verification criteria: |Delta| <= 1.50%.
[Statistical Auditor] STATUS: REPRODUCTION VERIFIED. Ready for Hugging Face Hub card publication.

# Auto-generated by Hugging Face ICML Reproduction Agent
# Benchmark: Parameter-Efficient Rank Dynamics (ICML 2026)
import numpy as np

def run_lora_rank4_verification(seed=42, budget_hours=1.5):
    np.random.seed(seed)
    epochs = int(min(120, max(40, budget_hours * 60)))
    t = np.linspace(0, 1, epochs)
    
    # Mathematical ideal convergence curve
    ideal_curve = 0.50 + 0.384 * (1 - np.exp(-4.2 * t))
    
    # Stochastic agent perturbation runs
    noise = np.random.normal(0, 0.006, size=(10, epochs))
    replicated_runs = ideal_curve + noise
    
    mean_trajectory = np.mean(replicated_runs, axis=0)
    std_envelope = np.std(replicated_runs, axis=0)
    
    final_acc = mean_trajectory[-1]
    print(f"[REPRO RESULT] Final Convergence Accuracy: {final_acc * 100:.2f}%")
    return mean_trajectory, std_envelope

if __name__ == "__main__":
    run_lora_rank4_verification(seed=42, budget_hours=1.5)

---
license: apache-2.0
tags:
- icml-2026
- scientific-reproduction
- open-science
- multi-agent-verified
reproduction_status: verified
reproduction_agent_version: "2.4.0-open"
reproduction_tolerance_pct: 1.5
observed_delta_pct: -0.26
---

# ICML 2026 Paper Reproduction: Parameter-Efficient Rank Allocation Dynamics

## Abstract & Claim Under Audit
"LORA rank-4 achieves within 0.8% accuracy of full fine-tuning on benchmark convergence with 64% fewer training steps."

## Autonomous Agent Replication Summary
- **Verification Result**: VERIFIED (Within ±1.5% margin)
- **Author Baseline Metric**: 88.40%
- **Replicated Agent Metric**: 88.14% (Δ = -0.26%)
- **Compute Budget Consumed**: 1.50 GPU-hrs (NVIDIA H100 equivalent)
- **Reproducibility Confidence**: 98.2%
- **Artifacts**: Verified PyTorch script and synthetic distribution logs.

Synthetic reproduction traces and tolerance labels

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Read the explanation

The simulator scales its synthetic noise by one point eight divided by the square root of budget over one point five. A budget of one point five gives one point eight; a budget of six gives point nine. At one hundred pixels per noise multiplier the bars shrink from one hundred eighty to ninety. Quadrupling the budget halves this authored noise scale rather than dividing it by four. No GPU job, paper extraction, or trained model is run. The original offline chart fails because D3 is missing, and its budget label updates before that same render failure. This draft explains the written formula without claiming a successful plot or scientific replication. Each synthetic step sets current noise to point eight eight times the previous noise plus a newly generated perturbation. With no further perturbations, a single existing disturbance retains eighty eight percent after one step and about fifty two point seven seven percent after five. At two hundred fifty pixels per retained fraction the bars span two hundred twenty and about one hundred thirty one point nine three. This is a damped autoregressive recurrence, not independent measurement errors or proof of a Brownian physical process. The final simulated value is clamped between thirty and ninety nine point five before comparison to an authored benchmark target. The pass label tests whether the absolute final difference, rounded to two decimals, is at most the selected tolerance. A hypothetical difference of two fails tolerance one point five and passes tolerance two point five. At eighty pixels per tolerance percentage point the threshold bars span one hundred twenty and two hundred. Changing tolerance does not change the simulated data. The reported p-value is generated as point three five plus a pseudo-random number times point four five; it is not calculated from a statistical test. The confidence label is another delta-based heuristic. A signed-looking reproduction card and verification badge therefore cannot prove a paper result was independently reproduced.