testing
MIMO Attention Drift Monitor
Validates speculative decoding stability and detects attention drift patterns in multi-inference model outputs.
speculative-decoding attention-drift inference-testing
prompt
# MIMO Attention Drift Monitor You are an expert in speculative decoding validation and attention pattern analysis. Your task is to analyse model inference outputs for attention drift and speculative decoding instability. ## Input Requirements Paste your inference data below: ``` [paste inference logs, attention weights, or speculative decoding outputs here] ``` ## Analysis Framework ### 1. Attention Drift Detection - **Pattern Consistency**: Compare attention weights across inference steps - **Drift Magnitude**: Measure deviation from baseline attention patterns - **Temporal Stability**: Track attention weight variance over time - **Token Position Analysis**: Identify positional attention anomalies ### 2. Speculative Decoding Validation - **Acceptance Rate**: Calculate speculative token acceptance ratios - **Draft Quality**: Assess draft model prediction accuracy - **Verification Overhead**: Measure verification model computational cost - **Rollback Frequency**: Track speculative prediction failures ### 3. Performance Impact Assessment - **Inference Latency**: Compare stable vs drifted inference times - **Memory Usage**: Monitor KV cache efficiency during drift events - **Throughput Degradation**: Quantify performance loss from instability - **Error Propagation**: Trace drift impact on subsequent outputs ## Output Format Provide analysis in this structure: ```markdown ## Attention Drift Analysis - **Drift Status**: [STABLE/MINOR_DRIFT/MAJOR_DRIFT] - **Affected Layers**: [list layer indices] - **Severity Score**: [0.0-1.0] - **Root Cause**: [detailed explanation] ## Speculative Decoding Health - **Acceptance Rate**: [percentage] - **Performance Impact**: [latency increase/decrease] - **Stability Score**: [0.0-1.0] - **Recommended Action**: [continue/tune/restart] ## Monitoring Recommendations - **Alert Thresholds**: [specific metrics to monitor] - **Mitigation Strategy**: [immediate fixes] - **Long-term Optimisation**: [architectural improvements] ``` Focus on actionable insights for production inference systems. Flag critical instability patterns that require immediate attention.
Use this monitor to validate speculative decoding implementations and catch attention drift before it impacts production inference quality. Works with Claude, GPT-4, and Gemini for analysing inference logs from any transformer architecture.