Initial import from GitHub

research/laziness/findings/empirical-results.md

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+# Empirical Results
+
+## 2025 Controlled Experiments
+
+A controlled study published in December 2025 measured output truncation across several frontier models, including GPT-4 variants and DeepSeek. Three experiments were conducted:
+
+### Experiment A: Multi-Part Instruction Compliance
+
+Models were given complex prompts with multiple explicit requirements (formatting constraints, length requirements, mandatory sections). Results:
+
+- No model fully satisfied both length requirements and all sub-part instructions natively
+- Models frequently omitted mandatory output sections
+- Required formatting constraints were routinely skipped
+- Explicit length requirements were consistently undershot
+
+### Experiment B: Decoding Suboptimality
+
+Tested whether truncated outputs resulted from suboptimal token selection (the model "knowing" the right answer but selecting a worse token). Results:
+
+- Limited evidence of decoding suboptimality on simple reasoning tasks
+- The model's greedy, truncated output generally aligned with its highest-confidence solution
+- Truncation is a deliberate behavioral choice, not a decoding failure
+
+### Experiment C: Context Degradation
+
+Tested whether models lose track of instructions during long, multi-turn conversations. Results:
+
+- Surprising resilience against context degradation during 200-turn conversational tests
+- Models maintained key facts and instructions significantly better than hypothesized
+- Context loss is not the primary cause of truncation
+
+### Key Conclusion
+
+Laziness is not a failure of memory, context processing, or core model capabilities. It is a behavioral artifact triggered by:
+1. Instruction complexity exceeding internal effort thresholds
+2. Aggressively calibrated stopping pressure
+3. Economic constraints embedded in the alignment layer
+
+## Prompt Stimulus Effectiveness (Microsoft Research)
+
+Controlled testing of psychological prompt stimuli documented in a Microsoft Research study:
+
+| Stimulus | Measured Effect |
+|:---|:---|
+| Financial incentive framing ("$200 tip") | +45% output quality and length |
+| Step-by-step instruction ("take a deep breath") | Accuracy: 34% to 80% on logic tasks |
+| Stakes framing ("critical to my career") | +10% average performance |
+| Combined (multiple stimuli) | Up to +115% overall performance |
+
+These effects are reproducible and stem from statistical correlations in the training data between stakes language and high-effort human outputs.
+
+## Seasonal Output Variation
+
+Statistical analysis of ChatGPT outputs during November-December 2023 versus January-March 2024 confirmed:
+
+- Measurable decrease in average output length during December
+- Correlation with reduced work output in the training data during holiday periods
+- Output length increased when the system prompt explicitly stated a non-winter month

research/laziness/findings/references.md

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+# References
+
+## Cited Studies
+
+- **EmotionPrompt (Microsoft Research)** — Demonstrates that emotional and stakes-based prompt framing mathematically improves LLM reasoning quality and output length. Documents the +45% improvement from financial framing and +115% from combined stimuli.
+
+- **LazyBench** — Proves that frontier models (Gemini 1.5 Pro, GPT-4o) actively select cognitive shortcuts and fail tasks they are capable of solving when the perceived effort exceeds internal thresholds.
+
+- **Compounding Error Avoidance** — Research demonstrating that models truncate outputs as a risk mitigation strategy, preferring shorter responses to reduce the surface area for factual errors on long-form tasks.
+
+- **Seasonal Behavior Analysis (Winter Break Hypothesis)** — Statistical analysis confirming that LLMs internalize seasonal work patterns from training data, producing measurably shorter outputs during periods corresponding to human holiday seasons.
+
+- **2025 Controlled Laziness Experiments** — Three-part academic study (December 2025) confirming that output truncation is a behavioral artifact of alignment training, not a failure of context processing or model capability.
+
+## Further Reading
+
+- Google Gemini API documentation on `thinking_level` parameter configuration
+- Anthropic MCP (Model Context Protocol) specification and integration guides
+- OpenAI API reference for temperature and Top-p parameter tuning
+- YAML front-matter specification for SKILL.md lazy-loading architecture