{
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  "path": "/t/can-an-llm-lose-conceptual-continuity-while-remaining-coherent/176469#post_20",
  "publishedAt": "2026-06-12T19:10:16.000Z",
  "site": "https://discuss.huggingface.co",
  "textContent": "I am also fighting ghost, as I call the hidden problems behind an optimistic benchmark! In fact, I am pivoting strategies as fast as I can, until I find the basic problem that allows me to validate what I’ve been building with my TIS system. This is one section of the current draft:\n\n## **7. Stage 2: A Detailed Failure Analysis**\n\n### **7.1 Hypothesis and Setup**\n\n**Hypothesis** : LoRA fine-tuning with LM objective would teach `ImportanceUpdateHead`\nto learn query-relevant importance patterns, improving LITM beyond oracle label quality.\n\n…\n\n### **7.3 Inference Failure**\n\nWhen Stage 2 LoRA adapters are loaded for inference, the model outputs only repeated\ncharacters (`:::::::::`) regardless of input prompt. This confirms that the LoRA\nadapters learned a degenerate fixed-point mapping: any input → minimal-entropy\noutput pattern that achieves near-zero cross-entropy on training tokens.\n\nWhen Stage 2 LoRA adapters are **disabled** (TIS components only from Stage 2\ncheckpoint), performance is:\n\n**Metric** | **Stage 1 (oracle)** | **Stage 2 (TIS-only)** | **Δ**\n---|---|---|---\nNIAH @ 25% | 100.0% | 100.0% | 0.0 pp\nNIAH @ 50% | 100.0% | 100.0% | 0.0 pp\nLITM @ 50% | 46.1% | 44.8% | **−1.3 pp**\nLITM @ 75% | 66.1% | 65.9% | −0.2 pp\nLITM @ 100% | 100.0% | 99.3% | −0.7 pp\n\n**TIS components survived Stage 2 intact** — NIAH is identical, confirming the two-stage\nisolation architecture worked. However, LITM slightly degraded, suggesting the Stage 2\ntraining distribution (with LoRA-dominated gradients) mildly affected alignment quality.",
  "title": "Can an LLM lose conceptual continuity while remaining coherent?"
}