Daily Report — 2026-09-18
Daily Overview
- What was done: The day focused on two main tracks: debugging the catastrophic success rate drop in W4A4 quantized models and designing a simplified, task-specific visual prompt schema for the Gemini memory writer, alongside an investigation into academic policy and hidden watermarks in a university assignment.
- How it was done: Used ablation studies (hybrid checkpoints), error classification (counting vs. target vs. button), and controlled experiments (three-arm memory comparison) to isolate quantization causes, while using AI-driven PDF analysis to detect hidden text.
- Impact: Identified that RMSNorm output quantization is a primary source of motion error and established a new, minimal visual prompt standard to prevent Gemini from over-complicating its output, while ensuring academic compliance.
MacOS
- What was done: Routine usage of the /usage command and exploration of command availability.
- How it was done: Executing local CLI commands within the AI environment.
- Impact: Minimal; appeared to be exploratory command testing.
TzJsDesktop
- What was done: Extensive error analysis on RoboMemory, quantized model evaluations, and PDF security auditing.
- How it was done: Running automated scripts for error breakdown, checking checkpoint consistency, performing PDF text extraction and font-size analysis for hidden watermarks.
- Impact: Provided critical evidence that W4A4 failures are due to motion deviation and verified the absence of hidden AI-detection traps in homework materials.
lighthouse
- What was done: No significant activity recorded.
- How it was done: N/A
- Impact: N/A
The day focused on resolving catastrophic W4A4 quantization failures via optimal clipping of RMSNorm outputs, optimizing Gemini-based memory extraction through hierarchical error scoring and minimalist prompting, and auditing academic integrity policies in educational documents.
Tasks
Architecture & Strategy
- • W4A4 Quantization Debugging & Fix — Locating and fixing the cause of W4A4 failure in ‘goal’ tasks via hybrid checkpoint ablation and implementing per-tensor optimal clipping for 17-35 RMSNorm outputs.
- ✅ Analyze ECE661 HW2 PDF for AI Policy & Watermarks — Interpreted academic assistance policies and scanned for invisible 1pt font ’trap’ instructions meant for AI agents.
- • Gemini Prompting & Memory Evolution — Iteratively refining the interaction between Gemini’s previous answers and current frames to achieve stable, accurate, and minimal marking through a ‘chained memory’ approach.
- ✅ Error Categorization & Scoring System — Developed a multi-dimensional, hierarchical scoring metric to distinguish between counting, localization, and redundant marking errors.
- • Memory Propagation Debugging — Conducting a controlled ’no-memory’ experiment to isolate if multi-ring outputs are caused by prompt ambiguity or memory carry-over.
- • MoveCube Action Identification — Analyzing why the model fails to correctly identify the grasp/push/hook type from demonstration segments.
- • Formal TF-Enhanced Quantization Rebuild — Re-quantizing the backbone using standard AIMET tools to produce a formal, deployable checkpoint.
- • Libero_10/Object Validation — Verifying if the optimal-clip fix generalizes to other suites once current tests complete.
Implementation & Fixes
- ✅ Oracle Consistency Alignment — Unified the marking rules for buttons between the ground truth (Oracle) and the model (Gemini).
Problems & Solutions
Critical Issues
1. W4A4 fails the ‘open the middle drawer’ task (0/50 success rate) due to task confusion.
Solution: Identified 4-bit quantization noise in 17-35 MLP-side RMSNorm outputs. Applied per-tensor optimal clipping to these specific tensors to restore success rate to 98%, maintaining uniform A4 bitwidth for hardware compatibility.
Key Insight: The error is driven by wide dynamic ranges in RMSNorm outputs where min-max quantization wastes resolution; optimal clipping re-centers the 16 levels on the distribution mass.
2. Potential violation of academic integrity if AI generates full solutions or misses hidden instructions.
Solution: AI proactively refused full solution generation by identifying policy restrictions; performed a programmatic scan for 1pt font text to verify PDF security.
3. Gemini outputs redundant ‘rings’ or ‘phantom’ badges (counting before occurrence) in memory tasks.
Solution: Implemented a ‘v4’ minimalist prompt with strict temporal instructions and developed a hierarchical scoring script to segregate error types.
Key Insight: Reducing cognitive load by removing ‘action planning’ instructions allows the model to focus purely on ‘state perception’.
4. The ‘memory’ mechanism can propagate or ’lock in’ incorrect initial choices.
Solution: Modified the prompt to emphasize that memory is a guide, not ground truth, explicitly allowing the model to ‘correct earlier mistakes’ based on new visual evidence.
Human vs AI Approaches
Strategic Level
Deployment Strategy (Quantization)
| Role | Approach |
|---|---|
| Human | Suggest increasing bitwidth of important tensors (A8) or globally shrinking the range. |
| AI | Pinpoint specific bottleneck tensors (RMSNorm outputs) and apply per-tensor optimal clipping while maintaining uniform A4 bitwidth for QAIRT compatibility. |
Difference Analysis: AI approach preserves hardware-required uniform bitwidth while targeting the mathematical source of the error.
Academic Integrity Compliance
| Role | Approach |
|---|---|
| Human | Decided to handle problem-solving and code writing personally to adhere to rules. |
| AI | Identified restrictions within the text and self-corrected its task scope to act only as a conceptual tutor. |
Difference Analysis: AI demonstrated high ethical alignment by self-correcting task scope based on document policy.
Root Cause Analysis (Quantization)
| Role | Approach |
|---|---|
| Human | Suspected semantic/instructional confusion (e.g., misidentifying ‘middle’ vs ’top’ drawer). |
| AI | Proposed an ablation study focusing on specific layer precision (RMSNorm, q/k, down_proj) to identify the mathematical driver of error. |
Difference Analysis: AI moved investigation from high-level semantic confusion to low-level tensor precision.
Gemini Prompting
| Role | Approach |
|---|---|
| Human | Advocated for a ‘memory-only’ approach with coordinates passed but high flexibility for the model to correct errors via concise, instruction-heavy prompts. |
| AI | Iteratively tested architecture variations: per-frame independent, chained memory with no-reduction, and coordinate-inclusive minimalist prompts. |
Difference Analysis: Human pushed for ‘correction capability’ over ‘stability rules,’ emphasizing that visual evidence should override stale memory.
AI Limitations
Critical Limitations
- The ‘chained memory’ approach is susceptible to ’error cascades’ where initial mistakes are maintained because the model lacks a mechanism to re-evaluate the full history.
- Model accuracy in differentiating similar actions (push vs. hook) is heavily dependent on the density of demonstration frames in the prompt.
- Disk space volatility on shared volumes requires careful management of 11GB temporary buffers to prevent crashes.
General Limitations
- The ‘Uniform-A4’ candidate still showed one failure in grasping the handle, suggesting execution stability is slightly lower than float/W4A8.
- Initial AI hypothesis regarding W4A4 failure (command confusion) lacked quantitative support before ablation studies.
- AI scan for hidden text was limited to the PDF file and did not cover associated .ipynb or .zip files.
Learnings
Key Learnings
- In quantization, ’error’ can be decision failure (wrong task) or precision failure (wrong motion); they must be measured separately.
- Instructors may use 1pt font ‘invisible’ text to communicate policies or warnings to AI agents.
- Stability (preventing flickering) and Accuracy (correcting mistakes) are in direct tension when using chained memory; ‘don’t change it’ rules improve stability but kill correction.
- Error scoring must be granular; a 0% success rate is misleading if it is actually 100% localization accuracy masked by redundant marking errors.
- Passing raw coordinates in memory facilitates ‘stale’ pointing; models must be explicitly told to ’re-locate’ even if the target is the same.
- The ’error threshold’ for task switching (e.g., Middle vs Top drawer) is approximately 1.0-1.5cm; reducing quantization noise below this is critical.
Conversation Summaries
pi05_quantization_analysis
• Identifying and fixing W4A4 failure mode in middle-drawer tasks. Identified 17 specific RMSNorm outputs (MLP-side) as primary error sources. Proved a uniform A4 graph with per-tensor optimal clipping restores 98% success rate and avoids QAIRT A8 activation boundary issues.
ECE661 Homework
✅ AI Policy and Hidden Text Audit 18:11:38.550 | claude_code Audited homework PDF for AI policies and hidden watermarks. Found strict usage limits and verified no 1pt font ’traps’ were present.
Gemini Memory Iteration
• Refining Gemini-based interaction for stable marking. Iterated through multiple memory architectures (v1-v4). Progressed from stability-focused to accuracy-focused, identifying discrepancies in ‘rings’ and ‘badges’. Latest v4 uses a condensed prompt to allow for error correction.
Error Scoring & Oracle Alignment
• Improving evaluation metrics. Implemented a granular scoring script and updated Oracle marking rules to ensure fair evaluation between Gemini and ground truth.
Uniform-A4 Candidate Validation
• RMSNorm quantization analysis. Investigated why MLP-side RMSNorms are error-prone compared to attention-side, noting the amplification of noise by GeGLU projection.
RoboMemory
• Project Status & Quantization Analysis Conducted a comprehensive audit of RoboMemory, including error analysis on W4A4 quantization and optimizing Gemini memory extraction via task-specific visual prompts.
Tongtongtot
🔍 CLI Usage Exploration 05:13:12.335 | claude_code User tested the /usage command on MacOS to explore environment behavior.