Daily Report — 2026-08-06

Daily Overview

• What was done: Audited and fixed a catastrophic silent quantization bug in pi0.5 that invalidated previous results, aligned scripts with upstream API changes, and initiated low-bit validation; concurrently finalized structural reorganization of the MIHD research report and completed clustering analysis; designed and launched an MVP experiment for RoboMemory comparing visual vs. text prompts, including smoke training validation.
• How it was done: Conducted adversarial code audits and internal state probing to identify missing encoding loads in pi0.5; used git history analysis to reconcile conflicting accuracy metrics; utilized multi-agent orchestration for MIHD report structuring and statistical audit; employed deep literature review and architectural refinement to define the RoboMemory MVP, followed by environment setup and training deployment on Tianhe3.
• Impact: Restored valid low-bit evaluation capability for pi0.5 preventing further compute waste; established a robust, statistically validated structure for MIHD deliverables; created a high-confidence, low-risk experimental pipeline for visual memory injection in robotics.

MacOS

• What was done: Orchestrated multi-agent workflows for MIHD report completion and conducted code audits/debugging for pi0.5 quantization scripts.
• How it was done: Used Claude Code CLI for file exploration, git operations, script editing, CUDA/CPU resource management, and agent delegation via Cursor IDE for parallel task execution.
• Impact: Corrected fundamental pipeline errors ensuring data validity; enabled massive parallelization of experimental runs and report generation.

TzJsDesktop

• What was done: Deployed drawn_memory modules, executed pilot data preprocessing on Tianhe3 via SSH, and managed git history for ErrorRecoveryBenchmark.
• How it was done: Utilized background SSH sessions, nohup processes, and custom deployment scripts for idempotent patching; used Codex to inspect branches and categorize commits.
• Impact: Achieved full pipeline connectivity from raw data to processed datasets; organized repository history for clarity.

lighthouse

• What was done: Performed deep code audit, bug fixing, and validation of pi0.5 quantized models using CPU-based workload; prepared evaluation environment configurations.
• How it was done: Used Monitor and background agents to audited the pipeline, identified OOM errors caused by load_checkpoint ignoring device flags, patched scripts, and resolved DNS/VPN issues for checkpoint downloads.
• Impact: Resolved catastrophic silent error in quantization results; ensured evaluation infrastructure readiness.

Resolved critical silent quantization failures in the pi0.5 pipeline and finalized progress reports for MIHD research while establishing the foundational infrastructure and MVP design for RoboMemory’s visual memory experiments.

Tasks

Architecture & Strategy

• ✅ Fix pi0.5 Silent Quantization Failure and Alignment — Discovered that activation encodings were generated but never loaded during evaluation, rendering results invalid. Fixed by adding load_encodings_to_sim calls. Merged 5 upstream commits to align scripts with new SpinQuant R1/R2 interfaces and rewrote pi05_quantize.sh to support per-component precision. Also fixed a silent downgrade bug in aimet_quant_types.
• 🔄 Validate pi0.5 Pipeline and Launch Low-Bit Matrix — Re-evaluating existing checkpoints with corrected metrics and launching comprehensive W4A4/W8A4 validation matrices. Resolved CPU OOM issues during quantization by isolating GPU environment variables.
• ✅ Finalize MIHD Research Report Structure and Analysis — Reorganized English and Chinese reports into Results/TODO/Appends. Completed HD P2 full-slide clustering (GT-A) with scGPT/TEDDY/NCL fusion, resolved App-M matrix gaps, and standardized query protocols. Conducted statistical audits using Holm correction.
• ✅ RoboMemory MVP: Visual vs Text Prompt Experiment Design — Pivoted project to an MVP comparing oracle-driven text coordinates vs. drawn overlays on RouteStick/PatternLock. Conducted literature review confirming novelty over VP-VLA/TraceVLA. Implemented subgoal parser/renderer and verified oracle signals from text metadata.
• ✅ RoboMemory: W1 Completion and Infrastructure Setup — Executed pi0.5 smoke training (1000 steps) on Tianhe3, verified loss convergence, and processed pilot3 data. Prepared evaluation client code on lighthouse and defined three-arm experimental setup (drawn/text/baseline).
• 🔄 Launch RoboMemory Training and Monitor Status — Launched parallel 30k step training for Drawn and Text arms on tianhe3 GPUs. Monitoring for convergence.
• ✅ ErrorRecoveryBenchmark Git Organization and Path Fixing — Categorized git history into distinct thematic commits. Updated hardcoded paths in scripts post-filerelocation on Tianhe3, verifying syntax and remote directory existence.

Problems & Solutions

Critical Issues

1. Silent Quantization Failure: pi0.5 evaluations showed valid success rates but activations were effectively unquantized because encodings were not loaded during inference.

Solution: Added load_encodings_to_sim into model.py for Vision, Backbone, and Action Expert components. Verified via probing QuantizationSimModel op_modes that activations moved from updateStats to quantizeDequantize. Merged upstream fix from Jinhee Kim’s fork.

Key Insight: AIMET-ONNX separates the quantized graph (float ONNX) from metadata (encodings JSON). Loading only the ONNX file without explicitly loading encodings results in unquantized inference despite having a ‘quantized’ checkpoint folder.

2. RoboMME Baseline Ambiguity and Novelty Concerns: Conflict between VP-VLA (visual noise) and TraceVLA (fine-tuning), questioning if visual prompting was already solved.

Solution: Narrowed scope to control experiment with oracle ground truth for both modalities. Literature review confirmed prior works focused on ‘current intent’ or required fine-tuning, establishing novelty for ‘past memory visualization’ on frozen VLMs.

Key Insight: Visual prompting provides value when information density exceeds textual embedding capacity (e.g., continuous trajectories vs. discrete coordinates).

3. Conflicting Evidence on Quantization Validity: Jinhee’s recent results showed significant drops, contradicting the silent bug hypothesis that should make all results invalid.

Solution: Investigated git history and discovered Jinhee had fixed the exact encoding loading bug in her fork earlier that day. This reconciled the anomaly between our old broken code and her valid/fixed results.

Key Insight: Human contextual knowledge of recent commits can quickly resolve logical anomalies that AI might attribute to complex model behaviors or data issues.

4. HVG+UNI2+NCL Geometric Gauge Flip: Cross-slide query showed extreme reversal (Layer 3 mapped to White Matter).

Solution: Proved via Procrustes alignment that the embedding space itself was flipped independently per slide. Updated report narrative to reflect this structural instability rather than data errors.

Key Insight: Per-slide learned graph norms can lead to arbitrary axis flips invisible in single-slide metrics but catastrophic for cross-slide transfer.

5. JAX CUDA Backend Failure: Empty CUDA_VISIBLE_DEVICES caused backend errors instead of fallback.

Solution: Set JAX_PLATFORMS=cpu for normalization stats, then specified explicit GPUs for training.

Key Insight: Empty device lists can cause specific backend errors in JAX; require explicit handling.

General Issues

6. CUDA OOM during CPU quantization: LeRepo’s loader ignored --host-device cpu flag, forcing weights to GPU.

Solution: Unset/override CUDA environment variables (CUDA_VISIBLE_DEVICES=) or patch pi05_quantize.sh to force fallback to CPU for base checkpoint loading before applying quantization.

Key Insight: Explicit API flags often don’t penetrate deeply into third-party loaders; environment-level isolation is sometimes required.

7. scGPT Binning Failure on Zero-UMI Tiles: Empty array min() operations crashed processing for tiles with zero valid cells despite total UMI > 0.

Solution: Filtered cells with non-zero vocab genes or accepted partial completion for those specific tiles. Identified as a data preprocessing edge case in Visium HD.

Key Insight: Visium HD tiles can contain zero-UMI cells that break downstream binning if not pre-filtered.

8. Disk Space Exhaustion: AIMET external weights filled root partition during quantization. Resolved by exporting TMPDIR and hardlinking shared components.

Solution: N/A

Key Insight: AIMET’s lazy extraction creates large temporary files; ignoring temp dirs is a common setup trap.

9. QFormer OOM on Full-Slide: Standard GPUs insufficient for self-attention on 17k spots.

Solution: Pinned to high-memory GPU (5000_ada) and implemented chunked forward pass logic. Rejected simple subsampling to preserve scientific protocol comparability.

Key Insight: Standard VRAM bounds are insufficient for full-slide self-attention without explicit memory strategies.

Human vs AI Approaches

Strategic Level

Root Cause of Silent Quantization Errors and Conflicting Results

Role	Approach
Human	Human suspected results were wrong due to lack of improvement. Noted that Jinhee’s conflicting 14.5% drop result prevented accepting the ‘fully broken’ narrative too quickly, serving as a logical anchor.
AI	AI launched adversarial auditing, probed QuantSim internal states (op_mode) to prove activations were unquantized, and investigated git blame/logs to resolve the timeline discrepancy with Jinhee’s results.

Difference Analysis: Human provided strategic suspicion and contextual anomaly detection; AI executed runtime inspection and historical verification to bridge ‘code says quantized’ vs ‘inference says float’.

RoboMemory Project Scope Definition

Role	Approach
Human	User rejected complex drawn-memory systems, insisting on a simple MVP question: ‘Does visual prompting help?’ and targeting tasks where text fails.
AI	Initial proposal over-engineered the solution. AI pivoted to identify the mechanism gap (text encoding loss for trajectories) and designed the controlled experiment based on human’s simplified constraint.

Difference Analysis: Human focused on fundamental question and task selection, discarding complexity. AI operationalized this into concrete oracle-based comparison design.

HVG Query Reversal Interpretation

Role	Approach
Human	Initially suspected data error or routing bug (‘completely opposite’), demanding investigation.
AI	Performed geometric analysis to prove it was a legitimate methodological limitation of independent graph learning (axis flips), not a data loading error.

Difference Analysis: Human sought a data error; AI proved it was a structural methodological limitation.

Visual Memory Injection Design and Oracle Sources

Role	Approach
Human	Focused on strategic constraints (zero-cost overlay) and questioned efficiency of scanning raw h5 files.
AI	Verified that existing pkl text fields contained all necessary signals, eliminating need for complex sidecar files. Provided implementation details for parser/renderer.

Difference Analysis: Human drove efficiency improvements by questioning assumptions; AI provided verification through schema analysis.

AI Limitations

Critical Limitations

• Failed to detect that Jinhee’s fork was ahead before launching long-running quantization jobs, leading to wasted compute on stale interfaces.

General Limitations

• Could not prevent LeRepo/LeRobot loaders from forcing CUDA despite SDK device hints, requiring environment variable workarounds.
• Reliance on WebSearch for very recent (2026) papers risks hallucination; manual verification of arXiv links was critical before drawing conclusions on novelty.
• Failed to interpret repetitive /usage commands as potential error/hang state, offering generic responses instead of investigating user intent.
• Initial AI proposal for QFormer fix suggested subsampling, which human correctly identified as altering scientific protocol rather than solving memory constraints.

Learnings

Key Learnings

• AIMET-ONNX quantization is not persistent across save/load cycles unless load_encodings_to_sim is explicitly called; always probe QuantSim op_modes post-fix.
• Always fetch upstream main before launching long-running quantitative jobs in collaborative forks to avoid building on stale interfaces.

Practical Learnings

• When integrating upstream changes that alter CLI interfaces, rewrite driver scripts entirely rather than patching; silent API mismatches cause wrong-data production.
• Visual memory signals can be derived entirely from text metadata in existing datasets, avoiding expensive segmentation or geometric projection computations.
• For HD full-slide analysis, using official 8µm tiling provides better comparability but requires careful handling of zero-cell tiles in binning steps.
• Text representations of continuous geometric trajectories lose significant information compared to discrete coordinates, justifying visual overlays for specific task families.
• Per-slide learned graph norms can lead to arbitrary axis flips invisible in single-slide metrics but catastrophic for cross-slide transfer.

Conversation Summaries

Qualcomm VLA Quantization (pi0.5)

✅ Quantization Bug Fix and API Alignment 19:07:08.257 | claude_code Discovered critical bug where pi0.5 evaluation ignored calibration encodings, rendering accuracy results invalid (activations remained FP16). Verified via QuantSim probing; found Jinhee Kim had fixed this in her fork earlier that day. Merged 5 upstream commits aligning with new SpinQuant R1/R2 interfaces. Fixed silent downgrade bug in aimet_quant_types and disk space issues. Launched low-bit validation matrix.

MIHD Research Project

✅ Report Restructuring and Clustering Analysis 00:00:00 | cursor Completed structural reorganization of progress reports (Results/TODO/Appends). Finalized HD P2 full-slide clustering (GT-A) with scGPT/TEDDY/NCL. Resolved App-M matrix gaps and standardized query protocols. Performed statistical audits with Holm correction. Addressed scGPT binning failures on zero-UMI tiles and HVG geometric gauge flips.

RoboMemory (RoboMME)

✅ MVP Design and Launch 04:37:11.950 | claude_code Pivoted from complex drawn-memory system to MVP comparing visual vs text prompts on RouteStick/PatternLock tasks. Literature review confirmed novelty over VP-VLA/TraceVLA. Implemented subgoal parser/renderer. Verified oracle signals from text metadata. Completed pi0.5 smoke training and pilot3 data preprocessing. Launched 30k step parallel training on Tianhe3.

ErrorRecoveryBenchmark

✅ Git Organization and Path Migration 04:15:13.994 | codex Organized repository history into distinct thematic commits (Evaluation, GR00T, OpenVLA-OFT). Updated hardcoded paths in scripts following file relocation on Tianhe3 cluster. Validated syntax and remote directory existence via SSH.

TzJsDesktop Dev Environment

❌ Repetitive Usage Command Execution 04:22:25.725 | claude_code User executed /usage command over 100 times without prompts. AI remained inactive. No substantive development work occurred.

Token Usage