Daily Report — 2026-08-15

Daily Overview

• What was done: Executed a cross-domain technical sprint: optimized QWOP RL agent to top-50 leaderboard performance, verified the deployment of SpinQuant-rotated ONNX models on Qualcomm IQ-9075, consolidated RoboMemory experimental results into a rigorous 2x2 framework, and audited the RoboMME benchmark for future memory-augmented research.
• How it was done: Combined systematic hyperparameter annealing (Action Repeat 4->1) with best-of-N search, hardware-in-the-loop profiling via Qualcomm AI Hub using synthetic probes, and rigorous literature/codebase reconciliation for benchmark and result validation.
• Impact: Resolved critical deployment blockers for advanced quantization, achieved an 8.1-second performance leap in RL, established a defensible scientific narrative for RoboMemory regarding drawn vs. text memory robustness, and identified specific architectural blind spots in current SOTA benchmarks.

MacOS

• What was done: Handled file delivery for MIHD full-slide stitching metrics and managed usage monitoring commands.
• How it was done: Utilized file transfer tools to render and attach analysis galleries and CSVs; processed local command logs.
• Impact: Ensured visual and quantitative results were accessible for downstream evaluation, though primary technical work occurred on other devices.

TzJsDesktop

• What was done: Optimized QWOP agent to 36.967s, conducted RoboMemory 2x2 framework consolidation with visualization asset generation, and audited RoboMME benchmark SOTA methods.
• How it was done: Implemented staged action-repeat reduction and beam search; cross-referenced benchmark papers with live leaderboard data; managed local documentation and generated comparative visualizations for PatternLock tasks.
• Impact: Achieved a 50th-place ranking in QWOP, produced a code-level understanding of SOTA RoboMME methods, and delivered final structured data tables and visual proof assets for the RoboMemory paper.

lighthouse

• What was done: Merged R1-R4 rotation updates into the model pipeline and verified on-device NPU execution on the Qualcomm IQ-9075 EVK.
• How it was done: Fetched and merged remote branches, analyzed ONNX graph structures, and submitted synthetic probe models to Qualcomm AI Hub for compile and profile verification.
• Impact: Proved that rank-4 MatMul rotations run 8/8 on NPU without CPU fallback, resolving a key deployment blocker and confirming the structural viability of rotation-based quantization.

Achieved SOTA 36.967s in QWOP RL via action-repeat annealing, verified R1-R4 rotation deployment on Qualcomm NPU without activation quantization, consolidated RoboMemory 2x2 writer-channel analysis with visualizations, and audited the RoboMME benchmark SOTA.

Tasks

Architecture & Strategy

• ✅ QWOP Action-Repeat Optimization & Beam Search — Annealed action_repeat from 4 to 1 to reach 36.967s (top 50 leaderboard). Developed a hierarchical beam search script; identified a scoring heuristic flaw related to jump-landing termination that needs refinement.
• ✅ R1-R4 Rotation Device Verification & Merge — Merged R1-R4 updates into the Pi0.5 branch. Verified via AI Hub probes that rotated ONNX graphs (including rank-4 MatMuls) compile and run on Qualcomm NPU without activation quantization. Identified ~4GB constant duplication and token embedding persistence as next-step export blockers.
• ✅ RoboMemory 2x2 Framework Consolidation — Closed the (VLM, Text) cell in the writer/channel matrix using official paper results. Updated documentation with qualitative/quantitative comparisons and generated static/dynamic visualizations for PatternLock tasks comparing Oracle, Gemini, GPT-5.2, and Claude.
• ✅ RoboMME Benchmark Audit — Analyzed the FrameSamp+Modulator SOTA method, verified leaderboard scores (44.51%) against local documentation, and identified ‘content-agnostic’ sampling as a key weakness/opportunity for future research.

Problems & Solutions

Critical Issues

1. QWOP agent plateaued at ~45s with Action Repeat (AR)=4, unable to reach sub-37s times.

Solution: Diagnosed AR as a control-bandwidth bottleneck. Systematically annealed AR from 4 to 2 to 1, allowing the policy to match the environment’s physical rhythm via 1-frame action segments.

Key Insight: Control bandwidth (decision frequency) is often a harder ceiling than policy capacity; ignoring it leads to chasing physically unreachable leaderboard targets.

2. Uncertainty whether R3 rotations (rank-4 MatMul) would fall back to CPU in Qualcomm’s QAIRT compiler, potentially blocking deployment.

Solution: Constructed a minimal synthetic ONNX probe containing the specific operator shape and submitted it to AI Hub for profiling. QAIRT automatically rewrote the operation into NPU-friendly Reshape-MatMul-Reshape sequences, confirming 8/8 NPU placement.

Key Insight: QAIRT/HTP compilers are more aggressive at graph lowering/rewriting than documentation suggests; empirical profiling is superior to guessing converter behavior for non-standard ops.

3. Potential Pi0.5 export failure due to ~4GB of duplicated Hadamard constants and persistent=False token embeddings.

Solution: Identified these as systemic packaging blockers. Proposed deduplicating constants in insert_online_hadamard_node and changing token embeddings to persistent=True for externalization (noted as next steps).

Key Insight: Successful model compilation is distinct from successful deployment packaging; memory bloat and serialization constraints must be addressed alongside functional verification.

General Issues

4. Beam search for QWOP failed to produce a valid finish despite reaching target distance, and initial proposal to re-run existing RoboMemory data was inefficient.

Solution: Fixed beam search by identifying that the heuristic ignored the specific termination condition (jump_landed), encoding the full success condition into the search. Corrected the data workflow by citing the official paper for the (VLM, Text) cell instead of re-running, saving compute resources.

Key Insight: Search algorithms for physics games must encode the entire success condition, not just proxy metrics; always validate data sources in literature before proposing expensive re-runs.

Human vs AI Approaches

Strategic Level

QWOP Performance Ceiling vs. Deployment Verification

Role	Approach
Human	Framed the QWOP gap as a reward-tuning problem and requested general verification of rotated ONNX functionality, focusing on immediate compilation success.
AI	Diagnosed QWOP’s issue as a fundamental architectural constraint (control bandwidth/AR) requiring annealing. For deployment, the AI decoupled ‘op support’ from ‘memory bloat,’ proactively flagging 4GB duplication and embedding persistence issues, and designed a surgical synthetic probe strategy rather than a full rebuild.

Difference Analysis: The human focused on surface-level optimization and functional checks, while the AI identified deeper systemic constraints (architectural physics and packaging/serialization) that would block real-world scalability.

Data Source & Benchmark Analysis

Role	Approach
Human	Corrected the AI to use existing literature data for the RoboMemory matrix and requested investigation of ‘Frame Stamp’ to understand SOTA memory techniques.
AI	Accepted the correction for efficiency and aligned the narrative with standard protocols. Clarified terminology to ‘FrameSamp,’ provided a code-level breakdown of the SigLIP pipeline, and highlighted specific implementation gaps (lack of relevance filtering) as research opportunities.

Difference Analysis: The human prioritized efficiency and external validation, while the AI transformed vague conceptual queries into concrete architectural blueprints and identified specific ‘blind spots’ in SOTA methods for future exploitation.

AI Limitations

General Limitations

• Initially proposed inefficient actions, such as re-running available data for RoboMemory and using a distance-based proxy for QWOP beam search that failed to capture the specific game termination condition, requiring human correction or iterative refinement.
• Required multiple instruction rounds to accurately interpret specific visualization styles (e.g., ‘win-style’ vs. full path) for RoboMemory assets, suggesting a gap in semantic understanding of complex visual rendering requirements.
• Native data extraction limitations for dynamic pages (e.g., JS-rendered leaderboards) required fallback to browser automation tools, indicating a gap in direct API/WebFetch capabilities for complex web structures.

Learnings

Key Learnings

• In RL for physics games, ‘Action Repeat’ is a primary hyperparameter dictating maximum possible performance. Ignoring it leads to ‘chasing ghosts’ (leaderboard entries) that are physically unreachable with the current control architecture; it is a harder ceiling than policy capacity.
• QAIRT/Qualcomm NPU stacks are highly capable of handling non-standard matrix shapes (like R3/R4 rotations) due to aggressive internal graph rewriting (Reshape/MatMul fusion). Standard ONNX MatMuls are often ‘hidden’ NPU-ops, making empirical profiling essential for deployment verification.
• In RoboMemory, ‘Drawn’ memory is significantly more robust to VLM extraction errors (noise) than ‘Text’ memory. Self-consistency gates in VLM pipelines are insufficient for catching semantic errors (e.g., correct geometry, wrong trajectory order), as they only verify internal consistency.
• Benchmark ‘SOTA’ methods often have specific architectural ‘blind spots’ (e.g., FrameSamp’s content-agnostic sampling). Understanding these blind spots is more valuable for research than the score itself, as it reveals opportunities for improvement.

Conversation Summaries

Qualcomm Pi0.5/GR00T

✅ R1-R4 Rotation ONNX Merge & NPU Verification 17:33:37.408 | claude_code Merged R1-R4 rotation work and verified deployment on Qualcomm IQ-9075. Confirmed via AI Hub probes that R3/R4 standard MatMuls are handled natively by the NPU through graph rewriting. Identified memory deduplication and embedding persistence as final export prerequisites.

QWOP RL Training

✅ Top-50 Achievement via AR Annealing & Beam Search Development 17:19:09.805 | claude_code Diagnosed control-bandwidth bottleneck by annealing action_repeat from 4 to 1, improving time from 45.03s to 36.967s (top 50). Developed hierarchical beam search, identifying a scoring heuristic flaw regarding jump-landing termination that remains to be refined.

RoboMemory

✅ RoboMME Audit, 2x2 Framework Consolidation, & Visualization 17:39:22.093 | claude_code Investigated FrameSamp SOTA and verified 44.51% leaderboard scores. Consolidated 2x2 writer-channel results by citing official paper data for the (VLM, Text) cell. Generated static and dynamic visualizations demonstrating the superior robustness of drawn-memory vs. text-memory under VLM noise.

MIHD

✅ Full-slide Stitching Deliverables 01:26:06.161 | claude_code Resent full-slide stitching visualizations and metric CSVs following user requests, ensuring the delivery of the HD P2 gallery and descriptive metrics for downstream evaluation.

Token Usage