Third-year undergraduate in Network Engineering at Guangzhou University. I build tools that audit deployed AI systems and run preregistered empirical studies in RL.

My contribution history starts with an early exploratory phase — small docs and fixes spread across many RL/ML repos. Since then I've deliberately narrowed to a few in-depth upstream fixes (below) and to my own research artifacts; the broad early PRs are left as-is rather than hidden.

Applying for Fall 2027 PhD entry (applications in Fall 2026) in Embodied AI / Reinforcement Learning / Robotics.

Academic Homepage | Blog | CV | ldz@e.gzhu.edu.cn

TransitTruth — open-source auditing tool that verifies whether an AI API relay actually serves the advertised model, using statistical behavioral fingerprints (chi-square / KS tests, Bayesian updating). A guided offline demo walks through the check with bundled seed data; the real audit runs locally. The technical report reports the animal-probe distribution distance gpt-4o vs gpt-4o-mini (TVD up to 0.90) and, separately, the combined pipeline's in-distribution resubstitution posterior (91.3% on the baseline's own reference — self-consistency, not held-out accuracy). Complements single-token fingerprinting work by focusing on fine-grained same-family distinctions.    Tech Report (PDF) · Live Demo

wm-audit — preregistered single-machine CPU audit suite that disentangles world-model perceptual fidelity (next-frame MSE / SSIM / NLL, plus fixed-filter stand-ins for LPIPS / rollout FVD — deterministic random-conv filter banks, not learned VGG/AlexNet backbones) from control utility (whether a planner can actually solve the task). A reduced pilot on one env (pixel_nav, 3 seeds × 3 capacities, 12k steps) found: pixel fidelity weakly predicts closed-loop return (H1 supported, |ρ|=0.32<0.4); control-relevant diagnostics did not (H2 not supported, ρ=0.26<0.6); INT8 compression did not degrade calibration faster than raw pixel fidelity (H3 not supported, n_pairs=9). This is a CPU reduced pilot, not the locked 200k-step × 5-seed × 2-env main run.

embodied-offpolicy-study — a Soft Actor-Critic implementation written from scratch in PyTorch (no Stable-Baselines3), run as a preregistered two-arm controlled study with a logged third-arm (FixedAlpha) amendment on Hopper-v4 (CPU, 120k env steps, 3 seeds/arm): Baseline (reward scale 1.0) vs RewardScaled (0.1) vs FixedAlpha (automatic temperature off, scale 0.1, α fixed at 0.2). Mean return ± SD (final window 105k–120k, n=3/arm): Baseline 1085±698, RewardScaled 1118±623, FixedAlpha 1404±632. Welch p: Baseline–FixedAlpha 0.59, Baseline–RewardScaled 0.95, FixedAlpha–RewardScaled 0.61; ANOVA F=0.22, p=0.81 — no statistically distinguishable effect detected (n=3). At 60k the reward-scaled arm looked ~3× better; extending to the preregistered 120k budget and adding the fixed-temperature control arm overturned that apparent gain as early seed noise.

rl-deploy-bench — cross-platform RL deployment and benchmarking toolkit. Export SB3 policies to ONNX/TorchScript, build TensorRT engines with FP16/INT8 quantization, and benchmark latency/throughput/accuracy across GPU and CPU from one config-driven CLI with auto-generated HTML reports. Jetson/edge is a target deployment path, not yet measured on device.

Merged — code fixes (3)

Merged — community & documentation (5, not code fixes)

• Eclectic-Sheep/sheeprl #340 — CONTRIBUTING guide and Code of Conduct.
• Algorineko/AgenticArXiv-RL #72 — MIT LICENSE, CONTRIBUTING, Code of Conduct, and CI workflow.
• FluxVLA #122 — typo rename bloat16 → bf16 in the BFloat16 support check.
• FluxVLA #123 — community docs: CONTRIBUTING, Code of Conduct, CHANGELOG, issue templates.
• scadastrangelove/awesome-ai-security-tools #165 — added TransitTruth to the WATCHLIST (merged 2026-10-21).

Under review (not yet merged) — selected code PRs in upstream RL/ML infrastructure

Other early exploratory PRs across smaller RL/ML projects remain open or unanswered — part of the broad phase noted above, not deep work.

Small, focused, offline tools that keep RL reporting and relay setup honest. They are zero-dependency local utilities (not services), each with its own test suite.

• seedlot — aggregate per-seed RL logs into mean ± std, IQR and 95% CI; blocks single-seed superiority claims.
• policy-parity — compare FP32 vs quantized/exported action logs (MAE, cosine, tolerance) before deployment.
• claim-boundary — check a draft's wording against the scope actually measured.
• relay-preflight — offline hygiene scan for relay configs (inline keys, Bearer, plaintext http).
• dsh-model-probe — a DSH plugin that runs the local model-authenticity check from a settings page.

Research notes and figures from the offline-policy study: GitHub Pages notebook.

• Fixing a bug in Microsoft TextWorld — one line of regex and the multi-command parsing behind it: root-cause writeup of merged PR #377. A greedy regex with DOTALL stitched two state blocks into one when a single line carried multiple commands.
• CI passed green but the package wouldn't import: numpy and scipy missing from requirements: postmortem on a dependency-declaration bug found while running the TransitTruth audit pipeline — tests on a dirty machine passing says nothing about a clean install.

RL / Robotics — PyTorch · Stable-Baselines3 · Gymnasium · MuJoCo Deployment — ONNX · ONNX Runtime · TensorRT · TorchScript · INT8/FP16 Systems — Python · C++ · Linux · Docker · Git · CI/CD · CUDA