PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Scalable and user friendly neural :brain: forecasting algorithms.
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
Lightning β‘οΈ fast forecasting with statistical and econometric models.
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Make AI coding agents architecture-aware: baseline-first, evidence-verified, drift-checked, and safe across long tasks.
Probabilistic Hierarchical forecasting π with statistical and econometric methods.
:bouncing_ball_person: Pytorch ReID: A tiny, friendly, strong pytorch implement of person re-id / vehicle re-id baseline. Tutorial πhttps://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial
High-quality implementations of standard and SOTA methods on a variety of tasks.
Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
ACM Multimedia2020 University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization :helicopter: annotates 1652 buildings in 72 universities around the world.
SBX: Stable Baselines Jax (SB3 + Jax) RL algorithms
Multi-Objective Reinforcement Learning algorithms implementations.
Evaluator-neutral AI evaluation evidence, baseline regression gates, and JSON, JUnit, and SARIF reports for CI.
A small, complete Python agent harness: tools, compaction, retries, and a single-file TUI. Built to learn and use as a baseline.
JLens Evolve v3: evidence-centric evolution platform (autonomous Teacher + local Qwen + official SWE-bench native evaluation). Branch codex/v3-evolution-10000 carries transport resilience, Router-key normalization, per-round temperature beam, and baseline fail-soft.
Reproducible monthly demand forecasting with Random Forest, chronological backtests, transparent baselines, and a guided local app.
Six security outcomes an enterprise must achieve to run AI agents, and the controls that evidence them. A draft for public comment.
AI humanizer skill for Claude Code, Codex and Cursor. Removes AI slop and the signs of AI writing, then measures the rewrite against human baselines.
Serving 4-bit Qwen3.8-27B on a single DGX Spark (GB10): 75 tok/s single-stream, 246 tok/s aggregate at 8-way concurrency. NVFP4 vs MixedInt4-AutoRound vs the FP8 baseline, measured on one harness β including why the quantization advantage collapses to +0.2% by c16.
M3D routing challenge: a reproducible EDA routing benchmark for a simplified monolithic-3D stack (generator, checker, scorer, baseline, visualization).
MiMo-V2.6-Flash-RL on 2x NVIDIA DGX Spark (GB10): vLLM TP2 + DFlash + vision/audio, fp8 KV 300K, four fixes for the stock image, measured baselines
Open visual probability research: encode an image once, answer multiple structured questions. Public data, trained weights, reproducible baselines and failure cases.
Audit AI agent skills, review usage evidence, and compare original, simplified, and baseline workflows.
A reinforcement learning environment for PCB autorouting, built on KiCad's push-and-shove router. Includes a decoder-only PPO/GRPO agent, LLM tool-calling and rule-based baselines, and a uniform 3-stage evaluation pipeline. The routing engine ships separately as the PCBWorld-Engine submodule (GPLv3).
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Vanilla RAG baseline for the Agent Memory Leaderboard