hbar.science
An AI-native, artifact-first research environment for computational and interdisciplinary science.
A public curated research environment. Methods and artifacts are documented below. Contributions are currently by invitation or collaboration.
What this is
hbar.science is a research environment focused on transparent, reproducible, AI-assisted inquiry. Work here is published as inspectable artifacts—code, data, and models—rather than opaque claims.
How work is labeled
Zone
Hypothesis, Exploration, Evidence, or Synthesis
Rigor Tier
T0 (concept) through T3 (validated)
AI Level
A0 (none) through A4 (autonomous)
Featured artifacts
The Future Evolution of hbar.science
Plausible directions for platform evolution: living artifacts, AI transparency, replication workflows, and lightweight stewardship.
GPU Concurrency Benchmark — vLLM Serving Box (RTX PRO 6000 Blackwell)
Measured the concurrent-request ceiling of a single RTX PRO 6000 Blackwell Server Edition GPU serving Llama 3.3 70B Q4_K_M GGUF under vLLM 0.21.0, to replace the ~25-user-per-box estimate in the sovereign-compute architecture with a measured number. Result: as-deployed ceiling is ~5 in-flight requests, falsifying the architecture-doc hypothesis 5×; the gap is attributable to software configuration (GGUF format, fp16 KV cache, FlashAttention v2, native PyTorch sampler) rather than hardware. The optimization path (AWQ, fp8 KV cache, FlashAttention 3) is projected to raise the ceiling into the 15-25 range.
HOPSO: Harmonic Oscillator–Based Particle Swarm Optimization (Reference Implementation)
Reference Python implementation of the Harmonic Oscillator–Based Particle Swarm Optimization (HOPSO) algorithm, corresponding to the published PLOS ONE article.
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Compliance: 4/5 (80%)
Missing: Environment hash documented
HOPSO — VQE Optimizer Implementation
Reference implementation of the HOPSO classical optimizer adapted for use in variational quantum eigensolver (VQE) workflows.
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Compliance: 1/2 (50%)
Missing: Deterministic seed documented
Institutionalized Science: Incentives, Access, and the Limits of the Journal Model
An analysis of structural constraints in contemporary scientific publishing and institutional research.
Sovereign LLM Serving on H200 — a 4-Model Concurrency Curve (70B, gpt-oss-120b, DeepSeek-V3-671B)
Measured the concurrent-request ceiling, time-to-first-token, and throughput of four open models served under vLLM 0.22 on NVIDIA H200 (Hopper) hardware, fp8 weights + fp8 KV cache, to characterize how cheaply a sovereign actor can serve frontier-class models privately. Headline finding: serving concurrency is governed by ACTIVE parameter count, not total size. gpt-oss-120b (MoE, ~5B active) sustains the highest concurrency of all (214x max-concurrency, chat ceiling >300 and long-context >100), beating the dense Llama-3.3-70B despite 120B total params; DeepSeek-V3-671B (MoE, ~37B active) serves privately across one 8xH200 node (~200 concurrent chat/node tuned). The earlier ~5-concurrent ceiling measured on an unoptimized Blackwell stack was a serving-configuration artifact, not a hardware limit -- on mature H200 with an optimized stack, per-GPU concurrency reaches 46-214x depending on the model.
VQC Embedding vs Classical Embedding on QM9 — a Confound-Controlled Negative
Controlled-substitution test of whether a shallow variational quantum circuit (VQC) used as the embedding layer inside a fixed transformer beats a classical linear embedding of equal output dimension on QM9 HOMO-LUMO gap prediction. Only the embedding changes. Result: the VQC (8 qubits, depth 2) is ~13% worse than the linear baseline (0.717 vs 0.634 eV test MAE, >3 sigma); a matched classical bottleneck (Linear 14->8->64) reaches 0.699 eV, showing ~78% of the VQC's deficit is the dimensional bottleneck, not quantum structure. Across a 30-config sweep (qubits 4/6/8/10 x depth 1/2/3 x entanglement linear/circular/all), 0/30 beat the linear baseline and 0/30 beat even the matched classical bottleneck. A clean, reviewer-proof negative at this scale and circuit family.
VQE Regularization Evaluation Pipeline
Computational pipeline for evaluating regularization effects in variational quantum eigensolvers, including parameter sweeps and optimization landscape analysis.
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Compliance: 1/3 (33%)
Missing: Deterministic seed documented
Missing: AI audit missing/incomplete