Independent Research

RAMGPT treats technical claims as hypotheses to test, not headlines to repeat. We independently reproduce published and community results, isolate the mechanisms behind them, and publish evidence that readers can inspect and challenge.

What we publish

• Reproduction Reports — independent attempts to reproduce published benchmark or performance claims.
• Claim Audits — checks for hidden assumptions, unfair baselines, benchmark leakage, cache effects, token-budget differences, and other confounders.
• Ablation Studies — controlled experiments that remove or change one mechanism at a time to identify what actually causes an observed gain.
• Performance Forensics — low-level investigation of latency, throughput, memory, storage I/O, page faults, utilization, quantization, and runtime behavior.
• Independent Benchmarks — versioned comparisons designed around exact artifacts, pinned references, reproducible workloads, and inspectable evidence.
• Research Notes — new technical observations discovered while reproducing or extending existing work.

Our experimental standard

A useful experiment starts with a falsifiable claim. We document the hardware, model artifact, software stack, settings, prompts or datasets, measurement method, and important limitations. When the explanation behind a result is uncertain, we prefer matched controls and ablations over speculation. When a result does not reproduce, we report that outcome rather than forcing a positive conclusion.

From reproduction to original research

Reproduction is the starting point, not the endpoint. The most valuable questions often appear after the first result: whether a reported gain survives an equal-token comparison, whether an SSD-offload claim holds under cold-cache conditions, whether a quantization result survives task-level evaluation, or whether an optimization still works on consumer hardware. Those unanswered questions become independent RAMGPT experiments.

Open evidence

Where practical, RAMGPT publishes benchmark inputs, summaries, scripts, manifests, provenance records, and other artifacts needed to inspect the result. RAMGPT QuantBench is the first standing research program built around this model.

Research standards and correction rules are documented in the editorial policy.