2026-10-11 17:09 UTC

model-compression

band: warmmomentum: stable score: 0.446
temperature history

Episodes (9)

Independent replications will determine whether compressed LLMs can pass standard behavioral-fidelity checks while suffering materially worse factual reliability or safety performance.
expirednovelscott: low
Independent reproduction will determine whether the reported lossless weight-compression method reduces GLM-5.2 memory requirements by roughly 25% without changing outputs or materially degrading inference performance.
expiredconvergesscott: medium
Independent testing will determine whether the English-focused Kimi K3 IQ2-XXS GGUF reduces storage from roughly 711GB to 478GB while preserving useful English-language capability.
expiredknownscott: medium
Independent evaluations will determine whether Haar-wavelet subband pruning materially reduces LLM inference memory or compute while preserving model quality.
expiredknownscott: low
Independent benchmarks and artifact review will determine whether the reported sub-2-bit 250M-parameter model can deliver useful local inference from a roughly 60MB deployment while using disk-backed compression for histories approaching 100 million tokens.
expiredknownscott: medium
Independent reproduction will determine whether Quantization-Aware Healing enables compressed 4-bit models to match or exceed their full-precision originals on useful evaluations.
expiredknownscott: medium
sanoTTS’s creator claims the released 294K-parameter multilingual speech stack fits in 337 KB and runs without an NPU on a $3 microcontroller, potentially making usable neural TTS practical on severely constrained edge hardware.
watchingconvergesscott: medium
Multiverse Computing claims its released Quasar 1.1 438B rebuild combines GLM-5.2 expert pruning with broader healing data, including quantum-generated samples, to improve reasoning and reduce output tokens by 37.6%, potentially lowering agent-serving costs without establishing a separate quantum-data advantage.
seedconvergesscott: low
Cactus Compute claims its released Whistle ASR model — 55M parameters in a 16.9MB file — mostly beats Whisper base across seven languages at ~9x less size and 6x the speed, and independent adoption on budget phones, wearables, and microcontrollers versus a quiet fade decides whether ultra-small ASR becomes a practical edge-inference default.
corroboratednovelscott: high

Trajectory notes