Can You Trust the Probability? Testing a Decision Model on 77 PubMed Papers
A decision model's probability is not how sure it is. On 77 PubMed papers, see how to check calibration and set a threshold from coverage and accuracy.
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One Forward Pass, Then Probabilities: How Decision Models Work and Are Trained
A decision model reads the logits of listed options after one forward pass and turns them into probabilities. Jev discloses RLCD but not its loss; open replicas publish their heads.
On one 16 GB card, the accuracy winner and the speed winner are not the same deployment
On one RTX 4060 Ti 16GB and one frozen set of 700 items, Ridge, OrcaSAQ-2, and Ternary Bonsai 2 scored 78.4%, 77.6%, and 74.6%. Wall clocks were 47.55, 62.59, and 34.01 minutes. This compares three deployments, not one set of weights.
What Is a Decision Model? Jev, Clef and the AI That Only Chooses
A decision model returns a probability for every pre-listed option in one forward pass and writes no text. Here is how it differs from LLM JSON output, where Jev and Clef stand, and when not to use one.
Ternary Bonsai 2 27B on a 16GB GPU: why multi-layer bakeoffs promoted it to daily default
On an RTX 4060 Ti 16GB seat, Prism Ternary Bonsai 2 27B (PQ2_0) was scored against a same-box Qwen3.8 dense daily model across short functional gates, long-context needles, and tool loops before a stable default alias pointed at the ternary engine.
Nine billion DNA letter swaps, pre-drawn as a map: what AlphaGenome Atlas ranks
DeepMind’s AlphaGenome Atlas precomputes molecular effects for about nine billion possible single-letter DNA changes and ranks them with AVI scores. A research traffic map, not a clinical diagnosis.
One Month of Local Model Benchmarking: I Overturned My Own Rankings
From acceleration knobs and extreme quantization to a nine-model comparison, a large-sample retest at month's end reshuffled the entire leaderboard. A record of a broken yardstick, small-sample error bars, and the current division of labor between pi and local models.
Local LLMs on One Mac — Which Cloud Generation Do They Match? Five Models, Benchmarked
I benchmarked five 4-bit local models on a single M4 Pro (48GB) with oMLX across eight benchmarks, mapped the speed/quality sweet spot, and calibrated which cloud generation they match. Plus a field report on three speculative-decoding traps (MTP / dflash / specprefill) and the cache trade-off that decides them.
Auto-Quant: LLM-Driven Quantitative Trading Research Loop
Auto-Quant lets LLMs autonomously iterate on FreqTrade strategies via backtesting loops. Interesting autoresearch concept, but deeply locked into crypto trading infrastructure. Only useful if you live in the FreqTrade ecosystem.
Why Is AI Getting Faster? Six Techniques Behind LLM Inference Acceleration
Starting from the everyday experience of watching ChatGPT type out words one by one, this article uses six everyday analogies to break down KV Cache, Paged Attention, Flash Attention, GQA, Continuous Batching, and Speculative Decoding — explaining how large language models serve more users faster on the same GPU.