logits
5 stories filed under logits on Beyond Market Intelligence. The newest of them: “How Predictable Is an AI's Supposedly Random Number”, “Precision scoring models that skip text generation for faster decisions”, and “Reward-aware search replaces blind sampling in best-of-N generation”. If an AI model is told to pick a "random" number, its output is far from unpredictable. Precision scoring models that skip text generation are making faster decisions possible, and the latest v2.1 updates to the 27B and 9B Jebadiah models show real progress. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every logits story on Beyond Market Intelligence, newest first.
How Predictable Is an AI's Supposedly Random Number
If an AI model is told to pick a "random" number, its output is far from unpredictable. We read the exact answer distribution from the logits of open models, both base and instruction-tuned, and turned that data into bits and guesses-to-crack. The results reveal how deterministic these systems actually are. It's a sobering look at what randomness means inside a neural network.
Precision scoring models that skip text generation for faster decisions
Precision scoring models that skip text generation are making faster decisions possible, and the latest v2.1 updates to the 27B and 9B Jebadiah models show real progress. Treating a decision as a closed-set scoring problem eliminates parsing and sampling entirely. The results speak clearly: the 27B jumped to 57.03 on the Decision Index, with strong gains in knowledge, language, and retrieval. The tool-use regression on When2Call is worth investigating, though.
Reward-aware search replaces blind sampling in best-of-N generation
Repetitive sampling in best-of-N generation is basically a blind search, even though most tasks using it are built around reward maximization. That irony isn't lost on us. With FLEET, we make generation reward-aware by attributing external rewards to specific tokens and using a modified MCTS to adjust logits. Testing on GSM8K and LiveCodeBench showed it matched baselines in half the iterations, or less. It's a smarter, more efficient path forward.
Streamlining softmax by reducing inputs without sacrificing accuracy
Reducing softmax's inputs from N to N-1 is a neat theoretical insight, those redundant parameters are doing nothing. If the logits sum to zero, the last one is already determined. The idea could trim the final layer and maybe nudge convergence. But the benefit feels negligible in practice, as the author suspects. Why not do it anyway? Because elegance doesn't always translate to speed. For a deeper look at streamlining neural architectures, our article on "Functional Gradient Descent with Adaptive Representations" explores similar efficiency gains.
Transforming LLM Efficiency: A DSP-Inspired Semantic Vocoder Approach
The idea of treating text like an audio signal is a sharp pivot. By splitting generation into a slow-rate planner and a fast-rate vocoder, this work tackles the inefficiency where predicting every token costs the same compute, regardless of its weight in meaning. The TinyStories results are compelling, showing faster convergence. However, the conditioning over-reliance is a real bottleneck. When the base model leans on semantic hashes, grammar suffers. This is a thoughtful exploration of a genuine problem.