A collection of LogitsProcessors to customize and enhance LLM behavior for specific tasks.
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Updated
Sep 16, 2026 - Python
A collection of LogitsProcessors to customize and enhance LLM behavior for specific tasks.
Research design for a Jev-native agent system:enable more options than jev provided with virtulization and paging, tool integration, external helper logits Top-k proposals with Jev-controlled fallback ,decision-aware hierarchical memory, and dependency-aware replanning.
Typed decisions (choice / score / yes-no) from local Qwen models on Apple Silicon. Probabilities come straight from the logits, no text generation. TypeSafe-compatible HTTP API, runs on MLX.
Plots how the logit values that are passed into the softmax function change over time as the model is trained.
Jev-style typed decisions from Qwen3.5-2B logits — one forward pass, zero decoding, zero fine-tuning.
Local frozen-backbone decision readout: evidence + criterion + options in, a probability per option out, in one forward pass. JevBench public set 0.805 / hard 0.604 with Qwen3.5-4B, no training.
Novel Hallucination detection method
Convert any causal LM into a Jev-style typed decision model — no training, no new weights. Measured honestly against JevBench, negative results included.
Tell recoverable LLM failures from structural ones by reading the failed trace. Two models, one vLLM pass, no extra forward pass.
Temporal analysis of LLM safety activation via logit-margin scores.
Detecting prompt toxicity from a small LLM's internal logits and embeddings, no separate moderation model needed. 99.18% AUPRC with a 64-neuron MLP.
Typed decisions from a local LLM in one forward pass. Reads the logits of the allowed options instead of generating text. ~44 ms per extra decision on the same document with llama.cpp prefix caching.
Selectia - A family of System One-style models fine-tuned from LFM 2.5, designed for one-pass typed decisions with calibrated probabilities. Decision API (Choice, Noul, Score) powered by open LLMs.
Fast, System One-inspired local AI decisions without text generation. One E4B Tetris snapshot game averaged 33.3 ms per request on an RTX 5090. Open source.
Ask a vision model a question, get the answer from its logits in one forward pass. No decoding, nothing trained. The confidence is the logit gap in nats.
This project bridges the gap between human language and machine-executable code by translating prompts into structured function calls with typed arguments. Built strictly with Python 3.10+ and Pydantic , it demonstrates how constrained decoding can ensure near-perfect reliability and strict JSON compliance, even on small 0.5B parameter models.
Convolutional Networks Training and Logits Saving
Composable token sampling over f32 logits with caller-supplied randomness
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