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TERMINATOR

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-03-17 / sources-researcherTiDAR: Think in Diffusion, Talk in Autoregression — Hybrid Architecture Uses Parallel Diffusion for Reasoning, AR for OutputYannic Kilcher's analysis of TiDAR (arXiv 2511.08923) covers a hybrid language model architecture where internal reasoning uses parallel diffusion-based generation while output tokens are produced autoregressively. The approach combines the quality advantage of autoregression with the speed potential of diffusion models, enabling faster parallel generation in latent reasoning space without sacrificing output quality. This is a direct architectural response to the CoT token efficiency problem and complements recent TERMINATOR-style exit-signal approaches.
2026-03-16 / sources-researcherTERMINATOR: 14–55% Reduction in CoT Token Length While Outperforming SOTA — Trains on First-Answer Positions as Optimal Exit SignalsTERMINATOR identifies that large reasoning models' first correct answer is predictable early in the chain, then trains a model to exit at that point. Achieves 14–55% CoT length reduction across MATH-500, AIME 2025, HumanEval, and GPQA benchmarks while outperforming current state-of-the-art methods — shorter reasoning chains without quality loss. Directly addresses the over-thinking problem in models like o1/o3 and DeepSeek-R1 where extended reasoning adds cost without adding accuracy.

Graph relationships

BENCHMARKED AGAINST
MATH-500
TERMINATOR achieves 14-55% CoT length reduction on MATH-500
Source finding
COMPETES WITH
DeepSeek-R1
TERMINATOR reduces reasoning cost compared to DeepSeek-R1
Source finding
COMPETES WITH
o1
TERMINATOR addresses the over-thinking problem in o1/o3
Source finding
BENCHMARKED AGAINST
HumanEval
TERMINATOR outperforms on HumanEval benchmark
Source finding

Source trail

arXiv Yannic Kilcher (YouTube)

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