S cubed: Spectral Null-Space Swap Makes Reasoning Models Efficient

arXiv:2609.37976 · cs.LG, cs.AI, cs.CL, cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.LG, cs.AI, cs.CL, cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Accuracy — 51× in this paper · explained in 1 episode
• Attention Entropy — 41× in this paper · explained in 2 episodes
• Benchmark — 25× in this paper · explained in 2 episodes
• Trace — 18× in this paper · explained in 2 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Hallucination — 5× in this paper · explained in 3 episodes
• Verifier — 5× in this paper · explained in 2 episodes
• Model Merging — 4× in this paper · explained in 2 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Core — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Chain-of-Thought (CoT) — 2× in this paper · explained in 4 episodes
• Cognitive Load — 2× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Interpretability — 2× in this paper · explained in 15 episodes
• Mixture-of-Experts (MoE) — 2× in this paper · explained in 7 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Reasoning Traces — 2× in this paper · explained in 3 episodes
• Vision-language models — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Agent Harness — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) Prompting — 1× in this paper · explained in 3 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• EMMA — 1× in this paper · explained in 2 episodes
• Entropy Collapse — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Mixture-of-Experts (MoE) Models — 1× in this paper · explained in 3 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Thinking mode — 1× in this paper · explained in 2 episodes

Sources

• CausalRM: Causal-Theoretic Reward Modeling for RLHF from Observational User Feedbacks

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