Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning

Md Muntaqim Meherab, Noor Islam S. Mohammad, Faiza Feroz

Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning: 3 upvotes on Hugging Face Daily Papers, #24 of 29 papers on 2026-03-12. Day-by-day upvote history.

Sparse autoencoders can localize where concepts live in language models, but not how they interact during multi-step reasoning. We propose Causal Concept Graphs (CCG): a directed acyclic graph over sparse, interpretable latent features, where edges capture learned causal dependencies between concepts. We combine task-conditioned sparse autoencoders for concept discovery with DAGMA-style differentiable structure learning for graph recovery and introduce the Causal Fidelity Score (CFS) to evaluate whether graph-guided interventions induce larger downstream effects than random ones. On ARC-Challenge, StrategyQA, and LogiQA with GPT-2 Medium, across five seeds (n{=}15 paired runs), CCG achieves CFS=5.654pm0.625, outperforming ROME-style tracing (3.382pm0.233), SAE-only ranking (2.479pm0.196), and a random baseline (1.032pm0.034), with p<0.0001 after Bonferroni correction. Learned graphs are sparse (5-6\% edge density), domain-specific, and stable across seeds.

Paper page on Hugging Face · arXiv

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