CASE: Causal Alignment and Structural Enforcement for Improving Chain-of-Thought Faithfulness
Quick Answer
The CASE framework enhances chain-of-thought (CoT) reasoning in large language models by enforcing causal alignment during training and structural constraints during inference.
Quick Take
It achieves a 37% relative improvement in CoT faithfulness across three models and four benchmarks while maintaining competitive accuracy. This approach mitigates direct instruction-to-answer shortcuts, ensuring that reasoning supports the final answer effectively.
Key Points
- CASE combines causal alignment and structural enforcement for improved CoT reasoning.
- Achieves a 37% average improvement in CoT faithfulness over strong baselines.
- Utilizes counterfactual-CoT and selective-loss fine-tuning during training.
- Masks direct attention from instruction to answer tokens during inference.
- Maintains competitive accuracy while enhancing reasoning interpretability.
DeepSignal Analysis
What happened
The CASE framework aims to improve chain-of-thought (CoT) reasoning in large language models by enforcing causal alignment during training and structural constraints during inference. It reportedly achieves a 37% relative improvement in CoT faithfulness across three models and four benchmarks while maintaining competitive accuracy.
Key evidence
- CASE enforces causal alignment by using counterfactual-CoT, biased-instruction, and empty-instruction datasets during training.
- The framework masks direct attention from instruction tokens to answer tokens during inference to prevent bypassing the generated CoT.
- Experiments demonstrate a 37% average per-setting relative improvement in CoT faithfulness over the strongest baselines across three models and four benchmarks.
Why it matters
Improving CoT faithfulness is crucial for enhancing the reliability and interpretability of large language models. The ability to ensure that reasoning supports final answers can reduce the risk of misleading outputs, which is particularly important in applications requiring high accuracy and trustworthiness.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract:Chain-of-thought (CoT) reasoning is widely used to improve both the performance and interpretability of large language models (LLMs), yet the generated reasoning may not faithfully support the final answer. We study this problem from a causal perspective, where a faithful CoT process should follow the chain $Z\rightarrow X\rightarrow Y$, with $Z$, $X$, and $Y$ denoting the instruction, reasoning chain, and final answer, respectively. In this process, the instruction should affect the answer only through the reasoning chain. However, conventional autoregressive LLMs condition answer generation on both the instruction and the CoT, which still allows a direct instruction-to-answer shortcut. To address this issue, we propose CASE, a framework that combines training-time causal alignment and inference-time structural enforcement. During training, CASE builds counterfactual-CoT, biased-instruction, and empty-instruction datasets, and applies selective-loss fine-tuning to strengthen CoT-to-answer dependence while suppressing instruction shortcuts. During inference, CASE masks direct attention from instruction tokens to answer tokens, preventing the model from bypassing the generated CoT. We provide an information-theoretic analysis showing how these components promote faithful chains. Experiments on three models and four benchmarks show that CASE achieves a 37\% average per-setting relative improvement in overall CoT faithfulness over the strongest baselines, exhibits stronger cross-dataset faithfulness transfer, and maintains competitive average accuracy. Code is available at this https URL.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2607.18820 [cs.CL] |
| (or arXiv:2607.18820v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2607.18820 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ziming Wang [view email]
[v1]
Tue, 21 Jul 2026 07:56:14 UTC (1,106 KB)
— Originally published at arxiv.org
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