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A Hierarchical Semantic Distillation Network, HSDN, is presented for compressing reasoning chains while preserving answer accuracy and logical coherence, and shows that graph guided compression is effective for high stakes financial reasoning tasks.
An LLM-based CI/CD analysis pipeline that combines repository enrichment, anti-pattern detection, stage mining, and recommendation generation over a large GitHub corpus is presented, arguing for CI/CD observability that combines diagnosis, context, and human review.
Context masking is established as necessary for attributing early answer availability to an explanation rather than its hidden input when early-prefix evidence disappears after masking.
The results support truncated CoT auditing as a lightweight process-level diagnostic for answer-driven reasoning in math tutoring explanations and support truncated CoT auditing as a lightweight process-level diagnostic for answer-driven reasoning in math tutoring explanations.
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