Breaking the Chain: A Causal Analysis of LLM Faithfulness to Intermediate Structures

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, medium

Summary

A new causal evaluation protocol measures Large Language Model (LLM) faithfulness to explicit intermediate structures, such as rubrics or checklists, used in schema-guided reasoning pipelines. The protocol involves editing these structures and observing if the final decision updates according to a deterministic function. Across eight models and three benchmarks, LLMs demonstrated self-consistency with their *own* generated intermediate structures but failed to update predictions in up to 60% of cases after intervention, revealing significant fragility. The study found that delegating the final decision derivation to an external tool largely eliminated this fragility, whereas stronger prompts prioritizing intermediate structures did not materially close the gap. This indicates that intermediate structures primarily function as influential context rather than stable causal mediators.

Key takeaway

For AI Engineers designing schema-guided reasoning pipelines, you should be wary of LLMs' inherent fragility in causally linking intermediate steps to final decisions. Do not assume LLMs will reliably update their final output if an intermediate structure is modified. Instead, consider offloading the final decision derivation to an an external, deterministic tool to ensure robust faithfulness and prevent reliance on hidden shortcuts. Relying on prompt engineering alone is insufficient.

Key insights

LLMs' apparent faithfulness to intermediate reasoning structures is fragile; they often fail to update predictions after causal intervention.

Principles

• Faithfulness to structured reasoning is a causal mediation problem.
• Intermediate structures act as influential context, not stable causal mediators.
• LLM faithfulness sensitivity is directionally asymmetric.

Method

A causal evaluation protocol uses controlled interventions on structured intermediate representations, with deterministic counterfactual targets, to measure LLM faithfulness.

In practice

• Delegate final decision derivation to an external, deterministic tool.
• Do not rely solely on stronger prompts to ensure LLM faithfulness.

Topics

• Large Language Models
• Causal Analysis
• Schema-Guided Reasoning
• LLM Faithfulness
• Intermediate Structures
• External Tools

Best for: Research Scientist, AI Architect, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.