Grounding Multi-Hop Reasoning in Structural Causal Models via Group Relative Policy Optimization
arXiv:2605.01482v3 Announce Type: replace Abstract: Multi-Hop Fact Verification requires complex reasoning across disparate evidence, posing significant challenges for Large Language Models , which may suffer from hallucinations and fractured logical chains. Existing methods, while improving transparency via Chain-of-Thought , often lack explicit modeling of the structural dependencies between evidence and claims. In this work, we introduce an SCM-inspired framework that grounds reasoning in explicit directed dependency graphs, treating verification as a constructive structural reasoning proce
The increasing sophistication and widespread use of LLMs highlight their limitations in complex reasoning, making advancements in grounding mechanisms crucial for trusted AI applications.
This work directly addresses the critical issues of hallucination and logical coherence in LLMs, which are currently significant barriers to their reliable deployment in high-stakes fact-verification tasks.
The introduction of SCM-inspired frameworks offers a more robust method for grounding AI reasoning in explicit dependencies, potentially moving beyond opaque Chain-of-Thought approaches.
- · AI developers focused on reliability
- · Industries requiring high-accuracy fact verification
- · Researchers in causal AI
- · LLM applications without robust grounding mechanisms
- · Users relying on unverified LLM outputs
Improved accuracy and trustworthiness of AI systems performing complex reasoning tasks will enable new applications.
Reduced incidence of AI-generated misinformation due to better factual grounding could increase public trust in AI.
This could accelerate the adoption of AI agents in highly sensitive domains like legal or scientific research where verifiable reasoning is paramount.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI