# Abductive Inference in Retrieval-Augmented Language Models: Generating and Validating Missing Premises

arXiv: https://arxiv.org/abs/2511.04020 · HTML: https://mlsanity.com/paper/2511.04020/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 11 of 11)

- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [RaDeR: Reasoning-aware Dense Retrieval Models](https://mlsanity.com/paper/2505.18405/) (2025)
- [Adapting LLMs for Efficient Context Processing through Soft Prompt Compression](https://mlsanity.com/paper/2404.04997/) (2024)
- [Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees](https://mlsanity.com/paper/2405.13290/) (2024)
- [Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning](https://mlsanity.com/paper/2504.06438/) (2025)
- [ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation](https://mlsanity.com/paper/2503.21729/) (2025)
- [Retrieving, Rethinking and Revising: The Chain-of-Verification Can Improve Retrieval Augmented Generation](https://mlsanity.com/paper/2410.05801/) (2024)
- [Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques](https://mlsanity.com/paper/2403.05801/) (2024)
- [Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs](https://mlsanity.com/paper/2404.12587/) (2024)
- [CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction](https://mlsanity.com/paper/2409.13701/) (2024)
- [Advancements in Natural Language Processing: Exploring Transformer-Based Architectures for Text Understanding](https://mlsanity.com/paper/2503.20227/) (2025)

## Built on by (most cited first, 9 of 9)

- [Wiring the 'Why': A Unified Taxonomy and Survey of Abductive Reasoning in LLMs](https://mlsanity.com/paper/2604.08016/) (2026)
- [Adaptive Detector-Verifier Framework for Zero-Shot Polyp Detection in Open-World Settings](https://mlsanity.com/paper/2512.12492/) (2025)
- [Adaptive Multi-Stage Patent Claim Generation with Unified Quality Assessment](https://mlsanity.com/paper/2601.09120/) (2026)
- [EVLA: An Electro-Aware Multimodal Assistant for Physically-Grounded Driving Reasoning and Control](https://mlsanity.com/paper/2606.28938/) (2026)
- [A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions](https://mlsanity.com/paper/2606.28943/) (2026)
- [Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework](https://mlsanity.com/paper/2607.01581/) (2026)
- [FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift](https://mlsanity.com/paper/2607.09695/) (2026)
- [CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLMs](https://mlsanity.com/paper/2608.14791/) (2026)
- [Graph-Based Modeling of Financial Volatility Dynamics](https://mlsanity.com/paper/2608.26127/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) enhanced with retrieval -- commonly referred to as Retrieval-Augmented Generation (RAG) -- have demonstrated strong performance in knowledge-intensive tasks. However, RAG pipelines often fail when retrieved evidence is incomplete, leaving gaps in the reasoning process. In such cases, abductive inference -- the process of generating plausible missing premises to explain observations -- offers a principled approach to bridge these gaps. In this paper, we propose a framework that integrates abductive inference into retrieval-augmented LLMs. Our method detects insufficient evidence, generates candidate missing premises, and validates them through consistency and plausibility checks. Experimental results on abductive reasoning and multi-hop QA benchmarks show that our approach improves both answer accuracy and reasoning faithfulness. This work highlights abductive inference as a promising direction for enhancing the robustness and explainability of RAG systems.