# Hallucination Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching

arXiv: https://arxiv.org/abs/2605.29055 · HTML: https://mlsanity.com/paper/2605.29055/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 13 of 13)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://mlsanity.com/paper/2310.12815/) (2023)
- [Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems](https://mlsanity.com/paper/2410.07283/) (2024)
- [Nested Learning: The Illusion of Deep Learning Architectures](https://mlsanity.com/paper/2512.24695/) (2025)
- [Signed-Prompt: A New Approach to Prevent Prompt Injection Attacks Against LLM-Integrated Applications](https://mlsanity.com/paper/2401.07612/) (2024)
- [PromptShield: Deployable Detection for Prompt Injection Attacks](https://mlsanity.com/paper/2501.15145/) (2025)
- [Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers](https://mlsanity.com/paper/2504.19254/) (2025)
- [Prompt Injection Detection and Mitigation via AI Multi-Agent NLP Frameworks](https://mlsanity.com/paper/2503.11517/) (2025)
- [Semantic Caching for Low-Cost LLM Serving: From Offline Learning to Online Adaptation](https://mlsanity.com/paper/2508.07675/) (2025)
- [Hallucination Mitigation using Agentic AI Natural Language-Based Frameworks](https://mlsanity.com/paper/2501.13946/) (2025)
- [Sentinel Agents for Secure and Trustworthy Agentic AI in Multi-Agent Systems](https://mlsanity.com/paper/2509.14956/) (2025)
- [AI Multi-Agent Interoperability Extension for Managing Multiparty Conversations](https://mlsanity.com/paper/2411.05828/) (2024)
- [Prompt Injection Mitigation with Agentic AI, Nested Learning, and AI Sustainability via Semantic Caching](https://mlsanity.com/paper/2601.13186/) (2026)

## Built on by (most cited first, 1 of 1)

- [Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog](https://mlsanity.com/paper/2607.21412/) (2026)

## Abstract (arXiv)

This paper describes an approach to hallucination detection and mitigation using a HOPE-inspired Nested Learning architecture with Continuum Memory Systems (CMS) and semantic similarity caching, tested on a hybrid benchmark of 310 prompts (217 epistemic-uncertainty prompts, 93 fabrication-induction stress tests). A three-stage pipeline orchestrated via the Open Floor Protocol is evaluated with five KPIs; four score the response and aggregate into a Total Hallucination Score. The score improves end to end by 6.1% of its attainable range, but 83.5% of that gain is attributable to a single dimension, Explicit Contextualization, while Factual Claim Density, the dimension closest to unsupported content, stays flat; 97.7% of the gain arrives at the first review stage. The fifth indicator, observability, is reported separately, since it registers the presence of an OFP annotation channel rather than a property of the response: it rises 147% at the review stage, the only stage carrying explicit hallucination markers, then falls back at the final stage, which does not propagate the channel. Three annotators independently labelled every final-stage response on the 93 stress prompts: in 10 of 93 cases (10.8%, 95% CI 5.9-18.7) the final answer still presents the invented item as real, at alpha=0.586, below the conventional threshold; Explicit Contextualization tracks these labels monotonically. Re-scoring all 930 outputs with Llama 3.1, Gemma 4, and Qwen 3 as judges confirms the gain and ranks the cross-family judges above the original evaluator against human labels (rho=-0.772 vs -0.477). Semantic caching serves 47.7% of model calls. A nominally multi-dimensional reliability score is thus effectively one-dimensional, and only that dimension has external support.