Retrieving the right level of context for a given query is a perennial challenge in information retrieval--too large a chunk dilutes semantic specificity, while chunks that are too small lack broader context. This paper introduces the Hierarchical Re-ranker Retriever (HRR), a framework designed to achieve both fine-grained and high-level context retrieval for large language model (LLM) applications. In HRR, documents are split into sentence-level and intermediate-level (512 tokens) chunks to maximize vector-search quality for both short and broad queries. We then employ a reranker that operates on these 512-token chunks, ensuring an optimal balance--neither too coarse nor too fine--for robust relevance scoring. Finally, top-ranked intermediate chunks are mapped to parent chunks (2048 tokens) to provide an LLM with sufficiently large context. We compare HRR against three widely used alternatives(details of them can be found in appendix section): 1. Base Retriever + Reranker 2. ChildToParent(C2P) Retriever + Reranker 3. SentenceToParent(S2P) Retriever + Reranker Experiments on two datasets--Yojana and Lendryl--demonstrate that HRR consistently outperforms these baselines in both Hit Rate (HR) and Mean Reciprocal Rank (MRR). On Yojana, HRR achieves a perfect 100% Hit Rate and an MRR of 96.15% which is 25% higher than Base Retriever and around 15% higher than C2P or S2P retriever. Similarly, on Lendryl, HRR attains MRR which is 20% and 10% higher than Base Retriever and C2P or S2P retriever respectively. These results confirm that a multi-stage retrieval strategy--fine-grained sentence-level and intermediate level(512 token) filtering, optimized 512 token reranking, and final parent-chunk(2048 token) mapping--delivers more accurate, context-rich retrieval well-suited for downstream LLM tasks.

Human respiration rate (HRR) is an important physiological metric for diagnosing a variety of health conditions from stress levels to heart conditions. Estimation of HRR is well-studied in controlled terrestrial environments, yet robotic estimation of HRR as an indicator of diver stress in underwater for underwater human robot interaction (UHRI) scenarios is to our knowledge unexplored. We introduce a novel system for robotic estimation of HRR from underwater visual data by utilizing bubbles from exhalation cycles in scuba diving to time respiration rate. We introduce a fuzzy labeling system that utilizes audio information to label a diverse dataset of diver breathing data on which we compare four different methods for characterizing the presence of bubbles in images. Figure 1: Robotic estimation of diver respiration rate during Ultimately we show that our method is effective at estimating a closed-water evaluation of the proposed detection HRR by comparing the respiration rate output system. The Aqua autonomous underwater vehicle [8] is with human analysts.

Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors \cite{Plate1995} by associating each vector with an abstract concept, and providing mathematical operations to manipulate vectors as if they were classic symbolic objects. This method has seen little use outside of older symbolic AI work and cognitive science. Our goal is to revisit this approach to understand if it is viable for enabling a hybrid neural-symbolic approach to learning as a differentiable component of a deep learning architecture. HRRs today are not effective in a differentiable solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space. In doing so we improve the concept retrieval efficacy of HRRs by over $100\times$. Using multi-label classification we demonstrate how to leverage the symbolic HRR properties to develop an output layer and loss function that is able to learn effectively, and allows us to investigate some of the pros and cons of an HRR neuro-symbolic learning approach.

Gentner and Markman (1992) suggested that the ability to deal with analogy will be a "Watershed or Waterloo" for connectionist models. They identified "structural alignment" as the central aspect of analogy making. They noted the apparent ease with which people can perform structural alignment in a wide variety of tasks and were pessimistic about the prospects for the development of a distributed connectionist model that could be useful in performing structural alignment. In this paper I describe how Holographic Reduced Representations (HRRs) (Plate, 1991; Plate, 1994), a fixed-width distributed representation for nested structures, can be used to obtain fast estimates of analogical similarity.

Gentner and Markman (1992) suggested that the ability to deal with analogy will be a "Watershed or Waterloo" for connectionist models. They identified "structural alignment" as the central aspect of analogy making. They noted the apparent ease with which people can perform structural alignment in a wide variety of tasks and were pessimistic about the prospects for the development of a distributed connectionist model that could be useful in performing structural alignment. In this paper I describe how Holographic Reduced Representations (HRRs) (Plate, 1991; Plate, 1994), a fixed-width distributed representation for nested structures, can be used to obtain fast estimates of analogical similarity.

Gentner and Markman (1992) suggested that the ability to deal with analogy will be a "Watershed or Waterloo" for connectionist models. They identified "structural alignment" as the central aspect of analogy making. They noted the apparent ease with which people can perform structural alignment in a wide variety of tasks and were pessimistic about the of a distributed connectionist model that could be useful inprospects for the development performing structural alignment. In this paper I describe how Holographic Reduced Representations (HRRs) (Plate, 1991; Plate, 1994), a fixed-width distributed representation for nested structures, can be used to obtain fast estimates of analogical similarity.