Latent Abstraction for Retrieval-Augmented Generation
summary
The gist
The gist The LAnR framework proposes a unified architecture where a single LLM performs encoding, retrieval, and generation entirely within its own latent space to enhance Retrieval-Augmented
In short
LAnR proposes a unified architecture where a single LLM handles encoding, retrieval, and generation within its latent space. It replaces explicit text queries with latent vectors from special tokens to retrieve documents directly from the model's representations. This allows for tight integration between retrieval and generation, enabling efficient multi-hop reasoning with an adaptive control head.
Key concepts
- Latent Query Construction
- Instead of creating text queries, LAnR uses a designated token [PRED] to generate a dense vector directly from the LLM's hidden states. This allows the model to create retrieval queries and document representations in the same shared mathematical space, eliminating separate embedding models.
- Retrieval Control Mechanism
- A lightweight Multi-Layer Perceptron (MLP) head is used to decide if currently retrieved evidence is enough or if more documents are needed. This control head takes the latent query vector as input and outputs a binary decision, guiding the iterative retrieval process efficiently.
- Multi-turn Training Objective
- The training combines next-token prediction loss with contrastive retrieval loss and a control head objective. The system learns to steer successive turns toward missing evidence by using positive documents sampled from previous turns, optimizing the entire loop for multi-hop reasoning.
- Entropy Signaling
- The entropy of token distributions serves as a reliable signal of evidence sufficiency during retrieval. Lower entropy in the retrieved set indicates that sufficient information has been gathered, which can be used to inform stopping decisions without needing full text generation at every step.
Terminology used across episodes
This episode discusses
- Latent Abstraction for Retrieval-Augmented Generation · Paper Radio
- M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
- ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
- Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning · Paper Radio
- In-context Autoencoder for Context Compression in a Large Language Model
- Think before you speak: Training Language Models With Pause Tokens
- Training Large Language Models to Reason in a Continuous Latent Space · Paper Radio
- Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG
- Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
- FREESON: Retriever-Free Retrieval-Augmented Reasoning via Corpus-Traversing MCTS
- Implicit Reasoning in Large Language Models: A Comprehensive Survey
- When Text Embedding Meets Large Language Model: A Comprehensive Survey
- SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs
- Search and Refine During Think: Facilitating Knowledge Refinement for Improved Retrieval-Augmented Reasoning
- Repetition Improves Language Model Embeddings
- Text Embeddings by Weakly-Supervised Contrastive Pre-training
- System-1.5 Reasoning: Traversal in Language and Latent Spaces with Dynamic Shortcuts
- Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
- SoftCoT++: Test-Time Scaling with Soft Chain-of-Thought Reasoning
- Qwen3 Technical Report
- The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook
The paper
Latent Abstraction for Retrieval-Augmented Generation · Read on arXiv
Center for AI Research, VinUniversity
Retrieval-Augmented Generation (RAG) has become a standard approach for enhancing large language models (LLMs) with external knowledge, mitigating hallucinations, and improving factuality. However, existing systems rely on generating natural language queries at each hop and maintaining a strict architectural separation between retriever and generator, preventing them from leveraging the full representational capacity of the LLM. We propose LAnR (Latent Abstraction for RAG), a unified framework in which a single LLM jointly performs encoding, retrieval, and generation entirely within its own latent space. Rather than generating textual queries, LAnR produces dense retrieval vectors from the hidden states of a designated[PRED] token and uses them to match against encoded document representations from the same model. Furthermore, LAnR adaptively decides when sufficient evidence has been retrieved using a lightweight MLP control head over those same hidden states, eliminating both the separate retriever and explicit token-level stopping reasoning. This design is motivated by our empirical observation that answer token entropy reliably signals retrieval sufficiency. Extensive experiments on six QA benchmarks spanning single-hop and multi-hop settings demonstrate that LAnR outperforms existing RAG methods, while achieving improved inference efficiency through reduced number of retrieval calls and tighter model integration.
Transcript
Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.
Tom: I'm Tom.
Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.
Tom: We're looking at the paper: "Latent Abstraction for Retrieval-Augmented Generation".
Jane: The gist The LAnR framework proposes a unified architecture where a single LLM performs encoding, retrieval, and generation entirely within its own latent space to enhance Retrieval-Augmented Generation (RAG) efficiency.
Tom: First, who's behind it and why it matters.
Title and authors: Tom: So we’re looking at the paper called "Latent Abstraction for Retrieval-Augmented Generation". It sounds like they’re trying to fix how RAG works right now where you have a separate system for searching and another one for answering.
Jane: Right, it sounds like they are proposing a way to get the retrieval and generation parts into one single brain inside the same large language model. That's pretty ambitious because it changes the whole architecture of how we use these models to find facts.
Lu: Exactly, it’s about unifying those components within a single architecture, which means they aren't doing separate encoding and retrieval processes anymore >
The paper's summary: Tom: So the core idea is that instead of making a natural language query like you would normally do to find documents, LAnR produces these dense retrieval vectors directly from the hidden states of a special token called PRED.
Jane: That means they are bypassing the whole step of generating that text query and instead using what’s already inside the model's memory to create a search vector. It’s like using the internal structure of the language model itself to ask questions >
Lu: They use this latent query vector to match against document representations that were also made by the same model, which creates this tight integration between retrieval and generation >
The paper's improvements: Tom: Beyond just making one unified system, they added a second part, which is an implicit retrieval control mechanism. This is a lightweight auxiliary head that looks at the hidden states and decides if you need to do more searching or if what you have is enough >
Jane: That’s interesting because it means the model doesn't just search once and give up; it can adapt dynamically based on its own internal state of evidence. It uses a lightweight MLP head for this decision-making >
Meng: From a deployment standpoint, that control head is crucial because if we have to run heavy retrieval systems every time, we want the model to decide when to stop searching before we waste compute resources >
Conclusion: Tom: So to wrap up, LAnR tries to replace explicit query generation with latent vectors and add that adaptive control head. The results show it performs better on multi-hop benchmarks and uses significantly less output tokens, generating only about five per query compared to systems like Search-R1 or AutoRefine >
Jane: It seems they are showing that this method can lead to higher cumulative gold recall while drastically cutting down the computational cost of getting answers from the LLM >
Lu: The ablation studies confirm that this lightweight MLP control head is what really drives the multi-hop gains, and scaling up to larger models consistently improves performance on complex reasoning tasks >
Meng: If you look at it practically, they are using substantially fewer documents than specialized retrievers while still hitting high recall scores on things like HotpotQA and 2WikiMQA > <ref:2604.17866#pg1>
Lalam: I'm quite confident in the findings here; the system demonstrates reduced computational cost generating only about five output tokens per query which is roughly thirty times fewer than Search-R1 or AutoRefine >
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