Knowledge Retrieval and Reasoning

Research on how AI systems acquire, store, and retrieve structured and unstructured knowledge to answer questions and generate informed responses. Covers RAG architectures, knowledge graphs, domain specialization, and the integration of external information into language model reasoning.

69 notes (primary) · 326 papers · 6 sub-topics

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Retrieval-Augmented Generation (RAG)

23 notes

Does lexical search outperform agentic navigation as corpus size grows?

As document collections scale, does a simple indexed search like BM25 become more reliable than an intelligent agent that explores the corpus sequentially? This matters because it shapes how to architect production RAG systems.

When should retrieval happen during model generation?

Explores whether retrieval should occur continuously, at fixed intervals, or only when the model signals uncertainty. Standard RAG retrieves once; long-form generation requires dynamic triggering based on confidence signals.

Can question features alone predict when to retrieve?

Can lightweight external features of a question—rather than expensive model uncertainty checks—reliably decide whether retrieval is needed? This matters because uncertainty-based methods promise efficiency but add computation.

Can you adapt retrieval models without accessing target data?

Explores whether dense retrieval systems can adapt to new domains using only a textual description, rather than actual target documents—especially relevant for privacy-restricted or competitive scenarios.

What do enterprise RAG systems need beyond accuracy?

Academic RAG benchmarks focus on question-answering accuracy, but enterprise deployments in regulated industries face five distinct requirements—compliance, security, scalability, integration, and domain expertise—that standard architectures don't address.

Can fine-tuning replace query augmentation for retrieval?

Query augmentation helps retrievers handle ambiguous queries but increases input cost. Does fine-tuning the retrieval model achieve comparable performance without this overhead?

Can long-context models resolve retriever-reader imbalance?

Traditional RAG systems force retrievers to find precise passages because readers had small context windows. Do modern long-context LLMs change what architecture makes sense?

Can query-time graph construction replace pre-built knowledge graphs?

Does building dependency graphs from individual queries at inference time offer a more flexible and cost-effective alternative to constructing knowledge graphs over entire document collections upfront?

Can retrieval learn what actually helps answer questions?

Standard RAG trains retrievers to find similar documents and generators to produce answers separately. But does surface similarity match what genuinely helps generate correct responses? This explores whether retrieval can receive feedback from answer quality.

Can knowledge graphs enable multi-hop reasoning in one retrieval step?

Standard RAG retrieves once but misses chains; iterative RAG follows chains but costs more. Can we encode multi-hop paths in a knowledge graph so one retrieval pass discovers them all?

Can long-context LLMs replace retrieval-augmented generation systems?

Explores whether loading entire corpora into LLM context windows can eliminate the need for separate retrieval systems, and what task types this approach handles well or poorly.

Can a model's partial response guide what to retrieve next?

Does using the model's in-progress output as a retrieval signal reveal information needs better than the original query alone? This explores whether generation itself can diagnose what documents are missing.

Does question type determine the right retrieval strategy?

Explores whether different non-factoid question types require distinct retrieval and decomposition approaches. Matters because standard RAG fails when applied uniformly to debate, comparison, and experience questions despite being effective for factoid queries.

Why do queries and documents occupy different embedding spaces?

Queries and documents express the same information in fundamentally different ways—short and interrogative versus long and declarative. Understanding this mismatch is crucial for why direct embedding retrieval often fails.

Can rationale-driven selection beat similarity re-ranking for evidence?

Can LLMs generate search guidance that outperforms traditional similarity-based evidence ranking? This matters because current re-ranking lacks interpretability and fails against adversarial attacks.

Can relevance guide grep search beyond just selecting documents?

Most retrieval systems use relevance only to pick top-k documents for the model. Could relevance instead shape how an agent explores the corpus—ordering traversal, setting entry points, and filtering matches—to find evidence faster?

Does synthetic content in search results hide ecosystem decay?

As AI-generated content dominates search rankings, do traditional accuracy metrics mask a silent loss of source diversity and ecosystem health? This matters because hidden fragility could make systems vulnerable to future corruption.

When should language models retrieve external knowledge versus use internal knowledge?

Can we model retrieval as a per-step decision problem rather than an always-on strategy? This matters because unnecessary retrieval adds noise and latency without improving accuracy.

Can document count be learned instead of fixed in RAG?

Standard RAG systems use a fixed number of documents regardless of query complexity. Can an RL agent learn to dynamically select both how many documents and their order based on what helps the generator produce correct answers?

Can retrieval systems ground answers in the right time?

Explores whether document retrieval for language models can distinguish between multiple versions of the same content from different time points, and whether adding temporal awareness to retrieval scoring helps answer time-sensitive questions accurately.

Can smaller language models outperform larger ones at graph extraction?

GraphRAG pipelines typically use large models for knowledge graph construction, but does extraction actually require world knowledge or just language skills? This explores whether compact specialized models can match larger general-purpose alternatives.

Why does retrieval-augmented generation fail in production?

RAG systems work in controlled demos but break in real-world deployment, especially for high-stakes domains like medicine and finance. Understanding the three structural failure modes reveals why.

Do vector embeddings actually measure task relevance?

Vector embeddings rank semantic similarity, but RAG systems need topical relevance. When these diverge—as with king/queen versus king/ruler—does similarity-based retrieval fail in production?

Domain Specialization in LLMs

3 notes

When do graph databases outperform vector embeddings for retrieval?

Vector similarity struggles with aggregate and relational queries that require traversing multiple entity connections. Can graph-oriented databases with deterministic queries solve this failure mode in enterprise domain applications?

Why do specialized models fail outside their domain?

Deep domain optimization creates sharp performance cliffs at domain boundaries. Specialized models generate plausible-sounding but ungrounded responses when queries fall outside their training scope, and often fail to signal their own ignorance.

Can organizing knowledge structures beat raw training data volume?

Does structuring domain knowledge into taxonomies during training enable models to learn more efficiently than simply increasing the amount of training data? This challenges assumptions about scaling knowledge injection.

Knowledge Graphs

2 notes

Can community detection enable RAG systems to answer global corpus questions?

Standard RAG struggles with corpus-wide questions that require understanding overall themes rather than retrieving specific passages. Can graph community detection overcome this limitation at scale?

How vulnerable is GraphRAG to tiny text manipulations?

GraphRAG converts raw text into knowledge graphs for question answering. This explores whether adversaries can degrade accuracy with minimal edits to source documents, and what makes the system susceptible.

Question Answering and Search

2 notes

How do logic units preserve procedural coherence better than chunks?

Can structured retrieval units with prerequisites, headers, bodies, and linkers maintain step-by-step coherence in how-to answers where fixed-size chunks fail? This matters because procedural questions require sequential logic and conditional branching that chunk-based RAG cannot support.

Can reference resolution work as a language modeling problem?

Can conversational, background, and on-screen references be resolved by converting them into text and using language models instead of specialized multimodal systems? This matters because it could enable efficient, on-device reference understanding.