Parquet Content-Defined Chunking

SemChunk-C: Semantic Segmentation for C Code

arXiv:2606. 23697v1 Announce Type: cross Abstract: Semantic segmentation of code written in a C-family language remains a challenging problem, due to the language's complex syntax, macro expansion, and irregular structural patterns.

UnWeaving the knots of GraphRAG -- turns out VectorRAG is almost enough

arXiv:2603. 29875v3 Announce Type: replace-cross Abstract: One of the key problems in Retrieval-augmented generation (RAG) systems is that chunk-based retrieval pipelines represent the source chunks as atomic objects, mixing the information contained within such a chunk into a single vector.

From Files to Chunks: Improving HF Storage Efficiency

Rethinking RAG in Long Videos: What to Retrieve and How to Use It?

arXiv:2606. 13141v1 Announce Type: new Abstract: Retrieval-augmented generation is moving beyond text into long, egocentric video, where systems must select query-relevant chunks across multiple modalities and temporal granularities.

QCFuse: Query-Aware Cache Fusion via Compressed View for Efficient RAG Serving

Retrieval-augmented generation (RAG) improves large language model (LLM) answer quality by grounding generation in external evidence, but processing retrieved contexts makes the prefill stage a dominant serving cost. RAG cache fusion reduces this cost by reusing precomputed key-value (KV) caches for retrieved chunks and selectively recomputing tokens under the current prompt.

QCFuse: Query-Aware Cache Fusion via Compressed View for Efficient RAG Serving

arXiv:2606. 05875v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves large language model (LLM) answer quality by grounding generation in external evidence, but processing retrieved contexts makes the prefill stage a dominant serving cost.

Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit

Enterprise Document Intelligence [Vol. 1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three approaches to deciding what fires, the audit _meta block, and a broker-corpus walkthrough The post Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit appeared first on Towards Data Science .

MLSkip: Data Skipping for ML Filters via Lightweight Metadata

arXiv:2606. 03946v1 Announce Type: cross Abstract: Database vendors recently released AI functions that can be used in filter predicates.

Verification Without Sufficiency: Per-Chunk Filtering Fails on Multi-Hop RAG, and Decomposition Repairs It

arXiv:2608. 00585v1 Announce Type: cross Abstract: Verification for retrieval-augmented generation usually scores each retrieved chunk and drops the ones that fail.

Optimizing Hypergraph-Based RAG: Toward Better Fact Extraction and Chunk Retrieval

arXiv:2607. 20506v1 Announce Type: new Abstract: GraphRAG enables deeper reasoning by structuring knowledge as graphs but struggles with n-ary facts.

Lightweight Chunk Selection for Mobile Retrieval-Augmented Generation

arXiv:2608. 03148v1 Announce Type: cross Abstract: RAG improves the factual grounding of LLM by incorporating external knowledge, but deploying RAG on mobile and edge devices remains challenging because retrieved context increases computation and memory.

Retrieve One Row from a Table, Not the Whole Table: Row-Level Chunks for RAG

Enterprise Document Intelligence [Vol. 1 #7sexies] - The unit of retrieval doesn’t have to be a page or a paragraph.