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Research2026-08-09 · source-backed
arXiv 2608.00765 compresses retrieved docs into query-conditioned visual representations, sidestepping the trade-off where hard compression is query-aware but weak and soft compression is strong but needs costly offline encoding. Beats both baselines across varying retrieval depths. The mechanism transfers without the visual encoder: a query-aware dynamic resolution pass that renders highly relevant passages at higher granularity while aggressively downsampling peripheral documents. Relevance-proportional budget allocation is something any RAG pipeline can adopt today.
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The paper names a failure mode called query dominance, where a high-capacity encoder lets the query dominate the latent state and renders retrieved evidence functionally irrelevant (arXiv 2608.16776). GRIP imposes capacity asymmetry: full-dimensional decoder access to the quer...
A July 2 evaluation tested semantic chunking against simple approaches on long structured academic theses using RAGAs, and the sophisticated method didn't win. Performance varied more with document formatting, preprocessing, and query type than with chunking strategy. The auth...
RAGAS-style evaluation checks correctness against a frozen snapshot, which means routine document updates and corrections can silently break production without moving a dashboard. This ASE 2026 paper defines 11 mutation operators perturbing at both the pre-chunk index level an...
READ (arXiv 2608.06305, submitted August 6) took a 780-page government financial report and asked 51 verified questions. Top-k embedding retrieval answered 15.7% of them correctly. The same agent loop, given three deterministic tools over MCP instead of a vector index, answere...
The study extracted 130 clean atomic state transitions from 707 real issues in SWE-bench Lite and Verified. Plain RAG scored 0.57-0.59 answer accuracy; an LLM reranker didn't help and added latency, about 18 seconds against 2.1. A (subject, relation, object) supersession memor...
arXiv 2608.13050 fed the same CTI report, same instructions and same LLM to Microsoft GraphRAG versus vector-similarity RAG, then rotated every IP, domain and file hash. In an APT28 case study the GraphRAG plan kept 100% of its detections firing, the naive plan kept 29%. Repli...
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