A practical guide to interpreting nuclear LLM benchmark results, focusing on why deployment decisions should require evidence traceability, hallucination checks, and auditability beyond test score gains.
How legal-structure chunking in an EU AI Act RAG corpus affects retrieval quality, citation traceability, and auditability.
Public research suggests rising LLM scores reflect tools, memory, and planning systems, not a simple march toward AGI.
How to separate session, RAG, and model parameter paths in generative AI to design confidentiality, deletion, and audit controls.
Agent bottlenecks are not just reasoning. Separate organizational knowledge into memory layers for reliability and control.
AI search can speed up answers, but citations, data, and technical details still require direct source verification.
ReContext highlights that long-context value depends on reusing evidence already in the prompt, not just larger windows.
How ContextNest frames context governance with a verifiable knowledge vault layer for auditable AI agents beyond retrieval quality.
Explores using sparse autoencoders to disentangle dense RAG embeddings for interpretable retrieval analysis and steering.
A look at using LLMs for single- and multi-truth data fusion, with implications for RAG, memory, and data quality.
KARLA explores retrieving facts during token generation, reframing RAG tradeoffs around noise, latency, cost, and attribution.
How RAG mixes past and current facts, causing stale-fact errors, and why temporal validity matters in retrieval.
RAGBench and LegalBench show why enterprise LLM evaluation must separate retrieval quality from domain-specific judgment.
In enterprise document RAG, retrieval granularity often matters more than reasoning. Why structure-aware search helps.
DistractionIF shows how RAG systems misread instruction-like noise in documents and why pipeline design matters.
Examines security risks in RAG when prompt injection and database poisoning combine across retrieval and indexing.
Industrial LLM hallucinations framed as a reproducibility problem, comparing five prompt strategies to reduce output variance across repeated runs.
RAG-Driver grounds driving explanations with retrieved expert demonstrations via RA-ICL, but evaluation still relies on BLEU, METEOR, and CIDEr.
Long-term memory can boost performance yet cause negative forward transfer as tasks evolve. Design deletion, summarization, and replacement policies.
A 3.5B-token combustion knowledgebase and CombustionQA benchmark unify knowledge injection and evaluation into one pipeline.
For long policy reports, context and upload limits push chunked workflows that separate evidence retrieval from drafting, improving traceability and quality.
A guide-driven dialogue study loop: paste fragments, then run understanding checks, structured explanations, and tailored quizzes.
Compare RAG vs parameter updates for long-term memory, then outline validation and gating needed for recursive self-improvement loops.
Learn how reranking after top-K retrieval improves ranking quality in RAG, and how to evaluate gains against added latency and cost.
