Knowledge Systems

TL;DR for operators The current enterprise RAG conversation still has a charmingly stubborn misconception: if the model hallucinates, buy better embeddings, increase the context window, add an agent, and hope the PowerPoint becomes true. The two papers here point in a less theatrical direction. One paper, Non-negative Elastic Net Decoding for Information Retrieval, argues that dense retrieval has a structural weakness: it scores each candidate independently, so it can retrieve several similar items instead of the complementary set actually needed to answer the query.1 The other, Agentic Hybrid RAG for Evidence-Grounded Muon Collider Analysis, shows what happens when retrieval is treated as a full evidence workflow: sparse and dense retrieval are fused, queries are decomposed under constraints, evidence is deduplicated and budgeted, and answers are judged for coverage, hallucination, and abstention.2 ...

RAG and the Art of Not Dropping the Answer A RAG team usually starts with a familiar ambition: make the retrieved context smarter. The raw document feels too long. The search snippet feels too primitive. The page structure looks messy. A query-focused summary sounds more elegant. A proposition list sounds more machine-readable. A paraphrase from a strong LLM sounds, at least cosmetically, like an upgrade. So the team builds another representation layer between retrieval and generation, hoping the model will reward the extra sophistication. ...

Evidence is not context. That is the small, expensive misunderstanding behind many enterprise RAG systems. A user asks a question, the system retrieves semantically similar chunks, the model reads them, and the answer arrives with a tone that suggests the matter has been settled. Very reassuring. Sometimes even correct. But in the situations where RAG is supposed to be most useful — compliance reviews, financial analysis, legal memos, medical evidence summaries, internal strategy briefings — the problem is often not that the system has too little relevant material. The problem is that the relevant material disagrees, overlaps, dates badly, or supports several competing interpretations at once. ...

Most document AI still behaves like a very diligent librarian with one bad habit: it files things by subject even when the useful question is about function. A customer support message about a refund, a legal paragraph about a breach, and a sales call transcript about price resistance may share almost no vocabulary. Standard embeddings will usually respect that difference. Finance goes with finance, legal goes with legal, complaints go with complaints. Neat shelves. Terrible diagnosis. ...

AI systems are very good at saying things. This is both the miracle and the invoice. In enterprise settings, the sentence itself is rarely the final product. A compliance officer does not only want an answer about whether a clause violates policy. A credit analyst does not only want a summary of why a borrower looks risky. A procurement team does not only want a generated explanation of why Vendor A seems eligible. They want to know what the system used, which rule it applied, where the uncertainty sits, and whether the conclusion survives when the evidence changes. ...