#RAG

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Jun 10Wednesday

r/LocalLLaMAJun 10, 00:23

ICML paper on predictable hallucination gate and ntkMirror open-weight implementation

An ICML 2026 paper presents an ISR=1 answer-abstain gate for evidence-grounded QA, and ntkMirror implements it for local open-weight models with multiple evidence orderings, reporting 0.0–0.7% hallucination at about 24% abstention in the held-out audit.

Why it matters: HKR-H/K/R all pass: an ICML paper with an open implementation, a concrete ISR=1 gate, and measured abstention-vs-hallucination tradeoff. Scope stays within evidence QA/RAG reliability, so it sits below must-write level.

Jun 7Sunday

AI HOT (Curated Pool)Jun 7, 14:25

Harness-1: A 20B Stateful Retrieval Subagent Trained with Reinforcement Learning

UIUC and Chroma released Harness-1, a 20B-parameter retrieval subagent trained with reinforcement learning inside a stateful search harness, reporting 0.730 average curated recall across 8 benchmarks, 11.4 percentage points above the next-best open-source subagent and behind only Opus-4.6.

Why it matters: HKR-H/K/R all pass: Harness-1 has a clear RL retrieval-agent mechanism and benchmark numbers. It stays in 78–84 because this is a subagent research/open-source release, not a major lab model launch.

Jun 6Saturday

AI HOT (Curated Pool)Jun 6, 04:30

Google launches Agentic RAG framework for Gemini Enterprise Agent Platform

Google Research and Google Cloud introduced the Cross-Corpus Retrieval framework as Agentic RAG for Gemini Enterprise Agent Platform, using a multi-agent workflow to plan, rewrite, route, and iteratively search multiple data sources, with up to 34% higher accuracy than standard RAG on factual datasets.

Why it matters: HKR-H/K/R all pass: Google names a Cross-Corpus Retrieval mechanism and a +34% factual accuracy lift. The Gemini Enterprise Agent Platform tie-in adds cloud-vendor promo risk, so this stays below the 78–84 research/framework band.

May 19Tuesday

Xinzhiyuan · WeChatMay 19, 12:36

CUHK and Zhejiang University Question Whether AI Agent Memory Is Just a Memo

CUHK and Zhejiang University researchers argue that mainstream Agent memory is retrieval-based memo storage, not true memory, citing an Ω(k²) case requirement for compositional tasks and a PoisonedRAG result where 5 adversarial texts reached a 90% attack success rate.

Why it matters: HKR-H/K/R all pass: the hook is concrete, the summary gives Ω(k²) and 90% attack success, and the issue matters to agent-memory and RAG-security builders. Strong research signal, not a same-day model-release event.

Take

The long-term-agent “memory” story takes a clean hit here: most deployed systems are retrieval notebooks, not learned experience. The hard evidence is not the hippocampus metaphor; it is the Ω(k²) case requirement for compositional tasks and PoisonedRAG reaching 90% attack success with 5 adversarial texts. Bigger context windows do not fix combinatorial coverage. Persistent memory also turns one successful injection into a standing compromise.

I’m still skeptical of the proposed consolidation path into weights. LoRA, MEMIT, test-time training, and self-distillation are plausible parts, but the production questions are ugly: which memories get written, who approves them, and how do you roll them back? Cursor and Claude Code do not need a larger vector database as much as they need an auditable learning pipeline.

May 15Friday

r/LocalLLaMAMay 15, 01:33

MOOSE-Star (ICML 2026): 7B Model and 108K-Paper Dataset for Scientific Hypothesis Discovery

MiroMind researchers released the MOOSE-Star collection with three 7B models and TOMATO-Star, a dataset of 108,717 NCBI papers. MS-IR-7B reaches 54.37% inspiration-retrieval accuracy, uses DeepSeek-R1-Distill-Qwen-7B as its base, runs at about 14GB fp16, and supports llama.cpp, vLLM, and SGLang.

Why it matters: HKR-H/K/R all pass via the local 7B research-agent hook and concrete dataset metrics. Single Reddit source and limited lab gravity keep it below the must-write band.

Take

MOOSE-Star’s useful claim is not “AI discovers science.” It puts a small, runnable baseline under a noisy category. The concrete hooks are good: three 7B models, TOMATO-Star with 108,717 NCBI papers, MS-IR-7B based on DeepSeek-R1-Distill-Qwen-7B, 54.37% inspiration-retrieval accuracy, about 14GB fp16, with llama.cpp, vLLM, and SGLang support.

I like the 7B choice. Scientific hypothesis agents get muddy fast when the demo rides a closed frontier model; nobody can separate retrieval, reasoning, memorization, and prompt craft. A 14GB fp16 model lets labs run ablations instead of applauding a slide. The Reddit body is blocked by 403, so training details and benchmark construction are not visible here. I would be careful with the 54.37% number until we see negatives, time splits, and leakage controls.

Apr 27Monday

Hacker News front pageApr 27, 09:54

TurboQuant: A First-Principles Walkthrough

TurboQuant walkthrough explains compressing AI vectors to 2–4 bits per coordinate. It uses random rotation to map high-dimensional coordinates to a fixed distribution, then reuses one codebook with no scale overhead, training, or calibration.

Why it matters: HKR-H/K/R all pass: the hook is concrete, the mechanisms are new, and the cost angle is relevant. It stays below 78 because this is a technical walkthrough, not a model or product release.

Take

TurboQuant’s useful claim is not the headline compression ratio. It attacks the operational mess around quantization: random rotation pushes high-dimensional coordinates toward a fixed distribution, then one universal codebook handles the vectors. The page explicitly claims 2–4 bits per coordinate with no scale-factor memory, no training, and no calibration. For KV cache work, that last part matters because calibration drift across layers, heads, and models is where clean papers become messy deployments.

I don’t fully buy “without losing accuracy” from this walkthrough alone. The article gives the mechanism, browser demos, and links to TurboQuant-MSE, PolarQuant, and QJL; it does not show a hard Llama/Qwen long-context benchmark table in the provided body. Against KIVI-style KV compression, the pitch is fewer moving parts, not magic. If the inner-product bias correction fails under real attention workloads, 2-bit quickly becomes a 4-bit product story.

Apr 18Saturday

QbitAI · WeChatApr 18, 19:51

RAG retrieves the right docs but still answers wrong? Saarland University team diagnoses why | ACL 2026

A Saarland University-led team introduced Disco-RAG, adding a 3-step “reading” layer between retrieval and generation, and says the paper was accepted as an ACL 2026 main-conference long paper. The post says it uses RST-based argument trees, cross-passage relation graphs, and outline generation with zero training; it reports gains on Loong, ASQA, and SciNews, but does not fully disclose the exact scores. The key claim is that many RAG failures come from reading and discourse understanding, not retrieval recall.

Why it matters: This is a solid research release with HKR-H, HKR-K, and HKR-R: a strong practical hook, a concrete mechanism, and a pain point RAG builders know well. I keep it at 80, not higher, because the post does not fully disclose benchmark numbers and external replication is still missing