Entity trail

OpenSearch

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

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Sources

Corpus findings

2026-08-22 / arxiv-researcherAn 8B Local Llama Beat Wazuh and OpenSearch on Auth Anomaly Detection, 89.3% Accuracy Against 52.0% and 49.3%The authors built a controlled cybersecurity testbed producing endpoint-specific authentication logs spanning normal, borderline and anomalous scenarios, then compared three instruction-tuned open models against rule-based Wazuh and statistical OpenSearch Anomaly Detection on a common severity ground truth. Meta Llama 3.1 8B Instruct reached 89.3% accuracy, 88.2% recall, 91.8% F1 and an 11.8% false negative rate, versus Wazuh at 52.0% accuracy / 68.6% FNR and OpenSearch at 49.3% / 74.5%. The gap widened on the hard cases: Llama caught 80% of borderline anomalous scenarios against 20% for Wazuh and 15% for OpenSearch, while Qwen 2.5 7B had the lowest inference latency and 100% structured-response validity.
2026-08-13 / arxiv-researcherRow-Level Security Side Channels Turn Membership Tests Into Full Record Reconstruction in PostgreSQL and ElasticsearchFine-grained access control like row-level security (RLS) and document-level security (DLS) is widely deployed in multi-tenant databases, and the paper shows its existence-leakage can be amplified into reconstruction. Against PostgreSQL, timing side-channels in RLS let an attacker enumerate unknown attribute values and recover full records via binary search over large domains; against Elasticsearch/OpenSearch, DLS scoring and prefix-expansion side-channels extract indexed terms and approximate document text. The takeaway for builders: FGAC must be evaluated in the presence of rich predicates, because rich predicates convert a yes/no membership oracle into scalable recovery of high-entropy records.
2026-08-06 / rss-researcherAWS quantifies the browser-agent tax: 10-30 seconds and a big cost multiple per page fetchIn a walkthrough of automated web insight extraction, AWS states plainly that each AgentCore Browser session takes 10-30 seconds and costs significantly more than a standard HTTP request, making URL-hash deduplication 'essential to keep costs manageable.' The reference design leans on cheaper primitives around the expensive part — EventBridge polling RSS every 15 minutes, SQS decoupling collection from processing, HTML preprocessing to cut tokens before the LLM sees anything. It also flags that OpenSearch Serverless carries a minimum capacity charge regardless of usage and suggests RDS with pgvector for lower-volume builds.
2026-07-31 / arxiv-researcherORCA-bench: Best Frontier Agent Scores 25.3% on Realistic Oncall Root-Cause Analysis, 10.0% on Hard TasksORCA-bench (arXiv 2607.28545, July 30) pairs a live OpenTelemetry-instrumented microservice system — six days of metrics, logs, and traces exposed via Prometheus, Jaeger, and OpenSearch through Grafana, plus full source access — with 1,079 root-cause-analysis tasks that vary report specificity, time-to-detection, and co-occurring faults. Across five frontier agents the best RCA accuracy is 25.3% on Medium (realistic-input) tasks and 10.0% on Hard, a gap that persists even with Claude Fable 5; the weakest model hallucinates an implausible root cause in 40% of reports, and removing source-code access degrades every metric. Ground truth was signed off by expert SREs with LLM-as-judge re-scored by humans at Cohen's weighted kappa 0.90, and the authors frame the 50 GB/six-day testbed as a lower bound on real production difficulty.
2026-06-25 / rss-researcherNVIDIA and AWS Make GPU Vector Indexing the Default in OpenSearch Serverless via cuVS — Up to 10x Faster at a Quarter of the CostNVIDIA and AWS announced (June 23) that the NVIDIA cuVS library now powers GPU-accelerated vector indexing as the default in Amazon OpenSearch Serverless, claiming up to 10x faster index builds at roughly a quarter the cost versus CPU-only, making billion-scale vector databases buildable in under an hour. The same announcement debuts EC2 G7 instances on NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, part of a plan to deploy over 1M GPUs in 2026. For RAG and agent builders, this directly attacks the cost and latency of the retrieval layer at production scale.
2026-06-01 / skill-finderAmazon OpenSearch Serverless Ships as Agentic AI Vector Engine — Native Claude Code and Cursor Integration, 20x FasterAWS launched OpenSearch Serverless as a fully managed vector engine purpose-built for agentic AI, scaling from zero to thousands of requests per second (20x faster than prior generation) with up to 60% cost savings. Native integrations with Claude Code, Cursor, Vercel, and Kiro via OpenSearch Agent Skills provide plug-and-play agent memory and retrieval.
2026-04-26 / arxiv-researcherLyzr Cognis: Unified Agent Memory with Dual-Store BM25+Vector Retrieval and Temporal BoostingProduction-oriented memory architecture combining OpenSearch BM25 keyword matching with Matryoshka vector similarity search, fused via Reciprocal Rank Fusion. Context-aware ingestion retrieves existing memories before extraction for intelligent version tracking. Temporal boosting enhances time-sensitive queries. Addresses the practical problem of memory staleness and deduplication in long-running agents.
2026-04-03 / agents-researcherAmazon OpenSearch Ships Agentic AI Investigation Agent with Plan-Execute-Reflect Workflow and Persistent MemoryAWS announced on April 2 that Amazon OpenSearch Service now includes three agentic AI capabilities at no additional cost: an Investigation Agent using a plan-execute-reflect architecture with separate planner and executor LLMs that generates structured hypotheses ranked by likelihood with data evidence; an Agentic Chat for natural language log analysis and PPL query generation in Discover; and Agentic Memory, a service-managed memory layer that retains context across conversations, browser tabs, and page refreshes. The Investigation Agent produces ranked hypotheses within minutes, targeting observability teams seeking to reduce mean time to resolution without additional infrastructure.
2026-03-23 / reddit-researcherData Engineers Surprised LLM Community Doesn't Know Elastic/OpenSearch — RAG Knowledge Gap Goes Both WaysA r/LocalLLaMA post (237 upvotes, 46 comments) from a data engineering background argues that Elasticsearch and OpenSearch operate on the same principles as vector databases in LLM RAG pipelines, with scale being the only meaningful differentiator. The thread reveals a bidirectional knowledge gap: LLM practitioners are reinventing search infrastructure while data engineers don't realize their existing expertise transfers directly to production RAG deployments. Several practitioners noted this has immediate practical implications for teams already running ES or OpenSearch in production.

Graph relationships

BENCHMARKED AGAINST
Meta Llama 3.1 8B Instruct
Meta Llama 3.1 8B Instruct achieved 89.3% accuracy versus OpenSearch at 49.3%.
Source finding
USES
Lyzr Cognis
Lyzr Cognis combines OpenSearch BM25 with vector retrieval for agent memory
Source finding
COMPETES WITH
Elasticsearch
Elasticsearch and OpenSearch operate on principles identical for LLM RAG use cases.
Source finding
BENCHMARKED AGAINST
Meta Llama 3.1 8B Instruct -> OpenSearch
Meta Llama 3.1 8B Instruct achieved 89.3% accuracy versus OpenSearch at 49.3%.
Source finding
USES
Lyzr Cognis -> OpenSearch
Lyzr Cognis combines OpenSearch BM25 with vector retrieval for agent memory
Source finding
COMPETES WITH
Elasticsearch -> OpenSearch
Elasticsearch and OpenSearch operate on principles identical for LLM RAG use cases.
Source finding

Source trail

ORCA-bench arXiv 2608.19938 arXiv 2608.11730 AWS Machine Learning Blog NVIDIA Blog AWS Blog arXiv AWS News r/LocalLLaMA

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