World Wire

Nace AI has released Drex for Search , which brings its Drex decision models to document search. Drex for Search is a retrieval layer that helps people and agents find evidence. Coding agents such as Claude Code, Codex, Cursor and OpenCode call it through CLI, SDKs, MCP or REST. In Nace’s evaluation, it scores 92.05% on BrowseComp-Plus with GPT-5, using 12.6 search calls per question. TL;DR Size: a hosted search system built to search over 1M+ files. It pairs with an answering model such as GPT-5, Gemini 3.5 Flash or Qwen 27B. Runs on: Nace’s managed cloud, a dedicated cloud, your own AWS or Azure account, or on-prem and air-gapped. Performance: ranked #1 or #2 on 3 of 4 public leaderboards, per Nace. Best: 77.5% R@1 on VisDocAgentBench, 10 points above Claude Opus 5 (67.5%). What did Nace AI release? Nace AI frames search as a series of decisions. Drex for Search applies its Drex models to those decisions. Drex scores given options with a...

Tsinghua University and CUHK researchers open-sourced AKV , a serving system for KV cache engineering in LLM agents. Instead of summarizing old history, agents drop selected KV cache entries and keep reusing the rest. Across 3 models and 4 agent benchmarks, average cost falls 27.7% . Average task score rises 1.7 points. The code is Apache-2.0 licensed and builds on SGLang . TL;DR What it is: Serving system plus policy library, no weights. Tested on GPT-OSS-120B (5.1B active), MiniMax-M2.7 (230B total, 10B active) and MiroThinker-1.7-Mini (30B total, 3B active). Runs on: NVIDIA GPUs via SGLang. Paper used 8 A800-80GB GPUs in BF16. Quick start serves GPT-OSS-120B on 2 GPUs (tensor parallel 2). Performance: 27.7% lower average cost and +1.7 points average score versus threshold summarization. Best: GPT-OSS-120B on SWE-bench Verified: cost per task 20.0 to 9.7 cents, score 40.0 to 43.2. Worst: MiniMax-M2.7 on BrowseComp-Plus scores 58.9, 3.4 points below Tex...

Anthropic has launched OSS Scanner , a free, opt-in vulnerability scanner for critical open-source projects. The Anthropic OSS Scanner runs the company’s strongest models, including Claude Mythos , against enrolled codebases. Reports go straight to maintainers without human review. That trade delivers faster findings, but shifts triage work onto project teams. 11 days after the October 8 launch, Anthropic reported 77 critical and 803 high model-rated findings . TL;DR Scale: 114 projects accepted from about 650 applications, as of October 19. Runs on: Anthropic’s infrastructure, free. Maintainers supply a Dockerfile; audits run in an isolated VM with no Internet. Performance: 85 of 97 audited critical and high findings (88%) met Anthropic’s disclosure bar. Best: Only 1 of 97 audited findings was a false positive. wolfSSL says 72 of 74 reports were valid. Worst: No human review before delivery. 11 of 97 audited findings duplicated known iss...