NewsPoetiq 54% ARC-AGI-2 Without Training a ModelPoetiq·high signalXBlueskyLinkedInCopy linkOpen-source meta-system beats frontier labs through orchestration not model training. Validates harness-over-model approach. .57/task.SourceSource pagePoetiq↳ Follow the threadPolicy dependency / Stack layerHierarchical Ransomware Agents Escalate to Dynamic and Memory Analysis Only on Specialist DisagreementarXiv 2609.04820Stack layer / Threat patternSBOM Tools Cover Only the First Two of Four Supply-Chain Propagation StagesarXiv 2609.05380Stack layer / ContrastTNW finds the contradiction inside OpenAI's own data: a 59.2% GPU cut to Astra-class training moved compute rather than removing itThe Next WebStack layer / ContrastSynthesizing tool-call training data by executing the tools first lets a 9B model nearly match a 27B onearXiv 2609.05395Stack layer / ContrastAstra scored 13% on MazeBench with no tools while r/OpenAI's top post was it clearing all 48 levels of 'I'm Not A Robot'r/singularity (166 upvotes) and r/OpenAI (1,124 upvotes)Policy dependency / Stack layerCodex routes MCP elicitations and tool approvals through one decision API, with model policy overriding the legacy review flagGitHubStack layer / ContrastIris trains 35B and 397B search agents by reverse-constructing questions from hyperlink structure, and reports benchmarks both with and without context managementarXiv / HuggingFace Daily PapersStack layer / Contrastτ^τ-bench Makes Agent Construction the Task: Claude Opus 5 Under Claude Code Passes 23.9% Against an 82.2% Expert CeilingarXiv 2609.04611