ResearchAgentic Critical Training — Why Actions Succeed Not Just What To DoarXiv·high signalXBlueskyLinkedInCopy linkNew training paradigm contrasting successful against suboptimal actions. Quality judgment as first-class training objective.SourceSource pagearXiv↳ Follow the threadStack layer / ContrastCapScope stops prompt injection by giving each coding subagent typed capabilities stored outside its contextarXivPolicy dependency / Stack layerCROSS-CATEGORY: Three Independent Agent-Action Gates Shipped in 48 Hours, All Judging the Command Against Stated IntentProduct Hunt, github.com/AGGIB/Stroq and rewarelabs.com (three independent sources; the 72% figure is Reware's own)Stack layer / ContrastShow-Harness gets frontier VLMs controlling robots zero-shot through discrete semantic action unitsarXivStack layer / ContrastBenchShield raises reward-hacking detection in agent benchmarks from 23-94% to 77-100% full-chain recall, using a lifecycle model of reward eventsarXivStack layer / ContrastEcdysis: fix the harness only for failure patterns that recur across tasks, not for each single failurearXiv 2609.11677Stack layer / ContrastDetecting benchmark leakage with code-specific features beat perplexity-only methods on eight code benchmarksarXiv 2609.09865Stack layer / ContrastFrontier models now learn arbitrary ciphers from prompting alone, and encrypted harmful content slips past commercial classifiers as gibberisharXiv 2609.09553Stack layer / ContrastVikingRAG Matches State-of-the-Art Structured-Document RAG Using 5.1-32.5% of the TokensarXiv