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Public story · 2026-03-10 · source-backed
New training paradigm that teaches agents why actions succeed by contrasting successful against suboptimal alternatives. Treats action quality judgment as a first-class training objective rather than afterthought reflection. arXiv 2603.08706
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AgentHijack deployed patches on author-controlled GitHub Pages and a locally hosted CSDN clone against five GUI-agent backends. Across 600 online cases: 84.5% success at the VLM output stage, 47.0% at action parsing, 20.3% end-to-end with verifiable environmental consequences....
DNative-Twin records an agentic decision as a typed trajectory linking observed state, path followed and the authority behind the action, then re-executes the mechanism in isolation. I like it for the stated limit: graph structure localizes represented changes but can't determ...
ArcticSwarm separates evidence gathering from evidence integration: subagents publish to a shared board, but gated isolation lets selected search tasks keep their own prior so parallel agents stop converging on an early candidate before alternatives are tested. On full BrowseC...
arXiv 2608.09902 wraps all 22 boss encounters of Dark Souls: Remastered in a containerized Gymnasium-style benchmark where each step is a real action against the running game. On DSLE-5, an expert system and an evolutionary baseline beat only the tutorial boss (63% and 43% pea...
arXiv 2608.03609 formalizes agentic systems over relational data as Stateful Tool-Enabled Agentic Deployments and proves verification against First-Order CTL specs is undecidable. Under a finite-domain restriction it becomes PSPACE-complete, but only if renaming opaque identif...
When an agent consolidates an external observation into long-term memory, attach platform-controlled metadata recording the source's trust level, then gate tool execution by matching action risk against supporting-memory authority. Laundered memories hit a 1.000 attack success...
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