A diffusion-based discovery framework that learns transition operators that transform existing artifacts into new candidates, and defines a diffusion mutation kernel, a learned transition distribution that preserves the structural regularities of feasible designs while moving between regions of the design space.
Dinithi Jayasuriya, A. Saravanan, Nilesh Ahuja et al.· 0 citations
Across 16 matched Baseline and Harness runs spanning AppWorld, Terminal-Bench, and $\tau^2$-Bench, the gate rejected 383 replay-decided proposals, showing that locally beneficial self-modifications can introduce collateral regressions often enough to materially affect gate decisions, providing direct empirical motivati...
Sina Tayebati, Divake Kumar, Nastaran Darabi et al.· 0 citations
This work introduces CURA (Certified Runtime Alarms for Computer-Use Agents), an external monitor that reads only harness-visible telemetry, with no model internals, extra LLM calls, or prompt changes, and turns the running trajectory into a sequential test with certified false-alarm control.
Divake Kumar, Sina Tayebati, Devashri Naik et al.· 1 citation
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