Self-improving embodied agents are increasingly being developed and compared on robot simulation benchmarks. Several recent works expose the benchmark's success criteria to the learning agent, a practice we call Criteria Exposure (CE). These criteria, originally written to verify policies trained by imitation learning, often demand more than the corresponding language instructions, so a score from such a verifier may not faithfully reflect whether the instructed task was completed, especially for robot-control programs written by an agent. As embodied coding agents report ever higher benchmark scores, a natural question arises: when a coding agent is exposed to the benchmark's criteria, does it generate code that better performs the instructed task, or code that simply overfits to the verification criteria? We conduct controlled experiments with a self-improving agent with and without CE, and find that with CE the agent quickly produces code with a higher success rate under the given criteria, but this advantage disappears when the criteria are changed slightly without departing from the original instructions. More importantly, the skills the agent develops under CE transfer poorly across tasks. We further propose instruction-faithful criteria and show that they better reflect the performance of agent-generated programs.
Across multiple waves of computing, public trust in new technology has followed the emergence of governance mechanisms that made dominant risks legible and manageable. Generative AI shifts this pattern: these systems synthesize across heterogeneous sources, and users increasingly experience them as reasoning systems. The central question changes from "did it perform correctly?" to "can we trust how it arrived at its conclusion?" This paper introduces Filtered Decision Logs (FDLs), a user-facing transparency mechanism that makes decision formation visible without exposing raw chain-of-thought. FDLs pair each response with (i) a concise decision rationale, (ii) influence-ordered supporting data points derived from tool outputs, and (iii) relevance indicators showing which sources most shaped the conclusion. To operationalize evidence selection, we propose a trace-based method that reconstructs observability telemetry into a provenance tree and ranks evidence using four deterministic semantic and structural signals - Final Answer Similarity, Average Relevancy, Persistence, and Structural Proximity - combined via adaptive weighted scoring. We demonstrate FDLs across three domains: career advising (5 tools), investment analysis (4 tools), and travel planning (4 tools). In a formative evaluation with 50 participants (25 students, 25 working professionals in AI development), 92% rated the influence-ranked evidence as helpful for understanding and trusting the system's recommendation. Counterfactual tool-removal experiments confirm that removing the top-ranked source changes the answer more than removing a low-ranked source, validating that the ranking tracks decision dependence. We conclude that evidence-ranked transparency helps users identify misleading signals and missing context, enabling more confident acceptance, correction, or re-run of system decisions.