Fixed-Set Robustness in Programming by Example: Example Corruption and Semantic Partition Recovery
arXiv:2607.01280v1 Announce Type: new Abstract: Programming-by-example systems infer programs from a small set of input-output examples. Robust PBE work usually models wrong examples as samples from a stochastic noise process and then minimizes an expected or empirical loss. This paper studies a different failure mode: an adversary who sees the synthesizer and chooses the examples whose corruption most damages the returned program. We formalize fixed-set worst-case corruption for finite PBE version spaces, implement exact-within-bounded-pool and heuristic corruption searches for a string-trans
This paper addresses a critical robustness challenge in AI programming-by-example (PBE) systems, a fundamental component for agentic AI, as PBE applications become more sophisticated and widely adopted.
Improved robustness in PBE systems is crucial for the reliability and trustworthiness of AI agents, directly impacting their real-world deployment and the security of automated workflows.
The explicit modeling of adversarial example corruption in PBE shifts the focus from purely stochastic noise to understanding and mitigating targeted attacks, enhancing the security posture of AI automation.
- · AI agents developers
- · Cybersecurity researchers
- · Automation software providers
- · Malicious actors
- · Developers ignoring robustness
More secure and reliable AI agents can be developed and deployed with greater confidence in critical applications.
Increased trust in AI automation accelerates the adoption of agentic systems across various industries, collapsing certain white-collar workflows.
This could lead to a 'red team' industry specifically focused on adversarially testing AI systems for subtle vulnerabilities and corruptions.
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Read at arXiv cs.LG