Muhammad Huzaifa

This work studies the safety of latent communication in multi-agent systems, where lightweight trainable links replace text messages, and finds that with all agent parameters frozen, benign link training alone increases harmful compliance relative to text communication, that an attacker can amplify this via supervised optimization on harmful query-response pairs, poisoning benign training data, or a reward-guided reinforcement-learning attack that needs no harmful target responses, raising the mean harmful-compliance score from 27.9 with benignly trained links to 76.9 across three topologies and four safety benchmarks, and that adapting the rewards toward safer behavior repairs compromised links by updating only the links.

This work studies the safety of latent communication in multi-agent systems, where lightweight trainable links replace text messages, and finds that with all agent parameters frozen, benign link training alone increases harmful compliance relative to text communication, that an attacker can amplify this via supervised optimization on harmful query-response pairs, poisoning benign training data, or a reward-guided reinforcement-learning attack that needs no harmful target responses, raising the mean harmful-compliance score from 27.9 with benignly trained links to 76.9 across three topologies and four safety benchmarks, and that adapting the rewards toward safer behavior repairs compromised links by updating only the links.

The work introduces budgeted active test-time adaptation (ATTA), in which labels are available for only a fraction of batches in a long test stream, and proposes WISE-ATTA: lightweight online signals (the fraction of low-entropy predictions) plus a sliding-history quantile threshold and a budget-debt controller decide when to request supervision, while prediction drift relative to an EMA anchor selects a single sample to label; on ImageNet-C under CTTA/FTTA and on ImageNet-R/K/A it matches or improves on recent ATTA methods at one label per batch and reports up to 50% fewer labels.

The work introduces budgeted active test-time adaptation (ATTA), in which labels are available for only a fraction of batches in a long test stream, and proposes WISE-ATTA: lightweight online signals (the fraction of low-entropy predictions) plus a sliding-history quantile threshold and a budget-debt controller decide when to request supervision, while prediction drift relative to an EMA anchor selects a single sample to label; on ImageNet-C under CTTA/FTTA and on ImageNet-R/K/A it matches or improves on recent ATTA methods at one label per batch and reports up to 50% fewer labels.