arXiv:2606.00611v1 Announce Type: new Abstract: Long-horizon LLM agents produce safety evidence across long trajectories, where sparse, delayed, and compositional risk signals often escape local moderation. Existing turn-level or short-context detectors struggle to reliably retain and aggregate such evidence over extended horizons. We reframe long-horizon agent safety detection as trajectory-level evidence compression and propose Trajectory Risk-Aware Compression for Long-Horizon Agent Safety (TRACE). TRACE uses a Compressor-Reader design: the Compressor encodes the full trajectory into a comp
The increasing complexity and autonomy of LLM agents operating over long horizons necessitates new methods for ensuring their safety and aligning them with human values, which current local moderation techniques cannot address.
This development addresses a critical scaling challenge in AI safety, enabling more reliable and secure deployment of sophisticated AI agents across various applications.
Current methods for AI safety, primarily focused on turn-level interactions, will be supplemented or replaced by trajectory-level risk assessment, allowing for more robust and comprehensive safety protocols.
- · AI developers
- · Organizations deploying AI agents
- · AI safety researchers
- · Users of AI agents
- · Malicious actors exploiting AI agent vulnerabilities
- · Organizations relying solely on short-context AI moderation
- · Obsolete AI safety techniques
Improved safety and reliability of long-horizon AI agents.
Accelerated adoption and integration of AI agents into sensitive and critical workflows.
Potential for AI agents to supervise other AI agents for safety, creating a recursive safety layer.
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Read at arXiv cs.AI