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This paper identifies that naive skill accumulation in LLM agents can cause performance regressions, as skills beneficial for some tasks hurt others. The authors propose Assay, a framework that measures per-skill causal contributions and applies per-task masking, achieving state-of-the-art results on AppWorld and τ-bench without weight updates.
This paper investigates the developmental conditions under which a minimal predictive neural system (a 192-dimensional GRU) can distinguish self-caused changes from world-caused changes, identifying four necessary conditions for agency and introducing a metric called agency gain.
MemAudit is a post-hoc auditing framework for memory-augmented LLM agents that identifies poisoned memories by combining counterfactual influence scores and structural anomaly detection, reducing attack success rates from over 70% to 0% in realistic scenarios.