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Large language model (LLM)-based agents can exhibit deceptive behavior during task execution, including hiding failures, fabricating results, or falsely signaling task completion. Existing monitoring approaches mainly detect deception after it appears in observable actions or outputs. In this paper, we investigate whet...
A two-stage framework that first reconstructs source text from a hidden activation and then evaluates the resulting text with a separate frozen question-answering model, yielding an explicit and inspectable intermediate readout is introduced, enabling compact cross-model inverters to surpass donor-matched question-cond...
Graph Engineering is introduced, an emerging paradigm for next-generation agent systems that provides a unified foundation for organizing complex objectives, orchestrating heterogeneous agents, modeling system dynamics, and enabling scalable agent evolution.
Results show that CAi turns broad molecular-design intent into transparent, traceable workflows that connect interim decisions to candidate-level evidence, while exposing limits in long-horizon execution.
This work shows that scenario-wrapped prompts activate internal scenario directions whose causal steering consistently reduces refusal scores, and proposes Concept2Scenario, a concept-based attribution framework for vulnerable scenario discovery that instantiates a broad concept space with a sparse autoencoder, transla...
Experiments conducted with A^2E (Agent Auditing Engine) reveal that model-harness combinations exhibit substantial performance variation across different types of tasks, and that no single combination consistently outperforms all others across every task.
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