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This paper designs TACT to revise student-generated actions, test the revisions through partner responses, and distill useful feedback into the student, which helps improve social goal attainment while reducing communication cost.
HVM-GraphRAG uses a holistic view to guide graph construction, thereby reducing noisy and conflicting graph updates and building reliable indices between concept-level graph nodes and supporting multimodal chunks and enabling the answering model to better integrate heterogeneous evidence.
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.
Extensive experiments on six real-world tabular benchmarks show that the proposed semantics-consistent tabular diffusion framework consistently improves distributional fidelity, semantic consistency, and downstream task utility over representative VAE, GAN, LLM, and diffusion-based baselines.
CORE introduces a decorrelated feature alignment module to directly align heterogeneous features into a unified representation space, which retains their semantic information, and formulates unified TAD as an in-context reconstruction problem, eliminating the need for labeled or synthesized anomalies.
A D-LLM hallucination detection framework that formulates the Denoising trajectories as Multivariate Time Series over learnable latent variables (DeMTS for short) that outperforms existing hallucination detection methods while maintaining strong robustness, efficiency, and cross-task transferability.
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