Long-form research reports generated by large language models drift, contradict themselves, and lose provenance: the same metric appears with different values, and rumor is quoted as confidently as an audited filing. We present a two-tier agentic system that separates a maintained, point-in-time knowledge library from...
Xing Zhang, Ya Cui, Guanghui Wang et al.· 0 citations
Compound LLM systems often solve a coordination problem by adding a higher-level LLM. The resulting meta-agent reads workers'outputs, writes the final answer, allocates later calls, and decides when to stop. It is expressive, but it also concentrates three control decisions in an opaque, order-sensitive model call. We...
Xing Zhang, Guang-Hui Wang, Ya Cui et al.· 0 citations
It is shown the metric itself can be the evolving object: the loop searches compositions of small typed drawback detectors under a full evolutionary lifecycle, selecting for agreement with a ten-item anchored reference set and regularizing by consensus over unlabeled outputs.
Xing Zhang, Guanghui Wang, Ya Cui et al.· arXiv.org· 0 citations
As large language models are increasingly deployed in real-world systems, safety failures can still lead to harmful outputs and dangerous misuse. We argue that the essence of safety is adversarial: many failures arise not from natural inputs alone, but from strategic attempts to evade model policies and safeguards. How...
Ting Ma, Xiufeng Huang, Benlei Cui et al.· 0 citations
EvalCEGAR is a pool of small Python operators that each flag a candidate for one named defect, or abstain, and vote, and borrows counterexample-guided abstraction refinement from program verification to score agents against a reliable automatic metric.
Xing Zhang, Ya Cui, Guanghui Wang et al.· 0 citations
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