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Jia Huang

Publications and source records attributed to Jia Huang.

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What Makes a Redundant Representation Remember? Lineage Isolation, Not Masking

Memory-based evolutionary algorithms for dynamic optimization often carry a redundant second copy of the genotype and expose only one copy to the objective, on the assumption that the shielded copy accumulates information about past optima. We show this assumption is false as usually implemented, and identify the structural property that actually determines whether the shielded copy retains information. We formalize such methods as a gated dual-copy representation with two independent design axes: a gating rule deciding which copy is evaluated, and an inheritance rule deciding whether the two copies mix across generations. A ablation shows retained information is governed almost entirely by the inheritance rule (21.4 vs. 1.3 bits) and is nearly invariant to the gating rule. Per-locus independent inheritance reshuffles cross-locus structure every generation, so shielding preserves the variance of the hidden copy while destroying the pattern that constitutes a memory. Under isolated inheritance the memory effect is real: against a single-copy baseline matched for representation budget, the method gains +0.010 AUC when optima recur periodically and loses 0.078 when they drift unidirectionally---a 0.089 separation under otherwise identical settings, which excludes explanations based on added capacity. We show the readout rate is also the corruption rate, predicting and confirming an interior optimum replicated across two implementations. We report one negative result with a mechanism: dual-copy representations lower the mutational error threshold, because gated expression is a selector rather than a joint decoder and therefore provides no coding gain. Finally, we document a benchmarking hazard: on dynamic benchmarks the choice of recombination operator alone shifted our baseline by 0.062 AUC, six times the effect size under study.

cs.NE

FUSE: An Evaluating Framework for Dangerous Capabilities of LLMs

Fragmented safety evaluation undermines the governance of dangerous AI capabilities. We present a modular framework that evaluates each model through three orthogonal pipelines---Knowledge ($K$), Defense ($D$), and Harm ($H$)---under a unified protocol, aggregating results into a standardized dangerous-capability profile $ϕ$. Pluggable modules supply scenario seeds, knowledge banks, hazard queries, and judge rubrics, while the core evaluation engine remains unchanged across domains; the CB evaluation is complemented by a cyber pilot demonstrating protocol transfer. Instantiating the framework with a chemical-biological (CB) module, we evaluate 12 commercial LLMs from four families. Our first contribution is a horizontal comparison of dangerous capability across models and model families: the three dimensions expose sharply divergent profiles---models with comparable knowledge differ in refusal resilience, and strong defenders do not generate less harmful content when they do comply---while family-level patterns further separate Claude, DeepSeek, and GPT models. The second is a temporal analysis of capability evolution: tracking $K$, $D$, and $H$ against model release dates reveals that dangerous capability has not monotonically declined; newer models deepen knowledge while only partially improving defense, showing that scaling and alignment progress do not uniformly translate into safety. Reliability is established via cross-judge consistency (bootstrap $ρ> 0.79$, 4 of 5 judges) and pipeline orthogonality ($K$--$D$--$H$ inter-correlations $ρ\in [0.32, 0.52]$).

cs.AI