Graph retrieval-augmented generation places retrieved subgraphs into the model's context window at query time, paying a recurring token cost and exposing source data on every call. We study an alternative: compiling a knowledge graph offline into a bank of LoRA adapters, one per entity, that serve as a parametric knowl...
Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov et al.· 0 citations
Autoregressive models are trained to predict a system's behavior one step at a time, and recursive generation allows the learned dynamics to unfold over long horizons. To what extent can such dynamics learned from local observations recover broader organization of an underlying system that was only partially observed d...
Yi-Lun Liu, Yi Zhang, Gan-Yu Wu et al.· 0 citations
The results establish that parametric knowledge graph memory is feasible for storing knowledge, and identify selecting and composing the right adapters by a mechanism other than semantic similarity as the central open problem—motivating a learned, query-conditioned composition mechanism.
Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov et al.· 0 citations
A controlled study isolates the source of MADA-RL's gains: the counterfactual advantage produces the highest critic improvement rate of any model evaluated, indicating that trained critics learn to correct generator errors rather than to imitate them.
Martino M. L. Pulici, Cuong Xuan Chu, Evgeny Kharlamov et al.· arXiv.org· 0 citations
According to Mendelian principles of controlled inheritance, Mendel G\"odel Machine (MGM) is introduced, which includes two new types of self-modification that better utilizes evidences accumulated and facilitates a faster and better convergence over single-trajectory baselines.
Changzhi Liu, Yilun Liu, Sikuan Yan et al.· 0 citations
MetaSkill-Evolve is introduced, a two-timescale framework that makes agentic skill improvement recursive and outperforms no-skill, static-skill, and single-level evolution baselines on three agentic benchmarks, improving held-out test accuracy over the raw backbone by +23.54, +16.09, and +1.92 points respectively.
Zefeng Wang, Minxi Yan, Jinhe Bi et al.· 4 citations
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