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Junkai Ji

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#artificial intelligence Preprint Sep 2026

Same Winners, Different Success Rates: Evaluating How LLM Agents Recover from Failures

It is proved that certifying exact population ties is impossible in finite time, and that the assignment of outcomes to checkpoints carries information beyond marginal outcome distributions, and proposed four diagnostic quantities that expose this failure mode with no additional data collection.

Dong Xu, Zhang-Fan Yang, Jian-Tao Wu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

ReplayLens: Auditing Agents'Use of Outcomes

When an agent reuses logged experience, a changed decision may reflect the recorded score, the action's name, or the record's position in storage. Standard memory evaluations do not reveal which relationship drives that change. We introduce ReplayLens, a black-box audit that changes one relationship in the stored histo...

Dong Xu, Zhang-Fan Yang, Jian-Tao Wu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

RoutePrism: Tracing Construction Order Effects in Agent Memory

Processing the same records in a different order can discard different evidence, yet endpoint accuracy alone cannot reveal what changed or whether it mattered. We introduce RoutePrism, a diagnostic protocol that builds memory twice from the same source pool in two processing orders, then traces which sources, compiled...

Dong Xu, Zhang-Fan Yang, Jian-Tao Wu et al. · 0 citations
Preprint Aug 2026

DegradeQuery: Counterfactual Tuple Pretraining for Context-Aware PROTAC Degradation Prediction

DegradeQuery, a context-aware prediction framework that converts label-missing records into a pretraining signal, is introduced and demonstrates that incompletely labeled PROTAC databases contain useful relational supervision and provide a practical route for learning context-aware degradation predictors from scarce ex...

Dong Xu, Zhang-Fan Yang, Jian-Tao Wu et al. · 0 citations
Jun 2025

READ: A Retrieval-Alignment Diffusion Framework for Structure-based Drug Design.

Structure-based drug design (SBDD) models are central to modern pharmaceutical research, enabling the rational exploration of protein-ligand interactions at atomic resolution. However, most existing approaches frame molecular generation as an isolated optimization or a one-to-one matching task, overlooking the shared b...

Dong Xu, Zhang-Fan Yang, Junchuang Cai et al. · 1 citation

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