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Bowen Jin

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Artificial Intelligence cond-mat.mtrl-sci Computation and Language Information Retrieval Machine Learning physics.app-ph

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preprint2026arXiv

F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking

Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate set. Large Language Models (LLMs) broaden this into a generative process: given a candidate pool, an LLM can generate a subset and order it within a single autoregressive pass. However, this flexibility introduces a new optimization challenge: the model must search a combinatorial output space while receiving utility feedback only after the full ranked list is generated. Because this feedback is defined over the completed sequence, it cannot distinguish whether a poor result arises from failing to generate a relevant subset or from failing to rank that subset correctly. This credit assignment gap makes end-to-end optimization unstable and sample-inefficient. Existing systems often address this by separating candidate generation from ranking. However, such decoupling remains misaligned with downstream utility because ranking is limited by the candidate set it receives. To bridge this gap, we propose a unified framework that performs both within a single autoregressive rollout and optimizes them end-to-end via factorized group-relative policy optimization (F-GRPO). Our framework factorizes the policy into candidate generation and ranking while sharing a single LLM backbone, and jointly trains them with an order-invariant coverage reward and a position-aware utility reward. To address the resulting phase-specific credit assignment problem, we use separate group-relative advantages for generation and ranking within a two-phase sequence-level objective. Across sequential recommendation and multi-hop question answering benchmarks, F-GRPO improves top-ranked performance over GRPO and decoupled baselines, outperforms supervised alternatives, and remains competitive with strong zero-shot rerankers, with no architectural changes at inference time.

preprint2026arXiv

Generating Leakage-Free Benchmarks for Robust RAG Evaluation

Retrieval-augmented generation (RAG) is widely used to augment large language models (LLMs) with external knowledge. However, many benchmark datasets, designed to test RAG performance, comprise many questions that can already be answered from an LLM's parametric memory. This leads to unreliable evaluation. We refer to this phenomenon as knowledge leakage: cases where RAG tasks are solvable without retrieval. This issue worsens over time due to benchmark aging. As benchmarks are reused for training, their contents are increasingly absorbed into model parameters, making them less effective for evaluating retrieval. We introduce SeedRG, a semi-synthetic benchmark generation pipeline that mitigates knowledge leakage and addresses the issue of benchmark aging. Starting from a seed benchmark dataset, SeedRG extracts a reasoning graph from question-context pairs to capture their underlying reasoning structure, and then generates new examples via type-constrained entity replacement. This process produces structurally similar but novel instances that are unlikely to exist in the model's parametric knowledge, while preserving the original reasoning patterns. To ensure quality, we incorporate two verification steps: (1) a reasoning-graph consistency check to maintain task difficulty, and (2) a knowledge-leakage filter to exclude instances answerable without retrieval.

preprint2026arXiv

Improving Scientific Document Retrieval with Academic Concept Index

Adapting general-domain retrievers to scientific domains is challenging due to the scarcity of large-scale domain-specific relevance annotations and the substantial mismatch in vocabulary and information needs. Recent approaches address these issues through two independent directions that leverage large language models (LLMs): (1) generating synthetic queries for fine-tuning, and (2) generating auxiliary contexts to support relevance matching. However, both directions overlook the diverse academic concepts embedded within scientific documents, often producing redundant or conceptually narrow queries and contexts. To address this limitation, we introduce an academic concept index, which extracts key concepts from papers and organizes them guided by an academic taxonomy. This structured index serves as a foundation for improving both directions. First, we enhance the synthetic query generation with concept coverage-based generation (CCQGen), which adaptively conditions LLMs on uncovered concepts to generate complementary queries with broader concept coverage. Second, we strengthen the context augmentation with concept-focused auxiliary contexts (CCExpand), which leverages a set of document snippets that serve as concise responses to the concept-aware CCQGen queries. Extensive experiments show that incorporating the academic concept index into both query generation and context augmentation leads to higher-quality queries, better conceptual alignment, and improved retrieval performance.

preprint2020arXiv

Amorphous Mo-Ta oxide nanotubes for long-term stable Mo oxide based supercapacitors

With a large-scale usage of portable electric appliances, a high demand for increasingly high density energy storage devices has emerged. MoO3 has, in principle, a large potential as negative electrode material in supercapacitive devices, due to high charge densities that can be obtained from its reversible redox reactions. Nevertheless, the extremely poor electrochemical stability of MoO3 in aqueous electrolytes prevents a practical use in high capacitance devices. In this work, we describe how to overcome this severe stability issue by forming amorphous molybdenum oxide/tantalum oxide nanotubes by anodic oxidation of a Mo-Ta alloy. The presence of a critical amount of Ta-oxide (> 20 at-%) prevents the electrochemical decay of the MoO3 phase and thus yields an extremely high stability. Due to the protection provided by tantalum oxide, no capacitance losses are measureable after 10000 charg-ing/discharging cycles.

preprint2016arXiv

Self-ordered Mo-oxide Nanotube Arrays as Precursor for Aligned MoOx/MoS2 Core-Shell Nanotubular Structures with a High Density of Reactive Sites

In the present work we demonstrate the self-organized formation of anodic Mo-oxide nanotube arrays grown on a Mo sheet under suitable electrochemical conditions in glycerol/NH4F electrolytes. The resulting amorphous tubes can be crystallized by annealing to MoO2 or MoO3. The tube walls then can be further sulfurized fully or partially to Mo-sulfide to form well-ordered arrays of vertically aligned MoOx/MoS2 nanotubes. Under optimized conditions, defined MoS2 sheets form on the oxide walls in a layer by layer low angle zig-zag arrangement that provide a high density of reactive stacking faults. These core-shell nanotube arrays, consisting of tubes with a conductive suboxide core and a functional high defect density MoS2 coating, are highly promising for applications such as electrocatalysis (hydrogen evolution) or ion insertion devices.

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