arXiv subjects

Shuguang Jiao

Publications and source records attributed to Shuguang Jiao.

3 recordsLinked to original sources

arXiv2026

CuraWeb: Joint Optimization of Quality, Redundancy, and Diversity for Web-Scale Pretraining Data

Open-web corpora curated via highly selective filters, such as FineWeb-Edu and DCLM, constitute the core of LLM pretraining data and have significantly advanced LLM performance. However, these pipelines typically rely on singular optimization objectives, which inevitably narrows distributional diversity and marginalizes long-tail knowledge, thereby restricting data coverage and underutilizing the vast potential of the open web. To address this limitation, we propose a novel curation paradigm that shifts from linear pruning to the joint optimization of quality, redundancy, and diversity. This framework synergizes dual-track cleaning (rule-based and model-driven) with hybrid deduplication (n-gram and semantic), while employing a multi-objective sampler to balance informational quality with distributional breadth. Applying this framework to Common Crawl, we construct CuraWeb, a 2T-token English corpus. Unlike existing resources, CuraWeb establishes an industrial-grade standard for data curation by recovering a more holistic data distribution with enhanced diversity and minimal redundancy, achieving broader coverage of long-tail knowledge across diverse domains. Experimental evaluations at the 3B scale demonstrate that CuraWeb significantly outperforms state-of-the-art baselines, yielding an average performance gain of 1.8\% across a wide range of benchmarks, particularly in knowledge-intensive and reasoning tasks.

cs.AI↗

arXiv2026

Doctor-RAG: A Failure-Aware Repair Framework for Agentic Retrieval-Augmented Generation

Agentic Retrieval-Augmented Generation interleaves retrieval and reasoning for multi-hop QA and complex knowledge tasks. As reasoning trajectories lengthen, failures become more frequent, while existing methods often either stop at diagnosis or rely on coarse replanning and rerun-style recovery, incurring high computational cost. We propose DoctorRAG (DR-RAG), a diagnose-and-repair framework that corrects failures via explicit error localization and prefix reuse. DR-RAG operates in two stages: (i) trajectory-level failure diagnosis, where a distilled diagnosis model jointly assesses evidence sufficiency, classifies the failure type, and localizes the earliest failure point; and (ii) tool-conditioned local repair that intervenes only at the diagnosed point while reusing conditionally valid prefixes and retrieved evidence. By separating error attribution from correction, DR-RAG avoids blind reruns in a post-hoc repair setting and enables targeted, efficient correction of known failed trajectories. Experiments on three multi-hop QA benchmarks across multiple agentic RAG baselines and backbone models show substantial improvements in answer accuracy.

cs.IR↗

arXiv2026

PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) has become a powerful framework for enhancing large language models in knowledge-intensive and reasoning tasks. However, as reasoning chains deepen or search trees expand, RAG systems often face two persistent failures: evidence forgetting, where retrieved knowledge is not effectively used, and inefficiency, caused by uncontrolled query expansions and redundant retrieval. These issues reveal a critical gap between retrieval and evidence utilization in current RAG architectures. We propose PruneRAG, a confidence-guided query decomposition framework that builds a structured query decomposition tree to perform stable and efficient reasoning. PruneRAG introduces three key mechanisms: adaptive node expansion that regulates tree width and depth, confidence-guided decisions that accept reliable answers and prune uncertain branches, and fine-grained retrieval that extracts entity-level anchors to improve retrieval precision. Together, these components preserve salient evidence throughout multi-hop reasoning while significantly reducing retrieval overhead. To better analyze evidence misuse, we define the Evidence Forgetting Rate as a metric to quantify cases where golden evidence is retrieved but not correctly used. Extensive experiments across various multi-hop QA benchmarks show that PruneRAG achieves superior accuracy and efficiency over state-of-the-art baselines.

cs.IR↗