arXiv:2608. 03138v1 Announce Type: cross Abstract: Generating a rigorous paper introduction with large language models (LLMs) remains challenging, since it requires coordinating background, gap identification, method and contribution within a coherent narrative.
arXiv:2608. 25826v1 Announce Type: cross Abstract: A recent line of synthetic-data work reconstructs the thinking behind existing text rather than rewriting the text itself, but it operates on short web passages, recovers only local thoughts, and leaves the structure of whole documents untouched.
A recent line of synthetic-data work reconstructs the thinking behind existing text rather than rewriting the text itself, but it operates on short web passages, recovers only local thoughts, and leav...
OARelatedWork is a large‑scale dataset for generating entire related‑work sections from open‑access papers, comprising 94,450 papers across multiple domains. It includes full texts of cited works and is designed so every source paper is available in full, enabling controlled evaluation of full‑text related‑work generation. The paper benchmarks various models, shows that large language models struggle with massive full‑text contexts, and introduces a statement‑level evaluation framework to better assess long‑form structured outputs.
arXiv:2508.14273v3 Announce Type: replace Abstract: As researchers increasingly adopt LLMs as writing assistants, generating high-quality research paper introductions remains both challenging and ess...
The paper presents InstruBPM, an instruction‑tuned open‑weight large language model that generates BPMN process models from natural language descriptions. By preparing paired text‑diagram data and applying parameter‑efficient fine‑tuning with quantization, the authors demonstrate that the tuned model outperforms both untuned open‑weight baselines and strong proprietary models in sequence, structural, and guideline‑conformance metrics. The approach is cost‑effective, privacy‑preserving, and supports on‑prem deployment, with publicly released models and scripts for reproducibility.
arXiv:2609.38510v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusi...
The paper "Limits of LLM Text Detectors in Education" argues that existing LLM‑generated text detectors assume a binary human/LLM distinction, which fails to capture realistic student‑AI collaboration. It introduces a contribution‑aware evaluation framework with eight student contribution levels and presents GEDE, a benchmark of over 900 human‑written and 12,500 generated essays across 886 tasks. Using GEDE, the authors evaluate four detection methods and find that most detectors perform poorly on intermediate contribution levels, especially LLM‑assisted revisions, raising concerns about false accusations.
DynEdit is a dynamic framework for editing long-sequence knowledge in large language models. It uses token-level entropy to guide non-uniform sliding windows, focusing on high-entropy, information-dense regions that are harder to edit. Experiments on three benchmarks and two backbone models show DynEdit outperforms baselines, achieving up to +14.0 BLEU points on challenging paraphrase queries and near-saturated semantic scores across diverse knowledge domains.
arXiv:2604. 01029v2 Announce Type: replace-cross Abstract: Multi-LLM revision pipelines, in which a second model reviews and improves a draft produced by a first, are widely assumed to derive their gains from genuine error correction.
The paper presents a benchmark that compares seven long‑form generation frameworks across three granularities—single chapter, multi‑chapter, and whole book—using an anchor‑based LLM‑as‑a‑judge protocol to evaluate outlines directly. Results show no single framework dominates across all settings; performance depends on how well a framework’s output form matches the target granularity, with SuperWriter excelling in length‑constrained single‑chapter mode but losing advantage in whole‑book mode. The study finds only moderate correlation between outline and writing quality, supporting the idea that these two stages should be evaluated separately.
GFlowPO introduces a probabilistic framework for optimizing language model prompts by treating prompt search as posterior inference over latent prompts, regularized by a meta-prompted reference-LM prior. The method first fine‑tunes a lightweight prompt‑LM using an off‑policy Generative Flow Network objective that reuses past prompt evaluations for sample‑efficient exploration. It then applies a training‑free Dynamic Memory Update to refine the meta‑prompt with diverse and top‑performing prompts, leading to consistent improvements over recent discrete prompt optimization baselines on few‑shot classification, instruction induction, and question‑answering tasks.
arXiv:2607. 22653v1 Announce Type: new Abstract: Large language models are increasingly used in recursive refinement workflows, where an initial draft is repeatedly revised by the same model.