The paper investigates how large language models (LLMs) tend to reuse a small set of character names in fiction, a phenomenon termed ‘name slop’. It evaluates eight prompt‑level strategies and introduces a new fine‑tuning‑free method called speculative naming, which uses a SlopTracker and a draft‑verify‑reject loop to reduce name concentration. Across 20 LLMs and 44,782 stories, speculative naming cuts the Slop Ratio by 95 % while preserving name naturalness and within‑story collision rates.
arXiv:2603. 18482v2 Announce Type: replace-cross Abstract: Standard decoding strategies for text generation, including top-$k$, nucleus sampling, and contrastive search, select tokens based on likelihood, restricting outputs to high-probability regions.
arXiv:2607. 04061v1 Announce Type: cross Abstract: Distinguishing Large Language Model (LLM) generated text from human writing is a critical and difficult challenge.
arXiv:2601. 02023v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) increasingly utilize massive context windows as working memory for autonomous tasks, their reliability fluctuates significantly depending on how information is distributed in real-world corpora.
The study investigates whether large language models (LLMs) rely on textual evidence or on their pre‑existing knowledge when annotating text. Using counterfactual prompts for 995 public figures, the authors find that LLMs often default to memory shortcuts—providing correct facts about a figure even when the text contains no supporting evidence. These shortcuts are amplified by evaluation design and incentives, but can be mitigated by name reminders or masking, though with trade‑offs in gender annotation.
arXiv:2608.05576v2 Announce Type: replace Abstract: When a large language model (LLM) writes Harry Potter fanfiction, it can produce fundamental elements of the Hogwarts universe, such as recognizabl...
The paper presents the Writerslogic systems for three PAN 2026 shared tasks—Reasoning Trajectory Detection, Voight‑Kampff Generative AI Detection, and Multi‑Author Writing Style Analysis—using a unified analytical framework that prioritizes feature robustness under distribution shift. The framework distinguishes domain‑anchored, domain‑portable, and domain‑invariant features, explaining why generator‑specific traits fail while vocabulary fingerprints, compression measures, and character n‑grams remain effective. The authors report first‑place source detection and third‑place safety classification on Reasoning Trajectory Detection, a top‑scoring ensemble for Voight‑Kampff, and a detailed design for Multi‑Author Writing Style Analysis that was not evaluated due to a platform mix‑up.
arXiv:2604.16656v2 Announce Type: replace Abstract: All languages are equal; when it comes to tokenization, some are more equal than others. Tokens are the hidden currency that dictates the cost and...
arXiv:2609.14677v1 Announce Type: new Abstract: Large language models (LLMs) have changed the way people engage with stories. Drawing on public chatbot logs, we can see that when users generate stori...
arXiv:2606. 00467v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-internalized priors interact with user-provided instructions.
The study investigates whether large language models (LLMs) can identify code they have generated, potentially leading to self‑favoring or collusive behavior. Experiments across 15 model‑benchmark pairs show that models can attribute authorship with balanced accuracy between 49% and 58%, but this ability largely stems from superficial cues such as solution length. Removing surface features like docstrings, comments, and type hints reduces attribution accuracy to chance, indicating that surface cues drive the effect.
When a tool-using agent is given the same task in a different language, does it still take the same steps? Multilingual evaluation rarely asks: it compares final answers and discards the actions.
arXiv:2508. 01656v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult.