MUDDLE is a benchmark designed to disentangle the effects of document length and topical distractors on document question‑answering systems. It contains 270 human‑annotated questions, each tested in five conditions: the source alone, the source with two or four hard negatives (topically similar), and the source with two or four random distractors matched in length and provenance. Experiments with GPT‑5‑mini show that hard negatives reduce accuracy more than length‑matched random distractors, indicating that topical similarity is a more significant source of error than length alone.
By Jason Luo, Saibilila Abudukelimu, Judy Song, Andrew Feng, Shivank Garg, Vasu Sharma, Kevin Zhu
Long-context evaluations often test whether a model can recover distant evidence, but recoverability does not guarantee behavioral influence. We test the prediction that a distant discourse anchor can...
arXiv:2605. 10828v2 Announce Type: replace Abstract: As large language models are increasingly deployed in retrieval-augmented generation and agentic systems that accumulate extensive context, understanding how distracting information affects long-context performance becomes critical.
By Muhan Gao, Zih-Ching Chen, Kuan-Hao Huang
The paper introduces MWE‑ECL, a bilingual diagnostic framework that tests whether distant discourse anchors can override local lexical priors in multi‑word expression interpretation. It evaluates models on a 0‑128K context grid, finding that while retrieval of anchors is near perfect, the ability to change locally preferred readings varies, especially when the model’s default conflicts with the anchor. The study shows that explicit recoverability does not always translate into behavioral influence, with gaps differing across models and languages.
By Wei He, Aline Villavicencio, Rodrigo Wilkens, Zhenyun Deng
arXiv:2606. 11198v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) systems inject external knowledge to improve LLM outputs, yet the format of injected content -- distinct from its semantic relevance -- can independently distort the model's attention distribution.
By Yuqi Zhang, Di Zhang
arXiv:2607. 21692v1 Announce Type: new Abstract: Sparse attention reduces the cost of long contexts by allowing each query to read only selected parts of the input.
By Jim Allchin