Large language models persistently produce literal translations of idiomatic expressions, but the source of this failure remains unclear. We introduce **IdiomDIT**, a diagnostic framework that jointly evaluates idiom **D**etection, idiom **I**nterpretation, and idiomatic **T**ranslation, attributing each error to a specific source. Across six language pairs and five models spanning 4B to 70B, IdiomDIT shows that literal translation error (LTE) can persist even after successful detection and interpretation, a know-but-error pattern we term Literal Translation Bias (LTB). This pattern is direction-specific: LTB dominates some directions, while others show greater knowledge deficits, likely reflecting lower idiom exposure in pretraining. We then use linear probing to test whether LTB is already reflected in the model’s pre-generation hidden state, recovering per-language literal translation directions with high accuracy, and ablating this direction reduces LTE. However, once we ensure no idiom’s data leaks across training and test, and subtract out each idiom’s mean hidden state before probing, accuracy collapses, confirming the probe was largely detecting which idiom it is looking at rather than a genuine bias signal. A matched random direction also reduces LTE just as much as the identified one. We release these controls as a reusable protocol for testing whether such hidden-state attribution claims in machine translation are genuine or confounded. The code is publicly available at https://github.com/NLP2CT/IdiomDIT.

We introduce LLM×MapReduce-V3, a hierarchically modular agent system designed for long-form survey generation. Building on the prior work, LLM×MapReduce-V2, this version incorporates a multi-agent architecture where individual functional components, such as skeleton initialization, digest construction, and skeleton refinement, are implemented as independent model-context-protocol (MCP) servers. These atomic servers can be aggregated into higher-level servers, creating a hierarchically structured system. A high-level planner agent dynamically orchestrates the workflow by selecting appropriate modules based on their MCP tool descriptions and the execution history. This modular decomposition facilitates human-in-the-loop intervention, affording users greater control and customization over the research process. Through a multi-turn interaction, the system precisely captures the intended research perspectives to generate a comprehensive skeleton, which is then developed into an in-depth survey. Human evaluations demonstrate that our system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning. Demo, video and code are available at https://github.com/thunlp/LLMxMapReduce.

We propose a training-free framework that enables large language models (LLMs) to effectively process long texts, using a divide-and-conquer strategy for comprehensive document understanding.The proposed LLM×MapReduce framework splits the entire document into several chunks for LLMs to read and then aggregates the intermediate outputs to produce the final response. The main challenge for divide-and-conquer long text processing frameworks lies in the risk of losing essential long-range information due to document splitting, which can lead the model to produce incomplete or incorrect answers based on the segmented texts.Disrupted long-range information can be classified into two categories: inter-chunk dependency and inter-chunk conflict.We design a structured information protocol to better cope with inter-chunk dependency and an in-context confidence calibration mechanism to resolve inter-chunk conflicts. Experiments demonstrate that LLM×MapReduce outperforms representative open-source and commercial long-context LLMs and is compatible with several models.Our framework can also function as a data synthesis engine, capable of generating high-quality long-alignment data using only short-context LLMs.