Large Language Models (LLMs), such as OpenAI’s o1 and DeepSeek’s R1, excel at advanced reasoning tasks like math and coding via Reinforcement Learning with Verifiable Rewards (RLVR), but still struggle with puzzles solvable by humans without domain knowledge. We introduce ENIGMATA, the first comprehensive suite tailored for improving LLMs with puzzle reasoning skills. It includes 36 tasks across 7 categories, each with: 1) a generator that produces unlimited examples with controllable difficulty, and 2) a rule-based verifier for automatic evaluation. This generator-verifier design supports scalable, multi-task RL training, fine-grained analysis, and seamless RLVR integration. We further propose ENIGMATA-Eval, a rigorous benchmark, and develop optimized multi-task RLVR strategies. Our trained model, Qwen2.5-32B-ENIGMATA, consistently surpasses o3-mini-high and o1 on the puzzle reasoning benchmarks like ENIGMATA-Eval, ARC-AGI (32.8%), and ARC-AGI 2 (0.6%). It also generalizes well to out-of-domain puzzle benchmarks and mathematical reasoning, with little multi-tasking trade-off. When trained on larger models like Seed1.5-Thinking (20B activated parameters and 200B total parameters), puzzle data from ENIGMATA further boosts SoTA performance on advanced math and STEM reasoning tasks such as AIME (2024-2025), BeyondAIME and GPQA (Diamond), showing nice generalization benefits of ENIGMATA. This work offers a unified, controllable framework for advancing logical reasoning in LLMs. Project page: https://seed-enigmata.github.io.

Inference scaling empowers LLMs with unprecedented reasoning ability, with reinforcement learning as the core technique to elicit complex reasoning. However, key technical details of state-of-the-art reasoning LLMs are concealed (such as in OpenAI o1 blog and DeepSeek R1 technical report), thus the community still struggles to reproduce their RL training results. We propose the **D**ecoupled Clip and **D**ynamic s**A**mpling **P**olicy **O**ptimization (**DAPO**) algorithm, and fully open-source a state-of-the-art large-scale RL system that achieves 50 points on AIME 2024 using Qwen2.5-32B base model. Unlike previous works that withhold training details, we introduce four key techniques of our algorithm that make large-scale LLM RL a success. In addition, we open-source our training code, which is built on the verl framework, along with a carefully curated and processed dataset. These components of our open-source system enhance reproducibility and support future research in large-scale LLM RL.

Fine-grained geographic named entity recognition involves the identification of precise locations within geographic text. Existing methods face challenges in effectively addressing this task, namely: (1) the complexity and long-dependency nature of various fine-grained locations, which pose difficulties for recognition, and (2) the presence of numerous out-of-vocabulary fine-grained locations that are not present in the training data . In this paper, we propose a Multi-scale Siamese Reconstruction Network (MSRN) to tackle these challenges. To address the first challenge, MSRN employs a multi-scale convolutional network to aggregate interactions among coarse-grained and fine-grained span features. To tackle the second challenge, MSRN introduces a siamese reconstruction network that corrupts the representation and then reconstructs it, preventing the model from relying on rote memorization of biased locations in the training data . This approach enhances the model’s understanding of entity context. We conduct experiments on four real-world fine-grained geographic named entity recognition datasets, which contain a variety of complex long fine-grained locations and geographic entities not present in the training data. These experiments illustrate the challenges commonly encountered in real-world engineering applications and deliver a solid foundation for evaluating the robustness and applicability of our proposed method. The experimental results demonstrate the superiority of MSRN over previous models, achieving significant performance improvements ranging from 2.1 to 3.0. Further analysis confirms the effectiveness of MSRN in recognizing complex long-dependency entities, with a performance improvement of 3.0, as well as out-of-vocabulary locations, with a performance improvement of 6.6 compared to models with similar parameter sizes.

The emergence of pre-trained models marks a significant juncture for the multilingual generation, offering unprecedented capabilities to comprehend and produce text across multiple languages. These models display commendable efficiency in high-resource languages. However, their performance notably falters in low-resource languages due to the extensive linguistic diversity encountered. Moreover, the existing works lack thorough analysis impairs the discovery of effective multilingual strategies, further complicating the advancement of current multilingual generation systems. This paper aims to appraise the efficacy of multilingual generation tasks, with a focus on summarization, through three resource availability scenarios: high-resource, low-resource, and zero-shot. We classify multilingual generation methodologies into three foundational categories based on their underlying modeling principles: Fine-tuning, Parameter-isolation, and Constraint-based approaches. Following this classification, we conduct a comprehensive comparative study of these methodologies across different resource contexts using two datasets that span six languages. This analysis provides insights into the unique advantages and limitations of each method. In addition, we introduce an innovative yet simple automatic metric LANGM designed to mitigate the prevalent problem of spurious correlations associated with language mixing. LANGM accurately measures the degree of code-mixing at the language level. Finally, we highlight several challenges and suggest potential avenues for future inquiry, aiming to spur further advancements within the field of multilingual text generation.

In document-level event extraction (DEE) tasks, a document typically contains many event records with multiple event roles.Therefore, accurately extracting all event records is a big challenge since the number of event records is not given.Previous works present the entitybased directed acyclic graph (EDAG) generation methods to autoregressively generate event roles, which requires a given generation order.Meanwhile, parallel methods are proposed to generate all event roles simultaneously, but suffer from the inadequate training which manifests zero accuracies on some event roles.In this paper, we propose an Iteratively Parallel Generation method with the Pre-Filling strategy (IPGPF).Event roles in an event record are generated in parallel to avoid order selection, and the event records are iteratively generated to utilize historical results.Experiments on two public datasets show our IPGPF improves 11.7 F1 than previous parallel models and up to 5.1 F1 than auto-regressive models under the control variable settings.Moreover, our enhanced IPGPF outperforms other entityenhanced models and achieves new state-ofthe-art performance 1 .* Work was done when Guanhua was an intern at ByteDance AI Lab.† Corresponding author. 1 Our code is available at https://github.com/ CarlanLark/IPGPF [S6] …, Jinggong Group increased its holdings of the company's stock by 182,038 shares through the secondary market on Dec 15, 2011,… [S7] …, the shares held by Jinggong Group in the company increased from 90,880,020 shares to 91,062,058 shares, … [S9] on Dec 16, 2011, Jinggong Group reduced its holdings of ... 35,000 shares, with an average price of 19.88.[S14] As of the date of this announcement, Jinggong Group holds 91,027,058 shares of the company, … EquityOverweight EquityHolder Jinggong Group

Given a natural language statement, how to verify its veracity against a large-scale textual knowledge source like Wikipedia? Most existing neural models make predictions without giving clues about which part of a false claim goes wrong. In this paper, we propose LOREN, an approach for interpretable fact verification. We decompose the verification of the whole claim at phrase-level, where the veracity of the phrases serves as explanations and can be aggregated into the final verdict according to logical rules. The key insight of LOREN is to represent claim phrase veracity as three-valued latent variables, which are regularized by aggregation logical rules. The final claim verification is based on all latent variables. Thus, LOREN enjoys the additional benefit of interpretability --- it is easy to explain how it reaches certain results with claim phrase veracity. Experiments on a public fact verification benchmark show that LOREN is competitive against previous approaches while enjoying the merit of faithful and accurate interpretability. The resources of LOREN are available at: https://github.com/jiangjiechen/LOREN.

Relevant articles recommendation plays an important role in online news platforms. Directly displaying recalled articles by a search engine lacks a deep understanding of the article contents. Generating clickable queries, on the other hand, summarizes an article in various aspects, which can be henceforth utilized to better connect relevant articles. Most existing approaches for generating article queries, however, do not consider the diversity of queries or whether they are appealing enough, which are essential for boosting user experience and platform drainage. To this end, we propose a Knowledge-Enhanced Diversified QuerY Generator (KEDY), which leverages an external knowledge graph (KG) as guidance. We diversify the query generation with the information of semantic neighbors of the entities in articles. We further constrain the diversification process with entity popularity knowledge to build appealing queries that users may be more interested in. The information within KG is propagated towards more popular entities with popularity-guided graph attention. We collect a news-query dataset from the search logs of a real-world search engine. Extensive experiments demonstrate our proposed KEDY can generate more diversified and insightful related queries than several strong baselines.

Automatically constructing taxonomy finds many applications in e-commerce and web search. One critical challenge is as data and business scope grow in real applications, new concepts are emerging and needed to be added to the existing taxonomy. Previous approaches focus on the taxonomy expansion, i.e. finding an appropriate hypernym concept from the taxonomy for a new query concept. In this paper, we formulate a new task, “taxonomy completion”, by discovering both the hypernym and hyponym concepts for a query. We propose Triplet Matching Network (TMN), to find the appropriate pairs for a given query concept. TMN consists of one primal scorer and multiple auxiliary scorers. These auxiliary scorers capture various fine-grained signals (e.g., query to hypernym or query to hyponym semantics), and the primal scorer makes a holistic prediction on triplet based on the internal feature representations of all auxiliary scorers. Also, an innovative channel-wise gating mechanism that retains task-specific information in concept representations is introduced to further boost model performance. Experiments on four real-world large-scale datasets show that TMN achieves the best performance on both taxonomy completion task and the previous taxonomy expansion task, outperforming existing methods.

Neural abstractive summarization systems have gained significant progress in recent years.However, abstractive summarization often produce inconsisitent statements or false facts.How to automatically generate highly abstract yet factually correct summaries?In this paper, we proposed an efficient weaksupervised adversarial data augmentation approach to form the factual consistency dataset.Based on the artificial dataset, we train an evaluation model that can not only make accurate and robust factual consistency discrimination but is also capable of making interpretable factual errors tracing by backpropagated gradient distribution on token embeddings.Experiments and analysis conduct on public annotated summarization and factual consistency datasets demonstrate our approach effective and reasonable.Our codes can be found at https://github.com/ parZival27/GrAdualCC

Sponsored search auction is a crucial component of modern search engines. It requires a set of candidate bidwords that advertisers can place bids on. Existing methods generate bidwords from search queries or advertisement content. However, they suffer from the data noise in (query, bidword) and (advertisement, bidword) pairs. In this paper, we propose a triangular bidword generation model (TRIDENT), which takes the high-quality data of paired (query, advertisement) as a supervision signal to indirectly guide the bidword generation process. Our proposed model is simple yet effective: by using bidword as the bridge between search query and advertisement, the generation of search query, advertisement and bidword can be jointly learned in the triangular training framework. This alleviates the problem that the training data of bidword may be noisy. Experimental results, including automatic and human evaluations, show that our proposed TRIDENT can generate relevant and diverse bidwords for both search queries and advertisements. Our evaluation on online real data validates the effectiveness of the TRIDENT's generated bidwords for product search.

We demonstrate a web-based software system, GraspSnooker, which is able to automatically generate Chinese text commentaries for snooker game videos. It consists of a video analyzer, a strategy predictor and a commentary generator. As far as we know, it is the first attempt on snooker commentary generation, which might be helpful for snooker learners to understand the game.

Text attribute transfer is modifying certain linguistic attributes (e.g.sentiment, style, authorship, etc.) of a sentence and transforming them from one type to another.In this paper, we aim to analyze and interpret what is changed during the transfer process.We start from the observation that in many existing models and datasets, certain words within a sentence play important roles in determining the sentence attribute class.These words are referred to as the Pivot Words.Based on these pivot words, we propose a lexical analysis framework, the Pivot Analysis, to quantitatively analyze the effects of these words in text attribute classification and transfer.We apply this framework to existing datasets and models, and show that: (1) the pivot words are strong features for the classification of sentence attributes; (2) to change the attribute of a sentence, many datasets only requires to change certain pivot words; (3) consequently, many transfer models only perform the lexical-level modification, while leaving higher-level sentence structures unchanged.Our work provides an in-depth understanding of linguistic attribute transfer and further identifies the future requirements and challenges of this task 1 .

Neural networks with tree-based sentence encoders have shown better results on many downstream tasks.Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior.To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders.Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated.This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling.Further analysis show that tree modeling gives better results when crucial words are closer to the final representation.Additional experiments give more clues on how to design an effective tree-based encoder.Our code is opensource and available at https://github.com/ExplorerFreda/TreeEnc.