OpenRaiser/Intern-Atlas huggingface dataset:
FactGuard: Leveraging Multi-Agent Systems to Generate Answerable and Unanswerable Questions for Enhanced Long-Context LLM Extraction
Venue: arXiv cs.CL 2025
Year: 2025

Extractive reading comprehension systems are designed to locate the correct answer to a question within a given text. However, a persistent challenge lies in ensuring these models maintain high accuracy in answering questions while reliably recognizing unanswerable queries. Despite significant advances in large language models (LLMs) for reading comprehension, this issue remains critical, particularly as the length of supported contexts continues to expand. To address this challenge, we propose an innovative data augmentation methodology grounded in a multi-agent collaborative framework. Unlike traditional methods, such as the costly human annotation process required for datasets like SQuAD 2.0, our method autonomously generates evidence-based question-answer pairs and systematically constructs unanswerable questions. Using this methodology, we developed the FactGuard-Bench dataset, which comprises 25,220 examples of both answerable and unanswerable question scenarios, with context lengths ranging from 8K to 128K. Experimental evaluations conducted on seven popular LLMs reveal that even the most advanced models achieve only 61.79% overall accuracy. Furthermore, we emphasize the importance of a model's ability to reason about unanswerable questions to avoid generating plausible but incorrect answers. By implementing efficient data selection and generation within the multi-agent collaborative framework, our method significantly reduces the traditionally high costs associated with manual annotation and provides valuable insights for the training and optimization of LLMs.
Stratified Expert Cloning for Retention-Aware Recommendation at Scale
Venue: arXiv cs.IR 2025
Year: 2025

User retention is critical in large-scale recommender systems, significantly influencing online platforms' long-term success. Existing methods typically focus on short-term engagement, neglecting the evolving dynamics of user behaviors over time. Reinforcement learning (RL) methods, though promising for optimizing long-term rewards, face challenges like delayed credit assignment and sample inefficiency. We introduce Stratified Expert Cloning (SEC), an imitation learning framework that leverages abundant interaction data from high-retention users to learn robust policies. SEC incorporates: 1) multi-level expert stratification to model diverse retention behaviors; 2) adaptive expert selection to dynamically match users with appropriate policies based on their state and retention history; and 3) action entropy regularization to enhance recommendation diversity and policy generalization. Extensive offline evaluations and online A/B tests on major video platforms (Kuaishou and Kuaishou Lite) with hundreds of millions of users validate SEC's effectiveness. Results show substantial improvements, achieving cumulative lifts of 0.098 percent and 0.122 percent in active days on the two platforms respectively, each translating into over 200,000 additional daily active users.
Lattice: Learning to Efficiently Compress the Memory
Venue: arXiv cs.LG 2025
Year: 2025

Attention mechanisms have revolutionized sequence learning but suffer from quadratic computational complexity. This paper introduces \model, a novel recurrent neural network (RNN) mechanism that leverages the inherent low-rank structure of K-V matrices to efficiently compress the cache into a fixed number of memory slots, achieving sub-quadratic complexity. We formulate this compression as an online optimization problem and derive a dynamic memory update rule based on a single gradient descent step. The resulting recurrence features a state- and input-dependent gating mechanism, offering an interpretable memory update process. The core innovation is the orthogonal update: each memory slot is updated exclusively with information orthogonal to its current state, hence incorporating only novel, non-redundant data to minimize interference with previously stored information. We derive an efficient computation for this orthogonal update rule and further approximate it with chunk-wise parallelization to ensure training scalability. Empirically, Lattice outperforms strong baselines on language modeling and associative recall tasks across diverse context lengths and model sizes, achieving superior memory efficiency with significantly reduced memory sizes.
Experimental Evaluation of Precise Placement of the Hollow Object with Asymmetric Pivot Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

In this paper, we present asymmetric pivot manipulation for picking up rigid hollow objects to achieve a hole grasp. The pivot motion, executed by a position-controlled robotic arm, enables the gripper to effectively grasp hollow objects placed horizontally such that one gripper finger is positioned inside the object's hole, while the other contacts its outer surface along the length. Hole grasp is widely employed by humans to manipulate hollow objects, facilitating precise placement and enabling efficient subsequent operations, such as tightly packing objects into trays or accurately inserting them into narrow machine slots in manufacturing processes. Asymmetric pivoting for hole grasping is applicable to hollow objects of various sizes and hole shapes, including bottles, cups, and ducts. We investigate the variable parameters that satisfy the force balance conditions for successful grasping configurations. Our method can be implemented using a commercially available parallel-jaw gripper installed directly on a robot arm without modification. Experimental verification confirmed that hole grasp can be achieved using our proposed asymmetric pivot manipulation for various hollow objects, demonstrating a high success rate. Two use cases, namely aligning and feeding hollow cylindrical objects, were experimentally demonstrated on the testbed to clearly showcase the advantages of the hole grasp approach.
xMTF: A Formula-Free Model for Reinforcement-Learning-Based Multi-Task Fusion in Recommender Systems
Venue: arXiv cs.IR 2025
Year: 2025

Recommender systems need to optimize various types of user feedback, e.g., clicks, likes, and shares. A typical recommender system handling multiple types of feedback has two components: a multi-task learning (MTL) module, predicting feedback such as click-through rate and like rate; and a multi-task fusion (MTF) module, integrating these predictions into a single score for item ranking. MTF is essential for ensuring user satisfaction, as it directly influences recommendation outcomes. Recently, reinforcement learning (RL) has been applied to MTF tasks to improve long-term user satisfaction. However, existing RL-based MTF methods are formula-based methods, which only adjust limited coefficients within pre-defined formulas. The pre-defined formulas restrict the RL search space and become a bottleneck for MTF. To overcome this, we propose a formula-free MTF framework. We demonstrate that any suitable fusion function can be expressed as a composition of single-variable monotonic functions, as per the Sprecher Representation Theorem. Leveraging this, we introduce a novel learnable monotonic fusion cell (MFC) to replace pre-defined formulas. We call this new MFC-based model eXtreme MTF (xMTF). Furthermore, we employ a two-stage hybrid (TSH) learning strategy to train xMTF effectively. By expanding the MTF search space, xMTF outperforms existing methods in extensive offline and online experiments.
Contrastive Decoupled Representation Learning and Regularization for Speech-Preserving Facial Expression Manipulation
Venue: arXiv cs.CV 2025
Year: 2025

Speech-preserving facial expression manipulation (SPFEM) aims to modify a talking head to display a specific reference emotion while preserving the mouth animation of source spoken contents. Thus, emotion and content information existing in reference and source inputs can provide direct and accurate supervision signals for SPFEM models. However, the intrinsic intertwining of these elements during the talking process poses challenges to their effectiveness as supervisory signals. In this work, we propose to learn content and emotion priors as guidance augmented with contrastive learning to learn decoupled content and emotion representation via an innovative Contrastive Decoupled Representation Learning (CDRL) algorithm. Specifically, a Contrastive Content Representation Learning (CCRL) module is designed to learn audio feature, which primarily contains content information, as content priors to guide learning content representation from the source input. Meanwhile, a Contrastive Emotion Representation Learning (CERL) module is proposed to make use of a pre-trained visual-language model to learn emotion prior, which is then used to guide learning emotion representation from the reference input. We further introduce emotion-aware and emotion-augmented contrastive learning to train CCRL and CERL modules, respectively, ensuring learning emotion-independent content representation and content-independent emotion representation. During SPFEM model training, the decoupled content and emotion representations are used to supervise the generation process, ensuring more accurate emotion manipulation together with audio-lip synchronization. Extensive experiments and evaluations on various benchmarks show the effectiveness of the proposed algorithm.
On the Suitability of Reinforcement Fine-Tuning to Visual Tasks
Venue: arXiv cs.CV 2025
Year: 2025

Reinforcement Fine-Tuning (RFT) is proved to be greatly valuable for enhancing the reasoning ability of LLMs. Researchers have been starting to apply RFT to MLLMs, hoping it will also enhance the capabilities of visual understanding. However, these works are at a very early stage and have not examined how suitable RFT actually is for visual tasks. In this work, we endeavor to understand the suitabilities and limitations of RFT for visual tasks, through experimental analysis and observations. We start by quantitative comparisons on various tasks, which shows RFT is generally better than SFT on visual tasks. %especially when the number of training samples are limited. To check whether such advantages are brought up by the reasoning process, we design a new reward that encourages the model to ``think'' more, whose results show more thinking can be beneficial for complicated tasks but harmful for simple tasks. We hope this study can provide more insight for the rapid advancements on this topic.
Towards Smarter Hiring: Are Zero-Shot and Few-Shot Pre-trained LLMs Ready for HR Spoken Interview Transcript Analysis?
Venue: arXiv cs.CL 2025
Year: 2025

This research paper presents a comprehensive analysis of the performance of prominent pre-trained large language models (LLMs), including GPT-4 Turbo, GPT-3.5 Turbo, text-davinci-003, text-babbage-001, text-curie-001, text-ada-001, llama-2-7b-chat, llama-2-13b-chat, and llama-2-70b-chat, in comparison to expert human evaluators in providing scores, identifying errors, and offering feedback and improvement suggestions to candidates during mock HR (Human Resources) interviews. We introduce a dataset called HURIT (Human Resource Interview Transcripts), which comprises 3,890 HR interview transcripts sourced from real-world HR interview scenarios. Our findings reveal that pre-trained LLMs, particularly GPT-4 Turbo and GPT-3.5 Turbo, exhibit commendable performance and are capable of producing evaluations comparable to those of expert human evaluators. Although these LLMs demonstrate proficiency in providing scores comparable to human experts in terms of human evaluation metrics, they frequently fail to identify errors and offer specific actionable advice for candidate performance improvement in HR interviews. Our research suggests that the current state-of-the-art pre-trained LLMs are not fully conducive for automatic deployment in an HR interview assessment. Instead, our findings advocate for a human-in-the-loop approach, to incorporate manual checks for inconsistencies and provisions for improving feedback quality as a more suitable strategy.
Separator Injection Attack: Uncovering Dialogue Biases in Large Language Models Caused by Role Separators
Venue: arXiv cs.CL 2025
Year: 2025

Conversational large language models (LLMs) have gained widespread attention due to their instruction-following capabilities. To ensure conversational LLMs follow instructions, role separators are employed to distinguish between different participants in a conversation. However, incorporating role separators introduces potential vulnerabilities. Misusing roles can lead to prompt injection attacks, which can easily misalign the model's behavior with the user's intentions, raising significant security concerns. Although various prompt injection attacks have been proposed, recent research has largely overlooked the impact of role separators on safety. This highlights the critical need to thoroughly understand the systemic weaknesses in dialogue systems caused by role separators. This paper identifies modeling weaknesses caused by role separators. Specifically, we observe a strong positional bias associated with role separators, which is inherent in the format of dialogue modeling and can be triggered by the insertion of role separators. We further develop the Separators Injection Attack (SIA), a new orthometric attack based on role separators. The experiment results show that SIA is efficient and extensive in manipulating model behavior with an average gain of 18.2% for manual methods and enhances the attack success rate to 100% with automatic methods.
STRIVE: A Think & Improve Approach with Iterative Refinement for Enhancing Question Quality Estimation
Venue: arXiv cs.CL 2025
Year: 2025

Automatically assessing question quality is crucial for educators as it saves time, ensures consistency, and provides immediate feedback for refining teaching materials. We propose a novel methodology called STRIVE (Structured Thinking and Refinement with multiLLMs for Improving Verified Question Estimation) using a series of Large Language Models (LLMs) for automatic question evaluation. This approach aims to improve the accuracy and depth of question quality assessment, ultimately supporting diverse learners and enhancing educational practices. The method estimates question quality in an automated manner by generating multiple evaluations based on the strengths and weaknesses of the provided question and then choosing the best solution generated by the LLM. Then the process is improved by iterative review and response with another LLM until the evaluation metric values converge. This sophisticated method of evaluating question quality improves the estimation of question quality by automating the task of question quality evaluation. Correlation scores show that using this proposed method helps to improve correlation with human judgments compared to the baseline method. Error analysis shows that metrics like relevance and appropriateness improve significantly relative to human judgments by using STRIVE.
Evaluating Speech-to-Text Systems with PennSound
Venue: arXiv cs.CL 2025
Year: 2025

A random sample of nearly 10 hours of speech from PennSound, the world's largest online collection of poetry readings and discussions, was used as a benchmark to evaluate several commercial and open-source speech-to-text systems. PennSound's wide variation in recording conditions and speech styles makes it a good representative for many other untranscribed audio collections. Reference transcripts were created by trained annotators, and system transcripts were produced from AWS, Azure, Google, IBM, NeMo, Rev.ai, Whisper, and Whisper.cpp. Based on word error rate, Rev.ai was the top performer, and Whisper was the top open source performer (as long as hallucinations were avoided). AWS had the best diarization error rates among three systems. However, WER and DER differences were slim, and various tradeoffs may motivate choosing different systems for different end users. We also examine the issue of hallucinations in Whisper. Users of Whisper should be cautioned to be aware of runtime options, and whether the speed vs accuracy trade off is acceptable.
QEMesh: Employing A Quadric Error Metrics-Based Representation for Mesh Generation
Venue: arXiv cs.CV 2025
Year: 2025

Mesh generation plays a crucial role in 3D content creation, as mesh is widely used in various industrial applications. Recent works have achieved impressive results but still face several issues, such as unrealistic patterns or pits on surfaces, thin parts missing, and incomplete structures. Most of these problems stem from the choice of shape representation or the capabilities of the generative network. To alleviate these, we extend PoNQ, a Quadric Error Metrics (QEM)-based representation, and propose a novel model, QEMesh, for high-quality mesh generation. PoNQ divides the shape surface into tiny patches, each represented by a point with its normal and QEM matrix, which preserves fine local geometry information. In our QEMesh, we regard these elements as generable parameters and design a unique latent diffusion model containing a novel multi-decoder VAE for PoNQ parameters generation. Given the latent code generated by the diffusion model, three parameter decoders produce several PoNQ parameters within each voxel cell, and an occupancy decoder predicts which voxel cells containing parameters to form the final shape. Extensive evaluations demonstrate that our method generates results with watertight surfaces and is comparable to state-of-the-art methods in several main metrics.
Unified Generative Search and Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Modern commercial platforms typically offer both search and recommendation functionalities to serve diverse user needs, making joint modeling of these tasks an appealing direction. While prior work has shown that integrating search and recommendation can be mutually beneficial, it also reveals a performance trade-off: enhancements in one task often come at the expense of the other. This challenge arises from their distinct information requirements: search emphasizes semantic relevance between queries and items, whereas recommendation depends more on collaborative signals among users and items. Effectively addressing this trade-off requires tackling two key problems: (1) integrating both semantic and collaborative signals into item representations, and (2) guiding the model to distinguish and adapt to the unique demands of search and recommendation. The emergence of generative retrieval with Large Language Models (LLMs) presents new possibilities. This paradigm encodes items as identifiers and frames both search and recommendation as sequential generation tasks, offering the flexibility to leverage multiple identifiers and task-specific prompts. In light of this, we introduce GenSAR, a unified generative framework for balanced search and recommendation. Our approach designs dual-purpose identifiers and tailored training strategies to incorporate complementary signals and align with task-specific objectives. Experiments on both public and commercial datasets demonstrate that GenSAR effectively reduces the trade-off and achieves state-of-the-art performance on both tasks.
Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regression
Venue: arXiv cs.LG 2025
Year: 2025

Survival Regression (SuR) is a key technique for modeling time to event in important applications such as clinical trials and semiconductor manufacturing. Currently, SuR algorithms belong to one of three classes: non-linear black-box -- allowing adaptability to many datasets but offering limited interpretability (e.g., tree ensembles); linear glass-box -- being easier to interpret but limited to modeling only linear interactions (e.g., Cox proportional hazards); and non-linear glass-box -- allowing adaptability and interpretability, but empirically found to have several limitations (e.g., explainable boosting machines, survival trees). In this work, we investigate whether Symbolic Regression (SR), i.e., the automated search of mathematical expressions from data, can lead to non-linear glass-box survival models that are interpretable and accurate. We propose an evolutionary, multi-objective, and multi-expression implementation of SR adapted to SuR. Our empirical results on five real-world datasets show that SR consistently outperforms traditional glass-box methods for SuR in terms of accuracy per number of dimensions in the model, while exhibiting comparable accuracy with black-box methods. Furthermore, we offer qualitative examples to assess the interpretability potential of SR models for SuR. Code at: https://github.com/lurovi/SurvivalMultiTree-pyNSGP.
Cross-Document Contextual Coreference Resolution in Knowledge Graphs
Venue: arXiv cs.CL 2025
Year: 2025

Coreference resolution across multiple documents poses a significant challenge in natural language processing, particularly within the domain of knowledge graphs. This study introduces an innovative method aimed at identifying and resolving references to the same entities that appear across differing texts, thus enhancing the coherence and collaboration of information. Our method employs a dynamic linking mechanism that associates entities in the knowledge graph with their corresponding textual mentions. By utilizing contextual embeddings along with graph-based inference strategies, we effectively capture the relationships and interactions among entities, thereby improving the accuracy of coreference resolution. Rigorous evaluations on various benchmark datasets highlight notable advancements in our approach over traditional methodologies. The results showcase how the contextual information derived from knowledge graphs enhances the understanding of complex relationships across documents, leading to better entity linking and information extraction capabilities in applications driven by knowledge. Our technique demonstrates substantial improvements in both precision and recall, underscoring its effectiveness in the area of cross-document coreference resolution.
Temporal Dynamic Embedding for Irregularly Sampled Time Series
Venue: arXiv cs.LG 2025
Year: 2025

In several practical applications, particularly healthcare, clinical data of each patient is individually recorded in a database at irregular intervals as required. This causes a sparse and irregularly sampled time series, which makes it difficult to handle as a structured representation of the prerequisites of neural network models. We therefore propose temporal dynamic embedding (TDE), which enables neural network models to receive data that change the number of variables over time. TDE regards each time series variable as an embedding vector evolving over time, instead of a conventional fixed structured representation, which causes a critical missing problem. For each time step, TDE allows for the selective adoption and aggregation of only observed variable subsets and represents the current status of patient based on current observations. The experiment was conducted on three clinical datasets: PhysioNet 2012, MIMIC-III, and PhysioNet 2019. The TDE model performed competitively or better than the imputation-based baseline and several recent state-of-the-art methods with reduced training runtime.