OpenRaiser/Intern-Atlas huggingface dataset:
MONICA: Real-Time Monitoring and Calibration of Chain-of-Thought Sycophancy in Large Reasoning Models
Venue: arXiv cs.AI 2025
Year: 2025

Large Reasoning Models (LRMs) suffer from sycophantic behavior, where models tend to agree with users' incorrect beliefs and follow misinformation rather than maintain independent reasoning. This behavior undermines model reliability and poses societal risks. Mitigating LRM sycophancy requires monitoring how this sycophancy emerges during the reasoning trajectory; however, current methods mainly focus on judging based on final answers and correcting them, without understanding how sycophancy develops during reasoning processes. To address this limitation, we propose MONICA, a novel Monitor-guided Calibration framework that monitors and mitigates sycophancy during model inference at the level of reasoning steps, without requiring the model to finish generating its complete answer. MONICA integrates a sycophantic monitor that provides real-time monitoring of sycophantic drift scores during response generation with a calibrator that dynamically suppresses sycophantic behavior when scores exceed predefined thresholds. Extensive experiments across 12 datasets and 3 LRMs demonstrate that our method effectively reduces sycophantic behavior in both intermediate reasoning steps and final answers, yielding robust performance improvements.
Personality over Precision: Exploring the Influence of Human-Likeness on ChatGPT Use for Search
Venue: arXiv cs.HC 2025
Year: 2025

Conversational search interfaces, like ChatGPT, offer an interactive, personalized, and engaging user experience compared to traditional search. On the downside, they are prone to cause overtrust issues where users rely on their responses even when they are incorrect. What aspects of the conversational interaction paradigm drive people to adopt it, and how it creates personalized experiences that lead to overtrust, is not clear. To understand the factors influencing the adoption of conversational interfaces, we conducted a survey with 173 participants. We examined user perceptions regarding trust, human-likeness (anthropomorphism), and design preferences between ChatGPT and Google. To better understand the overtrust phenomenon, we asked users about their willingness to trade off factuality for constructs like ease of use or human-likeness. Our analysis identified two distinct user groups: those who use both ChatGPT and Google daily (DUB), and those who primarily rely on Google (DUG). The DUB group exhibited higher trust in ChatGPT, perceiving it as more human-like, and expressed greater willingness to trade factual accuracy for enhanced personalization and conversational flow. Conversely, the DUG group showed lower trust toward ChatGPT but still appreciated aspects like ad-free experiences and responsive interactions. Demographic analysis further revealed nuanced patterns, with middle-aged adults using ChatGPT less frequently yet trusting it more, suggesting potential vulnerability to misinformation. Our findings contribute to understanding user segmentation, emphasizing the critical roles of personalization and human-likeness in conversational IR systems, and reveal important implications regarding users' willingness to compromise factual accuracy for more engaging interactions.
Guidelines for Building Indexes on Partially Cache-Coherent CXL Shared Memory
Venue: arXiv cs.OS 2025
Year: 2025

The \emph{Partial Cache-Coherence (PCC)} model maintains hardware cache coherence only within subsets of cores, enabling large-scale memory sharing with emerging memory interconnect technologies like Compute Express Link (CXL). However, PCC's relaxation of global cache coherence compromises the correctness of existing single-machine software. This paper focuses on building consistent and efficient indexes on PCC platforms. We present that existing indexes designed for cache-coherent platforms can be made consistent on PCC platforms following SP guidelines, i.e., we identify \emph{sync-data} and \emph{protected-data} according to the index's concurrency control mechanisms, and synchronize them accordingly. However, conversion with SP guidelines introduces performance overhead. To mitigate the overhead, we identify several unique performance bottlenecks on PCC platforms, and propose P$^3$ guidelines (i.e., using Out-of-\underline{P}lace update, Re\underline{P}licated shared variable, S\underline{P}eculative Reading) to improve the efficiency of converted indexes on PCC platforms. With SP and P$^3$ guidelines, we convert and optimize two indexes (CLevelHash and BwTree) for PCC platforms. Evaluation shows that converted indexes' throughput improves up to 16$\times$ following P$^3$ guidelines, and the optimized indexes outperform their message-passing-based and disaggregated-memory-based counterparts by up to 16$\times$ and 19$\times$.
NOAH: Benchmarking Narrative Prior driven Hallucination and Omission in Video Large Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Video large language models (Video LLMs) have recently achieved strong performance on tasks such as captioning, summarization, and question answering. Many models and training methods explicitly encourage continuity across events to enhance narrative coherence. While this improves fluency, it also introduces an inductive bias that prioritizes storyline consistency over strict grounding in visual evidence. We identify this bias, which we call narrative prior, as a key driver of two errors: hallucinations, where non-existent events are introduced or existing ones are misinterpreted, and omissions, where factual events are suppressed because they are misaligned with surrounding context. To systematically evaluate narrative prior-induced errors, we introduce NOAH, a large-scale benchmark that constructs composite videos by inserting clips from other sources into target videos. By varying semantic similarity and insertion position, our benchmark enables controlled and scalable analysis of narrative priors. We design one captioning task with tailored metrics and three QA tasks - Existence, Temporal, and Narrative - yielding more than 60K evaluation samples. Extensive experiments yield three key findings: (i) most Video LLMs exhibit hallucinations and omissions driven by narrative priors, (ii) the patterns of these errors vary across architectures and depend on event similarity and insertion position, and (iii) reliance on narrative priors intensifies under sampling with fewer frames, amplifying errors when event continuity is weak. We establish NOAH as the first standardized evaluation of narrative prior-induced hallucination and omission in Video LLMs, providing a foundation for developing more reliable and trustworthy models. Our benchmark and code are available at https://anonymous550520.github.io/.
Zooming into Comics: Region-Aware RL Improves Fine-Grained Comic Understanding in Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Complex visual narratives, such as comics, present a significant challenge to Vision-Language Models (VLMs). Despite excelling on natural images, VLMs often struggle with stylized line art, onomatopoeia, and densely packed multi-panel layouts. To address this gap, we introduce AI4VA-FG, the first fine-grained and comprehensive benchmark for VLM-based comic understanding. It spans tasks from foundational recognition and detection to high-level character reasoning and narrative construction, supported by dense annotations for characters, poses, and depth. Beyond that, we evaluate state-of-the-art proprietary models, including GPT-4o and Gemini-2.5, and open-source models such as Qwen2.5-VL, revealing substantial performance deficits across core tasks of our benchmarks and underscoring that comic understanding remains an unsolved challenge. To enhance VLMs' capabilities in this domain, we systematically investigate post-training strategies, including supervised fine-tuning on solutions (SFT-S), supervised fine-tuning on reasoning trajectories (SFT-R), and reinforcement learning (RL). Beyond that, inspired by the emerging "Thinking with Images" paradigm, we propose Region-Aware Reinforcement Learning (RARL) for VLMs, which trains models to dynamically attend to relevant regions through zoom-in operations. We observe that when applied to the Qwen2.5-VL model, RL and RARL yield significant gains in low-level entity recognition and high-level storyline ordering, paving the way for more accurate and efficient VLM applications in the comics domain.
Route Experts by Sequence, not by Token
Venue: arXiv cs.LG 2025
Year: 2025

Mixture-of-Experts (MoE) architectures scale large language models (LLMs) by activating only a subset of experts per token, but the standard TopK routing assigns the same fixed number of experts to all tokens, ignoring their varying complexity. Prior adaptive routing methods introduce additional modules and hyperparameters, often requiring costly retraining from scratch. We propose Sequence-level TopK (SeqTopK), a minimal modification that shifts the expert budget from the token level to the sequence level. By selecting the top $T \cdot K$ experts across all $T$ tokens, SeqTopK enables end-to-end learned dynamic allocation -- assigning more experts to difficult tokens and fewer to easy ones -- while preserving the same overall budget. SeqTopK requires only a few lines of code, adds less than 1% overhead, and remains fully compatible with pretrained MoE models. Experiments across math, coding, law, and writing show consistent improvements over TopK and prior parameter-free adaptive methods, with gains that become substantially larger under higher sparsity (up to 16.9%). These results highlight SeqTopK as a simple, efficient, and scalable routing strategy, particularly well-suited for the extreme sparsity regimes of next-generation LLMs. Code is available at https://github.com/Y-Research-SBU/SeqTopK.
Automatically Identifying Solution-Related Content in Issue Report Discussions with Language Models
Venue: arXiv cs.SE 2025
Year: 2025

During issue resolution, software developers rely on issue reports to discuss solutions for defects, feature requests, and other changes. These discussions contain proposed solutions-from design changes to code implementations-as well as their evaluations. Locating solution-related content is essential for investigating reopened issues, addressing regressions, reusing solutions, and understanding code change rationale. Manually understanding long discussions to identify such content can be difficult and time-consuming. This paper automates solution identification using language models as supervised classifiers. We investigate three applications-embeddings, prompting, and fine-tuning-across three classifier types: traditional ML models (MLMs), pre-trained language models (PLMs), and large language models (LLMs). Using 356 Mozilla Firefox issues, we created a dataset to train and evaluate six MLMs, four PLMs, and two LLMs across 68 configurations. Results show that MLMs with LLM embeddings outperform TF-IDF features, prompting underperforms, and fine-tuned LLMs achieve the highest performance, with LLAMAft reaching 0.716 F1 score. Ensembles of the best models further improve results (0.737 F1). Misclassifications often arise from misleading clues or missing context, highlighting the need for context-aware classifiers. Models trained on Mozilla transfer to other projects, with a small amount of project-specific data, further enhancing results. This work supports software maintenance, issue understanding, and solution reuse.
Differential Space-Time Block Coding for Phase-Unsynchronized Cell-Free MIMO Downlink
Venue: arXiv cs.IT 2025
Year: 2025

In the downlink of a cell-free massive multiple-input multiple-output (CF-mMIMO) system, spectral efficiency gains critically rely on joint coherent transmission, as all access points (APs) must align their transmitted signals in phase at the user equipment (UE). Achieving such phase alignment is technically challenging, as it requires tight synchronization among geographically distributed APs. In this paper, we address this issue by introducing a differential space-time block coding (DSTBC) approach that bypasses the need for AP phase synchronization. We first provide analytic bounds to the achievable spectral efficiency of CF-mMIMO with phase-unsynchronized APs. Then, we propose a DSTBC-based transmission scheme specifically tailored to CF-mMIMO, which operates without channel state information and does not require any form of phase synchronization among the APs. We derive a closed-form expression for the resulting signal-to-interference-plus-noise ratio (SINR), enabling quantitative comparisons among different DSTBC schemes. Numerical simulations confirm that phase misalignments can significantly impair system performance. In contrast, the proposed DSTBC scheme successfully mitigates these effects, achieving performance comparable to that of fully synchronized systems.
Efficient Approximation of Volterra Series for High-Dimensional Systems
Venue: arXiv cs.LG 2025
Year: 2025

The identification of high-dimensional nonlinear dynamical systems via the Volterra series has significant potential, but has been severely hindered by the curse of dimensionality. Tensor Network (TN) methods such as the Modified Alternating Linear Scheme (MVMALS) have been a breakthrough for the field, offering a tractable approach by exploiting the low-rank structure in Volterra kernels. However, these techniques still encounter prohibitive computational and memory bottlenecks due to high-order polynomial scaling with respect to input dimension. To overcome this barrier, we introduce the Tensor Head Averaging (THA) algorithm, which significantly reduces complexity by constructing an ensemble of localized MVMALS models trained on small subsets of the input space. In this paper, we present a theoretical foundation for the THA algorithm. We establish observable, finite-sample bounds on the error between the THA ensemble and a full MVMALS model, and we derive an exact decomposition of the squared error. This decomposition is used to analyze the manner in which subset models implicitly compensate for omitted dynamics. We quantify this effect, and prove that correlation between the included and omitted dynamics creates an optimization incentive which drives THA's performance toward accuracy superior to a simple truncation of a full MVMALS model. THA thus offers a scalable and theoretically grounded approach for identifying previously intractable high-dimensional systems.
Breaking the Dyadic Barrier: Rethinking Fairness in Link Prediction Beyond Demographic Parity
Venue: arXiv cs.LG 2025
Year: 2025

Link prediction is a fundamental task in graph machine learning with applications, ranging from social recommendation to knowledge graph completion. Fairness in this setting is critical, as biased predictions can exacerbate societal inequalities. Prior work adopts a dyadic definition of fairness, enforcing fairness through demographic parity between intra-group and inter-group link predictions. However, we show that this dyadic framing can obscure underlying disparities across subgroups, allowing systemic biases to go undetected. Moreover, we argue that demographic parity does not meet desired properties for fairness assessment in ranking-based tasks such as link prediction. We formalize the limitations of existing fairness evaluations and propose a framework that enables a more expressive assessment. Additionally, we propose a lightweight post-processing method combined with decoupled link predictors that effectively mitigates bias and achieves state-of-the-art fairness-utility trade-offs.
HyperEF 2.0: Spectral Hypergraph Coarsening via Krylov Subspace Expansion and Resistance-based Local Clustering
Venue: arXiv cs.SI 2025
Year: 2025

This paper introduces HyperEF 2.0, a scalable framework for spectral coarsening and clustering of large-scale hypergraphs through hyperedge effective resistances, aiming to decompose hypergraphs into multiple node clusters with a small number of inter-cluster hyperedges. Building on the recent HyperEF framework, our approach offers three primary contributions. Specifically, first, by leveraging the expanded Krylov subspace exploiting both clique and star expansions of hyperedges, we can significantly improve the approximation accuracy of effective resistances. Second, we propose a resistance-based local clustering scheme for merging small isolated nodes into nearby clusters, yielding more balanced clusters with substantially improved conductance. Third, the proposed HyperEF 2.0 enables the integration of resistance-based hyperedge weighting and community detection into a multilevel hypergraph partitioning tool, achieving state-of-the-art performance. Extensive experiments on real-world VLSI benchmarks show that HyperEF 2.0 can more effectively coarsen hypergraphs without compromising their structural properties, while delivering much better solution quality (e.g. conductance) than the state-of-the-art hypergraph coarsening methods, such as HyperEF and HyperSF. Moreover, compared to leading hypergraph partitioners such as hMETIS, SpecPart, MedPart, and KaHyPar, our framework consistently achieves smaller cut sizes. In terms of runtime, HyperEF 2.0 attains up to a 4.5x speedup over the latest flow-based local clustering algorithm, HyperSF, demonstrating both superior efficiency and partitioning quality.
Improving Asset Allocation in a Fast Moving Consumer Goods B2B Company: An Interpretable Machine Learning Framework for Commercial Cooler Assignment Based on Multi-Tier Growth Targets
Venue: arXiv cs.LG 2025
Year: 2025

In the fast-moving consumer goods (FMCG) industry, deciding where to place physical assets, such as commercial beverage coolers, can directly impact revenue growth and execution efficiency. Although churn prediction and demand forecasting have been widely studied in B2B contexts, the use of machine learning to guide asset allocation remains relatively unexplored. This paper presents a framework focused on predicting which beverage clients are most likely to deliver strong returns in volume after receiving a cooler. Using a private dataset from a well-known Central American brewing and beverage company of 3,119 B2B traditional trade channel clients that received a cooler from 2022-01 to 2024-07, and tracking 12 months of sales transactions before and after cooler installation, three growth thresholds were defined: 10%, 30% and 50% growth in sales volume year over year. The analysis compares results of machine learning models such as XGBoost, LightGBM, and CatBoost combined with SHAP for interpretable feature analysis in order to have insights into improving business operations related to cooler allocation; the results show that the best model has AUC scores of 0.857, 0.877, and 0.898 across the thresholds on the validation set. Simulations suggest that this approach can improve ROI because it better selects potential clients to grow at the expected level and increases cost savings by not assigning clients that will not grow, compared to traditional volume-based approaches with substantial business management recommendations
HiMo-CLIP: Modeling Semantic Hierarchy and Monotonicity in Vision-Language Alignment
Venue: arXiv cs.CV 2025
Year: 2025

Contrastive vision-language models like CLIP have achieved impressive results in image-text retrieval by aligning image and text representations in a shared embedding space. However, these models often treat text as flat sequences, limiting their ability to handle complex, compositional, and long-form descriptions. In particular, they fail to capture two essential properties of language: semantic hierarchy, which reflects the multi-level compositional structure of text, and semantic monotonicity, where richer descriptions should result in stronger alignment with visual content.To address these limitations, we propose HiMo-CLIP, a representation-level framework that enhances CLIP-style models without modifying the encoder architecture. HiMo-CLIP introduces two key components: a hierarchical decomposition (HiDe) module that extracts latent semantic components from long-form text via in-batch PCA, enabling flexible, batch-aware alignment across different semantic granularities, and a monotonicity-aware contrastive loss (MoLo) that jointly aligns global and component-level representations, encouraging the model to internalize semantic ordering and alignment strength as a function of textual completeness.These components work in concert to produce structured, cognitively-aligned cross-modal representations. Experiments on multiple image-text retrieval benchmarks show that HiMo-CLIP consistently outperforms strong baselines, particularly under long or compositional descriptions. The code is available at https://github.com/UnicomAI/HiMo-CLIP.
Dual-Pathway Fusion of EHRs and Knowledge Graphs for Predicting Unseen Drug-Drug Interactions
Venue: arXiv cs.LG 2025
Year: 2025

Drug-drug interactions (DDIs) remain a major source of preventable harm, and many clinically important mechanisms are still unknown. Existing models either rely on pharmacologic knowledge graphs (KGs), which fail on unseen drugs, or on electronic health records (EHRs), which are noisy, temporal, and site-dependent. We introduce, to our knowledge, the first system that conditions KG relation scoring on patient-level EHR context and distills that reasoning into an EHR-only model for zero-shot inference. A fusion "Teacher" learns mechanism-specific relations for drug pairs represented in both sources, while a distilled "Student" generalizes to new or rarely used drugs without KG access at inference. Both operate under a shared ontology (set) of pharmacologic mechanisms (drug relations) to produce interpretable, auditable alerts rather than opaque risk scores. Trained on a multi-institution EHR corpus paired with a curated DrugBank DDI graph, and evaluated using a clinically aligned, decision-focused protocol with leakage-safe negatives that avoid artificially easy pairs, the system maintains precision across multi-institutuion test data, produces mechanism-specific, clinically consistent predictions, reduces false alerts (higher precision) at comparable overall detection performance (F1), and misses fewer true interactions compared to prior methods. Case studies further show zero-shot identification of clinically recognized CYP-mediated and pharmacodynamic mechanisms for drugs absent from the KG, supporting real-world use in clinical decision support and pharmacovigilance.
EONSim: An NPU Simulator for On-Chip Memory and Embedding Vector Operations
Venue: arXiv cs.AR 2025
Year: 2025

Embedding vector operations are a key component of modern deep neural network workloads. Unlike matrix operations with deterministic access patterns, embedding vector operations exhibit input data-dependent and non-deterministic memory accesses. Existing neural processing unit (NPU) simulators focus on matrix computations with simple double-buffered on-chip memory systems, lacking the modeling capability for realistic embedding behavior. Next-generation NPUs, however, call for more flexible on-chip memory architectures that can support diverse access and management schemes required by embedding workloads. To enable flexible exploration and design of emerging NPU architectures, we present EONSim, an NPU simulator that holistically models both matrix and embedding vector operations. EONSim integrates a validated performance model for matrix computations with detailed memory simulation for embedding accesses, supporting various on-chip memory management policies. Validated against TPUv6e, EONSim achieves an average inference time error of 1.4\% and an average on-chip memory access count error of 2.2\%.