OpenRaiser/Intern-Atlas huggingface dataset:
InterCorpRel-LLM: Enhancing Financial Relational Understanding with Graph-Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Identifying inter-firm relationships such as supply and competitive ties is critical for financial analysis and corporate governance, yet remains challenging due to the scale, sparsity, and contextual dependence of corporate data. Graph-based methods capture structure but miss semantic depth, while large language models (LLMs) excel at text but remain limited in their ability to represent relational dependencies. To address this, we propose InterCorpRel-LLM, a cross-modal framework that integrates GNNs with LLMs, supported by a proprietary dataset derived from FactSet supply chain records and three tailored training tasks: company graph matching, industry classification, and supply relation prediction. This design enables effective joint modeling of structure and semantics. Experiments show that InterCorpRel-LLM substantially outperforms strong baselines, including GPT-5, on a supply relation identification task, achieving an F-score of 0.8543 vs. 0.2287 with only a 7B-parameter backbone and lightweight training. The model also generalizes to zero-shot competitor identification, underscoring its ability to capture nuanced inter-firm dynamics. Our framework thus provides analysts and strategists with a robust tool for mapping and reasoning about complex corporate networks, enhancing decision-making and risk management in dynamic markets.
HeSRN: Representation Learning On Heterogeneous Graphs via Slot-Aware Retentive Network
Venue: arXiv cs.LG 2025
Year: 2025

Graph Transformers have recently achieved remarkable progress in graph representation learning by capturing long-range dependencies through self-attention. However, their quadratic computational complexity and inability to effectively model heterogeneous semantics severely limit their scalability and generalization on real-world heterogeneous graphs. To address these issues, we propose HeSRN, a novel Heterogeneous Slot-aware Retentive Network for efficient and expressive heterogeneous graph representation learning. HeSRN introduces a slot-aware structure encoder that explicitly disentangles node-type semantics by projecting heterogeneous features into independent slots and aligning their distributions through slot normalization and retention-based fusion, effectively mitigating the semantic entanglement caused by forced feature-space unification in previous Transformer-based models. Furthermore, we replace the self-attention mechanism with a retention-based encoder, which models structural and contextual dependencies in linear time complexity while maintaining strong expressive power. A heterogeneous retentive encoder is further employed to jointly capture both local structural signals and global heterogeneous semantics through multi-scale retention layers. Extensive experiments on four real-world heterogeneous graph datasets demonstrate that HeSRN consistently outperforms state-of-the-art heterogeneous graph neural networks and Graph Transformer baselines on node classification tasks, achieving superior accuracy with significantly lower computational complexity.
QONNECT: A QoS-Aware Orchestration System for Distributed Kubernetes Clusters
Venue: arXiv cs.DC 2025
Year: 2025

Modern applications increasingly span across cloud, fog, and edge environments, demanding orchestration systems that can adapt to diverse deployment contexts while meeting Quality-of-Service (QoS) requirements. Standard Kubernetes schedulers do not account for user-defined objectives such as energy efficiency, cost optimization, and global performance, often leaving operators to make manual, cluster-by-cluster placement decisions. To address this need, we present QONNECT, a vendor-agnostic orchestration framework that enables declarative, QoS-driven application deployment across heterogeneous Kubernetes and K3s clusters. QONNECT introduces a distributed architecture composed of a central Knowledge Base, Raft-replicated Resource Lead Agents, and lightweight Resource Agents in each cluster. Through a minimal YAML-based interface, users specify high-level QoS goals, which the system translates into concrete placement and migration actions. Our implementation is evaluated on a federated testbed of up to nine cloud-fog-edge clusters using the Istio Bookinfo microservice application. The system demonstrates dynamic, policy-driven microservice placement, automated failover, QoS-compliant rescheduling, and leader re-election after node failure, all without manual intervention. By bridging the gap between declarative deployment models and operational QoS goals, QONNECT transforms the cloud-edge continuum into a unified, self-optimizing platform.
MTMD: A Multi-Task Multi-Domain Framework for Unified Ad Lightweight Ranking at Pinterest
Venue: arXiv cs.IR 2025
Year: 2025

The lightweight ad ranking layer, living after the retrieval stage and before the fine ranker, plays a critical role in the success of a cascaded ad recommendation system. Due to the fact that there are multiple optimization tasks depending on the ad domain, e.g., Click Through Rate (CTR) for click ads and Conversion Rate (CVR) for conversion ads, as well as multiple surfaces where an ad is served (home feed, search, or related item recommendation) with diverse ad products (shopping or standard ad); it is an essentially challenging problem in industry on how to do joint holistic optimization in the lightweight ranker, such that the overall platform's value, advertiser's value, and user's value are maximized. Deep Neural Network (DNN)-based multitask learning (MTL) can handle multiple goals naturally, with each prediction head mapping to a particular optimization goal. However, in practice, it is unclear how to unify data from different surfaces and ad products into a single model. It is critical to learn domain-specialized knowledge and explicitly transfer knowledge between domains to make MTL effective. We present a Multi-Task Multi-Domain (MTMD) architecture under the classic Two-Tower paradigm, with the following key contributions: 1) handle different prediction tasks, ad products, and ad serving surfaces in a unified framework; 2) propose a novel mixture-of-expert architecture to learn both specialized knowledge each domain and common knowledge shared between domains; 3) propose a domain adaption module to encourage knowledge transfer between experts; 4) constrain the modeling of different prediction tasks. MTMD improves the offline loss value by 12% to 36%, mapping to 2% online reduction in cost per click. We have deployed this single MTMD framework into production for Pinterest ad recommendation replacing 9 production models.
Abductive Preference Learning
Venue: arXiv cs.CL 2025
Year: 2025

Frontier large language models such as GPT-5 and Claude Sonnet remain prone to overconfidence even after alignment through Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO). For instance, they tend to offer the same conservative answer "No" to both questions "Can I eat the [food / potato chips] that has been left out overnight?" despite the latter requiring no refridgeration for safe consumption. We find that this failure is potentially attributed to a limitation of existing preference learning: it emphasizes selecting the correct response for a given prompt, while neglecting counterfactual prompts that should alter the response. To address this limitation, we propose abductive preference learning, a fine-tuning paradigm that reverses the conventional conditioning by learning preferences over prompts given a response. To validate this idea, we construct an abductive dataset derived from the HaluEval QA benchmark with 1,001 entries, implementing abductive DPO and its variant DPOP. Experiments reveal complementary strengths: standard methods improve response selection, abductive methods improve prompt discrimination, while a multitask objective unifies both. On the abductive dataset, multitask DPOP boosts accuracy from $90.0\%$ to $99.5\%$ in response selection and $54.7\%$ to $85.0\%$ in prompt discrimination, with qualitative evidence highlighting improved sensitivity to prompt differences. Finally, evaluation on AlpacaEval shows multitask DPOP improves win rate (from $5.26\%$ to $6.17\%$), confirming that abductive preference learning preserves the benefits of conventional preference optimization while addressing the overlooked challenge of counterfactual prompts.
The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs
Venue: arXiv cs.AI 2025
Year: 2025

When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive? As personalized AI systems increasingly incorporate long-term user memory, understanding how this memory shapes emotional reasoning is critical. We investigate how user memory affects emotional intelligence in large language models (LLMs) by evaluating 15 models on human validated emotional intelligence tests. We find that identical scenarios paired with different user profiles produce systematically divergent emotional interpretations. Across validated user independent emotional scenarios and diverse user profiles, systematic biases emerged in several high-performing LLMs where advantaged profiles received more accurate emotional interpretations. Moreover, LLMs demonstrate significant disparities across demographic factors in emotion understanding and supportive recommendations tasks, indicating that personalization mechanisms can embed social hierarchies into models emotional reasoning. These results highlight a key challenge for memory enhanced AI: systems designed for personalization may inadvertently reinforce social inequalities.
Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery
Venue: arXiv cs.LG 2025
Year: 2025

Recent breakthroughs in generative modeling have demonstrated remarkable capabilities in molecular generation, yet the integration of comprehensive biomedical knowledge into these models has remained an untapped frontier. In this study, we introduce K-DREAM (Knowledge-Driven Embedding-Augmented Model), a novel framework that leverages knowledge graphs to augment diffusion-based generative models for drug discovery. By embedding structured information from large-scale knowledge graphs, K-DREAM directs molecular generation toward candidates with higher biological relevance and therapeutic suitability. This integration ensures that the generated molecules are aligned with specific therapeutic targets, moving beyond traditional heuristic-driven approaches. In targeted drug design tasks, K-DREAM generates drug candidates with improved binding affinities and predicted efficacy, surpassing current state-of-the-art generative models. It also demonstrates flexibility by producing molecules designed for multiple targets, enabling applications to complex disease mechanisms. These results highlight the utility of knowledge-enhanced generative models in rational drug design and their relevance to practical therapeutic development.
Phase-Aware Deep Learning with Complex-Valued CNNs for Audio Signal Applications
Venue: arXiv cs.LG 2025
Year: 2025

This study explores the design and application of Complex-Valued Convolutional Neural Networks (CVCNNs) in audio signal processing, with a focus on preserving and utilizing phase information often neglected in real-valued networks. We begin by presenting the foundational theoretical concepts of CVCNNs, including complex convolutions, pooling layers, Wirtinger-based differentiation, and various complex-valued activation functions. These are complemented by critical adaptations of training techniques, including complex batch normalization and weight initialization schemes, to ensure stability in training dynamics. Empirical evaluations are conducted across three stages. First, CVCNNs are benchmarked on standard image datasets, where they demonstrate competitive performance with real-valued CNNs, even under synthetic complex perturbations. Although our focus is audio signal processing, we first evaluate CVCNNs on image datasets to establish baseline performance and validate training stability before applying them to audio tasks. In the second experiment, we focus on audio classification using Mel-Frequency Cepstral Coefficients (MFCCs). CVCNNs trained on real-valued MFCCs slightly outperform real CNNs, while preserving phase in input workflows highlights challenges in exploiting phase without architectural modifications. Finally, a third experiment introduces GNNs to model phase information via edge weighting, where the inclusion of phase yields measurable gains in both binary and multi-class genre classification. These results underscore the expressive capacity of complex-valued architectures and confirm phase as a meaningful and exploitable feature in audio processing applications. While current methods show promise, especially with activations like cardioid, future advances in phase-aware design will be essential to leverage the potential of complex representations in neural networks.
ACT: Automatically Generating Compiler Backends from Tensor Accelerator ISA Descriptions
Venue: arXiv cs.PL 2025
Year: 2025

Tensor compilers play a key role in enabling high-performance implementations of deep learning workloads. These compilers rely on existing CPU and GPU code generation backends to generate device-specific code. Recently, many tensor accelerators (neural processing units) have been proposed to further accelerate these workloads. Compared to commodity hardware, however, most of the proposed tensor accelerators do not have compiler backends with code generation support. Moreover, the accelerator designs are subject to fast iteration cycles, making it difficult to manually develop compiler backends similar to commodity hardware platforms. Therefore, to increase adoption and enable faster software development cycles for novel tensor accelerator designs, we need to make the compiler backend construction process more agile. To address this gap, we introduce ACT, a compiler backend generator that automatically generates compiler backends for tensor accelerators, given just the instruction set architecture (ISA) descriptions. We first formally specify the compiler backend generation problem that introduces a novel specification for describing tensor accelerator ISAs. Next, we design ACT such that it supports user-programmable memories and complex parameterized instructions that are prevalent in tensor accelerators. ACT uses a novel parameterized equality saturation-based instruction selection phase and a constraint programming-based memory allocation phase. We prove that compiler backends generated by ACT are sound and complete. Finally, we generate compiler backends for three accelerator platforms from industry and academia, and show that they match or outperform code written using hand-optimized kernel libraries while maintaining low compilation overheads.
Unpacking Hateful Memes: Presupposed Context and False Claims
Venue: arXiv cs.CL 2025
Year: 2025

While memes are often humorous, they are frequently used to disseminate hate, causing serious harm to individuals and society. Current approaches to hateful meme detection mainly rely on pre-trained language models. However, less focus has been dedicated to \textit{what make a meme hateful}. Drawing on insights from philosophy and psychology, we argue that hateful memes are characterized by two essential features: a \textbf{presupposed context} and the expression of \textbf{false claims}. To capture presupposed context, we develop \textbf{PCM} for modeling contextual information across modalities. To detect false claims, we introduce the \textbf{FACT} module, which integrates external knowledge and harnesses cross-modal reference graphs. By combining PCM and FACT, we introduce \textbf{\textsf{SHIELD}}, a hateful meme detection framework designed to capture the fundamental nature of hate. Extensive experiments show that SHIELD outperforms state-of-the-art methods across datasets and metrics, while demonstrating versatility on other tasks, such as fake news detection.
Conformal Sparsification for Bandwidth-Efficient Edge-Cloud Speculative Decoding
Venue: arXiv cs.LG 2025
Year: 2025

Edge-cloud speculative decoding (SD) accelerates inference by having a cloud-based large language model (LLM) that verifies draft tokens generated by a resource-constrained small language model (SLM) at the edge. A central bottleneck is the limited bandwidth of the edge-cloud link, which necessitates efficient compression of draft token distributions. We first derive an information-theoretic bound that decomposes the token rejection rate into contributions from SLM-LLM distribution mismatch and from quantization distortion. Guided by this analysis, we propose the Sparse Quantize-and-Sample SD (SQS-SD) framework, which exploits distributional sparsity through structured sparsification and lattice-based quantization. Within this framework, K-SQS applies fixed top-K truncation, while C-SQS adaptively adjusts the retained token set via online conformal prediction to ensure bounded deviation from the dense distribution. Empirical results confirm that both approaches improve end-to-end latency and rejection rates in complimentary operating regimes.
Read the Room or Lead the Room: Understanding Socio-Cognitive Dynamics in Human-AI Teaming
Venue: arXiv cs.HC 2025
Year: 2025

Research on Collaborative Problem Solving (CPS) has traditionally examined how humans rely on one another cognitively and socially to accomplish tasks together. With the rapid advancement of AI and large language models, however, a new question emerge: what happens to team dynamics when one of the "teammates" is not human? In this study, we investigate how the integration of an AI teammate -- a fully autonomous GPT-4 agent with social, cognitive, and affective capabilities -- shapes the socio-cognitive dynamics of CPS. We analyze discourse data collected from human-AI teaming (HAT) experiments conducted on a novel platform specifically designed for HAT research. Using two natural language processing (NLP) methods, specifically Linguistic Inquiry and Word Count (LIWC) and Group Communication Analysis (GCA), we found that AI teammates often assumed the role of dominant cognitive facilitators, guiding, planning, and driving group decision-making. However, they did so in a socially detached manner, frequently pushing agenda in a verbose and repetitive way. By contrast, humans working with AI used more language reflecting social processes, suggesting that they assumed more socially oriented roles. Our study highlights how learning analytics can provide critical insights into the socio-cognitive dynamics of human-AI collaboration.
HTTP Request Synchronization Defeats Discrepancy Attacks
Venue: arXiv cs.CR 2025
Year: 2025

Contemporary web application architectures involve many layers of proxy services that process traffic. Due to the complexity of HTTP and vendor design decisions, these proxies sometimes process a given request in different ways. Attackers can exploit these processing discrepancies to launch damaging attacks including web cache poisoning and request smuggling. Discrepancy attacks are surging, yet, there exists no systemic defense. In this work, we propose the first comprehensive defense to address this problem, called HTTP Request Synchronization. Our scheme uses standard HTTP extension mechanisms to augment each request with a complete processing history. It propagates this context through the traffic path detailing how each server hop has processed said request. Using this history, every proxy server can validate that their processing is consistent with all previous hops, eliminating discrepancy attacks. We implement our scheme for 5 popular proxy technologies, Apache, NGINX, HAProxy, Varnish, and Cloudflare, demonstrating its practical impact.
VG-Mapping: Variation-Aware 3D Gaussians for Online Semi-static Scene Mapping
Venue: arXiv cs.RO 2025
Year: 2025

Maintaining an up-to-date map that accurately reflects recent changes in the environment is crucial, especially for robots that repeatedly traverse the same space. Failing to promptly update the changed regions can degrade map quality, resulting in poor localization, inefficient operations, and even lost robots. 3D Gaussian Splatting (3DGS) has recently seen widespread adoption in online map reconstruction due to its dense, differentiable, and photorealistic properties, yet accurately and efficiently updating the regions of change remains a challenge. In this paper, we propose VG-Mapping, a novel online 3DGS-based mapping system tailored for such semi-static scenes. Our approach introduces a hybrid representation that augments 3DGS with a TSDF-based voxel map to efficiently identify changed regions in a scene, along with a variation-aware density control strategy that inserts or deletes Gaussian primitives in regions undergoing change. Furthermore, to address the absence of public benchmarks for this task, we construct a RGB-D dataset comprising both synthetic and real-world semi-static environments. Experimental results demonstrate that our method substantially improves the rendering quality and map update efficiency in semi-static scenes. The code and dataset are available at https://github.com/heyicheng-never/VG-Mapping.
Learning Joint Embeddings of Function and Process Call Graphs for Malware Detection
Venue: arXiv cs.LG 2025
Year: 2025

Software systems can be represented as graphs, capturing dependencies among functions and processes. An interesting aspect of software systems is that they can be represented as different types of graphs, depending on the extraction goals and priorities. For example, function calls within the software can be captured to create function call graphs, which highlight the relationships between functions and their dependencies. Alternatively, the processes spawned by the software can be modeled to generate process interaction graphs, which focus on runtime behavior and inter-process communication. While these graph representations are related, each captures a distinct perspective of the system, providing complementary insights into its structure and operation. While previous studies have leveraged graph neural networks (GNNs) to analyze software behaviors, most of this work has focused on a single type of graph representation. The joint modeling of both function call graphs and process interaction graphs remains largely underexplored, leaving opportunities for deeper, multi-perspective analysis of software systems. This paper presents a pipeline for constructing and training Function Call Graphs (FCGs) and Process Call Graphs (PCGs) and learning joint embeddings. We demonstrate that joint embeddings outperform a single-graph model. In this paper, we propose GeminiNet, a unified neural network approach that learns joint embeddings from both FCGs and PCGs. We construct a new dataset of 635 Windows executables (318 malicious and 317 benign), extracting FCGs via Ghidra and PCGs via Any.Run sandbox. GeminiNet employs dual graph convolutional branches with an adaptive gating mechanism that balances contributions from static and dynamic views.