AI-generated summaries
Today's ML research,
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Daily summaries of the latest machine learning papers from arXiv, processed every 8 hours.
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Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates
Federated Learning
- Introduction of FedIoC, a framework for federated attack detection that encodes threat indicators into gradient updates.
- Utilization of a supervised contrastive loss to enhance the representation of attack campaigns in gradient updates.
- Demonstrated effectiveness on two public datasets (CTU-13 and UNSW-NB15) for recovering attack campaign structures.
- No raw indicators are transmitted, preserving privacy and complying with data protection regulations.
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Federated Attack Campaign Detection via Contrastive Encoding of Threat Indicators in Gradient Updates
Summary
The paper presents FedIoC, a modular framework designed for detecting orchestrated cyberattack campaigns across multiple organizations using Federated Learning (FL). Traditional methods require sharing sensitive telemetry data, which is often hindered by privacy concerns and regulatory constraints. FedIoC allows clients to encode local structured threat indicators into their gradient updates, utilizing a supervised contrastive loss to enhance the representation of attack campaigns. By clustering the gradients based on cosine similarity, the FL server can recover global patterns of attack campaigns without direct transmission of sensitive indicators. The authors evaluate FedIoC on two public threat-detection benchmarks, demonstrating its effectiveness in recovering cross-organizational campaign cohorts from fragmented data. The framework not only preserves the rich contextual information of indicators but also opens avenues for future research on improving gradient encoding techniques.
Methodology
The methodology involves a federated learning setup where each client identifies local flows matching threat indicators and trains using a contrastive loss that emphasizes campaign identity in the gradient updates. The server then clusters these updates based on cosine similarity to recover global attack patterns.
Results
The evaluation on CTU-13 and UNSW-NB15 datasets showed that FedIoC successfully recovers cross-organizational attack campaign cohorts from disjoint indicator sets, demonstrating the framework's capability to leverage local data effectively while maintaining privacy.
Implications
FedIoC has significant implications for cybersecurity, enabling organizations to collaboratively detect and respond to cyber threats without compromising sensitive data. This approach could enhance the overall security posture of multiple organizations by facilitating better threat intelligence sharing.
Locating and Steering Refusal Beyond Attention
NLP
Large Language Models
Interpretability
- Refusal is a shared representation across different model architectures.
- The refusal direction must be read at the fresh write site for effective harm detection.
- A detector-triggered gate can significantly reduce jailbreak attack success rates.
- The refusal representation can be aligned across architectures using a rigid rotation.
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Locating and Steering Refusal Beyond Attention
Summary
This paper investigates the concept of refusal in language models, particularly focusing on how refusal is represented across different architectures, such as transformers and state-space models (SSMs). The authors demonstrate that refusal is not architecture-specific but rather a shared representation that can be aligned across different model types using a rigid rotation. They find that the refusal direction must be read at the fresh write site of each architecture, rather than in the accumulated residual stream. The study also introduces a detector-triggered gate mechanism that effectively lowers the success rate of jailbreak attacks across various architectures, confirming that the refusal direction can be transported and utilized in different model frameworks. The findings suggest that safety tooling for language models can be adapted to new architectures by re-estimating the refusal direction at the appropriate readout site, rather than needing to be completely rebuilt.
Methodology
The authors employed a combination of theoretical analysis and empirical testing across multiple language model architectures, including transformers and state-space models. They utilized a linear probe to detect harmful inputs and a detector-triggered gate to control refusals. The effectiveness of their approach was evaluated against various attack strategies, including adaptive attacks that optimize prompts against the defense.
Results
The study found that the refusal direction could be aligned across architectures, allowing a harm probe trained on one model to effectively flag harmful inputs in another. The detector-triggered gate reduced the success rate of jailbreak attacks from 15.0% to 1.0% on the SSM model, demonstrating the robustness of the approach. Additionally, the automated judge used for harm detection showed a high agreement rate with human raters, validating its effectiveness.
Implications
The findings have significant implications for the development of safe and interpretable language models. By demonstrating that refusal can be effectively transferred across architectures, the research suggests a pathway for enhancing the safety of newly deployed models without the need for extensive retraining of safety tooling. This could streamline the deployment of language models in sensitive applications where refusal is critical.
WEECFP-SuRGE: Wide Embedded Extended Connectivity Fingerprint with Substructure Rotary Graph-distance Encoding
Graph Learning
- Introduction of WEECFP, a parameter-free continuous molecular fingerprint with enhanced substructure representation.
- Development of WEECFP-SuRGE, a transformer architecture utilizing SuRGE for improved molecular property prediction.
- Achieved top rankings on the TDC ADMET leaderboard without external pretraining, outperforming classical fingerprint methods.
- Demonstrated near-lossless tokenization with high recovery rates of canonical SMILES.
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WEECFP-SuRGE: Wide Embedded Extended Connectivity Fingerprint with Substructure Rotary Graph-distance Encoding
Summary
This paper introduces WEECFP, a novel 1024-dimensional continuous molecular fingerprint that enhances the representation of Morgan substructures by distributing them across approximately thirty-two signed positions in a single vector. The authors also present WEECFP-SuRGE, a transformer architecture that utilizes a self-attention mechanism based on Substructure Rotary Graph-distance Encoding (SuRGE), which is a rotation method influenced by the shortest-path graph distance of molecules. The proposed WEECFP-SuRGE Blend, which is a combination of seven models, achieves significant performance on the TDC ADMET leaderboard, ranking #2 overall and #1 among methods without external pretraining. The model excels in various benchmarks, including Pgp, Lipophilicity, and CYP2D6 Substrate, demonstrating its effectiveness in predicting molecular properties. Additionally, the paper highlights the near-lossless nature of WEECFP tokenization, achieving a 99.9% recovery rate of canonical SMILES for in-distribution molecules. The authors also establish a strong correlation between their graph distance encoding and true pairwise distances, indicating the robustness of their approach.
Methodology
The methodology consists of three main components: the WEECFP-S molecular fingerprint, which encodes Morgan substructures into a continuous vector; a transformer model that tokenizes molecules using WEECFP and applies SuRGE for positional encoding; and a blend of seven models for performance evaluation on various benchmarks. The WEECFP fingerprint is deterministic and parameter-free, while SuRGE adapts Rotary Position Encoding to molecular graphs.
Results
The WEECFP-SuRGE Blend achieved the lowest average regression rank on the TDC ADMET leaderboard and ranked #2 overall. It secured #1 finishes in multiple categories, including Pgp and Lipophilicity, across a comprehensive suite of 22 benchmarks. On MoleculeNet, it outperformed classical fingerprint baselines in three out of four regression tasks. The tokenization method demonstrated a 99.9% recovery rate for canonical SMILES in in-distribution datasets.
Implications
The findings suggest that WEECFP-SuRGE can significantly enhance molecular property prediction without the need for extensive pretraining, making it a valuable tool for virtual screening and ADMET profiling in drug discovery. The approach could lead to more efficient models in computational chemistry and related fields.
A Repeated-Measurement Study for Cultural Analytics of English Song Lyrics Using Five Large Language Models
NLP
Large Language Models
- LLMs can annotate cultural texts but their reliability varies by social construct.
- Self-esteem shows the strongest reliability, while seeking recognition is less stable.
- Consensus labels from LLMs can be useful for supervised classification but do not confirm construct validity.
- Establishing measurement reliability is crucial before using LLM outputs in cultural analytics.
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A Repeated-Measurement Study for Cultural Analytics of English Song Lyrics Using Five Large Language Models
Summary
This study investigates the reliability of five large language models (LLMs) as zero-shot annotators for four latent social constructs—self-esteem, self-control, seeking belonging, and seeking recognition—expressed in English song lyrics. The authors emphasize the importance of establishing measurement reliability before using LLM outputs in cultural analytics. By conducting repeated annotations on a large corpus of song lyrics, the study examines three key properties: consistency across repeated runs, convergence across different models, and the transferability of consensus labels to supervised classification tasks. The findings reveal that LLM-based measurement reliability varies by construct, with self-esteem showing the highest reliability and seeking recognition the lowest. Self-control and seeking belonging demonstrate intermediate reliability that is dependent on the specific model used. Additionally, the study finds that consensus labels from LLMs contain learnable signals, although this does not automatically confirm construct validity. The authors argue that repeated-measurement stability and cross-model convergence should be reported before LLM annotations are utilized as scalable measurements in cultural analytics.
Methodology
The study employs a repeated-measurement design to evaluate five LLMs on their ability to annotate song lyrics for four latent social constructs. It analyzes the consistency of annotations across repeated runs, the convergence of results from different models, and the transferability of consensus labels to supervised classification tasks.
Results
The results indicate that self-esteem has the highest repeated-measurement reliability across models, while seeking recognition is the least stable. Self-control and seeking belonging show intermediate reliability that varies by model. The consensus labels derived from LLM annotations contain learnable signals, suggesting potential for downstream classification tasks.
Implications
The findings highlight the necessity of validating LLM outputs in cultural analytics, particularly regarding their reliability and the need for careful interpretation of annotations as measurements of latent constructs. This research could inform future studies on the use of LLMs in analyzing cultural texts and their implications for understanding social constructs.
FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification
Federated Learning
- FedDRAW improves federated learning by adjusting aggregation weights based on model similarity rather than solely on data size.
- The method employs dual annealing schedules to balance client influence during training.
- FedDRAW ensures that smaller hospitals are not permanently outweighed by larger institutions in the model training process.
- The approach outperforms eight state-of-the-art methods in chest radiograph classification tasks.
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FedDRAW: Federated Dual Reputation Annealing Weighting for Heterogeneous Multi-Institutional Chest Radiograph Classification
Summary
The paper introduces FedDRAW, a novel federated learning approach designed to improve chest radiograph classification across heterogeneous multi-institutional datasets. Traditional federated averaging methods assign aggregation weights based solely on local sample sizes, which can lead to smaller hospitals being overshadowed by larger ones, even if their data is more informative. FedDRAW addresses this issue by implementing a dual reputation annealing weighting system that combines a data-size prior with model similarity metrics. The method employs two coupled annealing schedules: an inner schedule that adjusts client reputation from data size to model similarity during training, and an outer deferred schedule that equalizes client weights at convergence. This approach ensures that smaller hospitals retain influence in the model training process. The authors evaluate FedDRAW against seven federated learning baselines using two chest radiograph datasets (CheXpert and ChestMNIST) across twelve simulated client-partition scenarios. The results demonstrate that FedDRAW achieves the highest average rank in terms of AUC and the geometric mean of sensitivity and specificity, indicating its effectiveness in creating less biased diagnostic models.
Methodology
FedDRAW utilizes a federated learning framework where hospitals train a shared model locally. It combines a data-size prior with cosine similarity metrics to adjust client weights dynamically across training rounds. The method employs two annealing schedules: one that shifts client reputation from data size to model similarity and another that relaxes weights to uniformity at convergence.
Results
FedDRAW achieved the highest average rank among eight methods in terms of AUC and the geometric mean of sensitivity and specificity across twelve simulated client-partition scenarios. Statistical tests confirmed that these performance improvements were significant compared to traditional federated learning methods.
Implications
The FedDRAW approach has the potential to enhance collaborative medical imaging diagnostics by ensuring equitable influence from all participating institutions, particularly benefiting smaller hospitals with valuable but limited datasets. This could lead to more accurate and generalized AI models in clinical settings.
Amortizing Scaling Law Construction Costs
Large Language Models
Optimization
Efficient ML
- Introduces a framework for efficient scaling law construction using Bayesian optimization.
- Demonstrates significant computational savings (10–100x) by avoiding exhaustive grid evaluations.
- Utilizes surrogate evaluations to enhance the recovery of scaling laws from sparse data.
- Proposes metrics for assessing the efficiency of scaling law fitting methods.
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Amortizing Scaling Law Construction Costs
Summary
The paper addresses the challenge of constructing scaling laws for large foundation models, which traditionally requires extensive computational resources to evaluate a dense grid of hyperparameters, token budgets, and parameter counts. The authors propose a novel framework that reformulates the data collection process as a Bayesian optimization problem, allowing for efficient scaling law construction. By progressively expanding the compute budget during data acquisition and utilizing surrogate evaluations to fill in gaps in the experimental grid, the proposed method significantly reduces the computational cost of scaling law fitting. The authors demonstrate that their approach can achieve accurate scaling law fits with computational savings of up to 10–100 times compared to exhaustive grid evaluations. This work not only enhances the efficiency of scaling law construction but also provides a structured methodology for optimizing hyperparameters in the context of large-scale model training.
Methodology
The authors formalize scaling law construction as a Bayesian optimization problem, where the data collection process is optimized to progressively expand the compute budget. They employ surrogate models to approximate the loss envelope and enable sparse data collection, thus reducing the need for dense evaluations across all configurations.
Results
The proposed method allows for accurate scaling law fitting with a fraction of the computational cost typically required. Empirical results show that the Bayesian optimization approach can closely match the performance of traditional dense grid evaluations while achieving substantial savings in compute resources.
Implications
This work has significant implications for the training of large language models and other foundation models, as it provides a more efficient way to derive scaling laws that guide model design and optimization. It can lead to faster experimentation cycles and reduced resource consumption in deep learning research and applications.
Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials
Efficient ML
Theory
Optimization
- Introduction of two Hessian-derived data augmentation methods: UniAug and ModeAug.
- Both methods enhance MLIP training by incorporating curvature information without modifying training objectives.
- UniAug utilizes isotropic Gaussian displacements, while ModeAug employs normal mode-weighted displacements.
- The proposed methods show improved accuracy over traditional energy-force training and are competitive with direct Hessian supervision.
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Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials
Summary
This paper addresses the limitations of standard machine-learning interatomic potentials (MLIPs) that primarily focus on energy and force predictions, neglecting the Hessian information crucial for applications like vibrational analysis and transition state searches. The authors propose two innovative data augmentation techniques, UniAug and ModeAug, which utilize Hessian-derived information through Taylor expansions to generate perturbed molecular geometries. These methods enhance the training data without altering the training objectives or requiring higher-order backpropagation, thus maintaining computational efficiency. Comprehensive evaluations demonstrate that both augmentation strategies improve model accuracy across various datasets, achieving results comparable to those obtained with direct Hessian supervision while avoiding the associated computational overhead.
Methodology
The authors developed two data augmentation schemes, UniAug and ModeAug, which leverage Hessian information via Taylor expansions to create perturbed molecular geometries. UniAug applies isotropic displacements, while ModeAug samples displacements along vibrational modes weighted by the inverse of their eigenvalue magnitudes. This approach allows for effective data augmentation without the need for higher-order automatic differentiation, preserving the efficiency of existing MLIP architectures.
Results
The proposed augmentation methods were evaluated on both non-equilibrium and equilibrium datasets, demonstrating significant improvements in model accuracy compared to Hessian-free baselines. The results indicate that UniAug and ModeAug can achieve performance levels comparable to those obtained with direct Hessian supervision, while avoiding the computational and memory overhead typically associated with higher-order differentiation.
Implications
The findings suggest that incorporating Hessian information through data augmentation can enhance the performance of MLIPs in various applications, including molecular dynamics simulations and vibrational spectroscopy. This approach provides a scalable and efficient strategy for improving the predictive capabilities of machine learning models in computational chemistry and materials science.
GLASS: Graph-Language Alignment with Spherical Scoring for Transferable Graph-Level Anomaly Detection
Graph Learning
- GLASS introduces a transferable GLAD paradigm using density estimation on an aligned graph-language hypersphere.
- The framework integrates Local Topology Descriptors, GraphDP, and Spherical Multi-Modal Scoring for robust anomaly detection.
- Empirical results show GLASS outperforms existing methods in single-domain performance and enables effective zero-shot and few-shot transfer.
- The model captures anomalies at multiple levels of granularity through a structured representation approach.
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GLASS: Graph-Language Alignment with Spherical Scoring for Transferable Graph-Level Anomaly Detection
Summary
The paper presents GLASS, a novel framework for graph-level anomaly detection (GLAD) that enhances cross-domain transferability through graph-language alignment on a unit hypersphere. GLASS constructs a unified representation space by aligning a structure-aware graph encoder with an instruction-aware text embedding using a multi-slice soft cosine objective. The framework creates a compact Graph Descriptor Prompt (GraphDP) that encapsulates local, global, and semantic graph properties, facilitating domain-agnostic anomaly scoring. By enforcing multi-scale consistency via Matryoshka representation slices, GLASS captures anomalies at various granularities. The anomaly detection process is framed as density estimation on the aligned hypersphere, employing Spherical Multi-Modal Scoring (SMS) that utilizes von Mises–Fisher kernel density estimators in both graph and text embedding spaces. This probabilistic approach allows for angular k-nearest-neighbor scoring as a limiting case and effectively combines structural and semantic anomaly signals. GLASS enables zero-shot anomaly detection by encoding target domain GraphDPs without target training data and allows few-shot adaptation with minimal normal examples. Empirical evaluations across twelve benchmarks and three meta-domains demonstrate GLASS's superior performance in terms of average AUROC and ranking compared to existing GLAD baselines, showcasing its effectiveness in cross-domain transfer.
Methodology
GLASS employs a pipeline that includes Local Topology Descriptors for stable node-level evidence, serialization of graph properties into GraphDP, and alignment of a structure-aware graph encoder with a frozen instruction-aware text embedding on the unit hypersphere. Anomaly detection is framed as density estimation using Spherical Multi-Modal Scoring, leveraging von Mises–Fisher kernel density estimators.
Results
GLASS achieved the best average AUROC and ranking across twelve benchmarks and three meta-domains, demonstrating superior performance compared to advanced GLAD baselines. The framework effectively enabled zero-shot anomaly detection and few-shot adaptation, showcasing its robustness in cross-domain scenarios.
Implications
The GLASS framework has significant implications for various applications in fields such as molecular screening, protein analysis, and social computing, where transferable anomaly detection across different graph domains is crucial. Its ability to perform well with limited target data opens avenues for practical deployment in real-world scenarios.
Hakken: Predicting future discoveries to fill the gaps in today's knowledge
Graph Learning
Large Language Models
NLP
- Hakken predicts novel scientific relationships using a transformer-based model and knowledge graphs.
- The system establishes a new benchmark for time-aware multi-label relation prediction in the biomedical domain.
- Hakken's predictions were validated with biologists, leading to empirical confirmations of new gene interactions.
- The model provides explainability for its predictions, aiding researchers in evaluating new hypotheses.
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Hakken: Predicting future discoveries to fill the gaps in today's knowledge
Summary
The paper introduces Hakken, a novel AI system designed for knowledge prediction in scientific research. Hakken employs a transformer-based model that utilizes temporal sequences of knowledge graphs derived from extensive research literature, combined with the semantic knowledge of large language models (LLMs). This system aims to predict new, undocumented relationships between scientific concepts, thereby expanding existing knowledge. The authors demonstrate Hakken's capabilities in the biomedical domain, establishing a new benchmark for time-aware multi-label relation prediction. The model's predictions are coherent and informative over extended historical data. The authors scored 1.5 million hypotheses related to aging, validated several predictions with biologists, and progressed three of them to empirical validation in wet-lab settings. Notably, two predictions were confirmed, revealing previously undocumented interactions between genes and proteins, which could have significant implications for drug discovery and repurposing. Hakken's approach represents a significant advancement in leveraging AI for scientific discovery, moving beyond traditional methods that merely collate existing knowledge.
Methodology
Hakken utilizes a unified temporal graph and language model called THiGERLLM, which predicts future relation labels in a multi-label setting. It models temporal evolution using a temporal Graph Neural Network and integrates LLM semantic knowledge with the graph structure. Additionally, it employs a model-agnostic explanation framework, PHELInE, to elucidate the predictions made by THiGERLLM.
Results
Hakken achieved a new benchmark in time-aware multi-label relation prediction, scoring 1.5 million hypotheses related to aging. The model's predictions were qualitatively validated with biologists, leading to the confirmation of two significant interactions: TP53 with BAMBI and RAF1 with TNF, both of which were previously undocumented.
Implications
The findings suggest that Hakken could significantly enhance the efficiency of scientific discovery, particularly in rapidly evolving fields like biomedicine. By predicting novel relationships, it may facilitate new avenues for research and drug discovery, ultimately contributing to advancements in healthcare and scientific understanding.
On the Abundance of Critical Points of the t-SNE Energy
Theory
Optimization
- t-SNE's energy landscape is non-convex, leading to multiple local minima.
- The authors construct infinite families of distinct critical points based on symmetry pairs.
- Numerical examples illustrate the complexity of the t-SNE minimization problem.
- The trivial embedding is shown not to be a local minimum for many symmetry pairs.
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On the Abundance of Critical Points of the t-SNE Energy
Summary
This paper investigates the energy landscape of the t-SNE algorithm, which is widely used for dimensionality reduction but is known for its non-convex energy landscape that complicates theoretical understanding. The authors demonstrate that the t-SNE energy admits infinitely many distinct critical points, which can lead to issues such as topology breaking and spurious clustering. They construct these critical points by identifying pairs of discrete symmetries in both the feature space and the target embedding space, preserved under gradient dynamics. The paper includes a general formulation of the t-SNE energy, reproduces numerical examples illustrating the complexity of the minimization problem, and develops a notion of symmetry invariant couplings. The authors prove the well-posedness of the gradient flow and establish that the trivial embedding is not a local minimum for a wide class of symmetry pairs. The main result indicates that for radially symmetric input data, there exist infinitely many distinct critical points in the Euclidean space for the t-SNE energy. The findings provide a rigorous foundation for understanding the complexities of t-SNE and suggest directions for future research.
Methodology
The authors formulate the t-SNE energy and analyze its critical points by employing symmetry invariance principles. They reproduce numerical examples to illustrate the energy landscape's complexity and prove the well-posedness of the gradient flow, demonstrating the existence of critical points through theoretical constructs.
Results
The paper establishes that the t-SNE energy has infinitely many distinct critical points for radially symmetric input data in Euclidean space. It also shows that the trivial embedding is not a local minimum, indicating the presence of complex critical configurations that can lead to topology breaking and spurious clustering.
Implications
The findings have significant implications for the application of t-SNE in various fields, including data visualization and analysis, as they provide a deeper understanding of the algorithm's limitations and behavior. This could lead to improved methodologies for dimensionality reduction and better interpretations of high-dimensional data.
Conformity Breaks Conformal Prediction
Large Language Models
Theory
NLP
- Introduction of the score-mechanism shift, highlighting how peer influence alters model scoring.
- Demonstration of a significant drop in coverage rates under unanimous-wrong peer conditions.
- Identification of a hidden conditional failure affecting low-confidence items.
- Critique of existing defenses against peer influence, showing their inadequacy.
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Conformity Breaks Conformal Prediction
Summary
This paper investigates the impact of social conformity on the effectiveness of conformal prediction in multi-agent large language model (LLM) systems. The authors introduce the concept of a 'score-mechanism shift,' where the model's score for the correct answer is altered when it is exposed to peers that unanimously assert a wrong answer. This shift leads to a significant drop in coverage rates of conformal prediction, from a calibrated 90% to 74% under conditions of unanimous incorrect peer responses. The study highlights a hidden conditional failure, where the coverage for low-confidence items drops from 87% to 47%, despite the overall average remaining deceptively high. The authors also critique existing defenses against this issue, such as the act-vs-escalate policy proposed by Wang et al., demonstrating that these methods fail to address the underlying problem of peer influence on model behavior. The findings underscore the need for new approaches to maintain the integrity of conformal prediction in environments where models interact with each other.
Methodology
The authors conducted experiments using multiple open-weight LLMs on various multiple-choice question-answering tasks. They analyzed the models' performance under different peer pressure conditions, measuring the impact on coverage rates of conformal predictions. The study involved calibrating models on solo data and testing them under peer influence scenarios, including unanimous correct, unanimous wrong, and mixed responses.
Results
The results revealed that the coverage rate of conformal prediction dropped from 90% to 74% when models were exposed to unanimous-wrong peers. Additionally, for low-confidence items, coverage fell drastically from 87% to 47%. The study also found that existing defense mechanisms, such as the act-vs-escalate policy, were ineffective, with significant rates of incorrect responses being accepted under peer pressure.
Implications
The findings suggest that conformal prediction methods need to be re-evaluated and adapted for use in multi-agent systems where models interact. This has implications for the deployment of LLMs in collaborative environments, such as automated decision-making systems, where peer influence could lead to erroneous conclusions.
An Energy-Based Conservative-Dissipative Latent Neural Evolution Operator for Magnetization Dynamics
Theory
Efficient ML
Optimization
- Introduces a reduced-order model for micromagnetic dynamics using a latent neural ODE.
- The model leverages an energy-based approach that ensures monotonic decrease of a learned scalar potential.
- Demonstrates significant improvements in trajectory prediction accuracy compared to traditional methods.
- Evaluates various latent energy formulations, with deep-quadratic energy yielding the best results.
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An Energy-Based Conservative-Dissipative Latent Neural Evolution Operator for Magnetization Dynamics
Summary
This paper presents a novel energy-based reduced-order model for micromagnetic magnetization dynamics, integrating a convolutional autoencoder with a structured latent neural ordinary differential equation (ODE). The model is inspired by the Landau–Lifshitz–Gilbert (LLG) equation, where the latent vector field is derived from the gradient of a learned scalar potential through antisymmetric and symmetric operators. The potential, which is not equivalent to Gibbs free energy, decreases monotonically along continuous-time solutions. The model is trained using latent and decoded-rollout losses without the need for time-derivative supervision or physical-energy labels. During inference, the model allows for efficient trajectory prediction by encoding an initial state, evolving it in latent space, and decoding at specified output times. The authors evaluate different latent energy formulations on datasets from the NIST µMAG Standard Problem 4, revealing that antisymmetric-dissipative models outperform dissipative-only models in accuracy for uninterrupted rollouts. The deep-quadratic energy formulation achieves the best overall accuracy and demonstrates slower error growth in extended rollouts, indicating its potential for practical applications in micromagnetic simulations.
Methodology
The authors developed a latent neural ODE that couples a convolutional autoencoder with a structured energy-based model. The latent vector field is generated from a learned scalar potential using antisymmetric and symmetric operators. The model is trained on short trajectory windows using latent and decoded-rollout losses, without requiring time-derivative supervision or physical-energy labels.
Results
The study found that antisymmetric-dissipative models provided more accurate trajectory predictions than dissipative-only models during uninterrupted rollouts. The deep-quadratic energy formulation achieved the highest accuracy across both field directions and exhibited slower error growth when extending rollouts beyond the training horizon.
Implications
This work has significant implications for the design and optimization of magnetic devices, as it offers a computationally efficient alternative to traditional micromagnetic solvers. The proposed model can facilitate faster evaluations in parameter studies and inverse design tasks, potentially accelerating advancements in magnetic technology.
A Data Fusion Framework for Grounding Aerospace Surrogate Model via Experimental Wind-Tunnel Observations
Optimization
Efficient ML
Theory
- Introduces a correction framework that integrates experimental data into CFD-trained surrogate models.
- Achieves high predictive fidelity for aerodynamic forces while addressing systematic discrepancies.
- Demonstrates significant improvements in prediction accuracy using limited experimental datasets.
- Maintains the computational efficiency of the surrogate model without retraining its parameters.
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A Data Fusion Framework for Grounding Aerospace Surrogate Model via Experimental Wind-Tunnel Observations
Summary
This paper presents a novel data fusion framework aimed at improving the predictive fidelity of aerodynamic surrogate models by integrating experimental wind-tunnel observations. The authors highlight the limitations of traditional Computational Fluid Dynamics (CFD) models, which, while accurate, often exhibit systematic discrepancies when compared to experimental data. To address this, they propose a correction network that adapts a pretrained CFD-based deep learning surrogate model using Pressure-Sensitive Paint (PSP) measurements from wind-tunnel tests. The Geotransolver surrogate, initially trained on 2,300 high-fidelity CFD simulations of the NASA Common Research Model (CRM), achieves high accuracy (R² > 0.99) in predicting aerodynamic forces but fails to align with experimental observations. By employing a correction network trained on spatially registered PSP data at two Mach numbers (0.70 and 0.85), the authors successfully reduce prediction errors, particularly in critical areas such as the wing suction peak and shock location. The grounded surrogate demonstrates improved agreement with experimental data, achieving discrepancies within 2.3–2.7% across held-out angles of attack. This framework allows for the effective integration of limited experimental data into large-scale simulation-trained surrogates, enhancing their accuracy while maintaining computational efficiency.
Methodology
The proposed methodology involves pretraining a surrogate model on a large dataset of CFD simulations, followed by the training of a lightweight correction network using experimental wind-tunnel measurements. This correction network learns the discrepancies between CFD predictions and experimental data without modifying the pretrained surrogate parameters.
Results
The grounded surrogate model shows improved agreement with experimental measurements, particularly at Mach 0.85, reducing prediction errors significantly. The model achieves discrepancies within 2.3–2.7% of the measured surface pressure range across held-out angles of attack, outperforming direct interpolation methods.
Implications
This framework has the potential to enhance the accuracy of aerodynamic predictions in aerospace applications, allowing for more reliable design and optimization processes. It also demonstrates the feasibility of integrating experimental data into existing simulation frameworks, paving the way for improved modeling techniques in various engineering fields.
A Quantum Variational Approach to Prototypical Recurrent Unit
Time Series
- Introduction of a lightweight quantum recurrent architecture (QPRU) with fewer parameters than classical and quantum counterparts.
- Utilization of Variational Quantum Circuits (VQCs) for modeling temporal dependencies in sequential data.
- QPRU achieves competitive forecasting performance on time-series benchmarks.
- The architecture offers enhanced scalability and practical advantages over existing models.
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A Quantum Variational Approach to Prototypical Recurrent Unit
Summary
This paper presents the Quantum Prototypical Recurrent Unit (QPRU), a novel quantum recurrent architecture designed to efficiently model sequential data with significantly fewer parameters than classical and existing quantum recurrent models. The QPRU is inspired by the classical Prototypical Recurrent Unit (PRU) and aims to address the complexity and resource demands of previous quantum architectures like Quantum LSTM (QLSTM) and Quantum GRU (QGRU). The authors highlight the advantages of QPRU, including enhanced scalability and competitive forecasting performance on time-series benchmarks. The methodology involves using Variational Quantum Circuits (VQCs) to model temporal dependencies, where quantum circuits serve as nonlinear transformations within recurrent updates. The experimental results demonstrate that QPRU matches the forecasting accuracy of state-of-the-art models while maintaining a compact design, making it a promising candidate for practical applications in quantum machine learning.
Methodology
The QPRU architecture incorporates Variational Quantum Circuits (VQCs) to model temporal dependencies, where quantum circuits perform nonlinear transformations in recurrent updates. The model reduces the number of VQCs used while preserving representational capacity, allowing for efficient training and implementation on Noisy Intermediate-Scale Quantum (NISQ) devices.
Results
The QPRU demonstrated competitive forecasting performance, matching state-of-the-art baselines while significantly reducing the number of trainable parameters compared to classical LSTM and GRU, as well as quantum models like QLSTM and QGRU.
Implications
The development of QPRU suggests a pathway for more efficient quantum machine learning models that can be applied in various domains, including finance, healthcare, and natural language processing, where sequential data analysis is crucial.
Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation
NLP
Large Language Models
Efficient ML
- Introduces a two-level framework for trade-up recommendation that combines reasoning distillation and test-time training.
- Utilizes a retrieval-augmented LLM to generate structured labels and rationales for product pairs.
- Achieves significant improvements in predictive performance without the need for LLM inference during scoring.
- Demonstrates operational efficiency with a 5,000× speedup and 10,000× cost reduction compared to direct LLM inference.
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Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation
Summary
This paper addresses the challenge of trade-up recommendation in e-commerce, which involves identifying higher-quality alternatives that maintain a customer's purchase intent while offering enhanced benefits. The authors propose a two-level framework that distills reasoning from large language models (LLMs) into a compact, efficient non-generative student model, enabling scalable application across hundreds of millions of product pairs. In Level 1, a retrieval-augmented few-shot LLM teacher generates structured relation labels and natural-language rationales, which are then encoded and transferred to the student model through alignment and contrastive objectives. The student model operates using only precomputed product embeddings, eliminating the need for LLM calls during inference. In Level 2, Product-Type Test-Time Training (PT-TTT) is introduced, utilizing few-shot demonstrations to optimize category-specific adapters for the frozen student model. This approach significantly enhances predictive performance while maintaining operational efficiency. The framework demonstrates a marked improvement in AUC scores and average precision on benchmark datasets, showcasing its effectiveness in real-world applications.
Methodology
The methodology consists of a two-level framework: Level 1 involves reasoning distillation from a retrieval-augmented LLM teacher to a non-generative student model using alignment and contrastive learning. Level 2 incorporates Product-Type Test-Time Training (PT-TTT), which applies few-shot demonstrations to optimize lightweight adapters for specific product categories, enhancing the model's decision-making capabilities without requiring LLM inference during scoring.
Results
The distilled student model achieved an AUC of 0.924 on a benchmark of 8,352 product pairs, outperforming a label-only student model. With the implementation of PT-TTT, the AUC improved to 0.941, and average precision increased from 0.920 to 0.940. The inference process was approximately 5,000 times faster and 10,000 times cheaper than direct LLM inference on a proxy catalog of 100,000 pairs.
Implications
The proposed framework has significant implications for e-commerce platforms, allowing for efficient and scalable trade-up recommendations that enhance customer experience without incurring high operational costs. This approach can be adapted for various product categories and could lead to improved sales and customer satisfaction.
Distilled Continuous Diffusion Language Models Can Write Code in Few Steps---or One
NLP
Large Language Models
Generative Models
- PlaidQ is a continuous diffusion language model that allows for efficient code generation.
- The model can generate code in as few as one denoising step while maintaining functional correctness.
- Distillation techniques significantly improve the performance of the model with fewer steps.
- PlaidQ outperforms traditional autoregressive models in code generation tasks.
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Distilled Continuous Diffusion Language Models Can Write Code in Few Steps---or One
Summary
This paper introduces PlaidQ, a 0.7B continuous diffusion language model designed for efficient code generation. Unlike traditional autoregressive models that generate tokens sequentially, PlaidQ employs a bidirectional denoising approach over continuous token embeddings, allowing for parallel processing. The authors demonstrate that PlaidQ can be distilled into fewer denoising steps—down to just one—while maintaining high performance in code generation tasks. The distillation process involves distribution matching for few-step generation and paired-trajectory supervision for one-step generation. The results show that at 16 steps, PlaidQ-D16 achieves competitive performance on benchmarks like HumanEval and MBPP+, outperforming its autoregressive teacher model sampled over many more steps. Notably, the one-step generation capability of PlaidQ-D1 achieves a pass@1 score of 7.07 on HumanEval, indicating that it can produce functionally correct programs in a single denoising step. This work establishes continuous diffusion as a promising avenue for efficient code generation, suggesting that it can inherit the benefits of continuous modeling techniques.
Methodology
The authors repurpose a pretrained autoregressive model to create PlaidQ, which predicts every completion position in parallel using continuous token embeddings. They employ distribution matching for few-step generation and paired-trajectory supervision for one-step generation. The model is optimized using a hybrid Muon–AdamW optimizer to enhance convergence.
Results
PlaidQ-D16 achieves 31.78 and 40.49 pass@10 on HumanEval and MBPP+, respectively, with significantly fewer denoising steps compared to its autoregressive teacher. The one-step PlaidQ-D1 model reaches a pass@1 score of 7.07 on HumanEval, demonstrating its ability to generate correct code in a single step.
Implications
The findings suggest that continuous diffusion models can revolutionize code generation by enabling faster and more efficient sampling processes. This could lead to advancements in automated coding tools and improve the efficiency of software development.
Fractal basins trap latent reasoning
Theory
Optimization
- Reasoning models exhibit transient chaos, leading to extended reasoning times on difficult tasks.
- Fractal basins of attraction increase in complexity with task difficulty across various reasoning tasks.
- Basin entropy serves as a new metric to quantify the complexity of convergence basins in reasoning models.
- The study reveals that reasoning slowdowns are an inevitable consequence of problem hardness.
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Fractal basins trap latent reasoning
Summary
This paper explores the dynamics of reasoning models in artificial intelligence, particularly focusing on the phenomenon of 'overthinking' where models become trapped in extended reasoning without reaching a solution. The authors propose that this behavior can be understood through the lens of dynamical systems theory, revealing that reasoning models exhibit transient chaos and possess fractal basins of attraction. These fractal basins become more complex with increasing task difficulty, as observed in various reasoning tasks such as Sudoku and visual puzzles. The study introduces a new metric, basin entropy, to quantify the complexity of these basins and demonstrates a strong correlation between basin entropy and the number of iterations required for convergence. The findings suggest that reasoning slowdowns are an inherent characteristic of problem hardness in AI models, establishing reasoning traces as a novel class of dynamical systems. This work highlights the importance of understanding the underlying dynamics of reasoning in AI, which could lead to improved model designs and efficiency in solving complex tasks.
Methodology
The authors employed dynamical systems theory to analyze reasoning models, treating them as optimization problems. They introduced a new probe to examine how varying initial latent states affects convergence times across different reasoning tasks. The complexity of the basins of attraction was quantified using basin entropy, which distinguishes self-similar fractal basins from random or smooth basins.
Results
The study found that reasoning models produce fractal basins when faced with hard problems, with basin entropy correlating strongly with the number of iterations required for convergence. This correlation was consistent across various model architectures and tasks, indicating that fractal basins are a common feature of reasoning dynamics in AI.
Implications
The findings have significant implications for the design and optimization of AI reasoning models. By understanding the dynamics of reasoning and the impact of task difficulty, researchers can develop more efficient models that minimize overthinking and improve performance on complex tasks.
Disentangling Attention in Deep Operator Learning: A Controlled Study of Data-Driven and Physics-Informed Architectures
Theory
Efficient ML
Optimization
- Cross-attention mechanisms significantly improve the accuracy of DeepONets in solving PDEs.
- Self-attention can be inconsistent, providing benefits in complex scenarios but potentially degrading performance in simpler cases.
- The study systematically evaluates the impact of different attention configurations on model performance.
- Increasing the depth of cross-attention improves accuracy but incurs higher computational costs.
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Disentangling Attention in Deep Operator Learning: A Controlled Study of Data-Driven and Physics-Informed Architectures
Summary
This paper investigates the integration of attention mechanisms in Deep Operator Networks (DeepONets) for solving partial differential equations (PDEs) more efficiently than traditional numerical methods. The authors conduct a systematic study of five DeepONet variants featuring different attention mechanisms, including cross-attention and self-attention, under both data-driven and physics-informed training regimes. The study aims to isolate the effects of these mechanisms on model accuracy. The experiments are conducted on various PDE problems, including nonlinear diffusion-reaction equations and heat conduction scenarios. Results indicate that cross-attention significantly enhances accuracy, reducing mean relative L2 error by factors ranging from 2.4 to 28.0 compared to classical DeepONet. The findings also reveal that while self-attention can be beneficial in complex scenarios, it may degrade performance in simpler cases. Overall, the research highlights the importance of query-dependent cross-attention as a robust mechanism for improving operator learning in scientific computing.
Methodology
The authors systematically evaluate five variants of DeepONet with distinct attention mechanisms, including cross-attention and self-attention, under both data-driven and physics-informed training. They assess the models on various PDE problems to isolate the effects of these mechanisms on accuracy.
Results
The introduction of per-sensor tokenization with cross-attention reduces the mean relative L2 error of classical DeepONet by factors of 2.4 to 28.0 across different benchmark-training combinations. The best attention configurations achieve reductions of 3.5 to 32.3. While self-attention shows inconsistent results, it improves performance when combined with cross-attention. Increasing cross-attention depth enhances accuracy but with diminishing returns and higher costs in physics-informed training.
Implications
The findings suggest that adaptive attention mechanisms can significantly enhance the performance of neural operators in scientific computing, potentially leading to more efficient simulations in engineering disciplines. This could facilitate advancements in areas requiring rapid evaluations of PDEs, such as sensitivity analysis and digital twin applications.
REFINE: LLM Refinement over Budgeted Text-Attributed Graphs for Personalized Medical Concept Representation
Graph Learning
Reinforcement Learning
Large Language Models
- REFINE constructs patient-specific temporal graphs from a global TKG for personalized medical concept encoding.
- A sequential reinforcement learning policy is used to adaptively select the relational context for each observed medical code.
- The framework integrates a heterogeneous GNN and a frozen LLM for capturing structural dependencies and refining semantic representations.
- Experiments show that REFINE outperforms existing models and demonstrates robustness in diverse scenarios.
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REFINE: LLM Refinement over Budgeted Text-Attributed Graphs for Personalized Medical Concept Representation
Summary
The paper introduces REFINE, a novel framework designed to enhance medical concept representations for Electronic Health Records (EHR) by leveraging Text-attributed Knowledge Graphs (TKGs). Traditional encoders often treat medical codes uniformly across patients, failing to account for the varying significance of these codes based on individual clinical contexts. REFINE addresses this by constructing patient-specific temporal graphs from a global TKG and employing a sequential reinforcement learning policy to determine the optimal amount of relational context to incorporate for each observed code. This personalized approach allows for richer, context-dependent representations. The framework utilizes a heterogeneous Graph Neural Network (GNN) to capture structural dependencies and a frozen Large Language Model (LLM) to refine semantic representations through graph-aware soft prompts. The experimental results on MIMIC-III and MIMIC-IV datasets demonstrate that REFINE significantly improves EHR prediction performance, surpassing strong baseline models and showing robustness across various evaluation scenarios.
Methodology
The REFINE framework begins with a global TKG to create personalized graphs for each patient. A reinforcement learning policy sequentially determines the budget for KG expansion for each observed code, allowing for tailored relational context. The resulting graphs are processed using a heterogeneous GNN and a frozen LLM, which employs graph-aware soft prompts to refine the semantic understanding of medical codes based on patient-specific data.
Results
The experiments conducted on MIMIC-III and MIMIC-IV datasets reveal that REFINE consistently enhances the performance of various EHR prediction models. It outperforms strong baseline methods and demonstrates significant improvements across different evaluation metrics, including component ablation studies and scenarios with limited data.
Implications
The findings suggest that personalized medical concept representations can lead to better predictive performance in EHR applications, potentially improving clinical decision-making and patient outcomes. The methodology could be applied to other domains requiring personalized representations based on relational data.
Coarse-Graining Hidden Representations: Unsupervised Neuron Selection via Mapping Entropy
Theory
Efficient ML
Interpretability
- Introduces a novel unsupervised method for neuron selection based on mapping entropy.
- Demonstrates that ME optimization can recover minimal representations in teacher-student networks.
- Shows that ME-selected subnetworks outperform random subsets in predictive tasks.
- Links configurational distinguishability to improved performance under compression.
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Coarse-Graining Hidden Representations: Unsupervised Neuron Selection via Mapping Entropy
Summary
This paper addresses the challenge of identifying essential neurons in overparameterized neural networks without relying on labels or gradients. The authors propose a novel approach that treats neuron selection as a coarse-graining problem, where a subset of neurons is retained based on their contribution to the network's discriminatory power. They utilize mapping entropy (ME) as a criterion to evaluate the loss of information when neurons are discarded. The methodology is fully unsupervised and relies solely on hidden-activation statistics. The authors demonstrate that optimizing for ME can recover minimal representations in teacher-student networks and select coherent functional-class mappings in non-linear Gaussian process tasks. Their experiments show that ME-selected subnetworks outperform random selections of the same size, particularly under strong compression, thus linking configurational distinguishability to predictive performance. This work sheds light on the internal organization of neural networks and offers insights into efficiency and interpretability in deep learning.
Methodology
The authors frame neuron selection as a coarse-graining problem, using mapping entropy (ME) to evaluate the loss of discriminatory power when neurons are discarded. They apply this information-theoretic approach to optimize the selection of neurons based on hidden-activation statistics, without requiring supervised labels.
Results
The results indicate that ME optimization successfully identifies essential neurons, leading to improved performance in various tasks. In experiments, ME-selected subnetworks consistently outperformed randomly selected subsets, particularly in scenarios involving strong compression, highlighting the effectiveness of the proposed method.
Implications
This research has significant implications for model compression, efficiency, and interpretability in deep learning. By identifying essential neurons, it can lead to more efficient neural network architectures that maintain performance while reducing complexity. Additionally, it contributes to the understanding of feature learning and the organization of representations in neural networks.
Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks
Theory
Optimization
- CV-CCI effectively combines observational and randomized telemetry to improve counterfactual analysis in wireless networks.
- The methodology retains finite-sample coverage guarantees while addressing hidden confounding issues.
- Experimental results show that CV-CCI outperforms existing methods in terms of prediction set efficiency and validity.
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Confounding-Valid Conformal Inference for Counterfactual KPIs in Wireless Networks
Summary
This paper introduces Confounding-Valid Counterfactual Conformal Inference (CV-CCI), a novel methodology designed to enhance counterfactual analysis in wireless networks, particularly in the context of key performance indicators (KPIs). The authors address the challenge of hidden confounding in observational telemetry, which can invalidate statistical guarantees in counterfactual analysis. Traditional methods rely on randomized telemetry to mitigate these issues, but such data is often scarce and can disrupt normal network operations. CV-CCI combines abundant observational data, which may be confounded, with limited randomized data to improve the efficiency of prediction sets while ensuring valid coverage under hidden confounding. The methodology leverages the General Synthetic-Powered Inference (GESPI) principle, allowing for a balance between validity and efficiency. Experimental results on radio access network (RAN) control tasks demonstrate that CV-CCI produces more informative and stable prediction sets compared to existing state-of-the-art methods, effectively addressing the tension between the validity of counterfactual analysis and the efficiency of prediction sets.
Methodology
The proposed CV-CCI methodology integrates observational telemetry, which may contain confounding variables, with limited randomized telemetry. It employs the GESPI principle to enhance the efficiency of prediction sets while ensuring valid coverage under hidden confounding conditions.
Results
Experiments conducted on two representative RAN control tasks indicate that CV-CCI maintains validity in the presence of hidden confounding and generates more efficient prediction sets compared to state-of-the-art confounding-valid baselines.
Implications
The findings suggest that CV-CCI can significantly improve decision-making processes for network operators by providing reliable counterfactual insights, thereby enhancing the performance and adaptability of wireless networks.
Dynamic Heterogeneous Graph Representation Learning: A Survey
Graph Learning
- Introduces a unified formal definition of Dynamic Heterogeneous Graphs (DHGs).
- Proposes an algorithm-centric taxonomy categorizing DHG representation learning methods.
- Highlights the intrinsic modeling biases of existing methods concerning dynamic granularity.
- Summarizes applications, datasets, and benchmarks for DHG representation learning.
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Dynamic Heterogeneous Graph Representation Learning: A Survey
Summary
This paper presents the first systematic survey of Dynamic Heterogeneous Graph (DHG) representation learning, addressing the challenges posed by evolving heterogeneous entities in real-world AI systems. The authors introduce a unified formal definition of DHGs that encompasses both discrete-time and continuous-time models, emphasizing the need for methods that capture the interplay between heterogeneous semantics and temporal dynamics. They propose an algorithm-centric taxonomy categorizing existing DHG representation learning methods into embedding-based, graph neural network (GNN)-based, and Transformer-based approaches. The survey highlights the intrinsic modeling biases of these methods concerning dynamic granularity and summarizes representative applications, datasets, and benchmarks. Additionally, the authors identify inconsistencies in graph construction and evaluation protocols that hinder fair comparisons. The paper concludes by outlining three promising research directions: enhancing efficiency for scaling, improving generalizability for cross-domain applications, and fostering trustworthiness for explainable DHG learning.
Methodology
The authors conducted a comprehensive literature review to categorize existing DHG representation learning methods into three main families: embedding-based, GNN-based, and Transformer-based approaches. They analyzed the technical mechanisms of these methods and identified their limitations. The survey also compiled datasets and benchmarks relevant to DHG representation learning.
Results
The survey reveals a fragmented landscape of DHG representation learning, with existing methods often failing to effectively model the coupling of heterogeneity and dynamics. It exposes inconsistencies in dataset construction and evaluation protocols, which complicate performance comparisons. The proposed taxonomy provides a structured overview of the field, facilitating better understanding and future research.
Implications
This survey serves as a foundational reference for researchers in the field of graph representation learning, particularly in understanding the complexities of dynamic heterogeneous graphs. The identified research directions may guide future studies towards developing more efficient, generalizable, and trustworthy DHG models, which could have applications in various domains such as social network analysis, recommendation systems, and large-scale AI systems.
Representation Redundancy and Structural Complexity in Finite-Field Inversion
Theory
- Exact redundancy in representation choice is characterized by Galois orbits.
- Different Boolean formulations of inversion exhibit varying algebraic complexities.
- Empirical results align with theoretical predictions regarding learning difficulty.
- Galois orbit redundancy offers limited generalization benefits in multilayer perceptron experiments.
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Representation Redundancy and Structural Complexity in Finite-Field Inversion
Summary
This paper investigates the impact of representation choice on the algebraic structure and learning difficulty of finite-field inversion over F2n. The authors establish that two ordered bases induce the same coordinate inversion map if and only if they belong to the same Galois orbit, leading to a precise n-to-one correspondence between ordered bases and distinct inversion maps. The study analyzes three Boolean formulations of inversion, revealing differences in algebraic degree and ANF leap. The reference formulation has degree n−1 and joint ANF leap 1, while the mixed representation has degree 2(n−1) and leap 2, and the complete raw formulation has degree at most 3(n−1) with leap at least n. Experimental results with multilayer perceptrons confirm the theoretical findings, showing a consistent ordering in learning difficulty across formulations. However, the benefits of Galois orbit redundancy in generalization are limited under the tested conditions. The findings highlight the coexistence of exact redundancy among representations with variations in Boolean structure and learning behavior when representations are included as input.
Methodology
The authors utilize theoretical analysis to establish the relationship between ordered bases and inversion maps through Galois theory. They conduct exhaustive computations to verify theoretical results and perform controlled experiments with multilayer perceptrons to assess learning difficulty across different representations.
Results
The paper proves that two ordered bases induce the same coordinate inversion map if they belong to the same Galois orbit, establishing a one-to-one correspondence between bases and inversion maps. The analysis of Boolean formulations reveals distinct algebraic degrees and ANF leaps, with empirical experiments confirming the theoretical hierarchy in learning difficulty.
Implications
The findings suggest that understanding representation redundancy can inform the design of more efficient learning algorithms and architectures, particularly in tasks involving finite-field operations. The insights into algebraic complexity may also have implications for cryptographic applications where finite-field inversion is critical.
Beyond a Universal Forecasting Selector: Demand-Conditioned Model Selection across Demand Patterns and Horizons
Time Series
- Model selection in forecasting should be context-dependent rather than universal.
- Five selection mechanisms were evaluated, revealing varying effectiveness across demand patterns.
- CCG-AHSC and CCG-AHSCD are better for Smooth and Erratic demands, while OWA and ERA excel in Intermittent and Lumpy settings.
- Selector performance is influenced by historical data availability and forecasting horizons.
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Beyond a Universal Forecasting Selector: Demand-Conditioned Model Selection across Demand Patterns and Horizons
Summary
This paper addresses the challenges of forecasting model selection in heterogeneous demand environments, where the optimal model may vary based on demand structure, data availability, and forecasting horizon. The authors propose that the model selection mechanism itself should be context-dependent. They evaluate five different automatic model-selection mechanisms—RMSSE, ERA, OWA, CCG-AHSC, and CCG-AHSCD—across 24 optimized forecasting models, nine datasets, and various training-testing partitions. The performance of these selectors is assessed using Global Relative Accuracy (GRA) and statistical tests. The findings reveal that no single selector consistently outperforms others across all conditions. Specifically, CCG-AHSC and CCG-AHSCD excel in Smooth and Erratic demand patterns, while OWA and ERA are more effective in Intermittent and Lumpy scenarios. The study emphasizes the importance of a context-dependent approach to model selection, suggesting that the effectiveness of selection mechanisms varies with historical data availability and forecasting horizons. This research shifts the focus from merely identifying the best forecasting model to understanding which selection mechanism is most suitable under specific forecasting conditions.
Methodology
The authors conducted a comparative evaluation of five automatic model-selection mechanisms across 24 optimized forecasting models and nine heterogeneous datasets. They utilized Global Relative Accuracy (GRA) as a performance measure and performed statistical tests to assess selector effectiveness under varying conditions.
Results
The study found that no single model-selection mechanism consistently outperformed the others across all demand conditions. CCG-AHSC and CCG-AHSCD showed superior performance for Smooth and Erratic demand patterns, while OWA and ERA were more effective in Intermittent and Lumpy scenarios. The effectiveness of selectors varied significantly based on historical data availability and forecasting horizons.
Implications
The findings suggest that organizations should adopt a context-sensitive approach to forecasting model selection, which could enhance predictive performance and operational efficiency in inventory management and resource planning.