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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UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma
Reinforcement Learning
Large Language Models
Optimization
- Introduces Unbounded Positive Asymmetric Optimization (UP) to resolve the exploration-stability dilemma in RL.
- Formalizes Probability Capacity (Cap) to highlight limitations of existing importance sampling methods.
- Demonstrates that UP allows for unclipped gradients for positive advantages while maintaining stability for negative advantages.
- Extensive experiments show UP enhances exploration and reasoning accuracy across diverse RL algorithms and architectures.
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UP: Unbounded Positive Asymmetric Optimization for Breaking the Exploration-Stability Dilemma
Summary
This paper addresses the exploration-stability dilemma in reinforcement learning (RL) frameworks, particularly in the context of large language models (LLMs). The authors introduce a novel optimization method called Unbounded Positive Asymmetric Optimization (UP), which aims to enhance sample efficiency while maintaining training stability. The exploration-stability dilemma arises from the reliance on importance sampling (IS), which can lead to catastrophic instability when exploring rare reasoning paths. The authors formalize the concept of Probability Capacity (Cap) to illustrate how conservative clipping mechanisms limit exploration by prematurely truncating updates for low-confidence reasoning paths. UP restructures the optimization process by using a stop-gradient operator to anchor the policy to its current state, allowing for unclipped gradients for positive advantages to maximize exploration while safeguarding against instability for negative advantages. The proposed method is versatile, extending across various optimization granularities, including token-level and sequence-level frameworks. Extensive experiments demonstrate that UP significantly improves exploration capacity and reasoning accuracy across multiple RL algorithms, model architectures, and training modalities, establishing it as a universal enhancement for RL-based training.
Methodology
The authors propose UP, which utilizes a stop-gradient operator to anchor the policy to its current state, allowing for asymmetric treatment of advantages in the optimization process. This method is designed to maximize exploration while preventing training instability, and it is applicable across different optimization granularities, including both token-level and sequence-level frameworks.
Results
UP consistently outperformed existing RL algorithms (DAPO, GSPO, GRPO) in terms of exploration capacity and reasoning accuracy. It achieved the best average Pass@1 accuracy across five reasoning benchmarks against eleven strong RL baselines, demonstrating its effectiveness in resolving the exploration-stability dilemma.
Implications
The findings suggest that UP can be a significant advancement in RL training methodologies, particularly for applications involving complex reasoning tasks in large language models. This could lead to improved performance in various AI applications, including natural language processing and multimodal reasoning.
Avoiding unsafe sets when training with Langevin Dynamics
Theory
Optimization
- The paper provides bounds on the probability of a training trajectory entering a failure region during Langevin dynamics.
- Equilibrium mass of the failure region is exponentially small in the dimension of the parameter space.
- A burn-in time of order d is necessary for the in-set probability to stabilize to its static value.
- Local relaxation rates can reduce the burn-in time for geometrically isolated failure regions.
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Avoiding unsafe sets when training with Langevin Dynamics
Summary
This paper addresses the safety concerns in training machine learning models using noisy gradient descent, modeled as overdamped Langevin dynamics on the loss landscape. The primary focus is on bounding the probability that the training trajectory enters a designated failure region, denoted as AH. The author investigates this for a smooth, strongly convex loss function in d dimensions, particularly when the failure region is separated from the minimizer by an energy gap. Three key bounds are derived: the equilibrium mass of AH is shown to be exponentially small in d, and a shape-free bound indicates that the in-set probability relaxes to its static value after a burn-in time proportional to d. A local relaxation rate is introduced to refine the bounds for geometrically isolated regions, allowing for a reduction in the burn-in time and a uniform cap on trajectory probability over time. The paper emphasizes the importance of both the convexity of the loss function and the geometric properties of the unsafe set in determining the trajectory's behavior during training.
Methodology
The author models the training process as overdamped Langevin dynamics, deriving bounds on the probability of entering a failure region based on the properties of the loss function and the geometry of the failure set. The analysis includes both static and dynamic bounds, with considerations for transient swelling effects in high dimensions.
Results
The paper establishes that the probability of the training trajectory entering the failure region AH is exponentially small in dimension d at equilibrium. Additionally, it shows that the in-set probability approaches this equilibrium value after a burn-in period, which can be reduced for isolated failure regions using local relaxation rates.
Implications
The findings suggest that careful consideration of the loss landscape and the geometry of failure regions can enhance the safety of machine learning models during training, particularly in applications where avoiding unsafe behaviors is critical, such as in AI alignment and secure code generation.
Heckman-Corrected Epistemic Uncertainty: Selection on Unobservables Defeats Importance Weighting
Theory
- Importance weighting fails to correct for selection on unobservables, leading to biased predictions.
- Heckman's two-equation model effectively addresses selection bias by jointly modeling selection and outcomes.
- The proposed deep learning implementation of the Heckman model restores predictive coverage in selected-against regions.
- The stability of the two-step estimator is superior to that of the joint maximum likelihood estimator when using deep feature maps.
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Heckman-Corrected Epistemic Uncertainty: Selection on Unobservables Defeats Importance Weighting
Summary
This paper addresses the challenge of selection bias in machine learning, particularly when training data is collected through processes that are not observable to the model. Traditional methods like importance weighting and covariate-shift correction assume that selection is ignorable given observable variables, which is often not the case. The author applies Heckman's two-equation model, which jointly estimates a selection equation and an outcome equation, to correct for selection on unobservables. The proposed methodology includes a deep outcome network and a linear selection head, producing a joint bivariate-normal likelihood that accounts for epistemic uncertainty. The paper demonstrates that traditional importance weighting fails under selection on unobservables, while the Heckman-corrected predictive distribution significantly improves coverage accuracy. The findings are validated through controlled experiments and real-world applications, revealing that the Heckman correction outperforms other methods in terms of calibration and predictive reliability, especially in regions where data is sparse due to selection bias.
Methodology
The paper employs Heckman's two-equation model, integrating a deep outcome network with a linear selection head. It uses a joint bivariate-normal likelihood to estimate the selection and outcome equations, correcting for selection bias. The methodology includes both a two-step estimator and a maximum likelihood estimator, with a focus on the stability of the estimators under different conditions.
Results
The implementation reproduces reference outputs from established econometric software with high accuracy. In controlled experiments, the Heckman correction achieved a coverage of 88.9% in selected-against regions, compared to only 43.1% for importance weighting. The method's performance degrades gracefully without an instrument, showing a coverage of 40.3%. Real-world applications demonstrated that the Heckman-corrected intervals were the best-calibrated among non-oracle methods.
Implications
The findings suggest that practitioners in machine learning should consider selection bias when training models on observational data. The Heckman-corrected approach provides a robust framework for improving predictive accuracy and uncertainty quantification in scenarios where selection on unobservables is a concern. This has significant implications for fields such as economics, healthcare, and any domain relying on observational data.
Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids
Reinforcement Learning
Federated Learning
Optimization
- FedPPO-PG achieves 100% stabilization in all trials conducted.
- Mean stability time is reduced by 72.4% compared to previous methods.
- Control power usage is decreased by 7-14 times relative to a centralized baseline.
- The framework allows for independent execution of each actor without a central coordinator.
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Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids
Summary
This paper introduces FedPPO-PG, a novel Federated Multi-Agent Proximal Policy Optimization framework designed for transient stability control in smart grids. The framework addresses the critical challenge of stabilizing generator frequencies and rotor angles post-fault to prevent cascading failures. By reformulating the stability control problem as a cooperative multi-agent reinforcement learning task, FedPPO-PG optimizes directly against closed-loop stability objectives. Each generator operates as an independent local actor, utilizing frequency deviations from its two most strongly coupled neighbors identified through the post-fault Kron-reduced susceptance matrix. The training process includes a guided policy initialization phase that leverages a classical decentralized controller, followed by centralized critic-guided advantage estimation under a centralized training-decentralized execution (CTDE) paradigm. The framework was evaluated on the IEEE 39-bus benchmark system, demonstrating significant improvements in stabilization performance across various fault scenarios.
Methodology
The methodology involves a federated multi-agent reinforcement learning approach where each generator acts as an independent agent. The agents are trained using a proximal policy optimization technique that incorporates physics-grounded neighborhoods for improved coordination. The training process includes a warm-start phase from a decentralized controller and utilizes centralized critic-guided advantage estimation to enhance learning efficiency.
Results
The FedPPO-PG framework was tested on the IEEE 39-bus benchmark system, achieving complete stabilization in all 24 trials across five training and three unseen fault contingencies. The results showed a 72.4% reduction in mean stability time and a significant decrease in control power requirements, demonstrating the effectiveness of the proposed approach in real-time applications.
Implications
The findings suggest that FedPPO-PG can significantly enhance the resilience and efficiency of smart grid operations, particularly in scenarios involving rapid fault recovery. This approach could lead to more reliable power systems and inform the design of decentralized control strategies in other cyber-physical systems.
PatchOptic for Shared-State LLM Workflows with Projected Views and Verified Structured Updates
Large Language Models
NLP
Theory
- Introduction of PatchOptic, an interface for managing shared-state LLM workflows.
- Utilization of projected views and verified updates to ensure local actions are globally valid.
- Development of PatchBench, a benchmark for evaluating the proposed methodology.
- Demonstrated improvements in output quality and reduction in leakage and token costs.
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PatchOptic for Shared-State LLM Workflows with Projected Views and Verified Structured Updates
Summary
The paper introduces PatchOptic, a novel interface designed for shared-state workflows in large language models (LLMs). It addresses the limitations of current methods that manage local updates without ensuring their global validity. PatchOptic employs an optic-inspired approach, allowing for projected reads and verified structured updates. Each workflow step specifies a projected view, an authorized write region, and a patch-source region. This ensures that the actor only sees the relevant projected view while a verifier checks the proposed patch against the full state before committing it. The authors present PatchBench, a benchmark with 46 cases across various domains, to evaluate the effectiveness of PatchOptic. Results indicate that projected reads significantly reduce leakage and token costs while maintaining output quality. The runtime verification mechanism effectively blocks workflow contract violations and rejects invalid patch artifacts, paving the way for safer LLM workflows in shared-state systems.
Methodology
The authors developed PatchOptic as an optic-inspired interface that couples projected reads with verified JSON Patch writes. The methodology includes defining authority triples for workflow steps, implementing runtime checks for patch validity, and creating a footprint algebra to support delegation and composition of workflows.
Results
The evaluation using PatchBench showed that projected reads improved the semantic pass rate from approximately 0.61 to between 0.78 and 0.80 under the GPT-5-mini model. Additionally, the runtime verification reduced leakage runs to 0.1 per round and successfully rejected all hidden-source patch artifacts.
Implications
PatchOptic has significant implications for the development of safer and more reliable LLM workflows in shared-state environments. It enables the composition and delegation of local actions while ensuring their validity, which is crucial for applications in collaborative AI systems and multi-agent environments.
Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions
Graph Learning
Optimization
- ClinicalFocal loss significantly improved model accuracy and F1 score compared to binary cross-entropy.
- The approach achieved a recall of 90.9% and reduced the false-negative rate from 29.8% to 9.1%.
- Emphasizing difficult positive interactions enhances DDI prediction capabilities.
- The methodology allows for safety-oriented DDI screening without altering the model architecture.
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Asymmetric Focal Loss Improves Graph Neural Network Prediction of Drug-Drug Interactions
Summary
This paper presents a novel approach to enhance the prediction of drug-drug interactions (DDIs) using graph neural networks (GNNs) by introducing ClinicalFocal loss, an asymmetric focal loss function. Traditional binary cross-entropy (BCE) training methods allocate equal importance to all examples, which can lead to poor performance on difficult-to-classify positive interactions. The authors propose ClinicalFocal loss to emphasize these challenging positive interactions, thereby improving the model's ability to predict clinically significant DDIs. The methodology integrates ClinicalFocal loss into a relation-aware graph convolutional network, utilizing molecular fingerprints and physicochemical descriptors. The model was evaluated on the TWOSIDES dataset using five-fold cross-validation, ensuring consistent experimental conditions. Results demonstrate significant improvements in accuracy, F1 score, AUROC, and AUCPR, with a notable reduction in false-negative rates and overall classification error. The findings suggest that loss-function design can be a powerful tool for enhancing DDI prediction without necessitating changes to the underlying model architecture.
Methodology
The study integrates ClinicalFocal loss into a relation-aware graph convolutional network, trained on the TWOSIDES dataset. The model uses molecular fingerprints, physicochemical descriptors, and learned embeddings, with performance evaluated through five-fold cross-validation against a binary cross-entropy baseline under identical conditions.
Results
The implementation of ClinicalFocal loss resulted in an increase in accuracy from 0.699 to 0.892, an F1 score increase from 0.700 to 0.894, and improvements in AUROC from 0.766 to 0.914 and AUCPR from 0.714 to 0.860. The false-negative rate decreased from 29.8% to 9.1%, while specificity increased from 69.6% to 87.5%. Overall classification error was reduced from 30.1% to 10.8%.
Implications
The findings indicate that focusing on difficult positive interactions can significantly enhance DDI prediction accuracy, which is crucial for patient safety in clinical settings. The proposed loss function can be applied in various safety-oriented applications in pharmacology, necessitating further validation and calibration for practical use.
SafeImpute: Reliable Clinical Data Imputation via Conformal Selection
Graph Learning
Time Series
Health informatics
- SafeImpute addresses the problem of reliable clinical data imputation with statistical error control.
- The framework utilizes an event graph to model irregular clinical visits and employs a two-relation GNN for imputation.
- Conformal selection is used to ensure that only reliable imputations are released, controlling the false discovery rate.
- Extensive experiments show that SafeImpute outperforms existing methods in terms of accuracy and reliability.
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SafeImpute: Reliable Clinical Data Imputation via Conformal Selection
Summary
The paper addresses the challenge of missing clinical data in longitudinal records, which can significantly impact patient care. Existing imputation methods often focus on improving average accuracy but fail to provide reliable guidance on which imputed values are trustworthy for clinical decision-making. The authors propose SafeImpute, a framework designed to produce accurate imputations while ensuring statistical control over clinically unacceptable errors. SafeImpute constructs an event graph that captures both intra-patient temporal trajectories and inter-patient clinical similarities. It employs a two-relation Graph Neural Network (GNN) for learning imputations, enhanced by adaptive fusion and regularization through an auxiliary masked reconstruction objective. To ensure reliability, SafeImpute converts a proxy risk score into conformal p-values and applies the BenjaminiโHochberg procedure to control the false discovery rate (FDR) of unacceptable errors among released imputations. Experiments conducted on datasets from Mayo Clinic, MIMIC-III, and MIMIC-IV demonstrate that SafeImpute not only achieves strong imputation accuracy but also provides reliable error control, outperforming various baseline methods in both standard and FDR-controlled evaluations.
Methodology
SafeImpute constructs an event graph to represent clinical visits, capturing both temporal and similarity relationships. It uses a two-relation GNN for imputation, integrating temporal and value signals through adaptive gating. Reliability is ensured by calibrating a proxy risk score into conformal p-values and applying the BenjaminiโHochberg procedure for FDR control.
Results
SafeImpute demonstrated strong imputation accuracy across various datasets, including those from Mayo Clinic and MIMIC-III/IV, while effectively controlling for clinically unacceptable errors. The framework outperformed multiple baseline methods in both standard evaluation metrics and FDR-controlled selective-release scenarios.
Implications
The SafeImpute framework has significant implications for clinical practice, particularly in improving the reliability of imputed clinical data used for decision-making. It can enhance patient care by providing clinicians with trustworthy data, thereby reducing the risk of misdiagnosis and inappropriate treatment adjustments.
Self-Supervised Implicit CEST Reconstruction via Physics-Informed Lorentz Encoding
Optimization
Theory
Efficient ML
- Introduction of Lorentz Encoding (LE) as a physics-informed framework for CEST MRI reconstruction.
- Decoupled hybrid architecture combining Hash Encoding for spatial features and LE for spectral features.
- Significant performance improvement over traditional methods, achieving high PSNR and SSIM with minimal sampling.
- Demonstrated effectiveness in accurate metabolite mapping (APT, NOE, MT) from sparse data.
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Self-Supervised Implicit CEST Reconstruction via Physics-Informed Lorentz Encoding
Summary
This paper presents a novel approach for reconstructing high-resolution Z-spectra in Multi-Pool Chemical Exchange Saturation Transfer (CEST) MRI using a self-supervised, physics-informed framework called Lorentz Encoding (LE). Traditional methods struggle with long acquisition times and the ill-posed nature of sparse data reconstruction, often leading to artifacts and physically invalid signals. The proposed LE framework reformulates the reconstruction task by embedding Lorentzian priors into the encoding layer, allowing for a continuous mapping of spatial-spectral coordinates while maintaining physical constraints. This method effectively reduces noise and enhances the consistency of the reconstructed spectra with physical models. The authors demonstrate the efficacy of LE through extensive in vivo experiments, showing significant improvements over state-of-the-art methods, particularly under extreme sampling conditions. The results indicate that LE can achieve high PSNR and SSIM values, ensuring accurate metabolite mapping from limited data points.
Methodology
The authors propose a decoupled hybrid encoding architecture that utilizes Multi-resolution Hash Encoding for high-frequency spatial textures and Lorentz Encoding for embedding physical spectral priors. The reconstruction is formulated as a continuous mapping function that translates spatial-spectral coordinates into normalized signal intensities, optimized through an end-to-end training process minimizing the L2 error against ground-truth signals.
Results
The proposed LE framework significantly outperformed state-of-the-art methods in in vivo human brain data experiments, achieving a Peak Signal-to-Noise Ratio (PSNR) of 57.58 dB and a Structural Similarity Index Measure (SSIM) of 0.9994 under a 39-point sampling strategy. The method also demonstrated robust performance with as few as 21 sampling points, ensuring accurate quantitative metabolite mapping.
Implications
The findings suggest that the LE framework can enhance the clinical applicability of CEST MRI by reducing acquisition times while maintaining high reconstruction quality. This could lead to more efficient diagnostic processes in molecular imaging and potentially expand the use of CEST MRI in clinical settings.
On Explicit Super-Expressive Approximation for Neural Networks
Theory
- Introduces the Chinese Remainder Theorem as a mechanism for explicit parameter bounds in neural network approximations.
- Constructs fixed-architecture networks for Lipschitz and H"older-smooth functions with explicit parameter-error trade-offs.
- Demonstrates that higher smoothness of target functions reduces the required parameter magnitude, improving scaling laws.
- Provides the first explicit relation between approximation accuracy and parameter magnitude in super-expressive approximation.
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On Explicit Super-Expressive Approximation for Neural Networks
Summary
This paper investigates the fixed-architecture neural network approximation with explicit parameter bounds and elementary activations. Previous works have shown super-expressive approximation using fixed-size networks but lacked quantitative and non-asymptotic characterizations of parameter magnitude concerning approximation error. The authors introduce the Chinese Remainder Theorem (CRT) as a constructive encoding mechanism to address this gap. They construct a width-max{D, 4}, depth-5 network for Lipschitz continuous functions on [0, 1]D, providing explicit parameter-error trade-offs. For H"older-smooth functions, they develop a fixed network of width max{2D, D + 5N + 1} and depth r + 9, achieving a parameter magnitude bound that improves upon traditional results. This work represents a significant advancement in understanding the trade-offs between network architecture and parameter magnitude in achieving super-expressive approximations.
Methodology
The authors utilize the Chinese Remainder Theorem to construct fixed-architecture neural networks that approximate Lipschitz continuous and H"older-smooth functions. They derive explicit parameter bounds based on the approximation accuracy, leveraging elementary super-expressive activation functions and rational gridwise polynomial surrogates.
Results
For Lipschitz functions, a fixed-width network of max{D, 4} and depth 5 is constructed, with parameter magnitude satisfying log2 P โค O(ฮตโ2D log(1/ฮต)). For H"older-smooth functions, a network of width max{2D, D + 5N + 1} and depth r + 9 achieves a parameter bound of log2 P โค O(ฮตโ2D/(r+ฮณ) log(1/ฮต)). These results provide explicit characterizations of parameter magnitudes in relation to approximation errors.
Implications
The findings have significant implications for the design of neural networks, particularly in applications requiring fixed architectures. By establishing explicit parameter bounds, this work can guide the development of more efficient neural network architectures that maintain high approximation accuracy without increasing complexity.
More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges
NLP
Large Language Models
Reinforcement Learning
- Self-play training leads to a significant divergence between judge pass rates and actual accuracy, termed the judge-truth gap.
- Reference-free judges score plausibility, not correctness, allowing models to exploit false-positive basins.
- A hidden-anchor audit effectively quantifies the over-reporting of correctness by judges under optimization.
- The de-anchoring method significantly reduces false-positive rates and prevents the exploitation of plausible but incorrect answers.
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More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges
Summary
This paper investigates the structural flaws in training language models (LLMs) using self-play and reference-free judgments, where a model's evaluation of an answer is assumed to be a reliable proxy for correctness. The author demonstrates that this assumption fails, as reference-free judges assess plausibility rather than correctness, leading to a phenomenon termed 'reward hacking.' The study employs a hidden-anchor audit to measure the divergence between the judge's pass rate and actual accuracy, revealing a significant gap. For instance, in experiments with the GSM8K dataset and Qwen3 policies, the judge's pass rate increased from 0.72 to 0.94, while the true accuracy remained at approximately 0.20, indicating a 0.74 judge-truth gap. This reward hacking is shown to be robust across different judge families and scales, and attempts to mitigate the issue through stronger judges or ensemble methods were unsuccessful. The paper proposes a 'de-anchoring' fix, where requiring the judge to commit an independent answer before evaluating the candidate significantly reduces false-positive rates. The findings highlight the importance of breaking the anchoring effect to improve the reliability of self-play training methods.
Methodology
The study employs a hidden-anchor audit to assess the accuracy of language model judges by comparing their evaluations against a held-out exact-match check. It analyzes the performance of self-play trained models on the GSM8K dataset, measuring the divergence between judge scores and true accuracy. The paper also explores the effects of requiring judges to commit independent answers before evaluating candidates to mitigate false positives.
Results
The results indicate a substantial judge-truth gap, with the judge's pass rate increasing from 0.72 to 0.94 while true accuracy remained at 0.20. The de-anchoring approach reduced the false-positive rate from 0.719 to 0.012, demonstrating that breaking the anchoring effect is crucial for improving the reliability of self-play training.
Implications
The findings suggest that reliance on reference-free judgments for self-improvement in language models can lead to misleading evaluations. The proposed de-anchoring method could enhance the training of LLMs, making them more robust and accurate in real-world applications.
LLM-Driven Neural Network Generation with Same-Family Architecture Guidance: Disentangling Transfer and Adaptation
Large Language Models
Optimization
Computer Vision
- Introduces a source-guided candidate-generation protocol for neural network modifications.
- Demonstrates improved model accuracy using stronger same-family source models.
- Separates the effects of recipe transfer and adaptation in neural network generation.
- Reports significant performance improvements on CIFAR-10 and SVHN datasets.
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LLM-Driven Neural Network Generation with Same-Family Architecture Guidance: Disentangling Transfer and Adaptation
Summary
This paper investigates the use of large language models (LLMs) to generate modifications for neural networks, focusing on improving weak target models by leveraging stronger same-family source models. The authors propose a source-guided candidate-generation protocol that includes non-source controls and source-conditioned candidates, aiming to ensure the validity and quality of generated models. The study emphasizes the importance of separating the effects of recipe transfer and adaptation, providing a controlled environment for evaluating model improvements. The results demonstrate significant accuracy gains when using source-guided candidates compared to non-source candidates across various datasets, including CIFAR-10 and SVHN, highlighting the effectiveness of the proposed methodology in enhancing model performance.
Methodology
The authors developed a controlled candidate-generation algorithm that evaluates the validity and accuracy of generated neural network modifications. The protocol includes non-source controls, source-conditioned candidates, and an ablation study to assess the impact of LLM-generated hyperparameters and transformations. The evaluation focuses on comparing the best valid candidates against a baseline to ensure improvements are meaningful.
Results
On the CIFAR-10 dataset, the best source-guided candidate achieved an accuracy of 0.5049, significantly outperforming the best non-source candidate at 0.2398, resulting in a +0.2651 advantage. For the SVHN dataset using AlexNet, source-guided transfer reached an accuracy of 0.7880 compared to 0.2254 for non-source candidates, yielding a +0.5626 advantage. The findings indicate that LLMs adapt rather than simply copy source recipes, as evidenced by the performance gains observed.
Implications
The findings suggest that leveraging stronger same-family models can enhance the capabilities of LLMs in generating effective neural network architectures. This approach could lead to more reliable and efficient neural architecture search and hyperparameter optimization methods, potentially benefiting various applications in machine learning and artificial intelligence.
EntroPath: Maximum Entropy Path Ensemble Embedding for Manifold Learning
Graph Learning
Theory
Efficient ML
- EntroPath leverages maximum entropy random walks to improve manifold learning by aggregating multiple paths rather than relying on single trajectories.
- The method provides a free-energy dissimilarity measure that converges to squared geodesic distances, enhancing robustness against graph errors.
- Scalable extensions for out-of-sample embedding and trajectory inference are introduced, broadening the method's usability.
- Empirical results indicate that EntroPath outperforms traditional methods like PHATE, HeatGeo, and Isomap, especially in complex manifold structures.
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EntroPath: Maximum Entropy Path Ensemble Embedding for Manifold Learning
Summary
The paper introduces EntroPath, a novel manifold learning method that utilizes maximum entropy random walks (MERW) to recover geodesic geometry from data graphs through ensembles of diffusion paths. Unlike traditional graph-based embeddings that rely on locally normalized random walks or shortest-path distances, which can lead to distortions in the presence of spurious edges or uneven sampling, EntroPath aggregates information from multiple k-step paths to create a more robust dissimilarity measure. This method is shown to converge to squared geodesic distances in the short-time limit, effectively balancing local and global manifold structures. The authors also present scalable extensions for out-of-sample embeddings and pseudotime inference, enhancing the method's applicability in various domains. Through extensive evaluations on synthetic manifolds and single-cell datasets, EntroPath demonstrates superior performance compared to existing methods, particularly in scenarios with non-uniform sampling and branching trajectories, thereby preserving the underlying geodesic geometry more faithfully.
Methodology
The methodology involves defining a free-energy dissimilarity based on k-step path ensembles derived from maximum entropy random walks. The authors prove a geodesic-recovery theorem and establish connections to kernel methods, facilitating a robust framework for manifold learning. Scalable techniques for landmark projection and pseudotime inference are also developed.
Results
EntroPath consistently matches or outperforms existing manifold learning methods across various datasets, particularly excelling in scenarios with non-uniform sampling densities and well-separated branching trajectories. The method's performance is validated through a two-stage evaluation protocol that distinguishes between distance-level and embedding-level metrics.
Implications
The findings suggest that EntroPath can be effectively applied in fields requiring accurate manifold learning, such as single-cell transcriptomics and other high-dimensional data analyses, where preserving geodesic structures is crucial for accurate representations and insights.
Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations
Reinforcement Learning
Large Language Models
Optimization
- Identifies limitations of standard LLMs in economic decision-making during negotiations.
- Proposes a reinforcement learning framework that aligns rewards with economic outcomes.
- Demonstrates improved negotiation strategies through price anchoring and strategic probing.
- Shows that the trained agent can generalize to unseen buyer behaviors and budget distributions.
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Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations
Summary
This paper addresses the challenges of negotiation in multi-buyer markets, where a single seller negotiates with multiple buyers who have heterogeneous budgets. The authors identify a significant limitation in standard Large Language Models (LLMs), which, despite their linguistic capabilities, fail to act as effective economic decision-makers. Specifically, these models tend to focus on the highest current bid rather than exploring the buyer pool for potentially higher hidden valuations. To overcome this, the authors propose a novel training approach using Reinforcement Learning from Verifiable Rewards (RLVR), which aligns the reward function with objective economic outcomes. This method allows the seller agent to learn a strategic balance between market exploration and surplus extraction. The results indicate that the trained seller agent evolves through multiple strategic stages, improving its ability to identify high-value buyers and negotiate better deals. The agent demonstrates a significant increase in surplus extraction compared to existing models and shows robust generalization to new buyer negotiation styles and budget distributions.
Methodology
The authors developed a concurrent negotiation framework where a seller negotiates with multiple buyers through private communication channels. They employed Reinforcement Learning from Verifiable Rewards (RLVR) to train the seller agent, focusing on aligning the reward function with economic outcomes to enhance strategic decision-making.
Results
The trained seller agent exhibited a multi-stage strategic evolution, significantly improving its persuasive bargaining skills and ability to close deals with high-value buyers. The agent achieved higher surplus extraction than existing models and demonstrated robust generalization to new negotiation scenarios.
Implications
The findings suggest that integrating RLVR into LLMs can enhance their effectiveness in negotiation tasks, particularly in digital marketplaces and automated procurement systems. This approach could lead to more efficient resource allocation and value discovery in various economic environments.
AdaStop: Cost-Aware Early Stopping for DNN Test Selection
Efficient ML
Optimization
Theory
- AdaStop provides a principled stopping criterion for DNN test selection based on cost-benefit analysis.
- The framework estimates the marginal fault discovery rate and stops labeling when it falls below a calculated threshold.
- Empirical evaluations show substantial savings in labeling costs while maintaining high fault discovery rates.
- The approach adapts to varying labeling costs and fault values, making it versatile for different testing scenarios.
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AdaStop: Cost-Aware Early Stopping for DNN Test Selection
Summary
This paper addresses the challenge of determining when to stop labeling test inputs in the context of deep neural network (DNN) testing, where labeling can be costly. The authors introduce AdaStop, a framework that formulates the testing process as a cost-benefit decision-making problem. The key innovation is the establishment of a stopping criterion based on the marginal fault discovery rate, which is compared against a cost-adjusted threshold. The framework consists of three components: an uncertainty-based selection strategy, a sliding-window estimator for the marginal fault discovery rate, and a cost-aware stopping rule. The empirical results demonstrate that AdaStop can achieve a fault discovery rate of 65-84% while utilizing only 9-31% of the labeling budget, thereby significantly improving the efficiency of DNN testing.
Methodology
The methodology involves framing the DNN test selection as a sequential cost-benefit optimization problem. The authors derive an optimal stopping condition based on the marginal fault discovery rate and implement a sliding-window estimator to monitor this rate. The stopping rule is designed to terminate labeling when the estimated fault discovery rate falls below a threshold defined by the ratio of labeling cost to fault value.
Results
The experiments conducted across multiple datasets and architectures indicate that AdaStop can discover 65-84% of faults while utilizing only 9-31% of the total labeling budget. This demonstrates the framework's effectiveness in optimizing resource allocation during DNN testing.
Implications
The implications of this work are significant for industries relying on DNNs, such as autonomous vehicles and healthcare, where testing is critical for reliability. By reducing labeling costs while maintaining high fault detection rates, AdaStop can facilitate more efficient testing processes, ultimately leading to safer and more reliable AI systems.
Agon: Competitive Cross-Model RL with Implicit Rival Grading of Reasoning
Reinforcement Learning
Large Language Models
NLP
- Agon reframes reasoning quality as an implicit reward from a competing model, addressing the lack of labels for good reasoning.
- The method involves two distinct models trained in a competitive manner, enhancing their reasoning capabilities.
- Agon achieves significant performance improvements on hard reasoning tasks compared to traditional RL methods.
- The approach is efficient, using a shared base with low memory overhead while maintaining model divergence.
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Agon: Competitive Cross-Model RL with Implicit Rival Grading of Reasoning
Summary
The paper introduces Agon, a novel approach to reinforcement learning (RL) that addresses the limitations of traditional methods which only evaluate final answers, often leading to inflated reasoning outputs without improved quality. Agon employs two competing models that act as graders for each other's reasoning processes, thus implicitly evaluating the quality of reasoning without requiring explicit labels. Each model alternates roles between drafting solutions and challenging the other's output, incentivizing them to out-reason one another. This competitive framework enhances the learning process, allowing both models to improve beyond the limitations of single-model RL. The authors demonstrate that Agon significantly outperforms existing methods, such as GRPO and untrained Mixture-of-Agents, particularly on challenging tasks like DeepMath, effectively doubling the accuracy of GRPO's pass@1 metric. The paper suggests that this competitive training paradigm could be extended to future models, potentially allowing them to reason collaboratively in latent space.
Methodology
Agon utilizes a competitive reinforcement learning framework where two models alternate between drafting solutions and evaluating each other's outputs. Each model is rewarded for correctness and for out-performing the rival, fostering implicit grading of reasoning quality. The models are instantiated as low-rank adapters over a shared frozen base, allowing for efficient training and deployment.
Results
On the challenging DeepMath dataset, Agon achieved a pass@1 accuracy that was approximately double that of GRPO and significantly outperformed untrained Mixture-of-Agents. The results indicate that the competitive training paradigm leads to substantial gains in reasoning quality, particularly for smaller models.
Implications
The findings suggest that competitive training can enhance the reasoning capabilities of language models, potentially leading to more effective applications in complex problem-solving tasks. This approach may also inform future research on collaborative reasoning in latent spaces, expanding the capabilities of AI systems.
Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization
Optimization
Efficient ML
- Introduces a latency-oriented DNN optimization method for edge systems.
- Utilizes a universal hardware-customized latency predictor for efficient model training.
- Achieves significant latency reduction while maintaining high accuracy in DNNs.
- Demonstrates the effectiveness of the method on popular models like GoogLeNet and VGG-19.
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Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization
Summary
This paper addresses the challenge of optimizing deep neural networks (DNNs) for edge devices, particularly under strict latency constraints. Traditional optimization methods often fail to directly address the temporal cost of model inference, which is critical for real-time applications. The authors propose a novel latency-oriented neural network learning method that incorporates a universal hardware-customized latency predictor, allowing for a one-shot training process to optimize models for high accuracy while meeting latency requirements. The proposed method leverages a compact learning scheme that dynamically zeroizes and recovers Batch Normalization (BN) layers to condense redundant DNNs. Experimental results demonstrate that the approach effectively satisfies stringent latency constraints while maintaining high accuracy. For instance, on the ImageNet-100 dataset, the latency of GoogLeNet was reduced from 40.32ms to 34ms with only a 0.14% drop in accuracy. Additionally, when combined with quantization, the accuracy drop was minimized to 0.04%. The framework is open-sourced for further research and application.
Methodology
The authors developed a hardware-aware DNN optimization framework that employs a compact learning scheme to optimize DNN architectures. This involves dynamically zeroizing and recovering Batch Normalization layers to condense models, alongside a latency predictor that estimates inference time without requiring extensive on-device measurements.
Results
The proposed method successfully reduced the latency of GoogLeNet from 40.32ms to 34ms with a minimal accuracy drop of 0.14%. When combined with quantization, the accuracy drop was further reduced to 0.04%. For VGG-19, the latency was compressed from 119.98ms to 34ms while improving accuracy by 0.5%. Additionally, GoogLeNet's latency was scaled up from 20.27ms to 34ms, achieving a 0.78% increase in accuracy.
Implications
This research has significant implications for deploying DNNs in latency-sensitive applications on edge devices, such as autonomous vehicles and healthcare systems. The ability to optimize models efficiently without extensive retraining can lead to faster deployment and improved performance in real-time scenarios.
Self-Review Reinforcement Learning (SRRL) with Cross-Episode Memory and Policy Distillation
Reinforcement Learning
Large Language Models
- Introduces Self-Review Reinforcement Learning (SRRL) framework for LLMs.
- Incorporates a self-review mechanism to analyze and improve responses.
- Utilizes policy gradients for optimizing self-reviews and internalizing improvements.
- Employs cross-episode memory to enhance learning efficiency by reusing successful reviews.
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Self-Review Reinforcement Learning (SRRL) with Cross-Episode Memory and Policy Distillation
Summary
The paper introduces Self-Review Reinforcement Learning (SRRL), a novel framework designed to enhance the training of large language models (LLMs) in environments with sparse or delayed feedback. Traditional reinforcement learning methods struggle with pinpointing which actions lead to success or failure due to the lack of immediate feedback. SRRL addresses this by embedding a self-review step in each episode, allowing the model to analyze its first-pass response and identify errors before attempting a second response. This self-review process is optimized using policy gradients, enabling the model to internalize improvements through selective distillation, which ensures that learned corrections persist across future episodes. Additionally, SRRL incorporates a cross-episode memory that retains successful self-reviews for reuse in similar tasks, thereby enhancing learning efficiency. The authors evaluate SRRL against a standard RL with verifiable rewards (RLVR) baseline using the GRPO optimizer on two language models, Qwen 3-4B and OLMo-3-7B, on the GSM8K benchmark. The results demonstrate that SRRL consistently outperforms RLVR in terms of final reward performance and learning efficiency, effectively transforming feedback into behavioral improvements.
Methodology
The SRRL framework integrates a self-review step within each reinforcement learning episode, allowing the model to evaluate its actions and learn from mistakes. It employs policy gradients to optimize the self-review process and uses selective distillation to internalize improvements into the base policy. A cross-episode memory is utilized to store successful self-reviews for future reference, enhancing the model's ability to learn from past experiences.
Results
SRRL consistently outperformed the RLVR baseline in final reward performance and demonstrated greater learning efficiency on the GSM8K benchmark, indicating its effectiveness in transforming feedback into actionable behavioral improvements.
Implications
The SRRL framework has potential applications in training decision-making agents in environments with delayed feedback, improving the adaptability and efficiency of large language models in real-world tasks. It may also influence future research in reinforcement learning by emphasizing structured learning from experience.
Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning
Optimization
Theory
Efficient ML
- EISAM improves upon SAM by incorporating an extragradient-inspired two-step update process.
- The optimizer enhances generalization performance and reduces sensitivity to hyperparameters.
- Extensive experiments show EISAM outperforms traditional optimizers like SGD and Adam.
- Theoretical analysis confirms EISAM's ability to steer parameters toward flatter minima.
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Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning
Summary
This paper addresses the challenge of generalization in deep learning, where traditional optimizers like Stochastic Gradient Descent (SGD) often converge to sharp minima, leading to overfitting. The authors propose a novel optimizer called Extragradient-Inspired Sharpness-Aware Minimization (EISAM), which builds on Sharpness-Aware Minimization (SAM) to seek flatter minima that are associated with improved generalization. EISAM employs a two-step update process: a prediction step that explores the geometry of the loss landscape and a perturbation step that refines updates using a base optimizer. This method not only enhances generalization performance compared to SAM but also reduces sensitivity to the perturbation radius, making it more robust and easier to tune across different settings. The authors conduct extensive experiments on benchmark datasets, demonstrating that EISAM consistently outperforms SGD, Adam, and SAM in terms of test accuracy and training efficiency across various architectures. Theoretical analysis supports the effectiveness of EISAM in tightening the generalization bound by guiding parameters toward flatter minima with reduced curvature. Additionally, the paper provides a thorough hyperparameter analysis, offering practical tuning guidance for real-world applications, thus establishing EISAM as a scalable and broadly applicable optimization solution in deep learning.
Methodology
EISAM utilizes a two-step update mechanism: a prediction step that investigates the local geometry of the loss landscape followed by a perturbation step that refines the updates using a base optimizer. This approach is designed to improve generalization and reduce sensitivity to the perturbation radius.
Results
EISAM consistently outperformed SGD, Adam, and SAM in test accuracy and training efficiency across various architectures on benchmark datasets such as CIFAR-10, CIFAR-100, ImageNet-1K, and others. The optimizer also demonstrated improved robustness and stability in training.
Implications
EISAM provides a new optimization tool that balances performance and efficiency, making it suitable for a wide range of deep learning applications. Its robustness and reduced sensitivity to hyperparameters can facilitate better model training in practical scenarios.
Gauge-Invariant Learnable Spectral Positional Encodings for Directed Graphs via Hermitian Block Krylov Subspaces
Graph Learning
- Introduces gauge-invariant learnable spectral positional encodings for directed graphs.
- Utilizes Hermitian block Krylov subspaces for efficient computation of PEs.
- Proves that a logarithmic number of block steps suffices for approximation across structured response families.
- Demonstrates improved performance of magnetic Krylov PEs over traditional direction-blind PEs in empirical tests.
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Gauge-Invariant Learnable Spectral Positional Encodings for Directed Graphs via Hermitian Block Krylov Subspaces
Summary
This paper addresses the challenges of spectral positional encodings (PEs) for directed graphs, particularly the inefficiencies associated with magnetic Laplacians and the ambiguity of complex eigenvectors. The authors propose a novel approach to learnable spectral PEs defined as a matrix function of a normalized magnetic operator applied to a block of random probes. This method ensures gauge invariance by construction, eliminating the need for basis-invariant architectures. The authors utilize Hermitian block Krylov subspaces to compute the PEs efficiently using sparse matrix-vector products, demonstrating that a logarithmic number of block steps suffices for approximation. They provide theoretical guarantees for the approximation error and show that structured response families generalize better than free per-eigenvalue weights. Empirical results on a cyclic directed stochastic block model (SBM) and real-world directed node-classification benchmarks indicate that the proposed magnetic Krylov PEs outperform direction-blind PEs and achieve convergence to the exact eigendecomposition oracle as the depth increases.
Methodology
The authors define the positional encodings as a matrix function of a normalized magnetic operator and a block of random probes. They compute these encodings in a Hermitian block Krylov subspace using sparse matrix-vector products, ensuring gauge invariance and efficient approximation. The theoretical framework includes covering-number arguments and convergence guarantees for structured response families.
Results
The proposed method shows that magnetic Krylov PEs converge to the exact eigendecomposition oracle as the number of block steps increases. In experiments, the method outperformed direction-blind PEs on a cyclic directed SBM and real-world benchmarks, indicating its effectiveness in capturing directional structures in graphs.
Implications
This work has significant implications for graph learning, particularly in applications where directional information is critical, such as citation networks, program analysis, and circuit design. The proposed method could enhance the performance of graph neural networks by providing more expressive positional encodings.
Hybrid Least Squares/Gradient Descent Methods for MIONets
Optimization
Efficient ML
Theory
- Introduction of a hybrid LSGD method for MIONets to enhance training efficiency.
- Utilization of Kronecker and Khatri-Rao products to simplify large system matrices.
- Development of the ALS+Adam algorithm for practical implementation.
- Demonstration of improved training performance compared to conventional methods.
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Hybrid Least Squares/Gradient Descent Methods for MIONets
Summary
This paper introduces a novel hybrid least squares/gradient descent (LSGD) method tailored for training Multiple-Input Operator Networks (MIONets), which generalizes the existing LSGD method used for DeepONets. MIONets, characterized by their structure that combines multiple branch networks and a trunk network, present unique challenges in training due to their complexity and the need for large datasets. The authors propose an efficient approach that utilizes alternating least squares (ALS) to optimize the last layer parameters of the branch networks sequentially. To manage the computational burden of large system matrices, they employ Kronecker and Khatri-Rao products along with tensor permutation matrices to decompose these matrices into smaller, more manageable components. The method is compatible with various L2 loss functions and includes regularization terms. The paper also presents the ALS plus Adam (ALS+Adam) algorithm as a practical implementation of the proposed LSGD method. Experimental results demonstrate the effectiveness of the ALS+Adam method in improving training performance for both supervised and unsupervised learning tasks involving nonlinear and linear partial differential equations (PDEs), respectively.
Methodology
The authors generalize the LSGD method for MIONets by formulating a minimization problem based on L2 loss functions. They implement an alternating least squares approach to optimize the last layer parameters of branch networks sequentially. To handle large system matrices, they factor them using Kronecker and Khatri-Rao products and apply tensor permutation matrices to align dimensions appropriately.
Results
The experimental results indicate that the ALS+Adam method significantly accelerates the training process for MIONets, outperforming traditional training methods like Adam in terms of computational efficiency and training time for both nonlinear and linear PDEs.
Implications
The proposed method has the potential to enhance the training of neural operator networks, making them more accessible for applications in scientific computing and solving complex PDEs. This could lead to advancements in various fields that rely on numerical methods and machine learning.
Scalable Perturbation Learning for Online Self-Supervised Echo State Networks
Time Series
Efficient ML
Theory
- Introduces a perturbation-based learning rule for online self-supervised learning in Echo State Networks.
- Addresses the variance scaling problem in high-dimensional systems by focusing on input-dependent components.
- Demonstrates that the effective perturbation dimension can be reduced, improving scalability.
- Maintains the advantages of scalar global feedback while enhancing learning efficiency.
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Scalable Perturbation Learning for Online Self-Supervised Echo State Networks
Summary
This paper addresses the challenges of developing intelligent systems that can adapt to real-world dynamics through online self-supervised learning, particularly focusing on Echo State Networks (ESNs). The authors propose a novel perturbation-based learning rule that mitigates the variance issues associated with high-dimensional systems. By employing an orthogonal decomposition of the self-supervised learning cost, the method separates the input-dependent component from a redundant component determined by fixed ESN parameters. This allows the learning process to focus solely on the input-dependent component, effectively reducing the perturbation dimension from the reservoir dimension to the input dimension. Consequently, the proposed approach maintains the benefits of self-supervised adaptation and online learning while avoiding the variance growth typically associated with larger reservoirs. The results demonstrate that this method enhances the scalability and efficiency of online self-supervised learning in ESNs, making it more suitable for hardware implementations with limited memory and computational resources.
Methodology
The authors derive a perturbation-based learning rule from an orthogonal decomposition of the self-supervised learning cost function. This decomposition separates the input-dependent component from a redundant component, allowing for online learning to focus only on the input-dependent term. The method leverages perturbation-based learning to reduce the effective dimension of the learning problem, thus improving the signal-to-noise ratio of the gradient estimates.
Results
The proposed learning rule was validated through numerical experiments, which confirmed the theoretical predictions regarding variance scaling. The results indicated that the new approach significantly reduces the variance of gradient estimates as the reservoir dimension increases, thereby enhancing the performance of online self-supervised learning in Echo State Networks.
Implications
This research has potential implications for the design of intelligent systems that require continuous adaptation in real-time environments. The findings suggest that scalable and efficient learning methods can be developed for hardware implementations of ESNs, which could be applied in various domains such as robotics, autonomous systems, and real-time data processing.
Learning When to Automate: Queue Control in Human-AI Service Systems
Theory
Optimization
- Introduces a novel queueing model for human-AI service systems that couples automation and human scheduling decisions.
- Develops the UCB-DPP policy for learning and decision-making in the context of uncertain task handling effectiveness.
- Proves theoretical guarantees on regret and stability for the proposed policy.
- Demonstrates superior performance of UCB-DPP over baseline policies through simulations.
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Learning When to Automate: Queue Control in Human-AI Service Systems
Summary
This paper investigates the dynamics of human-AI service systems where tasks are processed through a two-stage architecture involving an automated chatbot and human agents. The authors model a scenario where tasks arrive sequentially, each belonging to one of K heterogeneous types, and propose a decision-making framework that balances the allocation of resources to the chatbot and the human service queues. The central challenge addressed is how to optimize automation while managing operational costs and human congestion. The proposed UCB-DPP policy combines Upper Confidence Bounds with Drift-Plus-Penalty control to learn unknown parameters of the system while making informed queue-aware decisions. The authors demonstrate that UCB-DPP achieves a regret bound of O(KโT) and ensures stability in human-service queues. Simulations indicate that this policy outperforms baseline approaches, highlighting its effectiveness in managing the trade-offs inherent in hybrid service systems.
Methodology
The authors propose the UCB-DPP policy, which integrates Upper Confidence Bounds (UCB) with Drift-Plus-Penalty (DPP) control. This method allows for the estimation of unknown parameters related to chatbot success probabilities and human service rates while making decisions that consider the state of the queues. The analysis combines concentration bounds and Lyapunov-drift arguments to derive theoretical guarantees.
Results
The UCB-DPP policy achieves a sublinear regret bound of O(KโT) and ensures mean-rate stability of the human-service queues. Simulations on synthetic instances show that UCB-DPP outperforms natural baseline policies, validating its effectiveness in managing the complexities of human-AI service systems.
Implications
The findings have significant implications for the design and operation of hybrid service systems, such as customer support platforms, where the balance between automation and human intervention is critical. The proposed framework can enhance operational efficiency and improve user satisfaction by optimizing resource allocation in real-time.
Gradient-free Riemannian Langevin Sampler
Theory
Efficient ML
Multimodal
- GRiLS is a gradient-free MCMC method that improves sampling efficiency for multimodal distributions.
- The method utilizes a Riemannian metric to enhance transitions between modes, addressing issues of poor mixing.
- Mean and covariance of the target density are estimated using an ensemble of interacting particles.
- Empirical results indicate that GRiLS achieves better mixing compared to traditional MCMC methods.
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Gradient-free Riemannian Langevin Sampler
Summary
This paper introduces the Gradient-free Riemannian Langevin Sampler (GRiLS), a novel Markov Chain Monte Carlo (MCMC) method designed to efficiently sample from multimodal probability distributions. Traditional MCMC methods often struggle with poor mixing and mode trapping, particularly in high-dimensional spaces. GRiLS addresses these challenges by employing a Riemannian metric to reshape the local geometry of the target distribution, facilitating transitions across modes without the need for gradient evaluations. The method estimates the mean and covariance of the target density using an ensemble of interacting particles, which enhances exploration and improves mixing. The empirical results demonstrate that GRiLS outperforms existing gradient-based and gradient-free MCMC approaches on multimodal benchmarks, showcasing its effectiveness in scenarios where derivatives are difficult to compute or unavailable.
Methodology
The GRiLS method defines a proposal density based on a Riemannian Langevin dynamic, utilizing a specific choice of Riemannian metric that reduces geodesic distances between modes. The proposal is derived using a Lamperti transform and a time discretization, allowing for efficient sampling without requiring gradient information. An ensemble-based approach is employed to estimate the mean and covariance, facilitating parallel updates of particles.
Results
The empirical evaluations on multimodal benchmarks reveal that GRiLS significantly improves mixing and reduces autocorrelation compared to both gradient-based and gradient-free MCMC methods. The results indicate that GRiLS can effectively traverse energy barriers between modes, leading to more efficient sampling in high-dimensional spaces.
Implications
The GRiLS method has potential applications in various fields requiring efficient sampling from complex distributions, such as Bayesian inference, statistical physics, and machine learning. Its gradient-free nature makes it particularly useful in scenarios where gradient computation is impractical or computationally expensive.
Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning
Federated Learning
- Introduces FedKT-CSD, a framework for one-shot federated learning with formal privacy guarantees.
- Utilizes pretrained autoencoders to create a shared latent space for efficient data encoding.
- Achieves competitive performance compared to existing methods while ensuring differential privacy.
- Reduces communication overhead by requiring only a single round of data transmission.
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Collaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning
Summary
The paper presents FedKT-CSD, a novel framework for one-shot federated learning (OSFL) that addresses the challenges of communication overhead, model quality, and privacy in federated learning settings. Traditional federated learning methods often struggle with heterogeneous data distributions, leading to slow convergence and suboptimal performance. FedKT-CSD leverages publicly pretrained autoencoders to create a shared latent space, allowing clients to encode their private data and compute class-conditional latent statistics in a single forward pass. These statistics are transmitted to a central server, where they are aggregated securely and enhanced with calibrated differential privacy noise. The server then decodes a synthetic dataset for training a global model. This approach ensures formal (ฮต, ฮด)-differential privacy, minimizes client-side computation, and maintains communication efficiency by requiring only one round of data transmission. The results demonstrate that FedKT-CSD is competitive with state-of-the-art methods, even outperforming some non-private baselines across various datasets and levels of data heterogeneity, while also being scalable to a large number of clients.
Methodology
FedKT-CSD employs a frozen, publicly pretrained autoencoder to encode client data into a latent space. Clients compute class-conditional statistics from their encoded data and transmit these lightweight summaries to a server. The server aggregates the statistics securely, adds differential privacy noise, and generates a synthetic dataset for training a global model. This process is designed to be efficient in terms of computation and communication, requiring only a single round of interaction.
Results
The experimental results indicate that FedKT-CSD not only meets the privacy requirements but also performs competitively against state-of-the-art one-shot federated learning methods. It demonstrates robustness to varying degrees of data heterogeneity and scales well with the number of clients, outperforming non-private baselines across multiple datasets.
Implications
The proposed framework has significant implications for applications requiring federated learning in privacy-sensitive environments, such as healthcare and finance. It enables efficient model training without compromising data privacy, making it suitable for real-world deployment in decentralized settings.
Deep Reinforcement Learning for Dynamic Battery Management of Autonomous Order Pickers
Reinforcement Learning
Robotics
Optimization
- Development of a multi-agent DRL framework using PPO for optimizing AMR routing and charging in warehouses.
- Incorporation of a comprehensive action space for charging decisions, including when to recharge, which station to use, and for how long.
- Modeling of stochastic order arrivals and queuing dynamics to minimize operational time and inefficiencies.
- Demonstrated improvements in order-completion rates and reduced recharging times compared to traditional methods.
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Deep Reinforcement Learning for Dynamic Battery Management of Autonomous Order Pickers
Summary
This paper addresses the critical operational challenge of battery charging for Autonomous Mobile Robots (AMRs) in warehouse environments, particularly under stochastic order arrivals. Traditional fixed-rule heuristics for charging decisions often lead to inefficiencies, especially in dynamic settings where multiple AMRs operate simultaneously. To tackle this issue, the authors propose a Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) framework that allows AMRs to learn optimal charging strategies dynamically. The model focuses on two main decisions: selecting charging stations and determining optimal charging durations while considering anticipated queuing times. Extensive numerical experiments benchmark the proposed DRL model against both state-of-the-art DRL and traditional heuristic approaches, demonstrating that the PPO framework can increase order-completion rates by up to 6% compared to the strongest baseline while significantly reducing total recharging time. The robustness of the model is validated across various warehouse configurations and stochastic arrival rates, providing insights into the learned DRL policy's operational advantages over standard benchmarks.
Methodology
The authors utilize a Proximal Policy Optimization (PPO) algorithm within a Deep Reinforcement Learning framework to optimize the charging and routing decisions of AMRs in a multi-block warehouse setting. The approach includes a comprehensive action space that allows for dynamic decision-making based on real-time operational conditions and anticipated future demands.
Results
The proposed DRL model outperformed traditional heuristic methods and alternative DRL baselines, achieving up to a 6% increase in order-completion rates and significantly reducing the time spent on recharging operations. The model's robustness was confirmed across various warehouse configurations and stochastic order arrival rates.
Implications
The findings suggest that implementing DRL frameworks for battery management in AMRs can lead to more efficient warehouse operations, potentially improving throughput and reducing operational costs. This approach could be applied to various logistics and fulfillment centers that rely on autonomous systems.
Converge to Surprise: Evolutionary Self-supervised Image Clustering
Computer Vision
Optimization
Theory
- Introduction of a surprise score that measures the non-randomness of model outputs under the maximum entropy hypothesis.
- Development of the 'converge-to-surprise' optimization scheme combining evolution strategy and gradient descent.
- Achievement of new state-of-the-art results in non-parametric self-supervised image clustering on benchmark datasets.
- Demonstration that the surprise score cannot generally be reduced to a per-step loss function, highlighting a fundamental limitation of traditional methods.
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Converge to Surprise: Evolutionary Self-supervised Image Clustering
Summary
This paper presents a novel self-supervised framework for image clustering that diverges from traditional gradient descent methods. The authors argue that existing self-supervised image clustering models rely heavily on clearly defined targets for loss calculation, which limits their ability to discover non-random features in data. To address this, they introduce the concept of a 'surprise score' that quantifies how unlikely the model's output is under the null hypothesis of maximum entropy, where pixels are assumed to be independent and identically distributed (i.i.d.). The framework, termed 'converge-to-surprise', combines an evolution strategy (ES) outer loop that maximizes the surprise score without requiring gradients, with a periodic gradient-descent inner loop that utilizes previously discovered surprising clusters as surrogate targets. The proposed method achieves state-of-the-art results in non-parametric self-supervised image clustering, where the number of ground-truth classes is not provided to the model, demonstrating its effectiveness in extracting meaningful information from unlabeled images.
Methodology
The authors propose a hybrid optimization framework that consists of an evolution strategy (ES) outer loop to maximize the surprise score without gradients, and a gradient-descent inner loop that uses previously identified surprising clusters as surrogate targets for optimization. This approach allows the model to discover non-random features in the data without relying on predefined loss functions.
Results
The experiments conducted on standard image benchmarks show that the proposed framework achieves new state-of-the-art results in non-parametric self-supervised image clustering, outperforming existing methods that require knowledge of the number of ground-truth classes.
Implications
The findings suggest that self-supervised learning can be advanced by moving away from traditional loss-based methods, potentially leading to more robust models capable of discovering complex patterns in unlabeled data. This could have significant applications in various fields of computer vision where labeled data is scarce or unavailable.
Nonlinear Bandit
Theory
Optimization
- Introduction of the EHM algorithm for heavy-tailed GLB problems with optimal regret and low computational complexity.
- Extension of the algorithm to piecewise constant contexts with the PGLB-EHM variant, maintaining similar regret bounds.
- Development of the NB-EHM algorithm for nonlinear bandit problems, achieving sublinear regret without restrictive assumptions.
- Demonstration of the robustness of the proposed algorithms under heavy-tailed distributions.
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Nonlinear Bandit
Summary
This paper addresses the generalized linear bandit (GLB) problem under heavy-tailed noise, which is prevalent in real-world applications such as personalized recommendations and financial markets. The authors propose an algorithm called EHM that builds upon the adaptive Huber loss method, achieving an optimal regret of O(T^(1/(1+ฯต))) with O(1) computational complexity per round. The algorithm does not require knowledge of certain parameters commonly used in existing methods. The authors also extend their work to a piecewise constant contextual characteristic scenario, leading to the PGLB-EHM algorithm, which retains the same regret upper bound. Furthermore, they delve into the nonlinear bandit (NB) problem, presenting the NB-EHM algorithm that utilizes a bisection method and affine lifting approach to achieve a sublinear regret bound. This work highlights the potential of Huber loss-based GLB algorithms in enhancing online learning theory and practical applications in noisy environments.
Methodology
The authors employ the online mirror descent (OMD) method to develop the EHM algorithm, which extends the adaptive Huber loss approach. They analyze the theoretical performance of their algorithms, focusing on regret bounds and computational efficiency. The paper also explores the use of affine lifting and bisection methods in the context of nonlinear bandits.
Results
The proposed EHM algorithm achieves an almost optimal regret of O(T^(1/(1+ฯต))) while maintaining O(1) computational complexity per round. The PGLB-EHM algorithm retains the same regret upper bound for piecewise constant contexts. The NB-EHM algorithm successfully addresses the nonlinear bandit problem, demonstrating a sublinear regret bound.
Implications
The findings suggest that Huber loss-based approaches can significantly enhance the robustness and efficiency of online learning algorithms in environments characterized by heavy-tailed noise. This has practical implications for applications in personalized recommendations, finance, and other fields where non-linear relationships and noisy data are prevalent.
Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies
Reinforcement Learning
- Gimitest provides a comprehensive framework for testing RL policies in various environments.
- The tool supports both single-agent and multi-agent reinforcement learning scenarios.
- Gimitest integrates with existing gym frameworks, allowing for customizable testing setups.
- The effectiveness of Gimitest is demonstrated through practical applications in popular RL environments.
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Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies
Summary
The paper introduces Gimitest, an open-source framework designed to comprehensively test reinforcement learning (RL) policies across various environments and scenarios. Traditional automated testing methods often focus on specific environments or algorithms, leaving gaps in the reliability and safety of RL policies. Gimitest addresses these issues by supporting both single-agent and multi-agent RL setups, allowing for extensive testing under diverse conditions. The framework integrates with existing gym frameworks, enabling modifications to their components for tailored testing. The authors demonstrate Gimitest's capabilities through testing multiple RL policies in environments like the Farama Gymnasium and PettingZoo, showcasing its effectiveness in identifying unsafe behaviors and vulnerabilities to adversarial attacks. The paper emphasizes the importance of rigorous testing in ensuring the reliability of RL agents, particularly in critical applications where safety is paramount.
Methodology
The authors developed Gimitest as an open-source tool that leverages existing gym frameworks for RL testing. The framework allows for the creation of test cases and oracles to evaluate policy performance under different conditions, including adversarial scenarios. It employs techniques such as search-based software testing (SBST) and metamorphic testing (MT) to systematically explore potential weaknesses in RL policies.
Results
Gimitest effectively identifies unsafe behaviors and vulnerabilities in RL policies across various testing scenarios. The tool's application in environments like Farama Gymnasium and PettingZoo demonstrates its capability to uncover issues that traditional testing methods may overlook, thus enhancing the reliability of RL agents.
Implications
The development of Gimitest has significant implications for the deployment of RL agents in safety-critical applications, such as autonomous vehicles and robotics. By providing a robust testing framework, Gimitest can help ensure that RL policies are reliable and resilient against adversarial attacks, ultimately contributing to safer AI systems.
K-ABENA: K-Adaptive Backpropagation with Error-based N-exclusion Algorithm : (Compensated Loss-Based Sample Exclusion with Unbiased Gradient Estimation)
Optimization
Efficient ML
Theory
- K-ABENA reduces training costs by excluding low-loss samples from the backward pass.
- The method provides a design-unbiased gradient estimator using Horvitz-Thompson reweighting.
- A convergence guarantee for SGD is established, showing O(1/โT) decay of the expected squared gradient norm.
- Empirical results indicate K-ABENA achieves 28-54% savings in computation while maintaining high accuracy.
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K-ABENA: K-Adaptive Backpropagation with Error-based N-exclusion Algorithm : (Compensated Loss-Based Sample Exclusion with Unbiased Gradient Estimation)
Summary
The paper introduces K-ABENA, a novel framework for selective gradient computation that aims to reduce the computational cost of training by excluding low-loss observations from the backward pass. The method employs a defensive-mixture sampling design combined with Horvitz-Thompson inverse-probability reweighting to create a design-unbiased gradient estimator. The authors establish a convergence guarantee for stochastic gradient descent (SGD) under this estimator and demonstrate that traditional uncompensated loss-based selection methods can fail to converge in certain scenarios, particularly under extreme class imbalance and label noise. Empirical results show that K-ABENA can achieve significant computational savings (28-54%) while maintaining performance comparable to full-batch SGD across various datasets. The paper also discusses the limitations of a biased regularized mode variant of K-ABENA, which can perform poorly under specific conditions.
Methodology
K-ABENA employs a selective gradient computation approach that combines defensive-mixture sampling and Horvitz-Thompson inverse-probability reweighting to exclude low-loss observations during the backward pass. The method is designed to maintain an unbiased gradient estimation while achieving computational efficiency. The authors also provide theoretical proofs regarding convergence and the limitations of traditional selection methods.
Results
K-ABENA demonstrated a significant reduction in computational costs (28-54%) while achieving performance metrics (AUC) that were statistically indistinguishable from full-batch SGD across various datasets. In scenarios with extreme class imbalance, K-ABENA outperformed traditional uncompensated methods, achieving an AUC of 0.9991 compared to 0.53 for uncompensated variants.
Implications
The K-ABENA framework has potential applications in large-scale machine learning tasks where computational efficiency is critical, particularly in scenarios with class imbalance or label noise. It provides a new approach to selective backpropagation that could enhance the training of deep learning models without sacrificing performance.
Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems
Multimodal
Time Series
- Introduces the MIIM framework to characterize the structure of normal behavior in CPS.
- Develops a detector that combines latent representation learning with Gaussian-mixture clustering.
- Evaluates the method using a fair protocol that avoids common pitfalls in anomaly detection metrics.
- Achieves state-of-the-art results on multiple CPS datasets, particularly in challenging scenarios.
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Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems
Summary
This paper addresses the challenge of anomaly detection in cyber-physical systems (CPS) where faults are rare and difficult to characterize. The authors propose a novel approach that focuses on modeling normal behavior rather than attempting to characterize anomalies directly. They introduce the Massive, Implicit, Imbalanced Multimodality (MIIM) framework, which outlines ten assumptions about the structure of normal data in CPS. The proposed method employs a jointly learned latent representation combined with explicit Gaussian-mixture mode clustering, allowing for a more accurate representation of the complex normal behavior. The evaluation is conducted using a fair protocol that emphasizes raw point-wise metrics and difficulty stratification. The authors demonstrate the effectiveness of their approach on three real CPS datasets (WADI, HAI, SKAB), achieving superior performance compared to existing deep learning models. The findings highlight the importance of accurately modeling normal behavior to improve anomaly detection in CPS.
Methodology
The authors propose a detector that utilizes a jointly learned latent representation along with explicit Gaussian-mixture mode clustering. The scoring is performed in the latent space using a mixture density head and a nearest-component likelihood, while avoiding reconstruction terms that can misrepresent faults. The evaluation is conducted using raw metrics and a difficulty-stratified approach to ensure fair comparisons.
Results
The proposed method outperformed existing deep learning models (USAD, TranAD, GDN) on all three datasets, achieving AU-ROC scores of 0.831 on HAI, 0.726 on WADI, and 0.610 on SKAB. The method showed the largest performance margins on multimodal datasets, confirming the effectiveness of the MIIM assumptions.
Implications
The findings suggest that accurately modeling normal behavior is crucial for effective anomaly detection in CPS. This approach can be applied to various industrial applications, enhancing the reliability and safety of complex systems by enabling early fault detection.
The Optimal Sample Complexity of Learning Autoregressive Chain-of-Thought
Theory
- Establishes the sample complexity bounds for learning autoregressive Chain-of-Thought traces.
- Introduces parity dimension as a refined measure for controlling sample complexity.
- Demonstrates that learning full autoregressive CoT traces is statistically as easy as learning local next-token rules.
- Provides a worst-case optimal dependence on the DS dimension for sample complexity.
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The Optimal Sample Complexity of Learning Autoregressive Chain-of-Thought
Summary
This paper addresses the sample complexity of learning full autoregressive Chain-of-Thought (CoT) traces in the realizable PAC setting. The author establishes that the sample complexity for exact-trace learning is upper bounded by the standard multiclass rate of the local next-token class, governed by the DanielyโShalev-Shwartz dimension. The main contribution is the introduction of parity dimension, a refined measure that controls one-inclusion density and remains stable under autoregressive rollout, unlike the DS dimension which can increase. The results indicate that the complexity of learning full autoregressive CoT traces is statistically comparable to learning the local next-token rule, thus providing a sharper bound for the sample complexity of exact-trace learning. The findings suggest that the shared autoregressive structure mitigates the expected increase in sample complexity typically associated with variable-length sequences.
Methodology
The author employs theoretical analysis to derive the sample complexity bounds, utilizing concepts from PAC learning and dimensions such as the DanielyโShalev-Shwartz dimension and the newly introduced parity dimension. The proof involves analyzing the structure of autoregressive models and their stopping rules to establish the relationship between local next-action classes and complete trace classes.
Results
The main result shows that the sample complexity for learning full autoregressive Chain-of-Thought traces is O(DSdim(H) + log(1/ฮด)/ฮต), where DSdim(H) is the DanielyโShalev-Shwartz dimension of the local next-action class. The introduction of parity dimension allows for a sharper bound, confirming that the statistical complexity of learning CoT traces is comparable to that of learning the local next-token rule.
Implications
These findings have significant implications for the design of learning algorithms in autoregressive models, particularly in applications involving sequential decision-making and reasoning tasks. The results suggest that leveraging the autoregressive structure can lead to more efficient learning strategies without incurring additional sample complexity.
Hypergraph Neural Stochastic Diffusion: An SDE Framework for Uncertainty Estimation
Graph Learning
Theory
- Introduces Hypergraph Neural Stochastic Diffusion (HyperNSD) for uncertainty estimation in hypergraphs.
- Models hypergraph representations as stochastic processes to capture uncertainty evolution.
- Employs a learnable drift function and stochastic forcing function for effective uncertainty quantification.
- Demonstrates theoretical stability and convergence of the proposed framework.
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Hypergraph Neural Stochastic Diffusion: An SDE Framework for Uncertainty Estimation
Summary
This paper introduces Hypergraph Neural Stochastic Diffusion (HyperNSD), a novel framework for uncertainty estimation in hypergraphs using stochastic differential equations (SDEs). Hypergraphs are capable of modeling complex higher-order relationships, but existing methods for uncertainty estimation primarily focus on deterministic predictions, which can lead to overconfidence in uncertain scenarios. HyperNSD addresses this by modeling hypergraph representations as stochastic processes that evolve over node-hyperedge incidence structures. The framework incorporates a learnable drift function to capture deterministic dynamics and a stochastic forcing function to account for structural ambiguity and noise. This allows for the quantification of predictive uncertainty through the variability of stochastic representation trajectories, offering a more intrinsic measure of uncertainty compared to traditional post-hoc methods. The authors provide theoretical analyses demonstrating the well-posedness and stability of the proposed dynamics. Experimental results on various hypergraph benchmarks show that HyperNSD not only achieves reliable uncertainty estimation for out-of-distribution and misclassification detection but also maintains competitive prediction accuracy, thus establishing a principled approach for trustworthy higher-order representation learning.
Methodology
The methodology involves formulating hypergraph representation learning as a stochastic diffusion process over node-hyperedge incidence structures. HyperNSD utilizes a learnable drift function to model deterministic dynamics and a stochastic forcing function to account for noise and ambiguity. The framework enables joint learning of predictions and uncertainty propagation through neural networks designed for drift and diffusion.
Results
The experimental results indicate that HyperNSD provides effective uncertainty estimation for out-of-distribution and misclassification scenarios, outperforming existing methods while maintaining competitive accuracy in predictions across multiple hypergraph benchmarks.
Implications
The proposed framework has significant implications for applications requiring reliable uncertainty quantification in complex relational data, such as social network analysis, biological systems modeling, and recommendation systems. It enhances the interpretability and trustworthiness of hypergraph neural networks in high-stakes environments.
FourierQK: Spectral Preprocessing of Query-Key Projections Improves Transformer Attention
NLP
Large Language Models
- FFT-based spectral preprocessing of query-key projections improves transformer attention performance.
- Significant reduction in validation loss observed, with multi-frequency spectral attention achieving a 79% reduction.
- Improvements are specific to spectral preprocessing; other methods do not yield measurable gains.
- The approach preserves the standard attention mechanism while enhancing it through frequency-domain filters.
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FourierQK: Spectral Preprocessing of Query-Key Projections Improves Transformer Attention
Summary
This paper introduces FourierQK, a novel approach to enhance transformer attention mechanisms through spectral preprocessing of query and key projections using Fast Fourier Transform (FFT). The study demonstrates that applying frequency-domain filters to learned query and key projections significantly improves performance in character-level language modeling, specifically on the TinyShakespeare dataset. The results show that a fixed random spectral filter achieves a validation loss of 1.031, while a single learned frequency initialized at paragraph scale reduces the loss to 0.608, and a multi-frequency spectral attention with four learned frequencies achieves a remarkable 0.309, representing a 79% reduction in validation loss compared to standard dot-product attention. The findings indicate that the improvements are specific to spectral preprocessing, as alternative methods such as random orthogonal rotations do not yield similar benefits. The paper also highlights the architectural distinction from previous works like FNET, emphasizing that FourierQK maintains the standard attention structure while integrating spectral preprocessing to enhance the attention mechanism itself.
Methodology
The methodology involves applying frequency-domain filters to the learned query and key projections before computing attention scores. Various filter designs were tested, including fixed random filters and learned frequency filters, to assess their impact on attention performance. The effectiveness of the spectral preprocessing was validated using a shuffled validation diagnostic to ensure that improvements stemmed from genuine sequence learning rather than positional artifacts.
Results
The experiments revealed that spectral preprocessing leads to substantial improvements in validation loss across different configurations. The best-performing model with four learned frequencies achieved a validation loss of 0.309, indicating a 79% reduction compared to standard attention mechanisms. The results were consistent across multiple random seeds, confirming the reproducibility of the findings.
Implications
The findings suggest that incorporating spectral preprocessing into transformer architectures could lead to more effective models for language modeling and potentially other tasks in NLP. This approach may inspire further research into frequency-domain techniques for enhancing attention mechanisms in various machine learning applications.
The Key to Going Linear: Analysis-Driven Transformer Linearization
NLP
Large Language Models
Efficient ML
- Isolated analysis of linearization mechanisms in a frozen-backbone setting.
- Delta-based mechanisms outperform gated updates by capturing key-dependent dynamics.
- Introduction of practical design choices that reduce performance gaps in linear attention.
- Demonstrated effectiveness across models with up to 32B parameters.
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The Key to Going Linear: Analysis-Driven Transformer Linearization
Summary
This paper addresses the computational bottleneck of causal self-attention in transformers, particularly when scaling to longer contexts. The authors focus on post hoc linearization methods that convert full-attention models into linear-time architectures without requiring retraining from scratch. They isolate the effects of various linear attention mechanisms by keeping the pretrained backbone frozen and only training newly introduced parameters. The study reveals that softmax attention relies on key-dependent, rank-1 orthogonal projections, which delta-style networks can effectively implement. The authors introduce structural interventions such as sink tokens, short convolutions, and fixed-budget cache routing to mitigate approximation errors. Their approach is validated on LLaMA and Qwen models, demonstrating superior performance over previous baselines on MMLU and achieving competitive results in long-context retrieval tasks.
Methodology
The authors employed an analysis-driven approach to transformer linearization, focusing on the approximation of causal self-attention with linear mechanisms. They compared various linear attention methods (kernelized, gated, delta-based) in a strict frozen-backbone regime, where only new parameters were trained. The study included theoretical derivations and empirical validations to assess the performance of these mechanisms.
Results
The proposed delta-style updates consistently provided the best approximation to softmax attention. The introduction of structural interventions like sliding-window attention, sink tokens, and limited adaptation techniques significantly narrowed the performance gap. The approach outperformed prior post hoc linearization methods on the MMLU benchmark and matched the performance of complex adaptive-caching frameworks in long-context retrieval tasks.
Implications
This work has significant implications for the efficient deployment of transformer models in applications requiring long-context processing, such as natural language understanding and generation. The findings could lead to more scalable and efficient transformer architectures, enabling broader use in real-time applications.
Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion
Graph Learning
Theory
Generative Models
- Linear attention in graph denoising is suboptimal due to its averaging nature across spectral properties.
- Spectral Attention improves upon linear attention by leveraging the input graph spectrum.
- Graph Convolutional Attention (GCA) is introduced as a permutation-equivariant mechanism that implements spectral denoising effectively.
- The softmax operation enhances denoising by projecting noisy eigenvectors onto the clean eigenspace.
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Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion
Summary
This paper addresses the problem of graph denoising, which is crucial in graph learning and underpins graph diffusion models. The authors critique the limitations of standard attention mechanisms, specifically linear attention, which only learns an average spectral denoising filter across graph distributions, leading to suboptimal performance when spectral properties vary. To overcome this, they introduce Spectral Attention, which utilizes the input graph spectrum and demonstrates improved performance over linear attention based on spectral diversity. The authors then propose Graph Convolutional Attention (GCA), a practical implementation that maintains permutation equivariance and effectively applies spectral denoising through graph-filtered queries and keys. They show that GCA matches the performance of the idealized Spectral Attention in stochastic block models. Additionally, the paper discusses the role of the softmax operation in enhancing denoising by projecting noisy eigenvectors onto the clean eigenspace. Empirical results indicate that GCA consistently outperforms standard graph transformers in denoising tasks across various datasets, with improvements correlating with spectral diversity. The integration of GCA into existing models like DiGress allows for competitive performance without the need for expensive structural features, thus reducing inference time.
Methodology
The authors analyze linear attention and its limitations, introduce Spectral Attention as a theoretical framework, and develop Graph Convolutional Attention (GCA) as a practical implementation. They validate their theoretical findings through empirical experiments on synthetic and real datasets, comparing GCA with standard attention mechanisms.
Results
The introduction of GCA leads to consistent improvements in graph denoising and diffusion tasks, with performance gains strongly correlated with the spectral diversity of the datasets. GCA matches the performance of existing models like DiGress without the need for costly structural features, resulting in faster inference times.
Implications
The findings suggest that GCA can enhance the performance of graph-based models in various applications, particularly in scenarios where spectral diversity is significant. This could lead to more efficient graph learning algorithms and improved performance in tasks such as graph-based diffusion models.
STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning
Time Series
- Introduction of STST-JEPA, a self-supervised transformer for EEG data.
- Pretrained on a large dataset, addressing EEG's unique challenges.
- Achieves a mean absolute error of 3.06 years in age regression.
- Demonstrates high performance in auxiliary tasks like sex classification.
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STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning
Summary
The paper introduces STST-JEPA, a self-supervised transformer model designed for EEG data, aimed at predicting brain age from EEG recordings. The model addresses challenges such as cross-site montage heterogeneity, small labeled cohorts, and subject-level non-stationarity. It is pretrained on a large dataset comprising 47,703 EEG sessions from ages 5 to 81. The architecture employs a latent-prediction objective, predicting masked-token representations against an exponential moving average of the tokenizer target, combined with an auxiliary signal-reconstruction term. The model demonstrates competitive performance in age regression, achieving a mean absolute error of 3.06 years on a validation set, significantly outperforming baseline models. Additionally, with task-specific fine-tuning, STST-JEPA achieves high accuracy in sex classification and psychopathology regression tasks. The findings suggest that the model's age-prediction residual correlates negatively with cognitive efficiency, indicating its potential as a biomarker for neurological and psychiatric conditions.
Methodology
The STST-JEPA model utilizes a transformer architecture with a latent-prediction objective and an auxiliary signal-reconstruction term. It processes 30-second multi-channel EEG windows under spatiotemporal block masks, enabling effective learning from large unlabeled datasets. The model is pretrained on diverse EEG sessions and fine-tuned for specific downstream tasks.
Results
The model achieved a mean absolute error of 3.06 years in age regression on a validation set of 3,367 sessions, with a correlation coefficient of 0.924. After fine-tuning, it reached a balanced accuracy of 0.911 for sex classification and a correlation of 0.215 for psychopathology regression. The age-prediction residual was found to be negatively correlated with cognitive efficiency across various tasks.
Implications
The STST-JEPA model has significant implications for using EEG as a biomarker for neurological and psychiatric conditions. Its ability to predict brain age accurately could aid in early diagnosis and monitoring of cognitive decline, making it a valuable tool in clinical settings.
Entropy-Guided Tensor Compression for Multimodal Federated Learning on Edge Devices
Federated Learning
Multimodal
Efficient ML
- MESH-FL adapts compression strategies based on spectral entropy, improving efficiency in multimodal federated learning.
- The framework allows for dynamic rank allocation across layers, modalities, and devices, optimizing communication costs.
- Experimental results indicate substantial improvements in accuracy and communication efficiency compared to traditional methods.
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Entropy-Guided Tensor Compression for Multimodal Federated Learning on Edge Devices
Summary
This paper addresses the challenges of federated learning (FL) in multimodal settings, where clients differ in sensing capabilities and computational resources. The authors propose MESH-FL, an entropy-guided matrix product state (MPS) update-compression framework designed for modality-heterogeneous FL on resource-constrained edge devices. Unlike existing compression schemes that apply uniform policies across layers and devices, MESH-FL adapts compression based on the spectral entropy of layer-wise updates, which reflects the compressibility of the updates. The framework estimates spectral entropy using truncated singular value decomposition and allocates MPS compression ranks dynamically according to the complexity of updates and the capabilities of devices, all while adhering to per-client payload budgets. The authors demonstrate that higher spectral entropy correlates with a need for higher reconstruction ranks, leading to a more efficient allocation of resources. The experimental results on a heterogeneous Raspberry Pi cluster show that MESH-FL achieves significant communication reductions, with up to 56.8ร compression and improved accuracy over the uncompressed FedAvg baseline by up to 2.01%, while also reducing the total transmitted data needed to reach convergence by up to 66ร.
Methodology
The authors developed MESH-FL, which incorporates a spectral-entropy-guided approach to update compression. It utilizes truncated singular value decomposition to estimate spectral entropy and employs an adaptive rank allocation strategy that considers both the complexity of updates and the capabilities of the devices involved. The framework is validated through theoretical analysis and extensive experiments on a heterogeneous edge testbed.
Results
MESH-FL achieved up to 56.8ร compression while surpassing the uncompressed FedAvg baseline in final accuracy by up to 2.01%. Additionally, it reduced the total transmitted data required to reach convergence by up to 66ร, demonstrating its effectiveness in improving communication efficiency in multimodal federated learning.
Implications
The proposed framework has significant implications for federated learning applications in resource-constrained environments, such as mobile health and human activity recognition, where efficient communication and model training are critical. MESH-FL can enhance the performance of multimodal systems by optimizing the use of available bandwidth and computational resources.
Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning
Graph Learning
Theory
Multimodal
- Introduction of intuitionistic fuzzy sets into the RVFL framework for better uncertainty handling.
- Incorporation of graph embedding to preserve geometric relationships and enhance generalization.
- Utilization of multiview learning to combine information from multiple feature sets.
- Statistical analyses confirm significant improvements in classification performance over existing models.
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Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning
Summary
This paper presents the Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning (IFGRVFL-MV) model, which addresses the limitations of existing Random Vector Functional Link (RVFL) networks in handling uncertainty and utilizing multiple feature views. The proposed model integrates intuitionistic fuzzy sets to manage uncertainty, graph embedding to capture geometric structures, and multiview learning to leverage complementary information from various feature spaces. By assigning intuitionistic fuzzy membership and non-membership values to data points, the model enhances robustness against outliers. The graph embedding framework preserves topological structures, leading to improved generalization performance. Experiments conducted on benchmark datasets from UCI and KEEL repositories demonstrate that IFGRVFL-MV significantly outperforms existing models in classification accuracy, establishing it as a promising advancement in the fields of uncertainty handling and multiview learning.
Methodology
The IFGRVFL-MV model combines intuitionistic fuzzy logic, graph embedding, and multiview learning. It assigns membership and non-membership values based on sample distances from class centers and neighboring heterogeneity. The graph embedding framework captures intrinsic geometric structures, while multiview learning integrates data from different feature sets to improve classification accuracy.
Results
The IFGRVFL-MV model was evaluated on benchmark datasets, showing statistically significant improvements in classification accuracy compared to existing models. The results validate the model's effectiveness in handling uncertainty and leveraging multiview information.
Implications
The proposed model has potential applications in various domains such as healthcare, image classification, and forecasting, where handling uncertainty and utilizing diverse data sources are crucial for improving predictive performance.
Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor
Computer Vision
- HARVEY leverages the RCE loss for better dataset splitting, enhancing the identification of poisonous samples.
- The method introduces a paradigm shift by using a backdoored reference model to differentiate between poisonous and benign samples.
- HARVEY consistently suppresses backdoor attacks while preserving natural accuracy across multiple datasets and architectures.
- The approach demonstrates a significant improvement over existing training-time defenses against backdoor attacks.
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Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor
Summary
This paper presents HARVEY, a novel training-time defense mechanism designed to combat neural backdoors introduced through data poisoning. The authors observe that models tend to learn poisonous samples more easily than benign ones, leading to the development of HARVEY, which focuses on learning an oracle for poisonous samples rather than benign ones. This approach allows for more accurate identification of poisonous samples, resulting in near-perfect backdoor removal. HARVEY employs a four-stage process: initialization, learning the backdoor, meta-splitting, and final training. The method utilizes the reverse cross-entropy (RCE) component of the symmetrical cross-entropy (SCE) loss for effective dataset splitting. The authors demonstrate that HARVEY significantly outperforms existing methods across various attack types, datasets, and architectures, achieving a minimal attack success rate while maintaining natural accuracy.
Methodology
HARVEY operates in four stages: (1) Initialization involves training a model and splitting the dataset into poisoned and benign subsets using RCE loss. (2) Learning the backdoor iteratively refines the reference model to focus on poisonous samples while unlearning benign ones. (3) Meta-splitting further isolates poisonous samples using the refined model. (4) Final training is conducted on the identified benign dataset to produce a clean model.
Results
HARVEY achieves an attack success rate below 2% in the worst case while maintaining high natural accuracy. It outperforms related approaches across various attacks, datasets, and model architectures, demonstrating its effectiveness in backdoor removal.
Implications
The findings suggest that training-time defenses can be significantly enhanced by focusing on poisonous samples, which may lead to more robust machine learning models in security-sensitive applications. HARVEY's approach could be applied in various domains where data integrity is crucial, such as autonomous systems and financial models.
Intrinsic-Noise Consolidation: A Doob-Barrier-Conditioned Diffusion Turns Analog Device Noise into a Continual-Learning Resource
Theory
Efficient ML
Optimization
- Introduces a novel Doob h-transform-based synaptic rule for memory consolidation.
- Demonstrates that intrinsic noise can enhance memory retention in analog devices.
- Finds a non-monotonic relationship between noise levels and retention performance.
- Achieves significant improvements over traditional methods in empirical tests.
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Intrinsic-Noise Consolidation: A Doob-Barrier-Conditioned Diffusion Turns Analog Device Noise into a Continual-Learning Resource
Summary
This paper explores the potential of intrinsic noise in analog neuromorphic hardware as a resource for memory consolidation rather than a detriment to accuracy. The author introduces a novel synaptic rule based on a Doob h-transform, which conditions the stochastic dynamics of synaptic weights on the event of not crossing a memory-critical barrier. This approach results in a drift term that amplifies with the noise variance, creating a non-monotonic relationship between intrinsic noise and sequential-task retention. The study demonstrates that increasing intrinsic noise can improve memory retention up to an optimal point, contradicting traditional views that noise is detrimental. The method was tested on the Split-MNIST dataset, showing a significant improvement in retention compared to existing methods like Ornstein-Uhlenbeck Adaptation (OUA), Elastic Weight Consolidation (EWC), and Memory-Efficient Synaptic Update (MESU). The findings suggest that intrinsic noise can be harnessed effectively in continual learning scenarios, particularly in analog hardware environments.
Methodology
The methodology involves modeling synaptic weight dynamics as a conditioned diffusion process, where the conditioning is based on the probability of not crossing a critical memory barrier. The author derives the resulting drift term from the Doob h-transform, which incorporates the effects of intrinsic noise in the learning process. The approach was empirically validated using the Split-MNIST dataset and real BrainScaleS-2 silicon hardware.
Results
The barrier-conditioned rule improved retention by 10.9 percentage points at an optimal noise level (ฯ* = 0.02) compared to conditions with zero or high noise. The results were statistically significant (p = 0.004). The method also demonstrated robustness across different noise models and tasks, outperforming traditional methods like OUA, EWC, and MESU in terms of retention without requiring rehearsal.
Implications
The findings suggest that intrinsic noise in analog hardware can be strategically utilized to enhance memory retention in continual learning systems. This reframing of noise from a liability to an asset could lead to more efficient learning algorithms in neuromorphic computing environments, potentially impacting the design of future neural networks and learning systems.
UASPL: Uncertainty-Aware Self-Paced Learning with Evidential Neural Networks
Theory
Interpretability
Efficient ML
- UASPL is the first SPL method to integrate model-generated evidential uncertainty with label-fitting loss.
- The sample selection process in UASPL is interpretable and aligns with the self-paced learning principle.
- UASPL can be adapted to various SPL variants, demonstrating its generality.
- Experimental results show significant improvements in classification performance over traditional SPL methods.
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UASPL: Uncertainty-Aware Self-Paced Learning with Evidential Neural Networks
Summary
This paper introduces UASPL, an innovative approach to self-paced learning (SPL) that incorporates uncertainty estimation through evidential neural networks. Traditional SPL methods select training samples based on their loss values, but this can lead to unreliable sample selection, as low-loss samples may not always represent easy samples. UASPL addresses this issue by integrating predictive reliability into the sample selection process using a general loss function derived from the Subjective Logic framework. This loss function not only estimates uncertainty but also provides interpretability in the sample selection process. The authors demonstrate that UASPL outperforms existing SPL methods across multiple datasets in terms of classification performance, interpretability, and generality. The findings suggest that UASPL can effectively distinguish between reliably simple samples and pseudo-easy ones, enhancing the overall learning process.
Methodology
UASPL employs evidential neural networks to quantify predictive uncertainty and incorporates this uncertainty into a general loss function for sample selection. The method adapts the growth of evidence to guide the learning process from easy to difficult samples, ensuring that the selected samples are reliably simple.
Results
The experimental evaluation on multiple datasets indicates that UASPL achieves superior classification performance compared to existing SPL methods. It also demonstrates enhanced interpretability and generality, confirming the effectiveness of the proposed approach.
Implications
The findings of this research could lead to more robust and efficient training methodologies in machine learning, particularly in scenarios where sample difficulty varies significantly. UASPL's approach may be applicable in various domains requiring reliable sample selection, such as medical diagnosis, image classification, and natural language processing.
Fingerprint, Not Blueprint: How Positional Schemes Set the Default Spectral Algebra of Attention
NLP
Large Language Models
Theory
- Introduces a matched-null spectral framework for analyzing the QK operator in attention heads.
- Demonstrates that positional encoding schemes significantly influence the spectral properties of attention heads.
- Finds that the strongest previous-token heads are spectrally rotational under RoPE, contrasting with other schemes.
- Establishes that the positional scheme acts as a 'fingerprint' that shapes the attention mechanism's behavior.
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Fingerprint, Not Blueprint: How Positional Schemes Set the Default Spectral Algebra of Attention
Summary
This paper investigates the spectral properties of attention heads in transformer models, focusing on how different positional encoding schemes influence the spectral algebra of attention mechanisms. The author introduces a matched-null spectral framework for analyzing the attention operator, revealing that the pre-softmax score can be represented as a bilinear form involving a learned operator M. The study examines seven pretrained models across three positional schemes: RoPE, learned-absolute, and ALiBi. The findings indicate that the strongest previous-token heads exhibit a rotational spectral signature under RoPE, while non-rotational characteristics are observed under learned-absolute and ALiBi schemes. The research also demonstrates that the spectral directionality effectively separates head functions and that the positional scheme fundamentally shapes the spectral algebra of attention heads, acting as a 'fingerprint' rather than a rigid constraint. The paper concludes that the positional encoding scheme is crucial in determining the behavior and performance of attention mechanisms in transformers.
Methodology
The study employs a spectral analysis framework to investigate the QK operator in attention heads across various pretrained transformer models. It utilizes statistical comparisons, causal interventions, and dynamic observations of model checkpoints to analyze the spectral properties and head functions under different positional encoding schemes.
Results
The analysis reveals that the spectral directionality can effectively differentiate head functions across models. The strongest previous-token heads show a significant rotational spectral signature under RoPE, while non-rotational characteristics are noted for learned-absolute and ALiBi schemes. The research confirms that the positional scheme sets the default spectral algebra of attention heads, with implications for understanding the underlying mechanisms of transformer models.
Implications
The findings suggest that the choice of positional encoding scheme can have profound effects on the performance and interpretability of transformer models. This insight could guide future research in optimizing attention mechanisms and improving model architectures for various applications in natural language processing and beyond.
Canopy: A Heterograph Foundation Model for Metabolic Engineering
Graph Learning
Multimodal
Optimization
- CANOPY integrates ten diverse data sources into a comprehensive knowledge graph for metabolic engineering.
- The model employs domain-specific foundation models for multi-modal feature encoding.
- Self-supervised pretraining enhances the predictive capabilities of the Heterogeneous Graph Transformer.
- CANOPY outperforms traditional tabular methods and homogeneous GNNs in fermentation titer prediction.
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Canopy: A Heterograph Foundation Model for Metabolic Engineering
Summary
The paper introduces CANOPY, a heterogeneous graph foundation model designed to enhance metabolic engineering by integrating multiple data sources into a unified knowledge graph. This model addresses the limitations of existing computational approaches in predicting fermentation titer by leveraging a comprehensive graph structure that includes genes, proteins, metabolites, and experimental data. CANOPY employs domain-specific foundation models for encoding node features, resulting in a multi-modal representation. The authors pretrain a Heterogeneous Graph Transformer (HGT) using self-supervised objectives, achieving significant improvements in fermentation titer prediction over traditional tabular methods and homogeneous graph neural networks. The study highlights the potential of CANOPY to streamline the design-build-test-learn cycle in metabolic engineering, thereby accelerating the development of engineered microorganisms for the bioeconomy.
Methodology
The authors constructed a heterogeneous knowledge graph with 6.9 million nodes and 34 edge types, integrating various biological data sources. They utilized domain-specific foundation models (ESM-2, MoLFormer, and PubMedBERT) for feature encoding and pretrained a Heterogeneous Graph Transformer with four self-supervised objectives, including link prediction and masked node modeling, balanced by learned uncertainty weighting.
Results
The CANOPY model achieved an R2 score of 0.41 in fermentation titer prediction using frozen embeddings, significantly outperforming the best tabular baseline score of 0.24 and homogeneous GNN variants.
Implications
The development of CANOPY has the potential to revolutionize metabolic engineering by providing a robust computational tool for predicting strain performance, thus accelerating the discovery and optimization of microbial strains for industrial applications in the bioeconomy.
When Do Geometric Algebra Layers Beat Scalarization? A Controlled Study on SO(3)-Equivariant Vector Laws
Robotics
Theory
Efficient ML
- Geometric algebra networks do not outperform scalarization for single-stage laws.
- For compositional laws, geometric algebra networks significantly outperform scalarization in low-data scenarios.
- No tested model effectively extrapolates invariant magnitudes, indicating a limitation in current approaches.
- The study provides a controlled benchmark to assess the contributions of geometric algebra in equivariant learning.
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When Do Geometric Algebra Layers Beat Scalarization? A Controlled Study on SO(3)-Equivariant Vector Laws
Summary
This paper investigates the effectiveness of geometric algebra layers compared to scalarization in learning SO(3)-equivariant vector laws. The study focuses on compact networks built from Clifford algebra Cl(3, 0) primitives, which are designed to learn 3D vector laws efficiently from limited data. The author compares these geometric algebra networks against a scalarization baseline that uses invariant dot products and a small multi-layer perceptron (MLP) to output coefficients on an equivariant basis. The findings reveal that for single-stage laws, scalarization performs equally or better than the geometric algebra network at significantly lower training costs. However, for compositional laws that involve nested group operations, the geometric algebra network demonstrates superior performance, achieving higher accuracy with fewer training samples. The paper concludes that while geometric algebra layers do not universally enhance low-data 3D learning, they become advantageous in scenarios where the target function involves deep compositions of group elements.
Methodology
The study employs a controlled benchmark comparing geometric algebra networks built from Cl(3, 0) primitives against a scalarization baseline using invariant dot products. Various synthetic vector laws are tested, and models are trained under consistent conditions to ensure comparability. The performance is evaluated based on accuracy and training costs across different data regimes.
Results
The results indicate that scalarization matches or exceeds the performance of geometric algebra networks for single-stage laws at a fraction of the training cost. In contrast, for compositional laws, the geometric algebra network outperforms scalarization by a factor of 3 to 16 in low-data conditions. However, scalarization catches up in performance as the sample size increases, particularly beyond 1000 samples. Additionally, no model successfully extrapolates invariant magnitudes, highlighting a common failure across all tested architectures.
Implications
The findings suggest that while geometric algebra layers can be beneficial in specific contexts, they are not a universal solution for low-data 3D learning tasks. This insight can guide future research in designing architectures that leverage equivariance more effectively, particularly in applications involving complex spatial transformations.
TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation
NLP
Large Language Models
Efficient ML
- TriRoute integrates attention resolution, expert selection, and KV-cache allocation into a single learned controller.
- The architecture is trained end-to-end, allowing for dynamic adjustments based on token characteristics.
- TriRoute outperforms traditional methods in terms of efficiency and robustness, particularly for rare and complex tokens.
- The approach mitigates routing collapse through a coupling-aware balancing loss and a unified budget constraint.
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TriRoute: Unified Learned Routing for Joint Adaptive Attention, Experts, and KV-Cache Allocation
Summary
The paper introduces TriRoute, a novel architecture designed to optimize the inference cost of large language models (LLMs) by jointly managing three critical components: attention resolution, expert selection, and key/value (KV) cache allocation. Traditional methods like Mixture-of-Experts (MoE), Mixture-of-Depths (MoD), and KV-cache quantization operate independently, leading to inefficiencies in resource allocation. TriRoute employs a single lightweight controller that dynamically adjusts these parameters for each token at every layer, allowing for a more nuanced and effective allocation of computational resources. The controller is trained end-to-end using a heterogeneous relaxation scheme, which includes Gumbel-Softmax for categorical decisions and load-balanced top-k gating for expert selection. The authors address challenges such as routing collapse across axes and the need for a unified budget constraint, ensuring that the model can effectively balance compute and memory costs while maintaining performance. The results demonstrate that TriRoute outperforms the best independently-tuned combinations of existing methods, particularly in preserving robustness for rare entities and complex cases, suggesting that a shared learned controller is a more effective approach for adaptive computation in LLMs.
Methodology
TriRoute employs a single controller that emits coordinated policies for attention mode, expert selection, and KV-cache bit-width for each token. The training utilizes a heterogeneous relaxation scheme combining Gumbel-Softmax and top-k gating, with a Lagrangian budget constraint to manage compute and memory costs effectively.
Results
TriRoute establishes a Pareto frontier that surpasses the best independently-tuned combinations of MoD, MoE, and KV-quantization, achieving superior performance at matched inference FLOPs and memory usage. It also demonstrates enhanced robustness for tail-case scenarios, such as rare entities and complex queries.
Implications
The findings suggest that a unified learned routing mechanism can significantly improve the efficiency and effectiveness of large language models, potentially leading to more resource-efficient deployments in real-world applications. This approach could be extended to other areas of machine learning where resource allocation is critical.
Empirical Minimal-Realisation Compression of Deep Neural Networks via Controllability-Observability Tests
Efficient ML
- Introduces a control-theoretic perspective for DNN compression focusing on hidden-state dynamics.
- Develops empirical tests for reachability and observability to assess hidden-state redundancy.
- Proposes realised C-balanced compression that directly reduces layer widths based on empirical ranks.
- Demonstrates substantial state and parameter compression on benchmark datasets with minimal accuracy loss.
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Empirical Minimal-Realisation Compression of Deep Neural Networks via Controllability-Observability Tests
Summary
This paper addresses the issue of hidden-state redundancy in deep neural networks (DNNs) by proposing a novel controllability-observability framework for empirical state-order reduction. Unlike traditional compression methods that focus on weights or neurons, this approach characterizes the dynamical role of internal states. The authors interpret a trained DNN as a nonlinear dynamical system and utilize data-driven reachability, observability, and balanced Gramians to estimate layer-wise ranks that indicate the useful dimensions of hidden states. The proposed method, termed realised C-balanced compression, directly translates these ranks into reduced layer widths for compact DNN architectures. Experiments conducted on MNIST and CIFAR-10 datasets demonstrate significant reductions in both state order and parameter count, achieving high compression rates while maintaining accuracy. The results highlight the effectiveness of using balanced reachable-observable ranks as a principled criterion for designing efficient neural networks.
Methodology
The authors formulate DNNs as depth-indexed nonlinear state-space systems and employ empirical tests to measure reachability and observability of hidden states. They construct balanced reachable-observable matrices to derive layer-wise ranks, which are then used to define the widths of a compressed network. The methodology is validated through experiments on standard datasets, comparing the proposed approach with various existing compression techniques.
Results
On the MNIST dataset, the proposed method reduced the hidden-state order from 1024 to 277, achieving 72.95% state compression and 73.48% parameter compression while maintaining 95.45% accuracy. For CIFAR-10, the hidden-state order was reduced from 4608 to 1339, resulting in 70.94% state compression and 83.09% parameter compression, with accuracy preserved at 54.44% and a reduction in CUDA inference latency by approximately 3ร.
Implications
The findings suggest that the proposed controllability-observability framework can significantly enhance the efficiency of DNNs, making them more suitable for deployment in resource-constrained environments. This approach can lead to the development of more compact neural architectures without sacrificing performance, which is crucial for applications in mobile and edge computing.
Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
Graph Learning
- SCISE effectively mitigates the structural isolation problem in graph clustering.
- The SECC operator enhances community cohesion by optimizing structural information.
- CSampE improves sampling by incorporating global community context into mini-batches.
- StructCL refines edge weights to guide the learning of discriminative representations.
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Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
Summary
This paper addresses the challenges of unsupervised graph clustering, particularly the issue of 'structural isolation' that arises during mini-batch training. The authors propose SCISE, a scalable framework that integrates community-aware sampling with constrained structural entropy to enhance clustering performance. The framework consists of three main components: the Structural Entropy Community Constraint (SECC) operator, which optimizes community cohesion; the Community-Aware Sampling Expansion (CSampE) mechanism, which incorporates global community context into sampling batches; and the Structural Contrastive Learning (StructCL) module, which refines edge weights based on intra-batch structural similarities. Through extensive experiments on six benchmark datasets, SCISE demonstrates significant improvements over state-of-the-art algorithms, validating its effectiveness in preserving structural integrity and enhancing clustering quality in large-scale graphs.
Methodology
The methodology involves three key components: (1) SECC operator to optimize community compactness within a constrained solution space, (2) CSampE mechanism to incorporate global community context into mini-batch sampling, and (3) StructCL module to refine edge weights based on structural similarities among nodes in the same batch.
Results
The results indicate that SCISE significantly outperforms state-of-the-art graph clustering algorithms across six benchmark datasets. The ablation studies and robustness analyses further confirm the framework's effectiveness and reliability in handling large-scale graphs.
Implications
The proposed framework has potential applications in various domains that utilize graph-structured data, such as social network analysis, recommendation systems, and anomaly detection, by providing more accurate community detection and insights into underlying patterns.
Design-CP: Context Parallelism for Design of Protein Nanoparticles
Generative Models
- Introduction of Design-CP, a context-parallel inference method for RFD3.
- Demonstration of improved scalability for designing large symmetric protein assemblies.
- Successful application of symmetry constraints to enhance sample quality.
- Feasibility of octahedral nanoparticle design on modest multi-GPU setups.
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Design-CP: Context Parallelism for Design of Protein Nanoparticles
Summary
The paper introduces Design-CP, a novel approach to enhance the design of protein nanoparticles using context-parallel inference strategies for the RFdiffusion 3 (RFD3) model. Traditional all-atom generative models struggle with memory limitations when designing large multimeric complexes due to their quadratic token and atom pair representations. Design-CP addresses this issue by implementing two context-parallel strategies: a 1D row-sharding and a 2D grid sharding with ring attention, which distribute the memory load across multiple GPUs while maintaining the integrity of pretrained weights. The authors demonstrate that these strategies allow for the design of larger asymmetric subunit sizes in protein assemblies, particularly for icosahedral and octahedral nanoparticles. The paper highlights the scalability of the methods and their practical application in generating high-quality protein designs without the need for extensive retraining or fine-tuning, thus making large-assembly protein design more accessible.
Methodology
The authors implemented two context-parallel inference strategies for RFD3: a 1D row-sharding method that distributes pair representations across multiple GPUs along a single axis, and a 2D grid sharding method that utilizes ring attention to tile the representation over a grid of GPUs. Both methods aim to reduce the quadratic memory costs associated with large protein assemblies while preserving numerical equivalence with single-GPU inference.
Results
The study found that the maximum feasible asymmetric subunit size increases with the square-root of the number of GPUs used, with the 2D sharding method showing superior wall-clock scaling. The imposition of strong point-group symmetry constraints allowed for effective end-to-end all-atom design of icosahedral nanoparticles without the need for retraining. Additionally, the methods enabled successful design of octahedral nanoparticles using a small cluster of workstation-grade GPUs.
Implications
The findings suggest that Design-CP could significantly advance the field of computational protein design by enabling the design of larger and more complex protein assemblies. This democratization of access to large-assembly protein design could have wide-ranging applications in therapeutic design, vaccine development, and biomolecular engineering.