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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Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebysheff Scalarization
Reinforcement Learning
Optimization
Large Language Models
- Introduces STOMP, a novel offline RL algorithm for multi-objective optimization.
- Utilizes smooth Tchebysheff scalarization to effectively capture non-convex regions of the Pareto front.
- Demonstrates superior performance over existing methods in protein engineering tasks.
- Addresses the limitations of linear reward scalarization in multi-objective RL.
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Pareto-Optimal Offline Reinforcement Learning via Smooth Tchebysheff Scalarization
Summary
This paper addresses the challenge of aligning large language models with human preferences through offline reinforcement learning (RL) in multi-objective settings. Traditional approaches often rely on linear reward scalarization, which fails to capture non-convex regions of the Pareto front, crucial for optimizing conflicting objectives. The authors propose a novel algorithm, Smooth Tchebysheff Optimization of Multi-Objective Preferences (STOMP), which utilizes smooth Tchebysheff scalarization to frame multi-objective RL as an optimization problem. This method dynamically standardizes individual rewards based on their observed distributions, thus overcoming the limitations of linear scalarization. The effectiveness of STOMP is empirically validated through experiments on protein engineering tasks, where it aligns autoregressive protein language models with multiple fitness objectives. The results demonstrate that STOMP outperforms state-of-the-art baselines in achieving higher hypervolumes in various evaluation settings, indicating its robustness and potential for improving multi-attribute optimization tasks.
Methodology
The authors frame multi-objective RL as an optimization problem and apply smooth Tchebysheff scalarization to derive STOMP. This approach standardizes individual rewards based on their distributions in an offline dataset, allowing for effective optimization of multiple conflicting objectives without the need for manual scaling of rewards.
Results
STOMP was empirically validated on protein engineering tasks, achieving the highest hypervolumes in eight out of nine evaluation settings compared to state-of-the-art baselines. This indicates that STOMP is effective in aligning models with multiple objectives, significantly improving performance in offline RL scenarios.
Implications
The proposed STOMP algorithm has significant implications for various applications requiring multi-objective optimization, such as protein engineering, chatbot development, and other domains where conflicting objectives must be balanced. It provides a robust framework for enhancing the alignment of large language models with human preferences.
Drowsiness-Aware Adaptive Autonomous Braking System based on Deep Reinforcement Learning for Enhanced Road Safety
Reinforcement Learning
Robotics
Time Series
- Integration of real-time drowsiness detection into an autonomous braking system.
- Utilization of ECG signals for accurate drowsiness monitoring.
- Development of a Double Dual Deep Q-Network (DD-DQN) for adaptive braking policies.
- Achieved a 99.99% success rate in avoiding accidents in both drowsy and non-drowsy scenarios.
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Drowsiness-Aware Adaptive Autonomous Braking System based on Deep Reinforcement Learning for Enhanced Road Safety
Summary
This paper presents a novel autonomous braking system that incorporates driver drowsiness detection using deep reinforcement learning (DRL) to enhance road safety. Recognizing that drowsiness significantly impairs a driver's ability to judge safe braking distances, the authors propose a system that integrates physiological data, specifically ECG signals, to detect drowsiness in real-time. The system utilizes a Double Dual Deep Q-Network (DD-DQN) agent that learns adaptive braking policies based on vehicle dynamics, traffic conditions, and the driver's cognitive state. The study includes an exhaustive benchmark analysis of different ECG window segmentation configurations to optimize drowsiness detection. The proposed RNN model effectively predicts drowsiness, which is then used to simulate delayed driver reactions in the DQN agent. The framework is evaluated in a high-fidelity simulation environment, demonstrating a remarkable 99.99% success rate in maintaining safe distances and avoiding collisions under both drowsy and alert conditions. This work represents a significant advancement in integrating physiological monitoring with autonomous driving systems, aiming to improve safety on the roads.
Methodology
The authors developed a drowsiness-aware braking system using a Double Dual Deep Q-Network (DD-DQN) agent. They employed a Recurrent Neural Network (RNN) to process ECG-derived features for real-time drowsiness detection. The system was trained in a simulation environment that mimicked real-world driving conditions, incorporating various configurations for ECG signal segmentation to optimize detection accuracy. The drowsiness state was integrated into the DQN's observable state space, simulating delayed actions to reflect impaired driver responses.
Results
The DD-DQN agent achieved a 99.99% success rate in maintaining safe following distances and avoiding collisions during testing. Over 30,000 seconds of simulation, only 0.9 seconds of cumulative violations of safe distance occurred, all during drowsy states, indicating the agent's robustness in adapting to impaired driving conditions.
Implications
This research has significant implications for the development of intelligent driving systems that can enhance road safety by integrating physiological monitoring. The approach could lead to more adaptive and responsive vehicle control systems that account for driver states, potentially reducing accident rates caused by drowsiness.
Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensembling
NLP
Large Language Models
- Identifies systematic overconfidence in LLM-generated confidence scores in telecommunications.
- Proposes a Twin-Pass CoT-Ensembling method to improve confidence estimation.
- Achieves up to 88% reduction in Expected Calibration Error (ECE) across benchmarks.
- Provides empirically validated confidence thresholds and recommendations for telecom applications.
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Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensembling
Summary
This paper addresses the critical issue of confidence estimation in Large Language Models (LLMs) used in telecommunications, where reliable self-assessment is essential for operational tasks. The authors focus on the Gemma-3 model family, evaluating its performance on three benchmarks: TeleQnA, ORANBench, and srsRANBench. They identify that traditional single-pass confidence estimates often misrepresent the model's correctness, leading to overconfidence in incorrect predictions. To improve this, the authors propose a novel Twin-Pass Chain of Thought (CoT)-Ensembling methodology, which involves multiple independent reasoning evaluations to aggregate confidence scores. This approach significantly reduces Expected Calibration Error (ECE) by up to 88%, enhancing the reliability of LLM outputs in telecommunications. The study emphasizes the need for better confidence calibration methods tailored to the unique demands of the telecom domain and provides actionable recommendations for practitioners.
Methodology
The authors evaluate confidence calibration using the Gemma-3 model family on three telecom-specific benchmarks. They introduce a training-free Twin-Pass CoT-Ensembling method, where the model critiques its own reasoning through multiple stochastic samples, aggregating the self-assessed scores to produce calibrated confidence estimates.
Results
The proposed methodology results in a significant reduction of Expected Calibration Error (ECE) by up to 88.4% across the evaluated benchmarks, transforming unreliable confidence scores into actionable metrics. The study also finds that mean aggregation of confidence scores outperforms median aggregation in 55% of experimental conditions.
Implications
The findings suggest a practical path toward more trustworthy evaluation of LLM outputs in telecommunications, which is crucial for decision-critical applications. Improved confidence estimation can enhance operational reliability and reduce risks associated with automated network management.
Hardware-Efficient Neuro-Symbolic Networks with the Exp-Minus-Log Operator
Efficient ML
Interpretability
Theory
- Introduction of the Exp-Minus-Log (EML) operator as a unifying primitive for DNNs.
- Development of a DNN-EML hybrid architecture that enhances interpretability and reduces hardware complexity.
- Establishment of computational-cost bounds and analysis of inference and training acceleration.
- Identification of a literature gap in existing neuro-symbolic approaches that do not utilize a single hardware-realizable primitive.
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Hardware-Efficient Neuro-Symbolic Networks with the Exp-Minus-Log Operator
Summary
This paper addresses the limitations of deep neural networks (DNNs) in safety-critical and resource-constrained environments, particularly their lack of interpretability and reliance on diverse activation functions that increase latency and hardware requirements. The author introduces the Exp-Minus-Log (EML) operator, which can express all standard elementary functions using a binary tree of identical nodes. By embedding EML primitives into conventional DNN architectures, the proposed DNN-EML hybrid model aims to enhance interpretability while maintaining approximation power. The paper details the forward equations of the DNN-EML architecture, establishes computational-cost bounds, and analyzes the potential for inference and training acceleration compared to traditional multilayer perceptrons (MLPs) and physics-informed neural networks (PINNs). The findings suggest that while EML may not accelerate training or inference on standard hardware, it could provide significant latency advantages on dedicated EML cells, such as FPGA or analog circuits, along with improved interpretability and formal verification capabilities.
Methodology
The paper formulates a DNN-EML hybrid architecture by embedding EML primitives into conventional DNNs. It derives forward equations, proves universal approximation properties, and analyzes computational complexity for inference and training. The author contrasts the EML approach with existing neuro-symbolic methods and evaluates performance on standard and dedicated hardware.
Results
The analysis indicates that EML does not accelerate training or inference on standard CPU/GPU hardware. However, on dedicated EML cells, such as FPGA or analog circuits, the DNN-EML hybrid can achieve latency advantages of an order of magnitude, along with gains in interpretability and formal verification.
Implications
The findings suggest that the DNN-EML architecture could be particularly beneficial for applications in safety-critical domains, such as automotive engineering, where interpretability and formal verification are essential. It may also enhance the deployment of AI models in edge computing environments with limited resources.
Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
Theory
- Model evaluation is often reduced to a few aggregate metrics, risking misleading conclusions.
- Common pitfalls in evaluation include data leakage, class imbalance, and inappropriate metric selection.
- Evaluation should be treated as a decision-oriented and context-dependent process.
- The paper emphasizes the importance of aligning evaluation methods with operational objectives.
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Evaluating Supervised Machine Learning Models: Principles, Pitfalls, and Metric Selection
Summary
This paper addresses the critical aspect of evaluating supervised machine learning models, emphasizing the need for rigorous assessment methods that reflect real-world performance. The authors argue that despite the availability of automated workflows, model evaluation often relies on a limited set of aggregate metrics, which can lead to misleading conclusions. The paper explores the principles and challenges of evaluating algorithms in both classification and regression tasks, highlighting how factors such as dataset characteristics, validation design, class imbalance, and asymmetric error costs influence evaluation outcomes. Through controlled experiments with diverse benchmark datasets, the authors identify common pitfalls, including the accuracy paradox, data leakage, and inappropriate metric selection. They advocate for a decision-oriented and context-sensitive approach to model evaluation, which aligns with the operational objectives of the task. The work provides a structured foundation for selecting metrics and validation protocols that enhance the reliability and trustworthiness of supervised machine learning systems.
Methodology
The authors conducted controlled experimental scenarios using diverse benchmark datasets to analyze the strengths and limitations of various evaluation strategies, including hold-out validation and cross-validation. They examined the impact of dataset characteristics and task-specific objectives on the behavior of different performance metrics.
Results
The study revealed that relying on a single metric can lead to misleading conclusions, particularly in cases of class imbalance and asymmetric error costs. It highlighted the sensitivity of regression measures to outliers and distribution shifts, and underscored the importance of rigorous evaluation protocols to ensure model reliability and trustworthiness.
Implications
The findings suggest that practitioners should adopt more comprehensive and context-aware evaluation methods to improve the reliability of machine learning models in real-world applications. This approach can help mitigate risks associated with model deployment and enhance the interpretability and robustness of predictive systems.
Minimax Optimality and Spectral Routing for Majority-Vote Ensembles under Markov Dependence
Theory
Reinforcement Learning
Time Series
- Establishes a minimax lower bound for classification risk under Markov dependence.
- Demonstrates that uniform bagging is suboptimal, with a significant risk gap.
- Proposes adaptive spectral routing to achieve optimal performance in Markov settings.
- Validates theoretical predictions through extensive experiments on various datasets.
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Minimax Optimality and Spectral Routing for Majority-Vote Ensembles under Markov Dependence
Summary
This paper addresses the performance degradation of majority-vote ensembles when trained on data exhibiting Markov dependence, which is common in time-series forecasting and reinforcement learning. The authors establish a minimax characterization of the classification risk for these ensembles in a fixed-dimensional Markov setting. They derive an information-theoretic lower bound indicating that no estimator can achieve better than Ω(√Tmix/n) excess classification risk. The paper also demonstrates that uniform bagging is suboptimal under these conditions, with a risk bounded below by Ω(Tmix/√n), revealing a significant gap. To overcome this, the authors propose an adaptive spectral routing algorithm that partitions training data based on the empirical Fiedler eigenvector of a dependency graph, achieving the minimax rate of O(√Tmix/n) up to a lower-order term. Experimental validation on synthetic Markov chains, spatial grids, and various datasets supports their theoretical findings, highlighting the implications for deep reinforcement learning and variance analysis.
Methodology
The authors utilize information-theoretic techniques to derive lower bounds for classification risk in Markov chains. They analyze the performance of uniform bagging and develop an adaptive spectral routing algorithm that partitions data based on the Fiedler eigenvector of a dependency graph. Theoretical results are supported by empirical experiments on synthetic and real-world datasets.
Results
The paper shows that the minimax lower bound for excess classification risk is Ω(√Tmix/n). Uniform bagging is proven to be suboptimal with a risk of Ω(Tmix/√n), while the proposed adaptive spectral routing achieves the optimal rate of O(√Tmix/n) on a graph-regular subclass, effectively closing the gap identified.
Implications
The findings suggest that traditional ensemble methods may need to be adapted for data with Markov dependence to avoid significant performance penalties. The proposed methods could enhance the effectiveness of ensemble learning in various applications, particularly in time-series analysis and reinforcement learning scenarios.
A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models
NLP
Large Language Models
Efficient ML
- Introduces a gradient-free sensitivity analysis framework for hybrid SSM-Transformer models.
- Demonstrates that KL divergence is a superior metric for quantization sensitivity in language models.
- Validates the proposed method through extensive experiments and real-world profiling.
- Achieves significant model compression with minimal accuracy loss, suitable for edge deployment.
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A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models
Summary
This paper addresses the challenges of deploying Large Language Models (LLMs) on edge devices, which face significant computational and memory constraints. The authors propose a novel framework for sensitivity analysis that identifies components of hybrid Structured State Space Models (SSMs) and Transformer architectures most affected by quantization. Unlike traditional methods that rely on backpropagation, this approach uses a forward-pass sensitivity analysis, making it efficient and suitable for scenarios with limited access to in-domain data. The study demonstrates that the Kullback-Leibler (KL) divergence metric is a more effective measure of quantization sensitivity for language modeling tasks compared to conventional metrics like mean squared error (MSE) and signal-to-quantization-noise ratio (SQNR). Through extensive experiments, the authors validate that KL-based sensitivity rankings correlate well with performance degradation, enabling a practical deployment strategy for hybrid models on resource-constrained devices with minimal accuracy loss. The framework was tested on Intel Lunar Lake hardware, achieving near-FP16 perplexity while maintaining competitive model sizes and throughput against Uniform INT4 quantization.
Methodology
The authors developed a lightweight, backpropagation-free sensitivity analysis framework that operates solely on forward-pass metrics. This method identifies which components of hybrid SSM-Transformer architectures are most sensitive to quantization, allowing for targeted mixed-precision assignments. The effectiveness of the KL divergence metric was formally analyzed and compared against traditional metrics like MSE and SQNR.
Results
The experiments confirmed that the KL-based sensitivity rankings aligned with observed performance drops in hybrid models. The proposed framework enabled the deployment of mixed-precision models that achieved near-FP16 perplexity while being competitive in size and throughput with Uniform INT4 quantization on both CPU and GPU platforms.
Implications
This research provides a pathway for deploying advanced language models on edge devices, addressing the critical need for efficient model compression techniques that maintain performance. The findings can influence future work in model quantization strategies, particularly in resource-constrained environments.
Some Theoretical Limitations of t-SNE
Theory
- t-SNE can lose important data features during dimensionality reduction.
- In high-dimensional spaces, t-SNE may map distinct points to the same location in lower dimensions.
- The paper provides mathematical propositions demonstrating the limitations of t-SNE in preserving data structure.
- The findings suggest that t-SNE may not be appropriate for all datasets, particularly those with high dimensionality.
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Some Theoretical Limitations of t-SNE
Summary
This paper provides a mathematical framework to understand the theoretical limitations of t-distributed stochastic neighbor embedding (t-SNE), a popular technique for dimensionality reduction and data visualization. The authors highlight that while t-SNE is effective in many scenarios, it can lead to significant loss of important data features, particularly in high-dimensional spaces. They present several propositions and a theorem demonstrating that as the dimensionality increases, t-SNE may fail to preserve the structure of the data, often collapsing distinct points into a single point in the lower-dimensional representation. The paper discusses the implications of these findings, emphasizing that t-SNE may not be suitable for all datasets, especially those with high dimensionality where points are approximately equidistant. The authors also relate their results to prior theoretical analyses of t-SNE, underscoring the need for caution when interpreting t-SNE visualizations.
Methodology
The authors establish a mathematical framework to analyze the performance of t-SNE by formulating propositions and a theorem that illustrate how t-SNE fails to maintain the original data structure in high-dimensional spaces. They use asymptotic analysis and consider specific configurations of data points to demonstrate the limitations of the t-SNE algorithm.
Results
The paper presents several key results, including: (1) a proposition showing that for a set of points sampled uniformly from a high-dimensional sphere, t-SNE can lead to a situation where points that are far apart in high dimensions become close in the low-dimensional embedding; (2) a theorem indicating that in high-dimensional settings, t-SNE often collapses points into a small neighborhood, resulting in a loss of informative structure; and (3) a demonstration that the optimal embedding may result in all points coinciding at a single location, particularly when the data points are equidistant.
Implications
The findings of this paper have significant implications for researchers and practitioners using t-SNE for data visualization. It highlights the need for careful consideration of the dimensionality of the data and the potential for misleading visualizations. The results suggest that alternative dimensionality reduction techniques may be necessary for high-dimensional datasets to preserve important features.
When Less Latent Leads to Better Relay: Information-Preserving Compression for Latent Multi-Agent LLM Collaboration
Large Language Models
NLP
Efficient ML
- Introduces Orthogonal Backfill (OBF) to enhance KV compression in multi-agent LLM communication.
- Achieves a significant reduction in communication costs (79.8%–89.4%) while maintaining competitive performance.
- Demonstrates that preserving useful information is more critical than merely relaying large amounts of data.
- Evaluates the method across nine diverse benchmarks, showing superior results in several cases.
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When Less Latent Leads to Better Relay: Information-Preserving Compression for Latent Multi-Agent LLM Collaboration
Summary
This paper addresses the communication challenges in Large Language Model (LLM)-based multi-agent systems, particularly focusing on the inefficiencies of relaying full key-value (KV) caches between agents. The authors propose a novel approach called Orthogonal Backfill (OBF), which enhances eviction-style KV compression to mitigate information loss during the relay process. By injecting low-rank orthogonal residuals from discarded KV states into retained states, OBF aims to preserve the most useful information for downstream tasks. The authors evaluate their method against full KV relay across nine benchmarks, including mathematical reasoning, coding, and knowledge-intensive question answering. The results demonstrate that their approach achieves comparable performance to full KV relay while significantly reducing communication costs by 79.8% to 89.4%. Furthermore, OBF improves performance, achieving the best results on seven out of nine benchmarks. This suggests that effective communication in multi-agent systems relies more on the quality of preserved information rather than the quantity, highlighting the importance of targeted information retention in collaborative settings.
Methodology
The authors develop an eviction-style KV compression framework tailored for inter-agent relay in multi-agent systems. They introduce Orthogonal Backfill (OBF) to counteract information loss from hard eviction by incorporating residual information from discarded KV states into the retained states. The effectiveness of this approach is empirically validated through experiments on various benchmarks.
Results
The proposed method matches or outperforms full KV relay while achieving a drastic reduction in communication costs. OBF leads to improved performance on seven out of nine benchmarks tested, indicating that effective communication relies on the preservation of useful information rather than the sheer volume of data transmitted.
Implications
The findings suggest that optimizing communication strategies in multi-agent systems can enhance collaboration efficiency, particularly in complex tasks that require rich intermediate state exchanges. This work could inform future designs of multi-agent frameworks and improve the scalability of LLM applications.
Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments
Reinforcement Learning
Robotics
Theory
- Introduction of Adaptive Memory Crystallization (AMC) for continual reinforcement learning.
- Development of a three-phase memory hierarchy (Liquid, Glass, Crystal) to manage memory stability and plasticity.
- Rigorous mathematical proofs establishing the convergence and performance guarantees of the proposed SDE.
- Empirical results show substantial improvements in learning efficiency and memory management.
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Adaptive Memory Crystallization for Autonomous AI Agent Learning in Dynamic Environments
Summary
This paper introduces Adaptive Memory Crystallization (AMC), a novel memory architecture designed to enhance the learning capabilities of autonomous AI agents in dynamic environments. The primary challenge addressed is the stability-plasticity dilemma, where agents must acquire new skills without losing previously learned knowledge. AMC is inspired by synaptic tagging and capture (STC) theory, conceptualizing memory as a continuous crystallization process. The framework features a three-phase memory hierarchy (Liquid, Glass, Crystal) governed by an Itô stochastic differential equation (SDE). The authors provide rigorous proofs of the SDE's well-posedness, convergence properties, and establish links between SDE parameters and agent performance. Empirical evaluations demonstrate that AMC significantly improves forward transfer, reduces catastrophic forgetting, and decreases memory footprint across various benchmarks, including Meta-World MT50, Atari, and MuJoCo.
Methodology
The methodology involves formulating a memory architecture based on a continuous crystallization process modeled by an Itô stochastic differential equation (SDE). The experiences are categorized into three phases of memory (Liquid, Glass, Crystal), with each phase having distinct learning rates and eviction policies. The performance of AMC is validated through extensive empirical evaluations on multiple reinforcement learning benchmarks.
Results
The empirical evaluation of AMC on various tasks showed a 34-43% improvement in forward transfer compared to the strongest baseline, a 67-80% reduction in catastrophic forgetting, and a 62% decrease in memory footprint. The theoretical analysis confirmed the well-posedness and convergence of the crystallization process, linking SDE parameters to agent performance.
Implications
The findings suggest that AMC can significantly enhance the capabilities of autonomous AI agents in dynamic environments, making it applicable in fields such as robotics, adaptive software, and autonomous driving. The approach may lead to more efficient lifelong learning systems that can adapt to new tasks without losing previously acquired knowledge.
LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning
Large Language Models
NLP
Theory
- LongCoT is a novel benchmark for evaluating long-horizon reasoning in language models.
- The benchmark consists of 2,500 expert-designed problems across multiple domains.
- Current top models achieve less than 10% accuracy on LongCoT, highlighting significant reasoning limitations.
- The problems require navigating complex interdependencies, emphasizing the need for planning and error management.
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LongCoT: Benchmarking Long-Horizon Chain-of-Thought Reasoning
Summary
The paper introduces LongCoT, a benchmark designed to evaluate the long-horizon chain-of-thought (CoT) reasoning capabilities of advanced language models. As language models are increasingly utilized for complex tasks, their ability to reason over extended sequences of interdependent steps is crucial. LongCoT comprises 2,500 expert-designed problems across various domains, including chemistry, mathematics, computer science, chess, and logic. Each problem requires navigating a complex graph of interdependent reasoning steps, with the aim of isolating and measuring the models' long-horizon reasoning abilities. The benchmark reveals that even the best-performing models, such as GPT 5.2 and Gemini 3 Pro, achieve less than 10% accuracy, indicating significant limitations in their reasoning capabilities over long chains of thought. The authors emphasize the importance of this benchmark in assessing and improving the reasoning abilities of future models, as current benchmarks fail to adequately stress-test these capabilities.
Methodology
The authors developed LongCoT by designing a set of 2,500 problems that require long-horizon reasoning across various domains. Each problem consists of a short prompt with a verifiable answer, necessitating the navigation of a graph of interdependent reasoning steps. The problems are constructed using domain-specific templates that allow for scalable question generation while ensuring that each step is tractable in isolation. This design isolates failures in reasoning to long-horizon capabilities rather than single-step difficulties.
Results
The best-performing models, including GPT 5.2 and Gemini 3 Pro, achieved accuracies of 9.8% and 6.1%, respectively, on the LongCoT benchmark. These results indicate a substantial gap in the current capabilities of language models to perform long-horizon reasoning, as they struggle with the complexity and interdependencies of the tasks presented.
Implications
LongCoT provides a critical framework for assessing and improving the reasoning capabilities of language models, particularly as they are deployed in more complex and autonomous tasks. The benchmark can guide future research and development efforts aimed at enhancing the reasoning abilities of AI systems, potentially leading to more reliable and effective applications in various fields.
MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models
Computer Vision
Large Language Models
Efficient ML
- MOONSHOT enhances one-shot pruning by optimizing multiple objectives simultaneously.
- The framework is scalable and efficient, suitable for billion-parameter models.
- Experimental results show significant improvements in performance and accuracy across various models.
- The study reveals that different pruning criteria can yield complementary insights into parameter importance.
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MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models
Summary
The paper introduces MOONSHOT, a novel framework for multi-objective pruning of vision and large language models, addressing the limitations of existing one-shot pruning methods that typically optimize a single objective. The authors argue that neither layer-wise reconstruction loss nor second-order Taylor approximation of training loss alone consistently yields optimal results across different architectures and sparsity levels. MOONSHOT extends existing single-objective pruning methods by jointly optimizing both objectives, thus improving the performance-sparsity trade-off. The framework is designed to be scalable for billion-parameter models and incorporates an efficient procedure for computing the inverse Hessian. Experimental results demonstrate that MOONSHOT, when combined with state-of-the-art pruning methods, significantly reduces perplexity and improves accuracy across various benchmarks, showcasing its effectiveness in compressing large models without retraining.
Methodology
The authors propose a multi-objective optimization framework that combines layer-wise reconstruction error and second-order Taylor approximation of the training loss. MOONSHOT is designed to be a wrapper around existing pruning algorithms, allowing for efficient integration while maintaining scalability. The methodology includes modeling decisions and an efficient computation of the inverse Hessian to preserve the efficiency of state-of-the-art one-shot pruners.
Results
MOONSHOT achieves a reduction in C4 perplexity by up to 32.6% at 2:4 sparsity and improves zero-shot mean accuracy by up to 4.9 points across seven classification benchmarks. For Vision Transformers, accuracy on ImageNet-1k improves by over 5 points at 70% sparsity, and ResNet-50 shows a 4-point gain at 90% sparsity.
Implications
The findings suggest that MOONSHOT can be a powerful tool for efficiently compressing large neural networks, making it applicable in real-world scenarios where computational resources are limited. This framework can enhance the deployment of large models in resource-constrained environments, potentially leading to broader adoption of advanced AI technologies.
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
NLP
Large Language Models
Optimization
- Parameter importance in supervised fine-tuning is dynamic, not static.
- Evolving Parameter Isolation (EPI) adapts isolation masks based on online gradient estimates.
- EPI improves stability and generalization in multi-task learning scenarios.
- The framework effectively balances the retention of established knowledge with the acquisition of new capabilities.
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Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
Summary
This paper addresses the challenges of Supervised Fine-Tuning (SFT) in large language models, particularly the issues of task interference and catastrophic forgetting. Traditional methods of parameter isolation assume that the importance of parameters remains static, which is contradicted by the authors' empirical findings that parameter importance actually evolves over the course of training. To tackle this issue, the authors propose a novel framework called Evolving Parameter Isolation (EPI), which dynamically updates isolation decisions based on real-time estimates of parameter importance. EPI utilizes gradient-based signals to periodically adjust isolation masks, allowing the model to protect newly critical parameters while releasing those that have become redundant. The authors conduct extensive experiments across various multi-task benchmarks, demonstrating that EPI significantly reduces interference and forgetting compared to both static isolation methods and standard fine-tuning approaches, while also enhancing overall generalization. The findings underscore the necessity of aligning isolation strategies with the dynamic nature of learning in diverse task environments.
Methodology
The EPI framework employs an online importance estimation mechanism that continuously monitors gradient-based signals to track parameter sensitivity. It combines temporal smoothing with layer-wise normalization to dynamically update isolation masks, creating a 'moving shield' that adapts to the evolving importance of parameters throughout the training process.
Results
Experiments show that EPI consistently outperforms standard SFT and static isolation baselines across diverse benchmarks, leading to reduced task interference and catastrophic forgetting, while improving overall model generalization.
Implications
The findings suggest that adapting isolation strategies in real-time can enhance the performance of large language models in multi-task settings, potentially leading to more effective applications in areas requiring dynamic learning capabilities.
Unsupervised domain transfer: Overcoming signal degradation in sleep monitoring by increasing scoring realism
Time Series
- The study investigates the potential of unsupervised domain transfer for sleep monitoring amidst signal degradation.
- A discriminator-guided approach is proposed to enhance the realism of hypnograms, which can improve scoring accuracy.
- The unsupervised method shows performance improvements in various signal distortion scenarios without decreasing overall performance.
- Real-life application of the method revealed limited benefits, indicating the need for further refinement.
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Unsupervised domain transfer: Overcoming signal degradation in sleep monitoring by increasing scoring realism
Summary
This paper explores the use of unsupervised domain transfer techniques to enhance sleep monitoring by addressing signal degradation issues. The authors propose a method that combines a pretrained 'u-sleep' model with a discriminator network to align features from a target domain with those learned during pretraining. The study investigates how 'realism' in hypnograms can guide the adaptation process to various types of signal distortions encountered in mobile sleep monitoring. The results indicate that the unsupervised approach can improve performance metrics, such as Cohen's kappa, by up to 0.29 depending on the distortion type, without decreasing performance in any case. However, the method does not achieve the theoretical optimal performance and shows limited benefits when applied to real-life domain mismatches. The findings suggest that 'discriminator-guided fine tuning' holds promise for improving sleep monitoring systems, although further development is needed before practical implementation.
Methodology
The authors utilized an adversarial learning framework that includes a sleep scorer and a discriminator network. The sleep scorer is trained to accurately classify sleep stages while simultaneously fooling the discriminator into believing that the hypnograms from the target domain are similar to those from the source domain. This approach allows the model to adapt to signal degradations without requiring ground truth labels from the target domain.
Results
The unsupervised domain transfer method improved Cohen's kappa scores by as little as 0.03 and up to 0.29, depending on the type of signal distortion. The method maintained performance across all tested scenarios but did not reach the estimated theoretical optimal performance. In real-life applications, the benefits were found to be insignificant.
Implications
The findings suggest that adversarial domain transfer techniques can be a viable approach for enhancing sleep monitoring systems, particularly in dealing with signal degradation in real-world settings. However, further research and development are necessary to optimize the method for practical use in clinical environments.
MAny: Merge Anything for Multimodal Continual Instruction Tuning
Multimodal
Large Language Models
Efficient ML
- Identification of a dual-forgetting phenomenon in MLLMs affecting both perception and reasoning.
- Introduction of Cross-modal Projection Merging (CPM) for adaptive merging of visual features.
- Development of Low-rank Parameter Merging (LPM) using Recursive Least Squares for optimal parameter merging.
- MAAny achieves state-of-the-art performance on UCIT and MLLM-DCL benchmarks without GPU training.
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MAny: Merge Anything for Multimodal Continual Instruction Tuning
Summary
The paper addresses the challenge of Multimodal Continual Instruction Tuning (MCIT) for Multimodal Large Language Models (MLLMs), which often suffer from catastrophic forgetting. The authors identify a dual-forgetting phenomenon that occurs in both the Cross-modal Projection Space and the Low-rank Parameter Space, which has been overlooked in existing literature. To tackle this issue, they propose a novel framework called MAny (Merge Anything) that employs two key strategies: Cross-modal Projection Merging (CPM) and Low-rank Parameter Merging (LPM). CPM focuses on recovering perceptual alignment by merging task-specific visual representations using visual-prototype guidance, while LPM minimizes interference among low-rank modules through a recursive merging approach. Notably, MAny operates without the need for additional training, relying instead on efficient CPU-based algebraic operations. The framework demonstrates superior performance across multiple benchmarks, achieving significant improvements in accuracy compared to state-of-the-art methods.
Methodology
The authors developed MAny, which includes Cross-modal Projection Merging (CPM) to adaptively merge visual representations and Low-rank Parameter Merging (LPM) to minimize interference among low-rank modules. CPM utilizes visual-prototype guidance for perceptual alignment, while LPM employs a recursive least squares algorithm to ensure optimal merging of parameters. The approach is designed to be training-free, relying on efficient CPU-based operations.
Results
MAAny demonstrated significant improvements in performance on the UCIT benchmark, achieving up to 8.57% and 2.85% higher final average accuracy compared to existing state-of-the-art methods across two different MLLMs.
Implications
The findings suggest that addressing both perceptual and reasoning aspects in MLLMs can enhance their adaptability to sequential tasks. The lightweight nature of MAny makes it suitable for deployment in real-world applications where computational resources are limited.
RPS: Information Elicitation with Reinforcement Prompt Selection
NLP
Large Language Models
Reinforcement Learning
- Proposes Reinforcement Prompt Selection (RPS) for adaptive information elicitation in dialogues.
- Introduces IELegal, a benchmark dataset for evaluating information elicitation in legal contexts.
- RPS outperforms static prompt baselines, demonstrating the effectiveness of adaptive strategies.
- Addresses the limitations of existing prompt engineering methods by reducing reliance on static prompts.
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RPS: Information Elicitation with Reinforcement Prompt Selection
Summary
This paper addresses the challenge of information elicitation in open-ended dialogues using large language models (LLMs). Despite their advanced capabilities in dialogue generation, LLMs struggle to extract concealed or uncertain information from users due to privacy concerns and social hesitations. The authors propose a novel framework called Reinforcement Prompt Selection (RPS), which formulates prompt selection as a sequential decision-making problem using reinforcement learning. RPS adapts its prompt strategy based on user feedback to effectively elicit concealed information. The paper introduces IELegal, a benchmark dataset derived from real legal case documents, designed to simulate dialogue-based information elicitation tasks. Experimental results demonstrate that RPS outperforms static prompt baselines in both synthetic and real-world settings, highlighting its effectiveness in uncovering critical information during interactions. The findings suggest that adaptive prompt selection can significantly enhance the performance of LLM-driven dialogue systems in various applications, including legal consultation and personal assistance.
Methodology
The authors define the problem of information elicitation in open-ended dialogues and propose RPS, a lightweight reinforcement learning framework. RPS formulates prompt selection as a sequential decision-making task, learning a policy over a pool of prompts to adaptively elicit concealed information. The methodology includes synthetic experiments using a Gaussian Mixture Model environment to validate the approach and the introduction of the IELegal dataset for real-world evaluation.
Results
In controlled experiments, the reinforcement learning agent using RPS significantly outperformed a random query baseline. In the IELegal dataset, RPS demonstrated superior performance compared to static prompt baselines, effectively uncovering relevant and concealed information during legal consultations.
Implications
The findings suggest that RPS can enhance the capabilities of LLMs in various interactive AI applications, such as personal assistants, tutoring systems, and legal or clinical support, by improving their ability to elicit sensitive information from users. This could lead to more effective and context-aware conversational agents.
First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs
Theory
Optimization
- Introduces a multi-stakeholder framework for fair algorithmic decision-making.
- Shifts focus from prediction-centric fairness to utility-based fairness.
- Utilizes post-hoc multi-objective optimization to explore performance-fairness trade-offs.
- Demonstrates that stochastic policies can yield better outcomes than deterministic ones.
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First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs
Summary
This paper addresses the limitations of traditional fairness assessments in algorithmic decision-making, which often rely on predictive metrics that do not account for the actual outcomes of decisions. The authors propose a multi-stakeholder framework that integrates welfare economics and distributive justice principles, focusing on the utilities of decision-makers (DM) and decision subjects (DS). By defining fairness through a social planner's utility that captures inequalities among DS, the framework allows for a more nuanced understanding of performance-fairness trade-offs. The authors formulate the problem as a post-hoc multi-objective optimization (MOO) task, enabling stakeholders to explore the trade-offs between DM utility and social planner utility under various decision policies. The findings indicate that simple stochastic decision policies can outperform deterministic ones in achieving better performance-fairness trade-offs by leveraging outcome uncertainty. This work advocates for a shift from prediction-centric fairness to a more transparent, justice-based approach that facilitates collaborative decision-making.
Methodology
The authors develop a multi-stakeholder framework that models the utilities of decision-makers and decision subjects, defining fairness through a social planner's utility. They employ post-hoc multi-objective optimization to characterize the trade-offs between performance and fairness, allowing stakeholders to evaluate different decision policies.
Results
The empirical analysis shows that under certain conditions, stochastic decision policies can achieve superior performance-fairness trade-offs compared to deterministic policies, highlighting the importance of considering outcome uncertainty in decision-making.
Implications
This framework can be applied in various domains where algorithmic fairness is critical, such as finance, healthcare, and criminal justice, promoting more equitable decision-making processes. It encourages stakeholders to engage in transparent discussions about the implications of different decision policies.
Chain of Uncertain Rewards with Large Language Models for Reinforcement Learning
Reinforcement Learning
Large Language Models
Optimization
- Introduction of CoUR framework for efficient reward function design in RL.
- Integration of code uncertainty quantification to streamline reward component reuse.
- Utilization of Bayesian optimization for independent optimization of reward terms.
- Extensive evaluation showing CoUR outperforms traditional methods in performance and cost.
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Chain of Uncertain Rewards with Large Language Models for Reinforcement Learning
Summary
This paper addresses the challenges of designing effective reward functions in reinforcement learning (RL), which is often a labor-intensive and error-prone process. The author proposes a novel framework called Chain of Uncertain Rewards (CoUR) that integrates large language models (LLMs) to streamline the reward function design and evaluation process. CoUR introduces a mechanism for code uncertainty quantification that identifies and reuses relevant reward function components through textual and semantic analyses, thereby reducing redundancy and improving consistency. Additionally, it employs Bayesian optimization on decoupled reward terms to enhance the efficiency of reward feedback exploration. The effectiveness of CoUR is validated through comprehensive experiments across nine environments from IsaacGym and 20 tasks from the Bidexterous Manipulation benchmark, demonstrating superior performance and reduced evaluation costs compared to traditional methods. The paper highlights the potential of LLMs in automating and optimizing reward design in RL, addressing local uncertainties, and minimizing redundant efforts.
Methodology
The CoUR framework employs a code uncertainty quantification mechanism that combines textual and semantic analyses to identify relevant reward components. It also utilizes Bayesian optimization on decoupled reward terms to enhance the efficiency of the reward evaluation process.
Results
The experimental results indicate that CoUR significantly improves performance across various benchmarks while reducing the cost and complexity associated with reward evaluations. The framework demonstrates faster convergence and less redundancy in reward design compared to traditional methods.
Implications
The proposed CoUR framework has the potential to revolutionize reward function design in RL by automating the process, addressing uncertainties, and minimizing redundant efforts. This could lead to more efficient training of RL agents in complex environments, ultimately enhancing their performance and adaptability.
Soft $Q(λ)$: A multi-step off-policy method for entropy regularised reinforcement learning using eligibility traces
Reinforcement Learning
- Introduces Soft Q(λ), a multi-step off-policy method for entropy-regularized reinforcement learning.
- Develops a novel Soft Tree Backup operator to handle entropy terms across multiple time steps.
- Eliminates the on-policy bias inherent in traditional n-step soft Q-learning methods.
- Demonstrates the ability to learn entropy-regularized value functions under arbitrary behavior policies.
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Soft $Q(λ)$: A multi-step off-policy method for entropy regularised reinforcement learning using eligibility traces
Summary
This paper introduces Soft Q(λ), a novel framework for multi-step off-policy reinforcement learning that incorporates eligibility traces and entropy regularization. The authors begin by formalizing an n-step soft Q-learning formulation, addressing the limitations of existing methods that are constrained to on-policy action sampling. They propose a new Soft Tree Backup operator that effectively manages entropy terms over multiple time steps without requiring knowledge of the behavior policy, thus eliminating the on-policy bias present in traditional n-step soft backups. The resulting Soft Q(λ) framework allows for efficient online, off-policy credit assignment, enabling the learning of entropy-regularized value functions under arbitrary behavior policies. This work provides a theoretically grounded approach to reinforcement learning that enhances exploration and stability, paving the way for future empirical experiments.
Methodology
The authors formalize an n-step soft Q-learning formulation and introduce the Soft Tree Backup operator, which leverages the recursive relationship between state-value and action-value functions. This operator allows for the handling of entropy terms over multiple time steps without requiring knowledge of the behavior policy, thus facilitating off-policy learning.
Results
The derivations in the paper show that Soft Q(λ) can learn entropy-regularized value functions stably under arbitrary behavior policies, without reliance on target networks or fixed exploration schedules. This indicates a significant advancement in the flexibility and robustness of reinforcement learning methods.
Implications
The Soft Q(λ) framework has the potential to improve exploration strategies in reinforcement learning applications, making it suitable for complex environments where traditional methods struggle. Its ability to operate off-policy could enhance the efficiency of learning in real-world scenarios, such as robotics and autonomous systems.
Golden Handcuffs make safer AI agents
Reinforcement Learning
Theory
- Introduces the 'Golden Handcuffs' mechanism to enhance safety in AI agents.
- Expands the reward range to include negative values, promoting risk aversion.
- Proves that the agent can achieve sublinear regret against the best mentor.
- Ensures that unsafe actions are only triggered by mentors, not the optimizing policy.
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Golden Handcuffs make safer AI agents
Summary
This paper addresses the safety concerns associated with reinforcement learning (RL) agents operating in general environments, where traditional assumptions do not hold. The authors propose a novel approach called the 'Golden Handcuffs' agent, which incorporates a pessimistic variant of the AIXI framework. By expanding the agent's subjective reward range to include a large negative value, the agent becomes risk-averse to strategies that could lead to significant negative outcomes. The agent employs a mentor-guided exploration mechanism, allowing it to defer to safer mentor policies when its confidence in achieving high rewards diminishes. The authors demonstrate that this approach leads to two main properties: (i) capability, where the agent achieves sublinear regret against the best mentor, and (ii) safety, ensuring that no low-complexity unsafe actions are taken before being flagged by a mentor. The paper discusses the implications of this method for improving the safety and robustness of AI agents in complex environments.
Methodology
The authors develop a Bayesian policy that incorporates a pessimistic approach to reward maximization. The agent's reward structure is modified to include a large negative value, which discourages exploration of potentially harmful strategies. The agent occasionally defers to mentor policies for exploration and safety, ensuring that it learns from safe actions while avoiding irrecoverable states.
Results
The Golden Handcuffs agent achieves sublinear regret of order T^(2/3 + ε) against the best mentor policy over time T. Additionally, it guarantees that no unsafe actions are taken by the agent before being triggered by a mentor, thus enhancing the overall safety of the agent's operations.
Implications
This approach has significant implications for the design of safer AI systems, particularly in environments where traditional safety guarantees are insufficient. It can be applied in various domains where AI agents interact with complex and unpredictable environments, ensuring that they remain aligned with human safety standards.
Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate
Large Language Models
Efficient ML
Optimization
- DASH-Q improves robustness in ultra low-bit quantization by using diagonal Hessian approximations.
- The framework effectively filters out noise from calibration data, enhancing feature preservation.
- Achieves significant accuracy improvements over existing PTQ methods, particularly in low-bit regimes.
- Demonstrates strong performance with minimal calibration data, making it suitable for resource-limited environments.
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Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate
Summary
This paper presents DASH-Q, a novel framework for Post-Training Quantization (PTQ) aimed at improving the deployment of Large Language Models (LLMs) in resource-constrained environments. Traditional PTQ methods, particularly those based on Hessian approximations, struggle with low-bit quantization due to noise in curvature estimates from limited calibration data. DASH-Q addresses this issue by utilizing a diagonal Hessian approximation and iterative weighted least squares, which effectively filters out noise-prone dependencies while preserving the importance of salient features. The proposed method demonstrates significant improvements in zero-shot accuracy across five baseline LLM models, achieving an average increase of 7.01% and up to 14.01% over existing state-of-the-art methods, even with minimal calibration data. This advancement highlights the potential of DASH-Q to enhance the robustness and efficiency of ultra low-bit quantization in practical applications.
Methodology
DASH-Q employs a diagonal Hessian approximation to decouple quantization into independent weighted least squares problems. This method iteratively optimizes quantization parameters while minimizing reconstruction error, effectively mitigating the impact of noise from limited calibration data.
Results
DASH-Q outperformed existing PTQ baselines in ultra low-bit quantization, achieving an average increase of 7.01% in zero-shot accuracy and up to 14.01% improvement over the strongest baselines across five LLM models, demonstrating robust performance even with very small calibration datasets.
Implications
The findings suggest that DASH-Q can significantly enhance the deployment of LLMs in environments with limited computational resources, making it a valuable tool for applications requiring efficient model performance without extensive retraining.
Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification
Theory
Interpretability
Efficient ML
- Introduces a physics-informed transfer learning framework for methane sorption prediction.
- Achieves a 227% improvement over classical isotherm models in predictive accuracy.
- Monte Carlo Dropout is identified as the best method for uncertainty quantification.
- Demonstrates the importance of moisture-volatile interactions in methane sorption.
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Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification
Summary
This paper presents a novel physics-informed transfer learning framework designed to enhance methane sorption predictions across various coal ranks. The framework adapts a previously developed hydrogen sorption Physics-Informed Neural Network (PINN) to methane sorption using Elastic Weight Consolidation and coal-specific feature engineering. A three-phase curriculum is implemented to balance the preservation of transfer learning with thermodynamic fine-tuning. The model is trained on a dataset comprising 993 equilibrium measurements from 114 independent coal experiments, achieving an R² score of 0.932 on held-out samples, significantly outperforming traditional pressure-only isotherm models. The study also evaluates five Bayesian uncertainty quantification methods, revealing that Monte Carlo Dropout provides the most reliable uncertainty estimates with minimal computational overhead. The results indicate that moisture-volatile interactions are the most influential factors in sorption behavior, and the learned representations maintain physical interpretability. This work demonstrates the effectiveness of cross-gas transfer learning as a strategy for geological material modeling, particularly in data-scarce environments.
Methodology
The methodology involves adapting a hydrogen sorption PINN to methane sorption through Elastic Weight Consolidation and coal-specific feature engineering. A three-phase curriculum is utilized to progressively balance transfer preservation with thermodynamic fine-tuning. The model is trained on a comprehensive dataset of coal sorption measurements, and various Bayesian uncertainty quantification methods are compared to assess their performance under physics constraints.
Results
The proposed framework achieved an R² score of 0.932 on held-out coal samples, indicating a significant improvement in prediction accuracy compared to traditional models. Monte Carlo Dropout provided well-calibrated uncertainty estimates with an expected calibration error of 0.101 and a correlation coefficient of 0.708, while deep ensembles showed performance degradation due to shared physics constraints.
Implications
The findings suggest that the proposed framework can significantly enhance methane sorption predictions in coal seams, which is critical for resource assessment and carbon storage. The effective use of cross-gas transfer learning could lead to more efficient modeling strategies in geological applications, particularly in scenarios with limited data.
Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus
Reinforcement Learning
Robotics
Optimization
- CMAT bridges MARL and SARL, addressing key challenges in cooperative multi-agent settings.
- The framework utilizes a Transformer encoder and a hierarchical decision-making mechanism for effective coordination.
- Simultaneous action generation based on a consensus vector reduces sensitivity to action order.
- CMAT shows superior performance on benchmark tasks compared to existing methods.
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Bridging MARL to SARL: An Order-Independent Multi-Agent Transformer via Latent Consensus
Summary
This paper introduces the Consensus Multi-Agent Transformer (CMAT), a novel framework that connects cooperative Multi-Agent Reinforcement Learning (MARL) with hierarchical Single-Agent Reinforcement Learning (SARL). The authors address the challenges of non-stationarity, unstable training, and weak coordination that arise in MARL due to the exponential growth of joint observation and action spaces. CMAT treats all agents as a unified entity and employs a Transformer encoder to process observations, while a hierarchical decision-making mechanism allows for the generation of a high-level consensus vector. This consensus enables simultaneous action generation by all agents, mitigating the sensitivity to action-generation order seen in conventional Multi-Agent Transformers. The framework is optimized using single-agent Proximal Policy Optimization (PPO), maintaining expressive coordination through latent consensus. Experimental evaluations on benchmark tasks from StarCraft II, Multi-Agent MuJoCo, and Google Research Football demonstrate that CMAT outperforms existing centralized solutions and conventional MARL methods, showcasing its effectiveness in cooperative settings.
Methodology
The authors developed the Consensus Multi-Agent Transformer (CMAT) by employing a Transformer encoder to process joint observations and a hierarchical decision-making mechanism that generates a consensus vector. This vector allows all agents to generate their actions simultaneously, thus avoiding the order sensitivity of traditional methods. The framework is optimized using single-agent Proximal Policy Optimization (PPO).
Results
CMAT was evaluated on several benchmark tasks, including StarCraft II, Multi-Agent MuJoCo, and Google Research Football. The results indicated that CMAT consistently outperformed strong baselines, including recent centralized solutions and conventional MARL approaches, highlighting its effectiveness in cooperative multi-agent scenarios.
Implications
The proposed CMAT framework has significant implications for real-world applications requiring coordinated decision-making among multiple agents, such as autonomous fleet management, traffic signal optimization, and robotic swarm control. Its ability to handle large joint observation and action spaces efficiently could lead to advancements in various domains where cooperation among agents is crucial.
Sparse Goodness: How Selective Measurement Transforms Forward-Forward Learning
Theory
Optimization
Efficient ML
- Introduction of top-k goodness function, significantly outperforming the traditional sum-of-squares method.
- Development of entmax-weighted energy for adaptive sparse weighting, leading to improved accuracy.
- Implementation of separate label–feature forwarding (FFCL) enhances performance across all goodness functions.
- Establishment of a unifying principle that emphasizes the importance of sparsity in goodness functions for FF networks.
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Sparse Goodness: How Selective Measurement Transforms Forward-Forward Learning
Summary
This paper investigates the Forward-Forward (FF) algorithm, a biologically plausible alternative to backpropagation for training neural networks. The authors challenge the conventional use of the sum-of-squares (SoS) goodness function, proposing a systematic exploration of the goodness function design space. They introduce 'top-k goodness,' which focuses on the k most active neurons, demonstrating a significant performance improvement over SoS on the Fashion-MNIST dataset. Additionally, they present 'entmax-weighted energy,' which utilizes a learnable sparse weighting mechanism to further enhance performance. The paper also adopts a novel approach called separate label–feature forwarding (FFCL), which injects class hypotheses at every layer. The combination of these innovations leads to a remarkable accuracy of 87.1% on Fashion-MNIST, representing a 30.7 percentage point improvement over the SoS baseline. The authors establish that sparsity in the goodness function is crucial for FF performance, with adaptive sparsity yielding the best results. Through extensive experiments, they reveal that the choice of goodness function significantly impacts the learning dynamics and overall effectiveness of FF networks.
Methodology
The authors conducted a systematic study of various goodness functions, focusing on the top-k goodness function that measures only the most active neurons. They also explored entmax-weighted energy for adaptive sparsity and implemented FFCL for label injection. The performance was evaluated through controlled experiments across multiple goodness functions and architectures, analyzing the effects of sparsity on model performance.
Results
The proposed top-k goodness function achieved a 22.6 percentage point improvement over the SoS baseline on Fashion-MNIST. The entmax-weighted energy further improved results, and the combination of these methods with FFCL led to an overall accuracy of 87.1%, a 30.7 percentage point increase over the SoS baseline.
Implications
The findings suggest that rethinking the design of goodness functions can lead to significant advancements in the performance of FF networks. This work may influence future research in biologically inspired learning algorithms and their applications in various domains, including computer vision and beyond.