Retrieval-Augmented Generation for Scientific Code Understanding
研究通过构建检索增强生成系统，利用小型开源模型和离线解析处理，解决大型云托管模型在科学代码理解中的高成本和隐私问题。
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Advanced Brain Tissue Imaging with Data-Consistent Diffusion Priors in Laminographic X-Ray Nanoimaging
为解决X射线层析成像中因数据缺失导致的脑组织成像失真问题，提出LUCID框架，结合多视角扩散先验与投影域数据一致性方法恢复未测量信息。
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Hierarchical Bayesian Calibration with Bayesian Committee Machine
This work addresses the computational bottlenecks in Bayesian calibration for high-cost scientific experiments—such as particle accelerators—where traditional methods become infeasible due to the need for per-experiment parameter estimation and extensive forward simulations. To overcome these challenges, the authors propose a hierarchical Bayesian calibration framework that integrates the Kennedy–O’Hagan model with hierarchical priors to share information across experiments, thereby enhancing generalization. For the first time in this context, the Bayesian Committee Machine (BCM) is incorporated into Gaussian process surrogate modeling to enable scalable, parallelized inference. By combining the No-U-Turn Sampler (NUTS) with Julia’s automatic differentiation, the approach eliminates the need for custom approximate inference tuning. Experiments on standard benchmarks and Argonne wakefield accelerator data demonstrate that the method substantially reduces computational overhead while maintaining robust calibration performance, making it suitable for large-scale scientific modeling.
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Beyond Object Validation: Relational Conformance in Multi-Artifact Agent Releases
This work addresses the problem of global inconsistency in multi-component intelligent agent releases, where local validation passes but cross-component relational integrity fails due to the absence of holistic consistency guarantees. To tackle this, we propose the Schema-SIP Relational Consistency (SIP-RC) framework—the first systematic approach to formally define and mitigate relational inconsistency faults in multi-component deployments. SIP-RC models release packages as graph structures and integrates schema documentation with product contract principles to enable cross-component relational verification. Key mechanisms include declarative–evidential linkage, decision authority scoping, provenance tracking of derived components, and byte-level consistency checks. Preliminary experiments demonstrate the feasibility of the proposed framework, offering a practical and actionable paradigm for ensuring relational consistency in intelligent agent releases.
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Heterogeneous Agent Cohorts for Safe Open-Ended Exploration with Runtime Constraint Memory
This work addresses the tension in large language model agents between stifling innovation through static safety constraints and risking unsafe behavior via unconstrained interaction. The authors propose a heterogeneous multi-agent collaboration framework comprising three specialized roles: a Disrupter that generates unconventional solutions, a Validator that enforces hard runtime checks prior to tool invocation, and a Broker that stimulates creativity through distant analogies. Failed attempts are distilled via Monte Carlo Tree Search (MCTS) into lightweight, inheritable constraint patches termed “Scars,” while a credit-based communication scoring mechanism (CAS) dynamically regulates bandwidth allocation. Experimental results in a spatial semantic sandbox demonstrate significantly enhanced exploratory capability (p<0.01), complete elimination of execution-level safety violations, a 15.1% reduction in token consumption due to Scars, and a 55.9% decrease in total communication overhead under resource constraints attributable to CAS.
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Retrieval-Augmented Generation for Scientific Code Understanding
研究通过构建检索增强生成系统，利用小型开源模型和离线解析处理，解决大型云托管模型在科学代码理解中的高成本和隐私问题。
0 citationsRead paper
Advanced Brain Tissue Imaging with Data-Consistent Diffusion Priors in Laminographic X-Ray Nanoimaging
为解决X射线层析成像中因数据缺失导致的脑组织成像失真问题，提出LUCID框架，结合多视角扩散先验与投影域数据一致性方法恢复未测量信息。
0 citationsRead paper
Hierarchical Bayesian Calibration with Bayesian Committee Machine
This work addresses the computational bottlenecks in Bayesian calibration for high-cost scientific experiments—such as particle accelerators—where traditional methods become infeasible due to the need for per-experiment parameter estimation and extensive forward simulations. To overcome these challenges, the authors propose a hierarchical Bayesian calibration framework that integrates the Kennedy–O’Hagan model with hierarchical priors to share information across experiments, thereby enhancing generalization. For the first time in this context, the Bayesian Committee Machine (BCM) is incorporated into Gaussian process surrogate modeling to enable scalable, parallelized inference. By combining the No-U-Turn Sampler (NUTS) with Julia’s automatic differentiation, the approach eliminates the need for custom approximate inference tuning. Experiments on standard benchmarks and Argonne wakefield accelerator data demonstrate that the method substantially reduces computational overhead while maintaining robust calibration performance, making it suitable for large-scale scientific modeling.
0 citationsRead paper
Beyond Object Validation: Relational Conformance in Multi-Artifact Agent Releases
This work addresses the problem of global inconsistency in multi-component intelligent agent releases, where local validation passes but cross-component relational integrity fails due to the absence of holistic consistency guarantees. To tackle this, we propose the Schema-SIP Relational Consistency (SIP-RC) framework—the first systematic approach to formally define and mitigate relational inconsistency faults in multi-component deployments. SIP-RC models release packages as graph structures and integrates schema documentation with product contract principles to enable cross-component relational verification. Key mechanisms include declarative–evidential linkage, decision authority scoping, provenance tracking of derived components, and byte-level consistency checks. Preliminary experiments demonstrate the feasibility of the proposed framework, offering a practical and actionable paradigm for ensuring relational consistency in intelligent agent releases.
0 citationsRead paper
Heterogeneous Agent Cohorts for Safe Open-Ended Exploration with Runtime Constraint Memory
This work addresses the tension in large language model agents between stifling innovation through static safety constraints and risking unsafe behavior via unconstrained interaction. The authors propose a heterogeneous multi-agent collaboration framework comprising three specialized roles: a Disrupter that generates unconventional solutions, a Validator that enforces hard runtime checks prior to tool invocation, and a Broker that stimulates creativity through distant analogies. Failed attempts are distilled via Monte Carlo Tree Search (MCTS) into lightweight, inheritable constraint patches termed “Scars,” while a credit-based communication scoring mechanism (CAS) dynamically regulates bandwidth allocation. Experimental results in a spatial semantic sandbox demonstrate significantly enhanced exploratory capability (p<0.01), complete elimination of execution-level safety violations, a 15.1% reduction in token consumption due to Scars, and a 55.9% decrease in total communication overhead under resource constraints attributable to CAS.
0 citationsRead paper