Port-Hamiltonian neural networks extended to PDEs recover the Hamiltonian and dissipation of nonlinear string dynamics from data and outperform non-physics-informed baselines.
The annals of mathematical statistics , pages=
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Re-evaluating four LLM code-efficiency benchmarks with 30-run statistical testing shows 93.89% of 'performant' implementations are indistinguishable from baselines; a multi-agent test-generation framework reveals hidden significant improvements in ~24% of previously non-significant tasks.
Lamarckian inheritance improves evolutionary robotics performance in dynamic environments unless changes are conflicting and unpredictable; a change-detecting sensor restores benefits.
A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
VisDoc uses a GenAI pipeline grounded in CTML to restructure OSS onboarding docs, with small evaluations showing higher task success and lower cognitive load.
LLM-generated feedback was associated with faster time to solution for programming students than compiler messages alone, with less-guided versions showing slightly stronger effects.
AInterviewer is an open-source multi-agent platform for AI-led qualitative interviews that integrates controlled question administration with LLMs and supports local models via a web GUI.
citing papers explorer
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Identifying the nonlinear string dynamics with port-Hamiltonian neural networks
Port-Hamiltonian neural networks extended to PDEs recover the Hamiltonian and dissipation of nonlinear string dynamics from data and outperform non-physics-informed baselines.
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Rethinking Code Performance Benchmarks for LLMs
Re-evaluating four LLM code-efficiency benchmarks with 30-run statistical testing shows 93.89% of 'performant' implementations are indistinguishable from baselines; a multi-agent test-generation framework reveals hidden significant improvements in ~24% of previously non-significant tasks.
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Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics
Lamarckian inheritance improves evolutionary robotics performance in dynamic environments unless changes are conflicting and unpredictable; a change-detecting sensor restores benefits.
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Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives
A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
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Restructure This: Using AI to Restructure Onboarding Documents to Reduce Cognitive Overload
VisDoc uses a GenAI pipeline grounded in CTML to restructure OSS onboarding docs, with small evaluations showing higher task success and lower cognitive load.
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The Effects of Structured LLM-Generated Feedback on Programming Assignment Performance
LLM-generated feedback was associated with faster time to solution for programming students than compiler messages alone, with less-guided versions showing slightly stronger effects.
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AInterviewer: A Platform for Designing and Conducting AI-led Qualitative Interviews
AInterviewer is an open-source multi-agent platform for AI-led qualitative interviews that integrates controlled question administration with LLMs and supports local models via a web GUI.