Increasing LLM coding agents' reasoning effort raises cost and process complexity but does not reliably improve model quality across 140 controlled runs on networked anagram game data.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
Conformal Seasonal Pools is a training-free method that outperforms DeepNPTS on CRPS, quantile loss, and especially 95% coverage (0.89 vs 0.66) across six time-series datasets while being over 500x faster on CPU.
Hard-label delivery via multipass or SLS matches or beats soft-label training on annotator disagreement data when annotations are sparse and leads to flatter minima.
CredibleDFGO extends DFGO by training a weighting network with NLL and energy score supervision so that the Hessian-derived covariances better match actual positioning errors on UrbanNav scenes.
citing papers explorer
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An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery
Increasing LLM coding agents' reasoning effort raises cost and process complexity but does not reliably improve model quality across 140 controlled runs on networked anagram game data.
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Training-Free Probabilistic Time-Series Forecasting with Conformal Seasonal Pools
Conformal Seasonal Pools is a training-free method that outperforms DeepNPTS on CRPS, quantile loss, and especially 95% coverage (0.89 vs 0.66) across six time-series datasets while being over 500x faster on CPU.
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Same Target, Different Basins: Hard vs. Soft Labels for Annotator Distributions
Hard-label delivery via multipass or SLS matches or beats soft-label training on annotator disagreement data when annotations are sparse and leads to flatter minima.
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CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision
CredibleDFGO extends DFGO by training a weighting network with NLL and energy score supervision so that the Hessian-derived covariances better match actual positioning errors on UrbanNav scenes.