Auturnal170 Chinese and English research reviews connecting primary studies, mathematical models and inspectable evidence.
Computational genomics · v5
From sequence to multimodal tracks and then to verifiable variant effects: explaining the capabilities enabled by long context and the causal questions that remain unresolved.
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Medical AI · v5
Using RETFound as a thread, we examine label efficiency, external generalization, calibration, and workflow endpoints, distinguishing predictive capability from clinical benefit.
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RNA science · v5
From sequences to secondary and tertiary structures, and then to condition-dependent conformational ensembles; examining generalization beyond families and the limitations of structural metrics.
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Embodied intelligence · v6
Explain why action distributions cannot simply be averaged, then distinguish offline imitation from acceptance testing for real-world control.
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Trustworthy AI and methodology · v6
Using a linear model to explain why high-dimensional perturbations can accumulate, then distinguishing attack failure from evidence of robustness.
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Geophysics · v5
Detecting more events does not automatically mean a more accurate catalogue; seismic phases, event identities, location errors, and observation thresholds must be assessed layer by layer.
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Model architectures and systems · v6
Accounting for expert loads through token traffic explains why total parameters, activated parameters, and actual training costs are not interchangeable.
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Spatial biology · v5
Comparing cell mapping, composition deconvolution, and neighborhood representations to explain why expression similarity cannot uniquely determine cell locations or communication mechanisms.
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Efficient inference systems · v6
Deriving how the target model corrects the draft model, and why a high acceptance rate may still yield no end-to-end benefit.
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Sequence models · v6
Distinguish fixed-parameter linear systems from input-dependent selection, and examine costs linear in sequence length and the trade-offs of a compressed state.
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Data and training methods · v6
Decomposing “generate more” into testable questions of correctness, independence, and coverage.
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Model architecture and computation · v10
Placing mathematical operators, memory hierarchies, and autoregressive dependencies in a single analytical framework clarifies which speedups preserve the original computation and which change the model itself.
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