Evaluation

GENA-LM 110M efficient implementation, pseudo-perplexity agreement with the reference

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Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: GENA-LM 110M, efficient inference implementation (Saadat and Fellay 2026)	Protocol: Pseudo-perplexity agreement between reference and efficient implementations (Saadat and Fellay 2026 Figure 1) Dataset: Synthetic uniform random DNA sequences, seed 42 (Saadat and Fellay 2026)	r = 1.00 Pearson (Agreement of per-sequence pseudo-perplexity between the reference and the efficient implementation, 50 synthetic sequences of length 1000) unitless · higher Uncertainty: Not reported by the source Coverage: Not reported scored / Not reported eligible	Independent external evaluation · Source checked Methods, coverage and source GENA-LM 110M efficient implementation, pseudo-perplexity agreement with the reference saadat-fellay-20261015-protocol-pseudo-perplexity-agreement Aggregation: Not reported Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) · Figure 1 (page 4, raster), panel 'GENA-LM 110M', legend text 'r = 1.00'

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Evaluation procedure

Pseudo-perplexity agreement between reference and efficient implementations (Saadat and Fellay 2026 Figure 1)

Configuration: GENA-LM 110M, efficient inference implementation (Saadat and Fellay 2026)
Protocol: Pseudo-perplexity agreement between reference and efficient implementations (Saadat and Fellay 2026 Figure 1)
Dataset: Synthetic uniform random DNA sequences, seed 42 (Saadat and Fellay 2026)

origin: Independent external evaluation
configuration: Primary source as retrieved 2026-10-15
split: Not applicable
population: 50 uniform random DNA sequences of length 1000, seed 42
inputs: DNA sequence
adaptation: None (pretrained weights, masked-LM head)
aggregation: Pearson correlation across 50 per-sequence pseudo-perplexities
budget: Single pass per masked position
protocol id: saadat-fellay-20261015-protocol-pseudo-perplexity-agreement
dataset version: Not reported
metric implementation: Not reported

Metadata review: source checked. Unreported conditions prevent automatic comparisons.

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Split: Not applicable
Adaptation: None (pretrained weights, masked-LM head)
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Evidence table

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19 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-22-1b7cf027e4f6
Property and statement	Original source and location	Review and provenance
Comparison: adaptation None (pretrained weights, masked-LM head) Context-only references	Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Original source ↗ Figure 1, panel 'GENA-LM 110M' (page 4) Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) Retrieved: 2026-10-15T10:56:26Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.adaptation` Source artifact SHA-256: `4bc2452110f280a7ba8baa4c7c28beac89769511019666bc77fc33af9b3bcd60` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: aggregation Pearson correlation across 50 per-sequence pseudo-perplexities Context-only references	Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Original source ↗ Figure 1, panel 'GENA-LM 110M' (page 4) Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) Retrieved: 2026-10-15T10:56:26Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.aggregation` Source artifact SHA-256: `4bc2452110f280a7ba8baa4c7c28beac89769511019666bc77fc33af9b3bcd60` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: budget Single pass per masked position Context-only references	Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Original source ↗ Figure 1, panel 'GENA-LM 110M' (page 4) Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) Retrieved: 2026-10-15T10:56:26Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.budget` Source artifact SHA-256: `4bc2452110f280a7ba8baa4c7c28beac89769511019666bc77fc33af9b3bcd60` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: dataset version Not reported Context-only references	Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Original source ↗ Figure 1, panel 'GENA-LM 110M' (page 4) Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) Retrieved: 2026-10-15T10:56:26Z	missing or unspecified No individual claim review recorded independent paper Audit details Field: `attributes.comparison.dataset_version` Source artifact SHA-256: `4bc2452110f280a7ba8baa4c7c28beac89769511019666bc77fc33af9b3bcd60` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: inputs DNA sequence Context-only references	Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Original source ↗ Figure 1, panel 'GENA-LM 110M' (page 4) Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) Retrieved: 2026-10-15T10:56:26Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.inputs` Source artifact SHA-256: `4bc2452110f280a7ba8baa4c7c28beac89769511019666bc77fc33af9b3bcd60` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: metric implementation Not reported Context-only references	Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Original source ↗ Figure 1, panel 'GENA-LM 110M' (page 4) Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) Retrieved: 2026-10-15T10:56:26Z	missing or unspecified No individual claim review recorded independent paper Audit details Field: `attributes.comparison.metric_implementation` Source artifact SHA-256: `4bc2452110f280a7ba8baa4c7c28beac89769511019666bc77fc33af9b3bcd60` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: population 50 uniform random DNA sequences of length 1000, seed 42 Context-only references	Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Original source ↗ Figure 1, panel 'GENA-LM 110M' (page 4) Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) Retrieved: 2026-10-15T10:56:26Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.population` Source artifact SHA-256: `4bc2452110f280a7ba8baa4c7c28beac89769511019666bc77fc33af9b3bcd60` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: protocol id saadat-fellay-20261015-protocol-pseudo-perplexity-agreement Context-only references	Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Original source ↗ Figure 1, panel 'GENA-LM 110M' (page 4) Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) Retrieved: 2026-10-15T10:56:26Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.protocol_id` Source artifact SHA-256: `4bc2452110f280a7ba8baa4c7c28beac89769511019666bc77fc33af9b3bcd60` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Comparison: split Not applicable Context-only references	Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Original source ↗ Figure 1, panel 'GENA-LM 110M' (page 4) Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) Retrieved: 2026-10-15T10:56:26Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.comparison.split` Source artifact SHA-256: `4bc2452110f280a7ba8baa4c7c28beac89769511019666bc77fc33af9b3bcd60` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Inference hardware NVIDIA H100 (section 2.4); 'H100 94GB GPUs' (section 4) Context-only references	Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) Original source ↗ Figure 1, panel 'GENA-LM 110M' (page 4) Version: OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09) Retrieved: 2026-10-15T10:56:26Z	not individually reviewed No individual claim review recorded independent paper Audit details Field: `attributes.inference_hardware` Source artifact SHA-256: `4bc2452110f280a7ba8baa4c7c28beac89769511019666bc77fc33af9b3bcd60` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Release 2026-10-22-1b7cf027e4f6 · Record review: source checked

1 source record and release history

Optimizing genomic language models for efficient training, fine-tuning, and inference (Saadat and Fellay, ICLR 2026 workshop) · Original source · OpenReview PDF headed 'Published as a workshop paper at ICLR 2026' (11 pages; PDF CreationDate 2026-03-09)

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Technical metadata and extraction receipts

Stable ID: saadat-fellay-20261015-eval-gena-lm-110m-pseudo-perplexity-agreement

areas: dna-genomes
contexts: research
origin: independent_paper
protocol: saadat-fellay-20261015-protocol-pseudo-perplexity-agreement
version: Primary source as retrieved 2026-10-15
comparison: split: Not applicable; population: 50 uniform random DNA sequences of length 1000, seed 42; inputs: DNA sequence; adaptation: None (pretrained weights, masked-LM head); aggregation: Pearson correlation across 50 per-sequence pseudo-perplexities; budget: Single pass per masked position; protocol id: saadat-fellay-20261015-protocol-pseudo-perplexity-agreement; dataset version: Not reported; metric implementation: Not reported
source locator: Figure 1, panel 'GENA-LM 110M' (page 4)
inference hardware: NVIDIA H100 (section 2.4); 'H100 94GB GPUs' (section 4)
missing metadata: comparison.dataset version: reason: inapplicable; note: Synthetic sequences generated by the authors; comparison.metric implementation: reason: unreported; note: No code release is cited
review: reviewer: claude; reviewer note: Claude review agent; no human review claimed. Independent Claude (Opus 5.5) review agent, separate from the extraction agent that built this batch. Automated review only. Transcription, not reproduction.; reviewed at: 2026-10-15T22:00:00Z; artifact sha256: 4bc2452110f280a7ba8baa4c7c28beac89769511019666bc77fc33af9b3bcd60; retrieval url: https://openreview.net/pdf?id=NUleWh1GFx; method: source-hash-verification; pdf-text-parse; scope-review; method note: Links, origin, comparison fields and missing-metadata reasons checked against the PDF and the linked protocol.; note: Origin independent_paper checked: the authors (EPFL) are not among the GENA-LM (Fishman et al.) or Nucleotide Transformer (Dalla-Torre et al.) authors and cite both as other work.

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