mlsanityData to 7 Oct 2026

Local Gains and Fixed-Assignment Set Losses in Shared Set Decoders

arXiv 2608.14717 ↗First posted 12 Aug 2026cs.CV cs.LGcited by 0

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Abstract (arXiv)

A query-relation deletion can improve the edited slot while reducing the utility of the prediction set that contains it. We study this tension in two related ResNet-50 DETR-family checkpoints using recorded, selection-conditional evidence from 710 paired image-relation units per checkpoint. The primary comparison subtracts a matched active control, which deletes the same leader source at a different recorded recipient, from the selected target deletion. It is therefore a composite contrast rather than a same-recipient placebo. The target-minus-control contrast is locally positive and fixed-assignment negative in both checkpoints. The opposite-sign pattern occurs within 302/710 DETR units and 460/710 DINO units. After rematching, the corresponding counts are 285/710 and 433/710. Rematching and native selection absorb enough of the mean loss for DETR intervals to cross zero, whereas DINO intervals remain negative, so persistence across readouts differs by checkpoint. A fixed-map comparison between hard deletion and a mass-preserving edit also differs before rematching. That comparison is conditional on the outcome-blind map and does not establish same-dose transport. Local intervention success therefore does not determine the consequence for a jointly decoded set. The supported conclusion is selection-conditional deletion sensitivity whose persistence depends on the readout and intervention operator. We do not identify an intervention-invariant edge mechanism, detector-level degradation, population prevalence, or the value of a training-time regularizer.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 19 references that are in the map. First posted on arXiv 12 Aug 2026.

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References · 14

  1. 2020End-to-End Object Detection with Transformerscited by 7,384
  2. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,306
  3. 2019Attention is not Explanationcited by 916
  4. 2019Attention is not not Explanationcited by 629
  5. 2022DN-DETR: Accelerate DETR Training by Introducing Query DeNoisingcited by 486
  6. 2023Towards Best Practices of Activation Patching in Language Models: Metrics and Methodscited by 328
  7. 2022Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignmentcited by 96
  8. 2023Is This the Subspace You Are Looking for? An Interpretability Illusion for Subspace Activation Patchingcited by 75
  9. 2023Detection Transformer with Stable Matchingcited by 43
  10. 2024Relation DETR: Exploring Explicit Position Relation Prior for Object Detectioncited by 27
  11. 2022Causal Abstraction with Soft Interventionscited by 23
  12. 2026Causality is Key for Interpretability Claims to Generalisecited by 8
2 more
  1. 2026Beyond Importance: Interchange-Sobol Sensitivity Reveals Task-Specific Content Channels in Transformer Componentscited by 3
  2. 2025Dual-R-DETR: Resolving Query Competition with Pairwise Routing in Transformer Decoderscited by 2

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