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

arXiv:2608.14717v1 Announce Type: new Abstract: 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.

SourcearXiv Computer VisionAuthor: Ze Zhang, Yang Zhang

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[Submitted on 12 Aug 2026]

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

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Abstract: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.

Comments: 13 pages, 4 figures, 2 tables. An ancillary analysis-ready package supports exact aggregate reproduction without model inference. Code and reproduction package: this https URL

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

Cite as: arXiv:2608.14717 [cs.CV]

(or arXiv:2608.14717v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2608.14717

arXiv-issued DOI via DataCite

Submission history

From: Ze Zhang [view email] [v1] Wed, 12 Aug 2026 03:14:43 UTC (1,447 KB)

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Ancillary-file links:

Ancillary files (details):

ARTIFACT_MANIFEST.json

DATA_AND_MODEL_PROVENANCE.md

README.md

analysis_ready/ANALYSIS_READY_PROVENANCE.json

analysis_ready/gate_c_detr_r50_500.jsonl

analysis_ready/gate_c_dino_r50_4s_12e.jsonl

analysis_ready/h4_d_detr_r50_500.jsonl

analysis_ready/h4_d_dino_r50_4s_12e.jsonl

analysis_ready/t1_detr_r50_500.jsonl

analysis_ready/t1_dino_r50_4s_12e.jsonl

analysis_ready/t2_detr_r50_500.jsonl

analysis_ready/t2_dino_r50_4s_12e.jsonl

audit/H4_D_FULL_INTEGRITY_AUDIT.json

audit/H4_GATE_C_D_FULL_INTEGRITY_AUDIT_V2.json

audit/N1_REPLAY_INTEGRITY_AUDIT.json

audit/T0B_D_LOCAL_FULL_INTEGRITY.json

audit/T0B_REALIZED_DOSE_OVERLAP.json

audit/T0B_R_PILOT_LOCAL_INTEGRITY.json

audit/T0B_R_PILOT_MANIFEST.json

audit/T1_REALIZED_DOSE_MAPPING.json

audit/T2_CONFIRMATION_PAIR_MANIFEST.json

audit/T2_CONFIRMATION_POPULATION_MANIFEST.json

audit/T2_CONFIRMATION_V2_FULL_INTEGRITY.json

audit/T2_INPUT_CAPTURE_INTEGRITY.json

audit/T2_OUTCOME_BLIND_MAPPING.json

audit/T2_REALIZED_DOSE_OVERLAP.json

code/iclr_h4_aggregate.py

code/iclr_h4_gate_c_aggregate.py

code/iclr_n1_aggregate.py

code/iclr_transport_t0.py

code/iclr_transport_t0b_integrity.py

code/iclr_transport_t0b_overlap.py

code/iclr_transport_t1_aggregate.py

code/iclr_transport_t2_aggregate.py

code/iclr_transport_t2_confirmation_integrity.py

code/iclr_transport_t2_input_overlap.py

code/p1_artifact_manifest.py

code/p1_export_analysis_ready.py

code/p1_reproduce_from_analysis_ready.py

code/reference_src/future_qc/iclr_h4/estimands.py

code/reference_src/future_qc/iclr_n1/statistics.py

configs/fq_iclr_h4_gate_c_aggregation_v1.json

configs/fq_iclr_h4_gate_c_d_v2.json

configs/fq_iclr_h4_set_externality_v1.json

configs/fq_iclr_n1_aggregation_v1.json

configs/fq_iclr_n1_instrumented_replay_v1.json

configs/fq_iclr_transport_t0_v1.json

configs/fq_iclr_transport_t0b_v1.json

configs/fq_iclr_transport_t1_aggregation_v1.json

configs/fq_iclr_transport_t2_confirmation_execution_v2.json

configs/fq_iclr_transport_t2_confirmation_v1.json

configs/fq_iclr_transport_t2_input_overlap_v1.json

expected/H4_D_SCIENTIFIC_AGGREGATE.json

expected/H4_GATE_C_SCIENTIFIC_AGGREGATE.json

expected/N1_MECHANISM_AGGREGATE.json

expected/T0_COMMON_ESTIMAND_AUDIT.json

expected/T1_PRE_READOUT_LOCALIZATION.json

expected/T2_CONFIRMATION_INITIAL_128_AGGREGATE.json

reproduced/GATE_C_REPRODUCED.json

reproduced/H4_D_REPRODUCED.json

reproduced/REPRODUCTION_VALIDATION.json

reproduced/T1_REPRODUCED.json

reproduced/T2_REPRODUCED.json

(58 additional files not shown)

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