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

[2608.14717] Local Gains and Fixed-Assignment Set Losses in Shared Set Decoders Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2608.14717 (cs) [Submitted on 12 Aug 2026] Title: Local Gains and Fixed-Assignment Set Losses in Shared Set Decoders Authors: Ze Zhang , Yang Zhang View a PDF of the paper titled Local Gains and Fixed-Assignment Set Losses in Shared Set Decoders, by Ze Zhang and 1 other authors View PDF HTML (experimental) 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 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ze Zhang [ view email ] [v1] Wed, 12 Aug 2026 03:14:43 UTC (1,447 KB) Full-text links: Access Paper: View a PDF of the paper titled Local Gains and Fixed-Assignment Set Losses in Shared Set Decoders, by Ze Zhang and 1 other authors View PDF HTML (experimental) TeX Source view license 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) Current browse context: cs.CV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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