{ "schema_version": 1, "batch": "2026-10-30-overlap-components", "counts": { "selected_papers": 10, "components_accepted": 10, "gpu_validated_components": 6, "cpu_only_components": 4, "reference_only_high_cost_done": 0, "components_pending_verification": 0, "capability_reproductions_claimed": 0, "latest_category_entries": 1478, "latest_unique_identities": 1059, "new_bulletin_identities_since_previous_capture": 0, "selected_first_publications_after_previous_capture": 0, "selected_historical_official_source_backfills": 1, "selected_recent_overlap_backfills": 9 }, "selected_scope_complete": true, "delivery_status": "bounded_scope_complete", "all_sources_exhaustive": false, "review_watermark_advanced": false, "review_watermark": { "before": "2026-10-15", "after": "2026-10-15" }, "fixed_component_keys": [ "concur", "crop", "emma", "evalalloc", "fold-bias", "merlin", "mpopd", "pinger", "pinterest-rlbp", "swim" ], "reference_only_keys": [], "papers": [ { "domain": "post-training", "key": "concur", "title": "CONCUR: Concordant Auxiliary Rewards for Multi-Task, Multi-Reward Reinforcement Learning", "paper_url": "https://arxiv.org/abs/2610.36437v1", "detail_path": "post-training/2610.36437-concur/README.md", "priority": "P0", "first_author": "Jixuan Leng", "first_author_affiliation": "Carnegie Mellon University", "published_at": "2026-10-27T00:36:59Z", "cost_assessment": { "source": "v1 AppendixD Tables3/4, SectionsD.1/D.2", "model_scale": "Qwen3-4B/8B-Base和OLMo-3-7B；Gemma-4-26B-A4B训练/评测judge与8B reward model", "paper_configuration": "每run 4节点×8 NVIDIA RTX PRO6000，300steps，batch512、每prompt8rollouts，训练响应4K/8K、评测最长32K", "data_scale": "四任务混合，各模型每任务约9.8K–11.7K prompts，15项下游评测及多组估计器/消融", "available_resource": "单张NVIDIA A100或A30，或本地Mac", "estimated_cost_basis": "原文明确32GPU/run及多模型长rollout矩阵，单卡预算不适合完整实验；不是本地测量", "decision": "成本过高，不复现（审查 DONE）；不下载、不试完整训练、不自动重试。只验证无模型调用的CPU估计器组件。" }, "implementation_status": "component_only", "id": "2610.36437", "intake_class": "same_bulletin_backfill", "status": "implemented", "first_publication_after_previous_capture": false, "component_accepted": true, "component_implemented": true, "review_done": true, "full_experiment_status": "not_reproduced_high_cost", "full_experiment_review_done": true, "resolution": "bounded_l1_component_accepted", "requires_gpu_validation": false, "gpu_validation_passed": false, "component_evidence": "docs/post-training/2610.36437-concur/metrics/component-cpu-seeds42-44.json", "automatic_retry": false, "automatic_implementation_queue": false, "promotion_eligible": false, "evolve_integrated": false, "source_provenance": [ { "source_kind": "reviewed-fixed-primary-paper", "source_url": "https://arxiv.org/html/2610.36437v1" }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.CL/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.CL/recent?show=2000", "announced_dates": [ "2026-10-28" ] } ] }, { "domain": "agent-research", "key": "crop", "title": "CROP: Token-Efficient Reasoning in Large Language Models via Regularized Prompt Optimization", "paper_url": "https://arxiv.org/abs/2604.14214v1", "detail_path": "agent-research/2604.14214-crop/README.md", "priority": "P0", "first_author": "Deep Shah", "first_author_affiliation": "Google", "published_at": "2026-04-08T19:31:25Z", "cost_assessment": { "source_url": "https://arxiv.org/html/2604.14214v1#S4", "model_scale": "Gemini3.1Pro Thinking元优化器，Gemini2.0Flash双评估器，Gemini2.0Flash/Qwen2.5-7B目标模型", "paper_configuration": "GSM8K、LogiQA、BBH ObjectCounting，逐样本双文本评估、多轮提示优化、多基线与validation超参搜索；每轮T/B与总GPU/API费用未完整报告，不推定便宜", "available_resource": "单张NVIDIA A100或A30，或本地Mac", "decision": "多模型多数据集闭源API与7B反复推理矩阵成本过高，不复现（审查 DONE）；仅CPU纯控制组件，不下载权重、不调用模型/API、不自动重试" }, "implementation_status": "component_only", "id": "2604.14214", "intake_class": "historical_backfill", "status": "implemented", "first_publication_after_previous_capture": false, "component_accepted": true, "component_implemented": true, "review_done": true, "full_experiment_status": "not_reproduced_high_cost", "full_experiment_review_done": true, "resolution": "bounded_l1_component_accepted", "requires_gpu_validation": false, "gpu_validation_passed": false, "component_evidence": "docs/agent-research/2604.14214-crop/metrics/component-cpu-seeds42-44.json", "automatic_retry": false, "automatic_implementation_queue": false, "promotion_eligible": false, "evolve_integrated": false, "source_provenance": [ { "source_kind": "reviewed-fixed-primary-paper", "source_url": "https://arxiv.org/html/2604.14214v1" }, { "source_kind": "historical-google-directory-backfill", "source_url": "https://research.google/pubs/crop-token-efficient-reasoning-in-large-language-models-via-regularized-prompt-optimization/" } ] }, { "domain": "agent-research", "key": "emma", "title": "EMMA: Structured Episodic Memory for Multi-Agent Workflow Synthesis", "paper_url": "https://arxiv.org/abs/2610.35966v1", "detail_path": "agent-research/2610.35966-emma/README.md", "priority": "P1", "first_author": "Ruijia Niu", "first_author_affiliation": "University of California San Diego", "published_at": "2026-10-25T18:00:11Z", "cost_assessment": { "source": "v1 Sections5/AppendicesB/C", "model_scale": "五种闭源/开放LLM backbone，controller高推理强度，反思与多Agent执行均真实模型调用", "paper_configuration": "三benchmarks、多基线；每任务至多5次revision/6次fresh execution，每step至多30轮，executor600秒/24GiB", "data_scale": "KramaBench28训练+76评测；AppWorld90训练+168/417评测；BBEH-mini92训练+368评测", "available_resource": "单张NVIDIA A100或A30，或本地Mac", "estimated_cost_basis": "多模型多环境长轨迹反思矩阵，未给单卡可复现总时长；按成本不启动探针", "decision": "成本过高，不复现（审查 DONE）；不调用LLM或部署完整AppWorld矩阵。只交付结构记忆/守卫子组件，不自动重试。" }, "implementation_status": "component_only", "id": "2610.35966", "intake_class": "same_bulletin_backfill", "status": "implemented", "first_publication_after_previous_capture": false, "component_accepted": true, "component_implemented": true, "review_done": true, "full_experiment_status": "not_reproduced_high_cost", "full_experiment_review_done": true, "resolution": "bounded_l1_component_accepted", "requires_gpu_validation": false, "gpu_validation_passed": false, "component_evidence": "docs/agent-research/2610.35966-emma/metrics/component-cpu-seeds42-44.json", "automatic_retry": false, "automatic_implementation_queue": false, "promotion_eligible": false, "evolve_integrated": false, "source_provenance": [ { "source_kind": "reviewed-fixed-primary-paper", "source_url": "https://arxiv.org/html/2610.35966v1" }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.AI/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.AI/recent?show=2000", "announced_dates": [ "2026-10-28" ] } ] }, { "domain": "agent-research", "key": "evalalloc", "title": "Where Should the Evaluation Budget Go in LLM-Driven Agent Scaffold Evolution?", "paper_url": "https://arxiv.org/abs/2610.37276v1", "detail_path": "agent-research/2610.37276-evalalloc/README.md", "priority": "P1", "first_author": "Sichun Luo", "first_author_affiliation": "The University of Hong Kong", "published_at": "2026-10-27T14:06:26Z", "cost_assessment": { "source": "v1 Sections4.1/5/6", "model_scale": "LLM proposer/executor；额外实验含gpt-oss120B及Llama3.1-8B", "paper_configuration": "4benchmarks×2evolution frameworks×3runs，多基线；80/81 proposals×300SEARCHitems，额外DEV/TEST与多轮LLM执行", "data_scale": "HotpotQA/HoVer/DROP/IFBench，各300SEARCH/150DEV及294或300TEST；最终SH公共prefix30/60/120/300", "available_resource": "单张NVIDIA A100或A30，或本地Mac", "estimated_cost_basis": "长链LLM搜索/多臂跨基准矩阵明显超出低成本交付；未运行完整模型试探", "decision": "成本过高，不复现（审查 DONE）；只交付明确标注的统计/配对轮子组件，不声称全框架已接入或节省实际LLM费用，不自动重试。" }, "implementation_status": "component_only", "id": "2610.37276", "intake_class": "same_bulletin_backfill", "status": "implemented", "first_publication_after_previous_capture": false, "component_accepted": true, "component_implemented": true, "review_done": true, "full_experiment_status": "not_reproduced_high_cost", "full_experiment_review_done": true, "resolution": "bounded_l1_component_accepted", "requires_gpu_validation": false, "gpu_validation_passed": false, "component_evidence": "docs/agent-research/2610.37276-evalalloc/metrics/component-cpu-seeds42-44.json", "automatic_retry": false, "automatic_implementation_queue": false, "promotion_eligible": false, "evolve_integrated": false, "source_provenance": [ { "source_kind": "reviewed-fixed-primary-paper", "source_url": "https://arxiv.org/html/2610.37276v1" }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.AI/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.CL/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.LG/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.AI/recent?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.CL/recent?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.LG/recent?show=2000", "announced_dates": [ "2026-10-28" ] } ] }, { "domain": "foundation-models", "key": "fold-bias", "title": "Folding Attention Biases into Keys Makes Compacted KV Caches Run on Standard Kernels", "paper_url": "https://arxiv.org/abs/2610.36520v1", "detail_path": "foundation-models/2610.36520-fold-bias/README.md", "priority": "P1", "first_author": "Utkarsh Kanwat", "first_author_affiliation": "Independent Researcher", "published_at": "2026-10-27T02:22:37Z", "cost_assessment": { "source": "固定v1 Sections4-7及AppendixA.11 serving details：https://arxiv.org/html/2610.36520v1", "available_resource": "单张NVIDIA A100或A30，或本地Mac", "model_scale": "Qwen3 1.7B/4B/8B/14B、Llama-3.1-8B及更大模型系统矩阵", "data_scale": "QuALITY、LongHealth、LongBench长上下文质量和多预算比较", "paper_configuration": "实际prefill/AM校准、KV connector、backend正确性和serving容量/解码速度；部分大模型容量实验为随机权重", "estimated_cost_basis": "多模型/长上下文/serving协议只读审查，未实测完整GPU小时；小张量折叠和最小二乘无需模型", "decision": "完整质量/serving实验成本过高，不复现（审查DONE），不下载/探针/自动重试。仅真实BF16舍入后的value重拟合及同value误差界，不声称完整AM或vLLM路径。" }, "implementation_status": "component_only", "id": "2610.36520", "intake_class": "same_announcement_overlap_backfill", "status": "implemented", "first_publication_after_previous_capture": false, "component_accepted": true, "component_implemented": true, "review_done": true, "full_experiment_status": "not_reproduced_high_cost", "full_experiment_review_done": true, "resolution": "bounded_l1_component_accepted", "requires_gpu_validation": true, "gpu_validation_passed": true, "component_evidence": "docs/foundation-models/2610.36520-fold-bias/metrics/component-cuda-seeds42-44.json", "automatic_retry": false, "automatic_implementation_queue": false, "promotion_eligible": false, "evolve_integrated": false, "source_provenance": [ { "source_kind": "reviewed-fixed-primary-paper", "source_url": "https://arxiv.org/html/2610.36520v1" }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.CL/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.LG/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.CL/recent?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.LG/recent?show=2000", "announced_dates": [ "2026-10-28" ] } ] }, { "domain": "foundation-models", "key": "merlin", "title": "Multi-State Linear Attention with Generalized Second-Order Routing", "paper_url": "https://arxiv.org/abs/2610.37645v1", "detail_path": "foundation-models/2610.37645-merlin/README.md", "priority": "P0", "first_author": "Hee Seung Hwang", "first_author_affiliation": "Princeton University", "published_at": "2026-10-27T17:55:49Z", "cost_assessment": { "source": "固定v1 Section4.1 Training/Models及Table1：https://arxiv.org/html/2610.37645v1", "available_resource": "单张NVIDIA A100或A30，或本地Mac", "model_scale": "Gated DeltaNet-1.3B和Mamba-2-780M，多状态注意力改造", "data_scale": "2B tokens中期训练后再60M tokens，另有RULER和LongBench；原始预训练分别50B/300B tokens", "paper_configuration": "16K上下文、256token等长段、多backbone多路由方法和消融", "estimated_cost_basis": "论文模型/数据/评测矩阵的只读成本审查；未实测完整GPU小时，不虚构估计；L1只需外部小张量", "decision": "完整实验成本过高，不复现（审查DONE），不下载/探针/自动重试。仅实现矩统计、路由与已完成段遮罩，不声称完整注意力层或LLM能力。" }, "implementation_status": "component_only", "id": "2610.37645", "intake_class": "same_announcement_overlap_backfill", "status": "implemented", "first_publication_after_previous_capture": false, "component_accepted": true, "component_implemented": true, "review_done": true, "full_experiment_status": "not_reproduced_high_cost", "full_experiment_review_done": true, "resolution": "bounded_l1_component_accepted", "requires_gpu_validation": true, "gpu_validation_passed": true, "component_evidence": "docs/foundation-models/2610.37645-merlin/metrics/component-cuda-seeds42-44.json", "automatic_retry": false, "automatic_implementation_queue": false, "promotion_eligible": false, "evolve_integrated": false, "source_provenance": [ { "source_kind": "reviewed-fixed-primary-paper", "source_url": "https://arxiv.org/html/2610.37645v1" }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.CL/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.CL/recent?show=2000", "announced_dates": [ "2026-10-28" ] } ] }, { "domain": "post-training", "key": "mpopd", "title": "Proposing Better Rollouts for On-Policy Distillation", "paper_url": "https://arxiv.org/abs/2610.36477v2", "detail_path": "post-training/2610.36477-mpopd/README.md", "priority": "P1", "first_author": "Sirui Chen", "first_author_affiliation": "University of Illinois Urbana-Champaign", "published_at": "2026-10-27T01:33:51Z", "cost_assessment": { "source": "v2 Section4, AppendixA.1/A.2/B.3", "model_scale": "1.7B/4B/8B学生、独立proposal副本、4B/30B-A3B教师", "paper_configuration": "原配置2GPU、TP=2；学生/proposal交替更新，batch128/256，4K训练rollout，16K评测，每题至多32次采样", "data_scale": "DAPO-Math17K、ARPO10K、5K医学训练子集，16套评测及教师/支持集消融", "available_resource": "单张NVIDIA A100或A30，或本地Mac", "estimated_cost_basis": "多份模型与长rollout/多基准矩阵，无法以小tensor梯度验证代替原规模；未试跑完整实验", "decision": "成本过高，不复现（审查 DONE）；不下载、不试完整蒸馏、不自动重试。仅小tensor公式/梯度组件，CUDA验收单独绑定真实回执。" }, "implementation_status": "component_only", "id": "2610.36477", "intake_class": "same_bulletin_backfill_with_revision", "status": "implemented", "first_publication_after_previous_capture": false, "component_accepted": true, "component_implemented": true, "review_done": true, "full_experiment_status": "not_reproduced_high_cost", "full_experiment_review_done": true, "resolution": "bounded_l1_component_accepted", "requires_gpu_validation": true, "gpu_validation_passed": true, "component_evidence": "docs/post-training/2610.36477-mpopd/metrics/component-cuda-seeds42-44.json", "automatic_retry": false, "automatic_implementation_queue": false, "promotion_eligible": false, "evolve_integrated": false, "source_provenance": [ { "source_kind": "reviewed-fixed-primary-paper", "source_url": "https://arxiv.org/html/2610.36477v2" }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.AI/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.LG/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.AI/recent?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.LG/recent?show=2000", "announced_dates": [ "2026-10-28" ] } ] }, { "domain": "recommendation", "key": "pinger", "title": "PinGER: Token-Efficient Generative Retrieval with Semantic IDs", "paper_url": "https://arxiv.org/abs/2610.33435v1", "detail_path": "recommendation-components/2610.33435-pinger/README.md", "priority": "P0", "first_author": "Xinyi Li", "first_author_affiliation": "Pinterest", "published_at": "2026-10-24T04:24:45Z", "cost_assessment": { "source_url": "https://arxiv.org/html/2610.33435v1", "source_location": "Sections3–5/AppendixA", "available_resource": "one NVIDIA A100 or A30, or local Mac", "model_scale": "RQ-VAE plus 4-layer768-d decoder,5x2048codes,up to6144trainingtokens", "data_scale": "private PinCLIP/OmniSage/engagement data and order1billion production corpus", "paper_configuration": "multi-context/beam/feature/DPO comparisons, custom kernels and real serving/production A/B", "estimated_cost_basis": "full GPU-hours undisclosed; private data and billion-item serving make total cost/material requirements unavailable; only small formula tensors accepted", "decision": "完整实验成本过高，不复现（审查 DONE）；不下载、不试跑、不自动重试。只接受mask/单层前缀选择/分层DPO的L1组件，独立GPU验收不代表推荐器或服务栈复现。" }, "implementation_status": "component_only", "id": "2610.33435", "intake_class": "overlap_backfill", "status": "implemented", "first_publication_after_previous_capture": false, "component_accepted": true, "component_implemented": true, "review_done": true, "full_experiment_status": "not_reproduced_high_cost", "full_experiment_review_done": true, "resolution": "bounded_l1_component_accepted", "requires_gpu_validation": true, "gpu_validation_passed": true, "component_evidence": "docs/recommendation-components/2610.33435-pinger/metrics/component-cuda-seeds42-44.json", "automatic_retry": false, "automatic_implementation_queue": false, "promotion_eligible": false, "evolve_integrated": false, "source_provenance": [ { "source_kind": "reviewed-fixed-primary-paper", "source_url": "https://arxiv.org/html/2610.33435v1" }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.IR/recent?show=2000", "announced_dates": [ "2026-10-27" ] } ] }, { "domain": "recommendation", "key": "pinterest-rlbp", "title": "Reinforcement Learning for Controller Parameters in the Auto-Bidding System at Pinterest", "paper_url": "https://arxiv.org/abs/2610.33248v1", "detail_path": "recommendation-components/2610.33248-pinterest-rlbp/README.md", "priority": "P0", "first_author": "Yichu Zhou", "first_author_affiliation": "Pinterest Inc.", "published_at": "2026-10-23T19:52:02Z", "cost_assessment": { "source_url": "https://arxiv.org/html/2610.33248v1", "source_location": "Sections3–6", "available_resource": "one NVIDIA A100 or A30, or local Mac", "model_scale": "private state encoder/sharedmultiheadQnetwork;deployedgamma0contextualbandit,notdirectneuralbidder", "data_scale": "21dayprivateauctionlogs plus7dayheldout,live7dayA/Band2monthholdout", "paper_configuration": "five-seedconfigablations,logging/explorationpolicy,productionPIDandpaidtraffic", "estimated_cost_basis": "GPU-hours undisclosed; faithful complete cost uncertain and private production requirements unavailable, so no trial run; only bounded Eq3–11 tensors", "decision": "完整实验成本过高，不复现（审查 DONE）；不下载、不调用bidder、不自动重试。低成本张量组件只验收奖励/双头目标/动作/策略/IPS，不冒称真实广告策略效果。" }, "implementation_status": "component_only", "id": "2610.33248", "intake_class": "overlap_backfill", "status": "implemented", "first_publication_after_previous_capture": false, "component_accepted": true, "component_implemented": true, "review_done": true, "full_experiment_status": "not_reproduced_high_cost", "full_experiment_review_done": true, "resolution": "bounded_l1_component_accepted", "requires_gpu_validation": true, "gpu_validation_passed": true, "component_evidence": "docs/recommendation-components/2610.33248-pinterest-rlbp/metrics/component-cuda-seeds42-44.json", "automatic_retry": false, "automatic_implementation_queue": false, "promotion_eligible": false, "evolve_integrated": false, "source_provenance": [ { "source_kind": "reviewed-fixed-primary-paper", "source_url": "https://arxiv.org/html/2610.33248v1" }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.IR/recent?show=2000", "announced_dates": [ "2026-10-27" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.LG/recent?show=2000", "announced_dates": [ "2026-10-27" ] } ] }, { "domain": "foundation-models", "key": "swim", "title": "SWIM: Learning What Matters from Behaviors for JEPA World Models", "paper_url": "https://arxiv.org/abs/2610.36662v1", "detail_path": "foundation-models/2610.36662-swim/README.md", "priority": "P1", "first_author": "Jiaming Hu", "first_author_affiliation": "Boston University / Unity Technologies", "published_at": "2026-10-27T05:28:28Z", "cost_assessment": { "source": "固定v1 AppendixA/E/F和Sections3-5：https://arxiv.org/html/2610.36662v1", "available_resource": "单张NVIDIA A100或A30，或本地Mac", "model_scale": "1024hidden逆动力学及完整视觉encoder/action-conditioned LeWM world model", "data_scale": "离线轨迹、五类环境/变体、多种子及结构化干扰变量诊断", "paper_configuration": "Stage1 8000steps，hinge batch4096或InfoNCE batch1024/255 negatives，gate惩罚网格；另需world-model训练和CEM规划评测", "estimated_cost_basis": "模型/训练批量/仿真评测矩阵只读审查；论文未提供完整可迁移GPU小时，未运行全实验；L1缩小MLP及batch，保留目标定义", "decision": "完整world-model与规划实验成本过高，不复现（审查DONE），不下载/探针/自动重试。仅逆动力学门控目标、真实cyclic错配和冻结sqrt(g)距离，不声称完整JEPA或机器人策略。" }, "implementation_status": "component_only", "id": "2610.36662", "intake_class": "same_announcement_overlap_backfill", "status": "implemented", "first_publication_after_previous_capture": false, "component_accepted": true, "component_implemented": true, "review_done": true, "full_experiment_status": "not_reproduced_high_cost", "full_experiment_review_done": true, "resolution": "bounded_l1_component_accepted", "requires_gpu_validation": true, "gpu_validation_passed": true, "component_evidence": "docs/foundation-models/2610.36662-swim/metrics/component-cuda-seeds42-44.json", "automatic_retry": false, "automatic_implementation_queue": false, "promotion_eligible": false, "evolve_integrated": false, "source_provenance": [ { "source_kind": "reviewed-fixed-primary-paper", "source_url": "https://arxiv.org/html/2610.36662v1" }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.CV/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-new", "source_url": "https://arxiv.org/list/cs.RO/new?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.CV/recent?show=2000", "announced_dates": [ "2026-10-28" ] }, { "source_kind": "arxiv-recent", "source_url": "https://arxiv.org/list/cs.RO/recent?show=2000", "announced_dates": [ "2026-10-28" ] } ] } ], "updated_existing_keys": [], "additional_dispositions": [] }