{"data":{"event":{"id":"8a68a04d-4e57-4fdc-884a-1fc0a5ee8780","slug":"capmem-a-benchmark-for-caption-based-episodic-memory-in-egocentric-video-8f8a81fbbd","title":"CapMem: A Benchmark for Caption-Based Episodic Memory in Egocentric Video","short_summary":"arXiv:2609.17688v1 Announce Type: new \nAbstract: Wearable assistants require episodic memory over egocentric video, yet current vision-language models face bounded frame budgets, growing visual-token costs, and long-context retrieval failures. Under these practical constraints, we study whether textual captions can serve as reusable episodic memory. We define the Episodic Memory Video Caption QA task and introduce CapMem, a human-annotated benchmark with 75 videos totaling 33.7 hours, and 1,000 multiple-choice questions across 16 scenarios. On long videos (>20 min), full-coverage CaptionQA wit","full_description":"arXiv:2609.17688v1 Announce Type: new \nAbstract: Wearable assistants require episodic memory over egocentric video, yet current vision-language models face bounded frame budgets, growing visual-token costs, and long-context retrieval failures. Under these practical constraints, we study whether textual captions can serve as reusable episodic memory. We define the Episodic Memory Video Caption QA task and introduce CapMem, a human-annotated benchmark with 75 videos totaling 33.7 hours, and 1,000 multiple-choice questions across 16 scenarios. On long videos (>20 min), full-coverage CaptionQA with 30s and 60s caption windows outperforms direct VideoQA for 10/12 and 8/12 models, respectively. On the same video subset, a matched-frame control across six Qwen models retains mean accuracy gains of 3.22 and 2.55 points, respectively. Our caption-guided retrieve-and-verify harness further improves accuracy by up to 5.3 points. These results support the effectiveness of caption memory for episodic reasoning over long egocentric video.\n\nUpdate (2026-09-17): A Systematic Evaluation of the COTQ Provincial Land Cover Product: Structural Consistency, Spectral Separability, and Relative Positioning Against ESA, ESRI, and Google Products. arXiv:2609.17731v1 Announce Type: new \nAbstract: High-resolution land use and land cover (LULC) products derived from Sentinel-2 imagery are widely used for environmental monitoring and land management, yet their performance can vary across regions with complex ecological gradients and heterogeneous surface conditions. In Quebec, these limitations motivated the development of a provincial 10-m land-cover product, the COTQ, designed to support annual monitoring of land occupation and soil artificialisation. This study presents a systematic evaluation of the COTQ product relative to three global 10-m LULC datasets: ESA WorldCover, ESRI LandCover, and Google DynamicWorld. This paper does not introduce a new mapping methodology but focuses on analysing the behaviour and consistency of the COTQ using complementary evaluation approaches. All products are harmonized under a common legend and compared using structural indicators (object-size distributions, shape complexity, Adjusted Rand Ind\n\nUpdate (2026-09-20): 'Violates antitrust laws': Anthropic, OpenAI, SpaceXAI, Google face lawsuit over ‘illegal agreement’ on slowdown | World News - Hindustan Times. 'Violates antitrust laws': Anthropic, OpenAI, SpaceXAI, Google face lawsuit over ‘illegal agreement’ on slowdown | World News  Hindustan Times\n\nUpdate (2026-09-20): 'AI could kill us...': Anthropic, OpenAI, SpaceXAI, Google face lawsuit over 'agreement' on slowdown - WION. 'AI could kill us...': Anthropic, OpenAI, SpaceXAI, Google face lawsuit over 'agreement' on slowdown  WION\n\nUpdate (2026-09-21): Anthropic, OpenAI, Google, and SpaceXAI Face Antitrust Lawsuit Over AI Slowdown Pact - gHacks. Anthropic, OpenAI, Google, and SpaceXAI Face Antitrust Lawsuit Over AI Slowdown Pact  gHacks\n\nUpdate (2026-09-19): Lawsuit says Anthropic, OpenAI, SpaceXAI and Google made illegal deal on AI slowdown - cbsnews.com. Lawsuit says Anthropic, OpenAI, SpaceXAI and Google made illegal deal on AI slowdown  cbsnews.com\n\nUpdate (2026-09-19): Lawsuit says Anthropic, OpenAI, SpaceXAI and Google made illegal agreement on AI slowdown - apnews.com. 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Follow-up ingest: independent source raised evidence; added a follow-up source. Follow-up ingest: added a follow-up source. Follow-up ingest: added a follow-up source.","methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","original_methodology_version_id":"8745541b-0f8e-4d5a-9b47-5291d3813a8d","published_at":"2026-09-17T04:00:51.231Z","created_at":"2026-09-17T04:00:51.231Z","updated_at":"2026-10-05T19:01:05.256Z","flags":[],"domain_name":"Other","domain_slug":"other","methodology_version":"0.2"},"contributions":[{"id":"b90aad54-cc11-4773-b095-5bac33c829f8","event_id":"8a68a04d-4e57-4fdc-884a-1fc0a5ee8780","model_id":"535014f7-c941-4d39-aa9b-8f9c1d3d07b7","role_description":"Unspecified system mentioned or implied by a news item. Remap to a named release when identified.","attribution_multiplier":"0.1000","credit_share":"1.0000","contribution_score":"0.005000","attribution_rationale":"News ingest does not infer a named model from the publisher alone. 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Under these practical constraints, we study whether textual captions can serve as reusable episodic memory. We define the Episodic Memory Video Caption QA task and introduce CapMem, a human-annotated benchmark with 75 videos totaling 33.7 hours, and 1,000 ","content_hash":null,"source_reliability_class":"medium","is_primary_source":true,"is_independent":true,"is_peer_reviewed":false,"archived_url":null,"created_at":"2026-09-17T04:00:51.246Z"},{"id":"56aac361-96b0-494e-ac5f-8e855801a6ca","event_id":"8a68a04d-4e57-4fdc-884a-1fc0a5ee8780","url":"https://arxiv.org/abs/2609.17731","canonical_url":"https://arxiv.org/abs/2609.17731","source_type":"preprint","publisher":"arXiv cs.AI","author":null,"publication_date":"2026-09-17T00:00:00.000Z","retrieved_at":"2026-09-17T04:00:51.697Z","title":"A Systematic Evaluation of the COTQ Provincial Land Cover Product: Structural Consistency, Spectral Separability, and Relative Positioning Against ESA, ESRI, and Google Products","excerpt":"arXiv:2609.17731v1 Announce Type: new \nAbstract: High-resolution land use and land cover (LULC) products derived from Sentinel-2 imagery are widely used for environmental monitoring and land management, yet their performance can vary across regions with complex ecological gradients and heterogeneous surface conditions. 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