{"as_of":"2026-08-21T14:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:72216669e1bb74ea8c204bb01fc5027ae688c2931917b5c1b730a4904f5e94e1","coverage":[{"denominator":45,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":45,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:08:59.967867Z","state":"measured"},{"denominator":45,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":45,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.10782/citation-record","integrity":"/paper/2505.10782/integrity","json":"/paper/2505.10782/citation-record.json","paper":"/paper/2505.10782"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.803809Z","title":"A survey on multimodal large language models for autonomous driving,","venue":null,"work_id":"46b842bb-6348-4089-ad3e-44919b0ebe4a","year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.713697Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:5443e50eda9cd8b46bb6e70f8655a07bb1c87a33d6301326c9454b20cbcef80b","observation_id":"e55f02d6-a953-4b4d-9b8e-e3beb00f404d","resolution":{"observed_at":"2026-08-15T21:09:00.809601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.06886","last_updated":"2025-08-25T02:20:09Z","snapshot_observed_at":"2026-08-17T00:52:37.891311Z","submitted_at":"2024-07-09T14:14:47Z","title":"Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.06886","snapshot_observed_at":"2026-08-15T21:08:59.720640Z","title":"Aligning cyber space with physical world: A com- prehensive survey on embodied ai,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.720640Z"},"links":{"cited_paper":"/paper/2407.06886","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:8d1880b77ca523c70815a9b9ccd17e8dc545ca9252613d0949e7679da1bfafc5","observation_id":"cda135b1-e89e-4f4b-b5be-afd144536842","resolution":{"observed_at":"2026-08-15T21:08:59.720640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.16886","last_updated":"2023-12-30T04:59:21Z","snapshot_observed_at":"2026-08-21T10:53:34.016934Z","submitted_at":"2023-12-28T08:21:24Z","title":"MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.16886","snapshot_observed_at":"2026-08-15T21:08:59.727174Z","title":"Mobilevlm: A fast, reproducible and strong vision language assistant for mobile devices,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.727174Z"},"links":{"cited_paper":"/paper/2312.16886","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:394fce82b497fd9ddbc8e18c2a0f30382c3d843db47b2d537b3c43aa19a3cb6c","observation_id":"aead2e63-f361-4274-863c-3d6b06a59632","resolution":{"observed_at":"2026-08-15T21:08:59.727174Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.10542","last_updated":"2024-12-14T03:18:34Z","snapshot_observed_at":"2026-08-21T10:58:26.554681Z","submitted_at":"2024-09-01T12:09:33Z","title":"SAM4MLLM: Enhance Multi-Modal Large Language Model for Referring Expression Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.10542","snapshot_observed_at":"2026-08-15T21:08:59.732942Z","title":"Sam4mllm: Enhance multi-modal large language model for referring expression segmentation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.732942Z"},"links":{"cited_paper":"/paper/2409.10542","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:10cd72a77871d2c34b56fb97d5531b0053051c2334960e513a5ce09ee27056af","observation_id":"4623e4ae-0f1e-42c5-82b0-576452019351","resolution":{"observed_at":"2026-08-15T21:08:59.732942Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.787190Z","title":"Lmdrive: Closed-loop end-to-end driving with large language models,","venue":null,"work_id":"1fdeff31-0bab-4c26-961a-31d4605c0f43","year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.738669Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:235aba0085e02218f158698c0af9dfb946ed3858e264bd1165a4d1057fdeb3bf","observation_id":"2fe0d4e6-1d74-4c57-94cd-108dcb718945","resolution":{"observed_at":"2026-08-15T21:09:00.793113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.769567Z","title":"A 17–95.6 tops/w deep learning inference accelerator with per-vector scaled 4-bit quantization for transformers in 5nm,","venue":null,"work_id":"8ad7cc00-3345-473f-958c-318865fcae92","year":2022},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.744977Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:9f482d2931af3917d78bddcfc0f20b50698feb80bcf927fc360a0c30107dc859","observation_id":"d46bef63-fdbc-4467-a617-0be21a7cf847","resolution":{"observed_at":"2026-08-15T21:09:00.774986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.752126Z","title":"22.9 a 12nm 18.1tflops/w sparse transformer processor with entropy-based early exit, mixed-precision predication and fine- grained power management,","venue":null,"work_id":"616b1a9d-b86a-4881-a995-4aecf890ae97","year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.751115Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:03098d98e6198165c231cf2eaa5b267ef8e28b8df8424f306a11b43ff5c3005a","observation_id":"1d0e7c6c-7e23-4aa3-9fb8-dc48e6c0de77","resolution":{"observed_at":"2026-08-15T21:09:00.758379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.733879Z","title":"Hetegen: Efficient heterogeneous parallel inference for large language models on resource-constrained devices,","venue":null,"work_id":"5ca5ca3c-01af-4898-96b0-3c008fef2d88","year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.756512Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:c52848601a17b70ef8d3559a430582de233e52e91a663027946abab45e3a590d","observation_id":"161bdf0d-3d26-4b69-ab4f-c2bce746c1ba","resolution":{"observed_at":"2026-08-15T21:09:00.740407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.15552","last_updated":"2025-04-07T15:49:45Z","snapshot_observed_at":"2026-08-19T13:19:41.585520Z","submitted_at":"2023-06-27T15:24:24Z","title":"A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.15552","snapshot_observed_at":"2026-08-15T21:08:59.761887Z","title":"A survey on deep learning hardware accelerators for heterogeneous hpc platforms,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.761887Z"},"links":{"cited_paper":"/paper/2306.15552","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:42baf7afd579b2f515a552e8ad1cbcb26cab6f10afb778ee05c708badf55315e","observation_id":"3dc86209-cda5-4417-b3d1-c6aaf9c23707","resolution":{"observed_at":"2026-08-15T21:08:59.761887Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.716346Z","title":"Blueface: Integrating an accelerator into the core’s pipeline through algorithm-interface co- design for real-time socs,","venue":null,"work_id":"14769785-7c38-4b33-a712-de5fbc86fba9","year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.767541Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:c61bd4c21b7782b6432e77a129cad82da55eb3e84a3481f4d17eaa49587536cf","observation_id":"4adad00e-19b0-4674-b99f-2c9c4a187636","resolution":{"observed_at":"2026-08-15T21:09:00.722879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.699410Z","title":null,"venue":null,"work_id":"ef15e44d-dd6c-45a3-83d2-afbb01fe78d1","year":null},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.773883Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:62405d5f4fd428cb81ab387ade8f3cdc493a1bd1f7a7313e492d0a5bbf712dcb","observation_id":"fb586d93-5528-483d-a2c5-2523dfc57b67","resolution":{"observed_at":"2026-08-15T21:09:00.704454Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.679698Z","title":"Sapphire rapids: The next-generation intel xeon scal- able processor,","venue":null,"work_id":"6fd2d639-bc7c-47d9-b704-ac836629b513","year":2022},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.779349Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:9366894593c43fab81756f42f50eba5ed32f2b7d26a7c56959bd36d410a1663f","observation_id":"57570916-7428-4481-b528-76f2cf8781b0","resolution":{"observed_at":"2026-08-15T21:09:00.686065Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.661824Z","title":"Intel accelerators ecosystem: An soc-oriented perspec- tive,","venue":null,"work_id":"416b4104-341d-43dc-8d32-beb8fa2d9931","year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.785646Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:855420eaf22b8a178732ca1ecedbd8c08a91e50e487dc677c36ca434e083ef6e","observation_id":"e905ebb0-9a45-46f0-9863-7d5dbb4a2718","resolution":{"observed_at":"2026-08-15T21:09:00.667335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.644369Z","title":null,"venue":null,"work_id":"88e9d6a3-f578-432e-893a-017e0c6d1313","year":null},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.791149Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:7e97be30db07bca73b475a4025959e6dc619b38762acc4ab85af0cbb71836dbe","observation_id":"cc29c1cc-28f4-47b7-87ce-e23ad534cb5c","resolution":{"observed_at":"2026-08-15T21:09:00.650174Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.626833Z","title":null,"venue":null,"work_id":"815915d4-9081-457f-a45d-29b81ed36de2","year":null},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.796862Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:f47f500167386f25a5d4fb5d4af7c0924d04538017b27837a3b005cd503bc48c","observation_id":"82167fdd-592e-4584-9668-ca31e1ea78b1","resolution":{"observed_at":"2026-08-15T21:09:00.633246Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.609527Z","title":"Generative multimodal models are in-context learners,","venue":null,"work_id":"84bdd505-fe78-44a7-ae0e-583af61cc3f9","year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.802563Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:c979dd8969dbcc051aafbf53c2003a144d2e3ee86006186f40ac2ced9959b8d6","observation_id":"2d14b5a9-cbbf-41c6-a4c1-906ef83fa017","resolution":{"observed_at":"2026-08-15T21:09:00.615188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.15389","last_updated":"2023-03-27T17:02:21Z","snapshot_observed_at":"2026-08-20T09:51:02.717471Z","submitted_at":"2023-03-27T17:02:21Z","title":"EVA-CLIP: Improved Training Techniques for CLIP at Scale","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.15389","snapshot_observed_at":"2026-08-15T21:08:59.808197Z","title":"Eva-clip: Improved training techniques for clip at scale,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.808197Z"},"links":{"cited_paper":"/paper/2303.15389","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:6246e5aa38b29bc2729ae8870ebfaff0d4f83e7a76c7a1eaa53d2e0709b8d391","observation_id":"866eb2e2-1079-44cf-94ea-4e0cb73e7baa","resolution":{"observed_at":"2026-08-15T21:08:59.808197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:08:59.814071Z","title":"Visual instruction tuning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.814071Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:4efb141aea80927d8da997c6f9d568d059b464b97d17624931962cf0ed250019","observation_id":"4a12e346-7b85-4170-ad92-d39e3cb2f4a0","resolution":{"observed_at":"2026-08-15T21:08:59.814071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.575486Z","title":"Learning transferable visual models from natural lan- guage supervision,","venue":null,"work_id":"1859f546-fd6d-4320-af07-3bd15c25559d","year":2021},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.819738Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:26f4c0bd67846fcc9369a6f1c07ffc7914cfc66f339a8befc8171f50a9691f5d","observation_id":"4ae4d48e-a35c-46a7-8eac-13de2b1a7c29","resolution":{"observed_at":"2026-08-15T21:09:00.587567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.558932Z","title":"Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, march 2023,","venue":null,"work_id":"575c9e43-a1c5-40b3-8d2e-29ca3cb286cd","year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.825467Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:8e680be841ebc9c6e3deb74d6e20195e25af1ecc3fcae715f5603adaafb7b90a","observation_id":"0b05c7d3-efd1-45d2-a106-34ba268cf450","resolution":{"observed_at":"2026-08-15T21:09:00.564547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03766","last_updated":"2024-02-06T07:16:36Z","snapshot_observed_at":"2026-08-20T12:23:13.963013Z","submitted_at":"2024-02-06T07:16:36Z","title":"MobileVLM V2: Faster and Stronger Baseline for Vision Language Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03766","snapshot_observed_at":"2026-08-15T21:08:59.831451Z","title":"Mobilevlm v2: Faster and stronger baseline for vision language model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.831451Z"},"links":{"cited_paper":"/paper/2402.03766","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:5f774e51d0cd4ad0e1c3a4e4ad58af7a2438a315349c6033ab14a85d78be572a","observation_id":"6c6a3cb5-88f0-4d77-a250-f7c3b484f8c2","resolution":{"observed_at":"2026-08-15T21:08:59.831451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.16862","last_updated":"2024-06-21T07:08:59Z","snapshot_observed_at":"2026-08-19T13:39:19.209645Z","submitted_at":"2023-12-28T07:11:41Z","title":"TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.16862","snapshot_observed_at":"2026-08-15T21:08:59.838409Z","title":"Tinygpt-v: Efficient multimodal large language model via small backbones,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.838409Z"},"links":{"cited_paper":"/paper/2312.16862","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:ac0ed75771b1acb7e2f2e912cc11b3e4f4888c86b99eb83129efdc33b4513460","observation_id":"ca995e71-5c60-4969-b821-c0713ddc9d8d","resolution":{"observed_at":"2026-08-15T21:08:59.838409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.540311Z","title":"Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,","venue":null,"work_id":"d4eb465f-ab69-4a31-8b64-1363305b566b","year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.845035Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:b74256eab54991be3d07e03046c63fa8a6c68533d871ea3a61bc3b96ccdd84ef","observation_id":"5147dd3e-fd12-45db-9c20-ddce0372dc7f","resolution":{"observed_at":"2026-08-15T21:09:00.546354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.521726Z","title":"Phi-2: The surprising power of small language models,","venue":null,"work_id":"963c3d5a-43f8-4a7a-a1fb-1d9716cd6a80","year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.851515Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:b307037c1d21341c51b9e30839080574cdf66cb40f61af17f8e2e965cca6ca0a","observation_id":"3575af4b-7605-4411-83c5-3e6506ed4e46","resolution":{"observed_at":"2026-08-15T21:09:00.528956Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05935","last_updated":"2025-03-21T10:19:01Z","snapshot_observed_at":"2026-08-18T00:33:51.822601Z","submitted_at":"2024-02-08T18:59:48Z","title":"SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05935","snapshot_observed_at":"2026-08-15T21:08:59.857272Z","title":"Sphinx-x: Scaling data and parameters for a family of multi-modal large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.857272Z"},"links":{"cited_paper":"/paper/2402.05935","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:61224464a5802a801b00081c71ab0dff03f38701577b965604b941e940af6eee","observation_id":"a5a5f427-2944-4b03-a669-1447ad160854","resolution":{"observed_at":"2026-08-15T21:08:59.857272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-17T13:03:40.359628Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-15T21:08:59.863419Z","title":"Dinov2: Learning robust visual features without supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.863419Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:9d56a79c3fc0de05c3284eb44b61592d89cfba5c68b2d391d88e7cad9f49ac51","observation_id":"9a783c36-ba94-4b8e-ae3b-aad1f24327da","resolution":{"observed_at":"2026-08-15T21:08:59.863419Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02385","last_updated":"2024-06-04T02:05:30Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-01-04T17:54:59Z","title":"TinyLlama: An Open-Source Small Language Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02385","snapshot_observed_at":"2026-08-15T21:08:59.868956Z","title":"Tinyllama: An open-source small language model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.868956Z"},"links":{"cited_paper":"/paper/2401.02385","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:5b6a0df6a7dd096ec7b50739270ab62339e9b6521a1200818182ec94538f559e","observation_id":"23c43ede-cecf-4d68-af09-7ba35291d5c2","resolution":{"observed_at":"2026-08-15T21:08:59.868956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05525","last_updated":"2024-03-11T16:47:41Z","snapshot_observed_at":"2026-08-19T16:22:29.898436Z","submitted_at":"2024-03-08T18:46:00Z","title":"DeepSeek-VL: Towards Real-World Vision-Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05525","snapshot_observed_at":"2026-08-15T21:08:59.875147Z","title":"Deepseek-vl: towards real-world vision-language understanding,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.875147Z"},"links":{"cited_paper":"/paper/2403.05525","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:5c7ab666221e89db4725a2ce3f83a1d36a30fbe8e71dc90042bb68b5a2139b85","observation_id":"b77d90fc-1d5d-40cc-9353-0bf4d8fbbf44","resolution":{"observed_at":"2026-08-15T21:08:59.875147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.503367Z","title":"Sigmoid loss for language image pre-training,","venue":null,"work_id":"0df10f57-38c7-43bf-a160-55923243dc27","year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.881191Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:eeb830f39468d06916385e64cfea7a19b5c764c1526a34451d329929d17843a3","observation_id":"8295163a-a213-4db5-963c-61f385158b1e","resolution":{"observed_at":"2026-08-15T21:09:00.510211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.483326Z","title":"https://github.com/ thomas-yanxin/KarmaVLM, 2024","venue":null,"work_id":"04914366-0495-48e1-977c-1b3ba9827180","year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.886306Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:4f913c903c6894f34bc880d28fa4f3f918bda86fc47fb09ccb6cabbcae725f00","observation_id":"f96309bf-cd65-4706-9331-90fbaf0bf08e","resolution":{"observed_at":"2026-08-15T21:09:00.489811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16609","last_updated":"2023-09-28T17:07:49Z","snapshot_observed_at":"2026-08-20T15:31:01.041088Z","submitted_at":"2023-09-28T17:07:49Z","title":"Qwen Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.16609","snapshot_observed_at":"2026-08-15T21:08:59.892636Z","title":"Qwen technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.892636Z"},"links":{"cited_paper":"/paper/2309.16609","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:95fcdd3798f37fef46c739e8531ac36105ddc90cbcfc574f700c4616e50062ec","observation_id":"b2a0ca01-25f1-4766-ba57-f4c56656a1c4","resolution":{"observed_at":"2026-08-15T21:08:59.892636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-08-20T18:27:04.837880Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-15T21:08:59.898445Z","title":"Gemini: a family of highly capable multimodal models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.898445Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:139265f49a727ef779fac3db134bb4b5b98cda7b21f4233f67241eef6e3c9c26","observation_id":"5ef0f008-4fa6-475f-a89b-d4c157af22df","resolution":{"observed_at":"2026-08-15T21:08:59.898445Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-15T21:08:59.903769Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.903769Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:c71c51dbf1e8d01675bdd54e73cd32df73fa06daf52dc805939b90313f4c4aba","observation_id":"ef85ef08-1a8b-4e70-9339-24a237432a0e","resolution":{"observed_at":"2026-08-15T21:08:59.903769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.466161Z","title":"Improved baselines with visual instruction tuning,","venue":null,"work_id":"61b98dd0-37bd-4600-a2a3-deba32eadf63","year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.910236Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:6370f942448ce0369183edc6d5e19a9606fdb9f48c2848fd7945dc65194d1f62","observation_id":"78725f1d-e0bf-42ce-946a-fbe3abc9bb12","resolution":{"observed_at":"2026-08-15T21:09:00.471529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.449031Z","title":"Making the v in vqa matter: Elevating the role of image understanding in visual question answering,","venue":null,"work_id":"15ea0ccf-4553-4436-a2c4-57ac79e4e9dd","year":2019},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.915664Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:31373dcd5b5ff0a7c49cb0d52c0649b831f9b58d3b67a039ffe4a3d66881d1ba","observation_id":"219f60f8-56c6-49ca-b9b1-9e05988ca4f6","resolution":{"observed_at":"2026-08-15T21:09:00.454477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.09513","last_updated":"2022-10-17T07:46:47Z","snapshot_observed_at":"2026-08-19T12:38:24.427894Z","submitted_at":"2022-09-20T07:04:24Z","title":"Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.09513","snapshot_observed_at":"2026-08-15T21:08:59.920456Z","title":"Learn to explain: Multimodal reason- ing via thought chains for science question answering,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.920456Z"},"links":{"cited_paper":"/paper/2209.09513","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:b95dec9876ead47bd961842a8ce0e5bd9ed3525094ef372bf066088dded07649","observation_id":"4bb86683-20f2-4da2-a08b-8ac1e9a97d78","resolution":{"observed_at":"2026-08-15T21:08:59.920456Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.16125","last_updated":"2023-08-02T08:02:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-30T04:25:16Z","title":"SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.16125","snapshot_observed_at":"2026-08-15T21:08:59.926535Z","title":"Seed-bench: Benchmarking multimodal llms with generative comprehension,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.926535Z"},"links":{"cited_paper":"/paper/2307.16125","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:c01b54a805e9e5d0090d50b8fe168463c753d28876bc67672ad1cc15796162a1","observation_id":"79832185-419b-4fca-9fbc-6914c248d28d","resolution":{"observed_at":"2026-08-15T21:08:59.926535Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.430599Z","title":"Mmbench: Is your multi-modal model an all-around player?,","venue":null,"work_id":"c9932b6d-e713-4793-b2be-17a49ee141fc","year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.931660Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:8f36aac867248339e3343a2769065e126115911b3887b42fc6ae472dd4bffce0","observation_id":"81b8eb96-e791-404e-b5b9-44da71fda8fa","resolution":{"observed_at":"2026-08-15T21:09:00.436610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.14017","last_updated":"2023-02-27T18:18:13Z","snapshot_observed_at":"2026-08-19T14:54:56.848158Z","submitted_at":"2023-02-27T18:18:13Z","title":"Full Stack Optimization of Transformer Inference: a Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.14017","snapshot_observed_at":"2026-08-15T21:08:59.936655Z","title":"Full stack optimization of transformer inference: a survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.936655Z"},"links":{"cited_paper":"/paper/2302.14017","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:280907bf64012f0ca285852ec1ad7d44c68f1f636eb6b03f937697a65c6ec7eb","observation_id":"0c6e26bd-44e7-482e-a6d7-a681543d02d7","resolution":{"observed_at":"2026-08-15T21:08:59.936655Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-15T21:08:59.942603Z","title":"Llama 2: Open foundation and fine-tuned chat models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.942603Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:362333b8366e89886def3f73418c40785851778438c62db211586b9021875f01","observation_id":"6155361e-0497-41fb-a760-0cae93f7bba8","resolution":{"observed_at":"2026-08-15T21:08:59.942603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.06825","last_updated":"2023-10-10T17:54:58Z","snapshot_observed_at":"2026-08-17T20:30:34.016254Z","submitted_at":"2023-10-10T17:54:58Z","title":"Mistral 7B","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.06825","snapshot_observed_at":"2026-08-15T21:08:59.947708Z","title":"Mistral 7b,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.947708Z"},"links":{"cited_paper":"/paper/2310.06825","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:4a05be9f7708e0b40432cff7de8ed624efe3c608d16aa54d6de261d229d7a7a3","observation_id":"90573873-29e0-4b58-b210-a46426deb9b7","resolution":{"observed_at":"2026-08-15T21:08:59.947708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.14048","last_updated":"2023-12-18T19:10:00Z","snapshot_observed_at":"2026-08-20T21:36:51.212852Z","submitted_at":"2023-06-24T20:11:14Z","title":"H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.14048","snapshot_observed_at":"2026-08-15T21:08:59.953094Z","title":"H2o: Heavy- hitter oracle for efficient generative inference of large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.953094Z"},"links":{"cited_paper":"/paper/2306.14048","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:7abc9362737a474a4032e9601cef373876b267fee7b07d719b49a1741e5dfab9","observation_id":"09713d9d-77da-4c53-96fc-2cb8ec13a748","resolution":{"observed_at":"2026-08-15T21:08:59.953094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:09:00.411550Z","title":"Snitch: A tiny pseudo dual-issue processor for area and energy efficient execution of floating- point intensive workloads,","venue":null,"work_id":"7e046f4a-0791-4b1b-bfd6-8e0d9fe7074b","year":2020},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.958265Z"},"links":{"citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:0628695569d01e27d0a78996d3d65a16b55f686836b6ef557a6ad172c675c24c","observation_id":"bc40dc13-eed7-4148-af70-118779202a62","resolution":{"observed_at":"2026-08-15T21:09:00.418905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.11695","last_updated":"2024-05-06T17:47:01Z","snapshot_observed_at":"2026-08-20T04:15:03.506960Z","submitted_at":"2023-06-20T17:18:20Z","title":"A Simple and Effective Pruning Approach for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.11695","snapshot_observed_at":"2026-08-15T21:08:59.962787Z","title":"A simple and effective pruning approach for large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.962787Z"},"links":{"cited_paper":"/paper/2306.11695","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:dd9f199a149e5600cf14a4d9503e2e5fdabde883d7622fdfef1f5379ab491661","observation_id":"d9527fc6-078b-4862-b21d-97325a75440d","resolution":{"observed_at":"2026-08-15T21:08:59.962787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.08763","last_updated":"2024-11-03T10:25:47Z","snapshot_observed_at":"2026-08-16T14:01:20.113350Z","submitted_at":"2024-04-12T18:42:18Z","title":"CATS: Contextually-Aware Thresholding for Sparsity in Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.08763","snapshot_observed_at":"2026-08-15T21:08:59.967867Z","title":"Cats: Contextually-aware thresholding for sparsity in large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T21:08:59.967867Z"},"links":{"cited_paper":"/paper/2404.08763","citing_paper":"/paper/2505.10782"},"observation_digest":"sha256:417a97a0732c5df74ee604cb9974de99f2226898e86844af22ef8f5d2f47d373","observation_id":"23625ce6-77f0-4e34-b78c-111930edb60d","resolution":{"observed_at":"2026-08-15T21:08:59.967867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.10782","last_updated":"2025-05-16T01:46:37Z","latest_version":1,"primary_category":"cs.AR","snapshot_observed_at":"2026-08-19T17:49:23.167874Z","submitted_at":"2025-05-16T01:46:37Z","title":"EdgeMM: Multi-Core CPU with Heterogeneous AI-Extension and Activation-aware Weight Pruning for Multimodal LLMs at Edge"},"reference_resolution":{"displayed":45,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":26,"verified_exact":0,"verified_fuzzy":19},"total_outbound_references":45},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2505.10782."}