{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KBVO7DIZNH7RMBAOZXFWBBO6OJ","short_pith_number":"pith:KBVO7DIZ","schema_version":"1.0","canonical_sha256":"506aef8d1969ff16040ecdcb6085de724c9e961841010659a6349dbdf65601a8","source":{"kind":"arxiv","id":"2404.16710","version":4},"attestation_state":"computed","paper":{"title":"LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Ahmed A Aly, Ahmed Roman, Akshat Shrivastava, Anas Mahmoud, Basil Hosmer, Beidi Chen, Bilge Acun, Bram Wasti, Carole-Jean Wu, Diana Liskovich, Liangzhen Lai, Mostafa Elhoushi, Saurabh Agarwal","submitted_at":"2024-04-25T16:20:23Z","abstract_excerpt":"We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2404.16710","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-25T16:20:23Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"f933beee5ac1ee3830f1018c8042b30de17b9664e42fa3ce812e197cfe750939","abstract_canon_sha256":"ca8a564d1b659630ab105f08d5bc4d9154e5a219428bd28ecb0bffb4e579c20f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:22:17.582496Z","signature_b64":"JOC3F2uH0vfvz/IadQ/eaaTec0p0Q88+zM/nNcGrz0rTQpuAnHJ4QflC4j6SrUqPitPwxfLneDbxJJS6EG/4BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"506aef8d1969ff16040ecdcb6085de724c9e961841010659a6349dbdf65601a8","last_reissued_at":"2026-07-05T09:22:17.582059Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:22:17.582059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Ahmed A Aly, Ahmed Roman, Akshat Shrivastava, Anas Mahmoud, Basil Hosmer, Beidi Chen, Bilge Acun, Bram Wasti, Carole-Jean Wu, Diana Liskovich, Liangzhen Lai, Mostafa Elhoushi, Saurabh Agarwal","submitted_at":"2024-04-25T16:20:23Z","abstract_excerpt":"We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.16710","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2404.16710/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2404.16710","created_at":"2026-07-05T09:22:17.582115+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.16710v4","created_at":"2026-07-05T09:22:17.582115+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.16710","created_at":"2026-07-05T09:22:17.582115+00:00"},{"alias_kind":"pith_short_12","alias_value":"KBVO7DIZNH7R","created_at":"2026-07-05T09:22:17.582115+00:00"},{"alias_kind":"pith_short_16","alias_value":"KBVO7DIZNH7RMBAO","created_at":"2026-07-05T09:22:17.582115+00:00"},{"alias_kind":"pith_short_8","alias_value":"KBVO7DIZ","created_at":"2026-07-05T09:22:17.582115+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":14,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23670","citing_title":"Tapered Language Models","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2606.10935","citing_title":"CLP: Collocation-Length Prediction for Zero-Loss Adaptive Multi-Token Inference","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27538","citing_title":"The Context-Ready Transformer","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30217","citing_title":"Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28678","citing_title":"DREAM-R: Multimodal Speculative Reasoning with RL-Based Refined Drafting, Precise Verification, and Fully Parallel Execution","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2503.14075","citing_title":"Growing a Multi-head Twig via Distillation and Reinforcement Learning to Accelerate Large Vision-Language Models","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2510.12773","citing_title":"Dr.LLM: Dynamic Layer Routing in LLMs","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2509.08318","citing_title":"CalexNet: Soft Cascade-Aligned Training and Calibration for Lightweight Early-Exit Branches","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2509.24328","citing_title":"Speculative Verification: Exploiting Information Gain to Refine Speculative Decoding","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2502.05171","citing_title":"Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10875","citing_title":"Compute Where it Counts: Self Optimizing Language Models","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01106","citing_title":"Component-Aware Self-Speculative Decoding in Hybrid Language Models","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21026","citing_title":"MCAP: Deployment-Time Layer Profiling for Memory-Constrained LLM Inference","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20503","citing_title":"FASER: Fine-Grained Phase Management for Speculative Decoding in Dynamic LLM Serving","ref_index":12,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KBVO7DIZNH7RMBAOZXFWBBO6OJ","json":"https://pith.science/pith/KBVO7DIZNH7RMBAOZXFWBBO6OJ.json","graph_json":"https://pith.science/api/pith-number/KBVO7DIZNH7RMBAOZXFWBBO6OJ/graph.json","events_json":"https://pith.science/api/pith-number/KBVO7DIZNH7RMBAOZXFWBBO6OJ/events.json","paper":"https://pith.science/paper/KBVO7DIZ"},"agent_actions":{"view_html":"https://pith.science/pith/KBVO7DIZNH7RMBAOZXFWBBO6OJ","download_json":"https://pith.science/pith/KBVO7DIZNH7RMBAOZXFWBBO6OJ.json","view_paper":"https://pith.science/paper/KBVO7DIZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.16710&json=true","fetch_graph":"https://pith.science/api/pith-number/KBVO7DIZNH7RMBAOZXFWBBO6OJ/graph.json","fetch_events":"https://pith.science/api/pith-number/KBVO7DIZNH7RMBAOZXFWBBO6OJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KBVO7DIZNH7RMBAOZXFWBBO6OJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KBVO7DIZNH7RMBAOZXFWBBO6OJ/action/storage_attestation","attest_author":"https://pith.science/pith/KBVO7DIZNH7RMBAOZXFWBBO6OJ/action/author_attestation","sign_citation":"https://pith.science/pith/KBVO7DIZNH7RMBAOZXFWBBO6OJ/action/citation_signature","submit_replication":"https://pith.science/pith/KBVO7DIZNH7RMBAOZXFWBBO6OJ/action/replication_record"}},"created_at":"2026-07-05T09:22:17.582115+00:00","updated_at":"2026-07-05T09:22:17.582115+00:00"}