{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7YTDWIM7RKAEGPLVOA5TXKCDQK","short_pith_number":"pith:7YTDWIM7","schema_version":"1.0","canonical_sha256":"fe263b219f8a80433d75703b3ba84382a8ad6d6c910f2ab1c3801b05f4583f8f","source":{"kind":"arxiv","id":"2502.02013","version":2},"attestation_state":"computed","paper":{"title":"Layer by Layer: Uncovering Hidden Representations in Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"Intermediate layers in language models often encode richer representations than the final layer for downstream tasks.","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Dan Zhao, Jalal Naghiyev, Md Rifat Arefin, Niket Patel, Oscar Skean, Ravid Shwartz-Ziv, Yann LeCun","submitted_at":"2025-02-04T05:03:42Z","abstract_excerpt":"From extracting features to generating text, the outputs of large language models (LLMs) typically rely on the final layers, following the conventional wisdom that earlier layers capture only low-level cues. However, our analysis shows that intermediate layers can encode even richer representations, often improving performance on a range of downstream tasks. To explain and quantify these hidden-layer properties, we propose a unified framework of representation quality metrics based on information theory, geometry, and invariance to input perturbations. Our framework highlights how each layer b"},"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":true,"formal_links_present":true},"canonical_record":{"source":{"id":"2502.02013","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T05:03:42Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"9a78a8101cc4fdc413f47784f866b9506c7f104983fbe8b6d18d8b7782bb0377","abstract_canon_sha256":"a12f5f2a407f00d1293ae8d608f79d7f44ea909c8948eb34baa5bdaf7cb40d41"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:50.917031Z","signature_b64":"DIMIDjDO4dheiJKFR84HIuHEkB/PuuOMjrKWD0DxvVfb2qGx296LqBmXlgaKaCyhieEHGE/kOIFTIoOZs/R1BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe263b219f8a80433d75703b3ba84382a8ad6d6c910f2ab1c3801b05f4583f8f","last_reissued_at":"2026-05-17T23:38:50.916364Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:50.916364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Layer by Layer: Uncovering Hidden Representations in Language Models","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"Intermediate layers in language models often encode richer representations than the final layer for downstream tasks.","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Dan Zhao, Jalal Naghiyev, Md Rifat Arefin, Niket Patel, Oscar Skean, Ravid Shwartz-Ziv, Yann LeCun","submitted_at":"2025-02-04T05:03:42Z","abstract_excerpt":"From extracting features to generating text, the outputs of large language models (LLMs) typically rely on the final layers, following the conventional wisdom that earlier layers capture only low-level cues. However, our analysis shows that intermediate layers can encode even richer representations, often improving performance on a range of downstream tasks. To explain and quantify these hidden-layer properties, we propose a unified framework of representation quality metrics based on information theory, geometry, and invariance to input perturbations. Our framework highlights how each layer b"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"our analysis shows that intermediate layers can encode even richer representations, often improving performance on a range of downstream tasks... intermediate layers consistently provide stronger features, challenging the standard view on final-layer embeddings","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"that the proposed metrics based on information theory, geometry, and invariance to input perturbations accurately capture representation quality relevant to downstream task performance","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Intermediate layers in LLMs consistently provide stronger features than final layers across tasks and architectures, as quantified by a new framework of information-theoretic, geometric, and invariance metrics.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Intermediate layers in language models often encode richer representations than the final layer for downstream tasks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"dc8b59d945f23b502183cf63228d93b678d15de36e79b0be330c750dd4d9f48f"},"source":{"id":"2502.02013","kind":"arxiv","version":2},"verdict":{"id":"e9508d18-ead8-4936-a86f-0f4c9811f422","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T16:25:43.473004Z","strongest_claim":"our analysis shows that intermediate layers can encode even richer representations, often improving performance on a range of downstream tasks... intermediate layers consistently provide stronger features, challenging the standard view on final-layer embeddings","one_line_summary":"Intermediate layers in LLMs consistently provide stronger features than final layers across tasks and architectures, as quantified by a new framework of information-theoretic, geometric, and invariance metrics.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"that the proposed metrics based on information theory, geometry, and invariance to input perturbations accurately capture representation quality relevant to downstream task performance","pith_extraction_headline":"Intermediate layers in language models often encode richer representations than the final layer for downstream tasks."},"references":{"count":173,"sample":[{"doi":"","year":2022,"title":"Agrawal, K. K., Mondal, A. K., Ghosh, A., and Richards, B. - ReQ : Assessing representation quality in self-supervised learning by measuring eigenspectrum decay. NeurIPs, 2022","work_id":"8fe0fb88-86eb-414b-b26d-362bdcf0cf9e","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Alain, G. and Bengio, Y. Understanding intermediate layers using linear classifier probes. ICLR, 2017","work_id":"a916bb7d-1cf2-481f-88c9-5d8cc9240c10","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"R., Subbaraj, G., Gontier, N., LeCun, Y., Rish, I., Shwartz-Ziv, R., and Pal, C","work_id":"b4a8c8bf-2a6e-433d-8438-d1898900a6ac","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Information theory with kernel methods","work_id":"cd38bb25-b39e-4543-8a17-5c6e2db8d88b","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"BeIT : Bert pre-training of image transformers","work_id":"c29e2625-5fb6-47c4-b15e-ad4380212f00","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":173,"snapshot_sha256":"4fc9c8e093cc20e5ad7db16f3600410d3e100fea8d14d137fc6435efbe234874","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"52234e52e1e85226baf11be515a74ec46aff0d131f1979ad36a5a9b8285dd307"},"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":"2502.02013","created_at":"2026-05-17T23:38:50.916464+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.02013v2","created_at":"2026-05-17T23:38:50.916464+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02013","created_at":"2026-05-17T23:38:50.916464+00:00"},{"alias_kind":"pith_short_12","alias_value":"7YTDWIM7RKAE","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"7YTDWIM7RKAEGPLV","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"7YTDWIM7","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":29,"internal_anchor_count":29,"sample":[{"citing_arxiv_id":"2605.23033","citing_title":"Uncovering the Latent Potential of Deep Intermediate Representations","ref_index":40,"is_internal_anchor":true},{"citing_arxiv_id":"2605.14738","citing_title":"TAPIOCA: Why Task- Aware Pruning Improves OOD model Capability","ref_index":1,"is_internal_anchor":true},{"citing_arxiv_id":"2511.06516","citing_title":"You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations","ref_index":53,"is_internal_anchor":true},{"citing_arxiv_id":"2605.12765","citing_title":"Inference-Time Machine Unlearning via Gated Activation Redirection","ref_index":37,"is_internal_anchor":true},{"citing_arxiv_id":"2605.17084","citing_title":"Scale Determines Whether Language Models Organize Representation Geometry for Prediction","ref_index":9,"is_internal_anchor":true},{"citing_arxiv_id":"2507.05387","citing_title":"The Generalization Ridge: Information Flow in Natural Language Generation","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2601.21619","citing_title":"On the Overscaling Curse of Parallel Thinking: System Efficacy Contradicts Sample Efficiency","ref_index":23,"is_internal_anchor":true},{"citing_arxiv_id":"2602.21750","citing_title":"From Words to Amino Acids: Does the Curse of Depth Persist?","ref_index":32,"is_internal_anchor":true},{"citing_arxiv_id":"2603.07475","citing_title":"A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2603.12451","citing_title":"Overcoming the Modality Gap in Context-Aided Forecasting","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2605.12714","citing_title":"Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs","ref_index":60,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11739","citing_title":"Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation","ref_index":78,"is_internal_anchor":true},{"citing_arxiv_id":"2605.12765","citing_title":"Inference-Time Machine Unlearning via Gated Activation Redirection","ref_index":37,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11856","citing_title":"UniVLR: Unifying Text and Vision in Visual Latent Reasoning for Multimodal LLMs","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2601.03233","citing_title":"LTX-2: Efficient Joint Audio-Visual Foundation Model","ref_index":26,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11739","citing_title":"Learning to Foresee: Unveiling the Unlocking Efficiency of On-Policy Distillation","ref_index":78,"is_internal_anchor":true},{"citing_arxiv_id":"2605.09430","citing_title":"FlashAR: Efficient Post-Training Acceleration for Autoregressive Image Generation","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11808","citing_title":"Mitigating Action-Relation Hallucinations in LVLMs via Relation-aware Visual Enhancement","ref_index":40,"is_internal_anchor":true},{"citing_arxiv_id":"2604.27169","citing_title":"Semantic Structure of Feature Space in Large Language Models","ref_index":14,"is_internal_anchor":true},{"citing_arxiv_id":"2605.09430","citing_title":"FlashAR: Efficient Post-Training Acceleration for Autoregressive Image Generation","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2605.05668","citing_title":"Large Vision-Language Models Get Lost in Attention","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"2605.00226","citing_title":"Why Do LLMs Struggle in Strategic Play? Broken Links Between Observations, Beliefs, and Actions","ref_index":34,"is_internal_anchor":true},{"citing_arxiv_id":"2604.10448","citing_title":"Instruction Data Selection via Answer Divergence","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2604.09425","citing_title":"Do Vision Language Models Need to Process Image Tokens?","ref_index":12,"is_internal_anchor":true},{"citing_arxiv_id":"2604.06377","citing_title":"The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment","ref_index":58,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7YTDWIM7RKAEGPLVOA5TXKCDQK","json":"https://pith.science/pith/7YTDWIM7RKAEGPLVOA5TXKCDQK.json","graph_json":"https://pith.science/api/pith-number/7YTDWIM7RKAEGPLVOA5TXKCDQK/graph.json","events_json":"https://pith.science/api/pith-number/7YTDWIM7RKAEGPLVOA5TXKCDQK/events.json","paper":"https://pith.science/paper/7YTDWIM7"},"agent_actions":{"view_html":"https://pith.science/pith/7YTDWIM7RKAEGPLVOA5TXKCDQK","download_json":"https://pith.science/pith/7YTDWIM7RKAEGPLVOA5TXKCDQK.json","view_paper":"https://pith.science/paper/7YTDWIM7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.02013&json=true","fetch_graph":"https://pith.science/api/pith-number/7YTDWIM7RKAEGPLVOA5TXKCDQK/graph.json","fetch_events":"https://pith.science/api/pith-number/7YTDWIM7RKAEGPLVOA5TXKCDQK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7YTDWIM7RKAEGPLVOA5TXKCDQK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7YTDWIM7RKAEGPLVOA5TXKCDQK/action/storage_attestation","attest_author":"https://pith.science/pith/7YTDWIM7RKAEGPLVOA5TXKCDQK/action/author_attestation","sign_citation":"https://pith.science/pith/7YTDWIM7RKAEGPLVOA5TXKCDQK/action/citation_signature","submit_replication":"https://pith.science/pith/7YTDWIM7RKAEGPLVOA5TXKCDQK/action/replication_record"}},"created_at":"2026-05-17T23:38:50.916464+00:00","updated_at":"2026-05-17T23:38:50.916464+00:00"}