{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NRUDAJ65J47VYHCUTC3KKNZ3DF","short_pith_number":"pith:NRUDAJ65","schema_version":"1.0","canonical_sha256":"6c683027dd4f3f5c1c5498b6a5373b1968ba25b3c53307f3264a913496a2cfdb","source":{"kind":"arxiv","id":"2412.13171","version":1},"attestation_state":"computed","paper":{"title":"Compressed Chain of Thought: Efficient Reasoning Through Dense Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Language models can reason more accurately by generating compressed continuous tokens that stand in for full reasoning chains.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Benjamin Van Durme, Jeffrey Cheng","submitted_at":"2024-12-17T18:50:33Z","abstract_excerpt":"Chain-of-thought (CoT) decoding enables language models to improve reasoning performance at the cost of high generation latency in decoding. Recent proposals have explored variants of contemplation tokens, a term we introduce that refers to special tokens used during inference to allow for extra computation. Prior work has considered fixed-length sequences drawn from a discrete set of embeddings as contemplation tokens. Here we propose Compressed Chain-of-Thought (CCoT), a framework to generate contentful and continuous contemplation tokens of variable sequence length. The generated contemplat"},"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":"2412.13171","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-12-17T18:50:33Z","cross_cats_sorted":[],"title_canon_sha256":"c90be175ba0892e0bb9f340f2945ab6ac8db699543ec10a296369e88e1c85c64","abstract_canon_sha256":"c5c44621015278e87fe24c7e5eb44ccc9cdd13eafe424e2424c51a0f8874ed69"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:15.085595Z","signature_b64":"gei5leovLiaIh4VkV0GnpZOqwEaNipbkVeKpXif126DHF0Fj3Y5RWEr/tAkb+23wAExkIEsbZI/m7bRcmLKIDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c683027dd4f3f5c1c5498b6a5373b1968ba25b3c53307f3264a913496a2cfdb","last_reissued_at":"2026-05-17T23:38:15.084912Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:15.084912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Compressed Chain of Thought: Efficient Reasoning Through Dense Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Language models can reason more accurately by generating compressed continuous tokens that stand in for full reasoning chains.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Benjamin Van Durme, Jeffrey Cheng","submitted_at":"2024-12-17T18:50:33Z","abstract_excerpt":"Chain-of-thought (CoT) decoding enables language models to improve reasoning performance at the cost of high generation latency in decoding. Recent proposals have explored variants of contemplation tokens, a term we introduce that refers to special tokens used during inference to allow for extra computation. Prior work has considered fixed-length sequences drawn from a discrete set of embeddings as contemplation tokens. Here we propose Compressed Chain-of-Thought (CCoT), a framework to generate contentful and continuous contemplation tokens of variable sequence length. The generated contemplat"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Through experiments, we illustrate how CCoT enables additional reasoning over dense contentful representations to achieve corresponding improvements in accuracy. Moreover, the reasoning improvements can be adaptively modified on demand by controlling the number of contemplation tokens generated.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the generated continuous contemplation tokens actually encode and preserve the semantic content of explicit reasoning chains rather than functioning primarily as additional learned parameters or regularizers whose benefit is not tied to interpretable reasoning.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"CCoT generates variable-length continuous contemplation tokens that compress explicit reasoning chains, enabling additional dense reasoning and accuracy gains in off-the-shelf language models while allowing adaptive control of token count.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Language models can reason more accurately by generating compressed continuous tokens that stand in for full reasoning chains.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b938dfd0d3b94388959f5e33794de953f2e4ff4ea69a5650fc8c8898921d4b07"},"source":{"id":"2412.13171","kind":"arxiv","version":1},"verdict":{"id":"7b174ffb-b384-4af3-9706-d006f0f547de","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T04:44:18.665036Z","strongest_claim":"Through experiments, we illustrate how CCoT enables additional reasoning over dense contentful representations to achieve corresponding improvements in accuracy. Moreover, the reasoning improvements can be adaptively modified on demand by controlling the number of contemplation tokens generated.","one_line_summary":"CCoT generates variable-length continuous contemplation tokens that compress explicit reasoning chains, enabling additional dense reasoning and accuracy gains in off-the-shelf language models while allowing adaptive control of token count.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the generated continuous contemplation tokens actually encode and preserve the semantic content of explicit reasoning chains rather than functioning primarily as additional learned parameters or regularizers whose benefit is not tied to interpretable reasoning.","pith_extraction_headline":"Language models can reason more accurately by generating compressed continuous tokens that stand in for full reasoning chains."},"references":{"count":23,"sample":[{"doi":"","year":2006,"title":"arXiv preprint arXiv:2006.11527 , year=","work_id":"1be92d31-396f-48a6-bc33-0ae9363a096c","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Training Verifiers to Solve Math Word Problems","work_id":"acab1aa8-b4d6-40e0-a3ee-25341701dca2","ref_index":2,"cited_arxiv_id":"2110.14168","is_internal_anchor":true},{"doi":"","year":null,"title":"Implicit chain of thought reasoning via knowledge distillation","work_id":"9fdf9899-15b3-4c02-93fb-f7bb42f8f2d6","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step","work_id":"70541c9d-c3f3-48e4-be00-e7de1622f612","ref_index":4,"cited_arxiv_id":"2405.14838","is_internal_anchor":true},{"doi":"","year":null,"title":"In-context autoencoder for context compression in a large language model.arXiv preprint arXiv:2307.06945","work_id":"20a3380e-2b43-4509-a62c-260b1efae1c8","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":23,"snapshot_sha256":"03758753a25140aeeacf43ab0d5e2d008b16a38c942fefbc4f0daa098498ce85","internal_anchors":10},"formal_canon":{"evidence_count":2,"snapshot_sha256":"44764b041e36faa2c28696dfbc06f0c284413dff4a66788264c6fc50a1c59dfe"},"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":"2412.13171","created_at":"2026-05-17T23:38:15.085019+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.13171v1","created_at":"2026-05-17T23:38:15.085019+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.13171","created_at":"2026-05-17T23:38:15.085019+00:00"},{"alias_kind":"pith_short_12","alias_value":"NRUDAJ65J47V","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"NRUDAJ65J47VYHCU","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"NRUDAJ65","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":26,"internal_anchor_count":26,"sample":[{"citing_arxiv_id":"2605.23872","citing_title":"Training-Free Looped Transformers","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2501.19201","citing_title":"Efficient Reasoning with Hidden Thinking","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2605.21260","citing_title":"On the Cost and Benefit of Chain of Thought: A Learning-Theoretic Perspective","ref_index":25,"is_internal_anchor":true},{"citing_arxiv_id":"2510.03206","citing_title":"Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2502.21074","citing_title":"CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation","ref_index":86,"is_internal_anchor":true},{"citing_arxiv_id":"2509.24251","citing_title":"Latent Visual Reasoning","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2603.08899","citing_title":"ConFu: Contemplate the Future for Better Speculative Sampling","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10426","citing_title":"CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving","ref_index":24,"is_internal_anchor":true},{"citing_arxiv_id":"2503.16419","citing_title":"Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models","ref_index":20,"is_internal_anchor":true},{"citing_arxiv_id":"2604.03307","citing_title":"V-Reflection: Transforming MLLMs from Passive Observers to Active Interrogators","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2604.03679","citing_title":"LightThinker++: From Reasoning Compression to Memory Management","ref_index":18,"is_internal_anchor":true},{"citing_arxiv_id":"2604.10500","citing_title":"Visual Enhanced Depth Scaling for Multimodal Latent Reasoning","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2502.05171","citing_title":"Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach","ref_index":31,"is_internal_anchor":true},{"citing_arxiv_id":"2604.18486","citing_title":"Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation","ref_index":16,"is_internal_anchor":true},{"citing_arxiv_id":"2605.09104","citing_title":"Token Economics for LLM Agents: A Dual-View Study from Computing and Economics","ref_index":43,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10426","citing_title":"CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving","ref_index":24,"is_internal_anchor":true},{"citing_arxiv_id":"2604.10500","citing_title":"Visual Enhanced Depth Scaling for Multimodal Latent Reasoning","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2605.06165","citing_title":"Post Reasoning: Improving the Performance of Non-Thinking Models at No Cost","ref_index":78,"is_internal_anchor":true},{"citing_arxiv_id":"2605.06285","citing_title":"LatentRAG: Latent Reasoning and Retrieval for Efficient Agentic RAG","ref_index":28,"is_internal_anchor":true},{"citing_arxiv_id":"2604.22709","citing_title":"Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2604.10500","citing_title":"Visual Enhanced Depth Scaling for Multimodal Latent Reasoning","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2604.09757","citing_title":"MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2604.08299","citing_title":"SeLaR: Selective Latent Reasoning in Large Language Models","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2604.18486","citing_title":"Xiaomi OneVL: One-Step Latent Reasoning and Planning with Vision-Language Explanation","ref_index":16,"is_internal_anchor":true},{"citing_arxiv_id":"2604.17892","citing_title":"LEPO: Latent Reasoning Policy Optimization for Large Language Models","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NRUDAJ65J47VYHCUTC3KKNZ3DF","json":"https://pith.science/pith/NRUDAJ65J47VYHCUTC3KKNZ3DF.json","graph_json":"https://pith.science/api/pith-number/NRUDAJ65J47VYHCUTC3KKNZ3DF/graph.json","events_json":"https://pith.science/api/pith-number/NRUDAJ65J47VYHCUTC3KKNZ3DF/events.json","paper":"https://pith.science/paper/NRUDAJ65"},"agent_actions":{"view_html":"https://pith.science/pith/NRUDAJ65J47VYHCUTC3KKNZ3DF","download_json":"https://pith.science/pith/NRUDAJ65J47VYHCUTC3KKNZ3DF.json","view_paper":"https://pith.science/paper/NRUDAJ65","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.13171&json=true","fetch_graph":"https://pith.science/api/pith-number/NRUDAJ65J47VYHCUTC3KKNZ3DF/graph.json","fetch_events":"https://pith.science/api/pith-number/NRUDAJ65J47VYHCUTC3KKNZ3DF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NRUDAJ65J47VYHCUTC3KKNZ3DF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NRUDAJ65J47VYHCUTC3KKNZ3DF/action/storage_attestation","attest_author":"https://pith.science/pith/NRUDAJ65J47VYHCUTC3KKNZ3DF/action/author_attestation","sign_citation":"https://pith.science/pith/NRUDAJ65J47VYHCUTC3KKNZ3DF/action/citation_signature","submit_replication":"https://pith.science/pith/NRUDAJ65J47VYHCUTC3KKNZ3DF/action/replication_record"}},"created_at":"2026-05-17T23:38:15.085019+00:00","updated_at":"2026-05-17T23:38:15.085019+00:00"}