{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:T6Q27LRIBE3XCCEBEJFI74RTT4","short_pith_number":"pith:T6Q27LRI","schema_version":"1.0","canonical_sha256":"9fa1afae280937710881224a8ff2339f02ae0972ba6e947650ad90e9c71f5898","source":{"kind":"arxiv","id":"2302.01560","version":3},"attestation_state":"computed","paper":{"title":"Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"DEPS combines LLM-based interactive planning with a trainable goal selector to create a zero-shot multi-task agent that completes 70+ Minecraft tasks and nearly doubles prior performance.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anji Liu, Guanzhou Chen, Shaofei Cai, Xiaojian Ma, Yitao Liang, Zihao Wang","submitted_at":"2023-02-03T06:06:27Z","abstract_excerpt":"We investigate the challenge of task planning for multi-task embodied agents in open-world environments. Two main difficulties are identified: 1) executing plans in an open-world environment (e.g., Minecraft) necessitates accurate and multi-step reasoning due to the long-term nature of tasks, and 2) as vanilla planners do not consider how easy the current agent can achieve a given sub-task when ordering parallel sub-goals within a complicated plan, the resulting plan could be inefficient or even infeasible. To this end, we propose \"$\\underline{D}$escribe, $\\underline{E}$xplain, $\\underline{P}$"},"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":false},"canonical_record":{"source":{"id":"2302.01560","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-02-03T06:06:27Z","cross_cats_sorted":[],"title_canon_sha256":"e3fd620c9334ea0d3878e7ba3848de41d55fa299f2e33aaf50240dc0098199f3","abstract_canon_sha256":"2a168690c78dfe07b88973493558119fd2f0865dbabc19777c7e1122cd4b6b1b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:49.267639Z","signature_b64":"XaWcvTqaEn8DrQOOq1M9lPaDNouTmK8AYG1OhVU9k3bcIhewMVkwRfTp0svJzT2gHvVvuEaM+R7xg269EKvLBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9fa1afae280937710881224a8ff2339f02ae0972ba6e947650ad90e9c71f5898","last_reissued_at":"2026-05-17T23:38:49.267167Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:49.267167Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"DEPS combines LLM-based interactive planning with a trainable goal selector to create a zero-shot multi-task agent that completes 70+ Minecraft tasks and nearly doubles prior performance.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Anji Liu, Guanzhou Chen, Shaofei Cai, Xiaojian Ma, Yitao Liang, Zihao Wang","submitted_at":"2023-02-03T06:06:27Z","abstract_excerpt":"We investigate the challenge of task planning for multi-task embodied agents in open-world environments. Two main difficulties are identified: 1) executing plans in an open-world environment (e.g., Minecraft) necessitates accurate and multi-step reasoning due to the long-term nature of tasks, and 2) as vanilla planners do not consider how easy the current agent can achieve a given sub-task when ordering parallel sub-goals within a complicated plan, the resulting plan could be inefficient or even infeasible. To this end, we propose \"$\\underline{D}$escribe, $\\underline{E}$xplain, $\\underline{P}$"},"claims":{"count":3,"items":[{"kind":"strongest_claim","text":"Our experiments mark the milestone of the first zero-shot multi-task agent that can robustly accomplish 70+ Minecraft tasks and nearly double the overall performances.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That large language models can reliably produce accurate descriptions and self-explanations of plan failures without hallucination, and that the trained goal selector generalizes to estimate completion steps across diverse tasks.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DEPS combines LLM-based interactive planning with a trainable goal selector to create a zero-shot multi-task agent that completes 70+ Minecraft tasks and nearly doubles prior performance.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"}],"snapshot_sha256":"5468a78e3ef7478e80f22524291f86337e301b2d129c82ec0b06c84469ad86d1"},"source":{"id":"2302.01560","kind":"arxiv","version":3},"verdict":{"id":"cd7dafa9-292a-4bbe-82b3-f81a11658453","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T03:20:17.333742Z","strongest_claim":"Our experiments mark the milestone of the first zero-shot multi-task agent that can robustly accomplish 70+ Minecraft tasks and nearly double the overall performances.","one_line_summary":"DEPS combines LLM-based interactive planning with a trainable goal selector to create a zero-shot multi-task agent that completes 70+ Minecraft tasks and nearly doubles prior performance.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That large language models can reliably produce accurate descriptions and self-explanations of plan failures without hallucination, and that the trained goal selector generalizes to estimate completion steps across diverse tasks.","pith_extraction_headline":""},"references":{"count":71,"sample":[{"doi":"","year":2022,"title":"Flamingo: a Visual Language Model for Few-Shot Learning","work_id":"a110f764-38dc-41b2-a802-53744ecea1fc","ref_index":1,"cited_arxiv_id":"2204.14198","is_internal_anchor":true},{"doi":"","year":2017,"title":"P.-L. Bacon, J. Harb, and D. Precup. The option-critic architecture. In Proceedings of the AAAI conference on artificial intelligence, 2017. 1","work_id":"95e5f834-6420-4a4e-a0a0-c797157ae93e","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Video pretraining (vpt): Learning to act by watching unlabeled online videos","work_id":"2bf44de9-4b8f-4ab3-816e-2ffb5278c578","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"A. Brohan, Y . Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian, et al. Do as i can, not as i say: Grounding language in robotic affordances. In 6th Annual Confe","work_id":"970cbc3b-0b27-4b39-b3fc-d86e1bf02280","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1901,"title":"T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. Language models are few-shot learners. Advances in neural information process","work_id":"bc17df25-2629-4184-aac1-f5bb84bd1856","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":71,"snapshot_sha256":"e9dac2d608cc2a91f80f55beaeb0d8f60e6ed49f19f98ad20b562ce4f2a4c63f","internal_anchors":18},"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":"2302.01560","created_at":"2026-05-17T23:38:49.267243+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.01560v3","created_at":"2026-05-17T23:38:49.267243+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.01560","created_at":"2026-05-17T23:38:49.267243+00:00"},{"alias_kind":"pith_short_12","alias_value":"T6Q27LRIBE3X","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"T6Q27LRIBE3XCCEB","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"T6Q27LRI","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":33,"internal_anchor_count":33,"sample":[{"citing_arxiv_id":"2405.14093","citing_title":"A Survey on Vision-Language-Action Models for Embodied AI","ref_index":153,"is_internal_anchor":true},{"citing_arxiv_id":"2502.05907","citing_title":"EvolvingAgent: Curriculum Self-evolving Agent with Continual World Model for Long-Horizon Tasks","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2504.01990","citing_title":"Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems","ref_index":96,"is_internal_anchor":true},{"citing_arxiv_id":"2601.12358","citing_title":"From Prompts to Pavement: LMMs-based Agentic Behavior-Tree Generation Framework for Autonomous Vehicles","ref_index":15,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07358","citing_title":"A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16054","citing_title":"Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making","ref_index":186,"is_internal_anchor":true},{"citing_arxiv_id":"2605.19824","citing_title":"From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2401.03568","citing_title":"Agent AI: Surveying the Horizons of Multimodal Interaction","ref_index":199,"is_internal_anchor":true},{"citing_arxiv_id":"2510.05307","citing_title":"When Should Users Check? Modeling Confirmation Frequency inMulti-Step Agentic AI Tasks","ref_index":87,"is_internal_anchor":true},{"citing_arxiv_id":"2510.23853","citing_title":"Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception","ref_index":47,"is_internal_anchor":true},{"citing_arxiv_id":"2402.13116","citing_title":"A Survey on Knowledge Distillation of Large Language Models","ref_index":249,"is_internal_anchor":true},{"citing_arxiv_id":"2303.17491","citing_title":"Language Models can Solve Computer Tasks","ref_index":71,"is_internal_anchor":true},{"citing_arxiv_id":"2305.14992","citing_title":"Reasoning with Language Model is Planning with World Model","ref_index":103,"is_internal_anchor":true},{"citing_arxiv_id":"2310.06114","citing_title":"Learning Interactive Real-World Simulators","ref_index":236,"is_internal_anchor":true},{"citing_arxiv_id":"2305.17144","citing_title":"Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory","ref_index":26,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13527","citing_title":"MMSkills: Towards Multimodal Skills for General Visual Agents","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2308.11432","citing_title":"A Survey on Large Language Model based Autonomous Agents","ref_index":33,"is_internal_anchor":true},{"citing_arxiv_id":"2602.20867","citing_title":"SoK: Agentic Skills -- Beyond Tool Use in LLM Agents","ref_index":37,"is_internal_anchor":true},{"citing_arxiv_id":"2511.20857","citing_title":"Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory","ref_index":164,"is_internal_anchor":true},{"citing_arxiv_id":"2605.12755","citing_title":"State-Centric Decision Process","ref_index":47,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13527","citing_title":"MMSkills: Towards Multimodal Skills for General Visual Agents","ref_index":29,"is_internal_anchor":true},{"citing_arxiv_id":"2304.11477","citing_title":"LLM+P: Empowering Large Language Models with Optimal Planning Proficiency","ref_index":58,"is_internal_anchor":true},{"citing_arxiv_id":"2410.23218","citing_title":"OS-ATLAS: A Foundation Action Model for Generalist GUI Agents","ref_index":104,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11951","citing_title":"From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation","ref_index":44,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11223","citing_title":"Do Vision-Language-Models show human-like logical problem-solving capability in point and click puzzle games?","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T6Q27LRIBE3XCCEBEJFI74RTT4","json":"https://pith.science/pith/T6Q27LRIBE3XCCEBEJFI74RTT4.json","graph_json":"https://pith.science/api/pith-number/T6Q27LRIBE3XCCEBEJFI74RTT4/graph.json","events_json":"https://pith.science/api/pith-number/T6Q27LRIBE3XCCEBEJFI74RTT4/events.json","paper":"https://pith.science/paper/T6Q27LRI"},"agent_actions":{"view_html":"https://pith.science/pith/T6Q27LRIBE3XCCEBEJFI74RTT4","download_json":"https://pith.science/pith/T6Q27LRIBE3XCCEBEJFI74RTT4.json","view_paper":"https://pith.science/paper/T6Q27LRI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.01560&json=true","fetch_graph":"https://pith.science/api/pith-number/T6Q27LRIBE3XCCEBEJFI74RTT4/graph.json","fetch_events":"https://pith.science/api/pith-number/T6Q27LRIBE3XCCEBEJFI74RTT4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T6Q27LRIBE3XCCEBEJFI74RTT4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T6Q27LRIBE3XCCEBEJFI74RTT4/action/storage_attestation","attest_author":"https://pith.science/pith/T6Q27LRIBE3XCCEBEJFI74RTT4/action/author_attestation","sign_citation":"https://pith.science/pith/T6Q27LRIBE3XCCEBEJFI74RTT4/action/citation_signature","submit_replication":"https://pith.science/pith/T6Q27LRIBE3XCCEBEJFI74RTT4/action/replication_record"}},"created_at":"2026-05-17T23:38:49.267243+00:00","updated_at":"2026-05-17T23:38:49.267243+00:00"}