{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:2ZNIFROYKTL4LNMG3F73VE64A4","short_pith_number":"pith:2ZNIFROY","schema_version":"1.0","canonical_sha256":"d65a82c5d854d7c5b586d97fba93dc073a7311f077a96b4c22150edab6c68995","source":{"kind":"arxiv","id":"2107.08408","version":2},"attestation_state":"computed","paper":{"title":"Pre-trained Language Models as Prior Knowledge for Playing Text-based Games","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.MA","cs.RO"],"primary_cat":"cs.CL","authors_text":"Ashutosh Modi, Gargi Singh, Ishika Singh","submitted_at":"2021-07-18T10:28:48Z","abstract_excerpt":"Recently, text world games have been proposed to enable artificial agents to understand and reason about real-world scenarios. These text-based games are challenging for artificial agents, as it requires an understanding of and interaction using natural language in a partially observable environment. Agents observe the environment via textual descriptions designed to be challenging enough for even human players. Past approaches have not paid enough attention to the language understanding capability of the proposed agents. Typically, these approaches train from scratch, an agent that learns bot"},"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":"2107.08408","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-07-18T10:28:48Z","cross_cats_sorted":["cs.AI","cs.MA","cs.RO"],"title_canon_sha256":"06dafd805f92b4229b67078af0c287efbf90ee9f87d849d3cd08eec8122299c7","abstract_canon_sha256":"739f3dab720f435081ab38e6d72e315e582bc183b2d2cb2990fed3375c352c50"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:43:25.645258Z","signature_b64":"QT3S8I0NkXMrA8Wjqe0i5FF2lj2VkEPBlBeO3LLsQywsrTErM4vPhOV/e+Nfrh3hRDUHTUlX9ZMK/AbOJexKBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d65a82c5d854d7c5b586d97fba93dc073a7311f077a96b4c22150edab6c68995","last_reissued_at":"2026-07-05T03:43:25.644857Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:43:25.644857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Pre-trained Language Models as Prior Knowledge for Playing Text-based Games","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.MA","cs.RO"],"primary_cat":"cs.CL","authors_text":"Ashutosh Modi, Gargi Singh, Ishika Singh","submitted_at":"2021-07-18T10:28:48Z","abstract_excerpt":"Recently, text world games have been proposed to enable artificial agents to understand and reason about real-world scenarios. These text-based games are challenging for artificial agents, as it requires an understanding of and interaction using natural language in a partially observable environment. Agents observe the environment via textual descriptions designed to be challenging enough for even human players. Past approaches have not paid enough attention to the language understanding capability of the proposed agents. Typically, these approaches train from scratch, an agent that learns bot"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.08408","kind":"arxiv","version":2},"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/2107.08408/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":"2107.08408","created_at":"2026-07-05T03:43:25.644914+00:00"},{"alias_kind":"arxiv_version","alias_value":"2107.08408v2","created_at":"2026-07-05T03:43:25.644914+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.08408","created_at":"2026-07-05T03:43:25.644914+00:00"},{"alias_kind":"pith_short_12","alias_value":"2ZNIFROYKTL4","created_at":"2026-07-05T03:43:25.644914+00:00"},{"alias_kind":"pith_short_16","alias_value":"2ZNIFROYKTL4LNMG","created_at":"2026-07-05T03:43:25.644914+00:00"},{"alias_kind":"pith_short_8","alias_value":"2ZNIFROY","created_at":"2026-07-05T03:43:25.644914+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2311.01468","citing_title":"Remember what you did so you know what to do next","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2410.17448","citing_title":"In Context Learning and Reasoning for Symbolic Regression with Large Language Models","ref_index":60,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2ZNIFROYKTL4LNMG3F73VE64A4","json":"https://pith.science/pith/2ZNIFROYKTL4LNMG3F73VE64A4.json","graph_json":"https://pith.science/api/pith-number/2ZNIFROYKTL4LNMG3F73VE64A4/graph.json","events_json":"https://pith.science/api/pith-number/2ZNIFROYKTL4LNMG3F73VE64A4/events.json","paper":"https://pith.science/paper/2ZNIFROY"},"agent_actions":{"view_html":"https://pith.science/pith/2ZNIFROYKTL4LNMG3F73VE64A4","download_json":"https://pith.science/pith/2ZNIFROYKTL4LNMG3F73VE64A4.json","view_paper":"https://pith.science/paper/2ZNIFROY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2107.08408&json=true","fetch_graph":"https://pith.science/api/pith-number/2ZNIFROYKTL4LNMG3F73VE64A4/graph.json","fetch_events":"https://pith.science/api/pith-number/2ZNIFROYKTL4LNMG3F73VE64A4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2ZNIFROYKTL4LNMG3F73VE64A4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2ZNIFROYKTL4LNMG3F73VE64A4/action/storage_attestation","attest_author":"https://pith.science/pith/2ZNIFROYKTL4LNMG3F73VE64A4/action/author_attestation","sign_citation":"https://pith.science/pith/2ZNIFROYKTL4LNMG3F73VE64A4/action/citation_signature","submit_replication":"https://pith.science/pith/2ZNIFROYKTL4LNMG3F73VE64A4/action/replication_record"}},"created_at":"2026-07-05T03:43:25.644914+00:00","updated_at":"2026-07-05T03:43:25.644914+00:00"}