{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:K6E2MM5DZE7AANNQEGZHTOD7SM","short_pith_number":"pith:K6E2MM5D","schema_version":"1.0","canonical_sha256":"5789a633a3c93e0035b021b279b87f93019e84449256715f416c0bebda3274fe","source":{"kind":"arxiv","id":"2601.16312","version":2},"attestation_state":"computed","paper":{"title":"Teaching and Evaluating LLMs to Reason About Polymer Design Related Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Small language models trained on PolyBench match or beat larger models on polymer design reasoning tasks.","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Benjamin Hsiao, Dikshya Mohanty, Mohammad Saqib Hasan, Niranjan Balasubramanian, Size Zheng, Syed Mostofa Monsur","submitted_at":"2026-01-22T20:39:18Z","abstract_excerpt":"Research in AI4Science has shown promise in many science applications, including polymer design. However, current LLMs are ineffective in this problem space because: (i) most models lack polymer-specific knowledge, and (ii) existing aligned models have limited coverage of knowledge and capabilities relevant to polymer design. Addressing this, we introduce PolyBench, a large-scale training and test benchmark dataset of more than 125K polymer design-related tasks, leveraging a knowledge base of more than 13 million data points obtained from experimental and synthetic data sources to ensure broad"},"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":true},"canonical_record":{"source":{"id":"2601.16312","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-22T20:39:18Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"690891f005264361c851591daaa8434dba4a55cccd41566ee0e0daa257756ebb","abstract_canon_sha256":"67d42ed87e302305fc5f7a16d7d99301ce1c15dbaa41f7aaddf44b6b7bd6a9d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:00.200561Z","signature_b64":"CMi/GemuEaC1wlGnJbCa7d1JeWJG6/Hbwns7YNzLsbP0sNNgP/MMFJYVHWa1CQte1S9ZaIb30Llwo/IOHUWTBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5789a633a3c93e0035b021b279b87f93019e84449256715f416c0bebda3274fe","last_reissued_at":"2026-05-17T23:39:00.199888Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:00.199888Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Teaching and Evaluating LLMs to Reason About Polymer Design Related Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Small language models trained on PolyBench match or beat larger models on polymer design reasoning tasks.","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Benjamin Hsiao, Dikshya Mohanty, Mohammad Saqib Hasan, Niranjan Balasubramanian, Size Zheng, Syed Mostofa Monsur","submitted_at":"2026-01-22T20:39:18Z","abstract_excerpt":"Research in AI4Science has shown promise in many science applications, including polymer design. However, current LLMs are ineffective in this problem space because: (i) most models lack polymer-specific knowledge, and (ii) existing aligned models have limited coverage of knowledge and capabilities relevant to polymer design. Addressing this, we introduce PolyBench, a large-scale training and test benchmark dataset of more than 125K polymer design-related tasks, leveraging a knowledge base of more than 13 million data points obtained from experimental and synthetic data sources to ensure broad"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"small language models (SLMs) with 7B to 14B parameters, trained on PolyBench, outperform similar-sized models and remain competitive with closed-source frontier LLMs on PolyBench's test dataset, while demonstrating performance gains on external polymer benchmarks.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The tasks in PolyBench and the underlying knowledge base of 13 million data points accurately represent the knowledge and reasoning demands of real-world polymer design without major gaps, biases, or inaccuracies from the experimental and synthetic sources.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Small 7B-14B parameter language models trained on the new PolyBench dataset for polymer design tasks outperform similar-sized models and compete with large frontier LLMs while improving on external benchmarks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Small language models trained on PolyBench match or beat larger models on polymer design reasoning tasks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"e0017d5f117bfced1a30a9a94f4b66f10767b0397058ed71bedc6e05c5da0f27"},"source":{"id":"2601.16312","kind":"arxiv","version":2},"verdict":{"id":"6a5c5619-10db-4c94-9197-016d580cb279","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T11:31:08.017151Z","strongest_claim":"small language models (SLMs) with 7B to 14B parameters, trained on PolyBench, outperform similar-sized models and remain competitive with closed-source frontier LLMs on PolyBench's test dataset, while demonstrating performance gains on external polymer benchmarks.","one_line_summary":"Small 7B-14B parameter language models trained on the new PolyBench dataset for polymer design tasks outperform similar-sized models and compete with large frontier LLMs while improving on external benchmarks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The tasks in PolyBench and the underlying knowledge base of 13 million data points accurately represent the knowledge and reasoning demands of real-world polymer design without major gaps, biases, or inaccuracies from the experimental and synthetic sources.","pith_extraction_headline":"Small language models trained on PolyBench match or beat larger models on polymer design reasoning tasks."},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"bafe42996f3a0f8bcc9c7d6a962d13e8a8bc0170e8a6d33427bf00128be8f92b"},"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":"2601.16312","created_at":"2026-05-17T23:39:00.200022+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.16312v2","created_at":"2026-05-17T23:39:00.200022+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.16312","created_at":"2026-05-17T23:39:00.200022+00:00"},{"alias_kind":"pith_short_12","alias_value":"K6E2MM5DZE7A","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"K6E2MM5DZE7AANNQ","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"K6E2MM5D","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K6E2MM5DZE7AANNQEGZHTOD7SM","json":"https://pith.science/pith/K6E2MM5DZE7AANNQEGZHTOD7SM.json","graph_json":"https://pith.science/api/pith-number/K6E2MM5DZE7AANNQEGZHTOD7SM/graph.json","events_json":"https://pith.science/api/pith-number/K6E2MM5DZE7AANNQEGZHTOD7SM/events.json","paper":"https://pith.science/paper/K6E2MM5D"},"agent_actions":{"view_html":"https://pith.science/pith/K6E2MM5DZE7AANNQEGZHTOD7SM","download_json":"https://pith.science/pith/K6E2MM5DZE7AANNQEGZHTOD7SM.json","view_paper":"https://pith.science/paper/K6E2MM5D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.16312&json=true","fetch_graph":"https://pith.science/api/pith-number/K6E2MM5DZE7AANNQEGZHTOD7SM/graph.json","fetch_events":"https://pith.science/api/pith-number/K6E2MM5DZE7AANNQEGZHTOD7SM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K6E2MM5DZE7AANNQEGZHTOD7SM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K6E2MM5DZE7AANNQEGZHTOD7SM/action/storage_attestation","attest_author":"https://pith.science/pith/K6E2MM5DZE7AANNQEGZHTOD7SM/action/author_attestation","sign_citation":"https://pith.science/pith/K6E2MM5DZE7AANNQEGZHTOD7SM/action/citation_signature","submit_replication":"https://pith.science/pith/K6E2MM5DZE7AANNQEGZHTOD7SM/action/replication_record"}},"created_at":"2026-05-17T23:39:00.200022+00:00","updated_at":"2026-05-17T23:39:00.200022+00:00"}