{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:OCLMPFAXSRX4UL77Q3NBRCWHFW","short_pith_number":"pith:OCLMPFAX","canonical_record":{"source":{"id":"2506.13109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-16T05:37:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7641c81ac80b22452984850f20a359cae66b4d36f22368488a1d001b41b28c7e","abstract_canon_sha256":"b8d4b7841e53aca1f21ba5ec0cb826cbea36828e7d542f01acc8d6be0f1fc3cf"},"schema_version":"1.0"},"canonical_sha256":"7096c79417946fca2fff86da188ac72db22ce85ff60dea8226307b819c626d6d","source":{"kind":"arxiv","id":"2506.13109","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13109","created_at":"2026-07-05T11:22:05Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13109v1","created_at":"2026-07-05T11:22:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13109","created_at":"2026-07-05T11:22:05Z"},{"alias_kind":"pith_short_12","alias_value":"OCLMPFAXSRX4","created_at":"2026-07-05T11:22:05Z"},{"alias_kind":"pith_short_16","alias_value":"OCLMPFAXSRX4UL77","created_at":"2026-07-05T11:22:05Z"},{"alias_kind":"pith_short_8","alias_value":"OCLMPFAX","created_at":"2026-07-05T11:22:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:OCLMPFAXSRX4UL77Q3NBRCWHFW","target":"record","payload":{"canonical_record":{"source":{"id":"2506.13109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-16T05:37:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7641c81ac80b22452984850f20a359cae66b4d36f22368488a1d001b41b28c7e","abstract_canon_sha256":"b8d4b7841e53aca1f21ba5ec0cb826cbea36828e7d542f01acc8d6be0f1fc3cf"},"schema_version":"1.0"},"canonical_sha256":"7096c79417946fca2fff86da188ac72db22ce85ff60dea8226307b819c626d6d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:05.283769Z","signature_b64":"t9b/5SGHo88ACxvgQIlXDgKi1sLWtrgvj/aEbKlv0Y7yG+1d5WiL/6tAow1e33Rub01ovPUDNdnGcxFW9zP7Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7096c79417946fca2fff86da188ac72db22ce85ff60dea8226307b819c626d6d","last_reissued_at":"2026-07-05T11:22:05.283115Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:05.283115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.13109","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:22:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t9k8kpk3jyupQlRbLTgl7gYIWocsrbVZHohGI9f17Dh+aCgPPdT1SfWd51YkUFaCGG9eeV8Kd/QIjPkPcn+eAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:11:44.025133Z"},"content_sha256":"728d5ef2eee10dd3f184d04f013ae7d45b2a1fe503666616ba1782eaabea96eb","schema_version":"1.0","event_id":"sha256:728d5ef2eee10dd3f184d04f013ae7d45b2a1fe503666616ba1782eaabea96eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:OCLMPFAXSRX4UL77Q3NBRCWHFW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Leveraging In-Context Learning for Language Model Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ashish Sabharwal, Ben Bogin, Sameer Singh, Shivanshu Gupta, Tushar Khot","submitted_at":"2025-06-16T05:37:49Z","abstract_excerpt":"In-context learning (ICL) with dynamically selected demonstrations combines the flexibility of prompting large language models (LLMs) with the ability to leverage training data to improve performance. While ICL has been highly successful for prediction and generation tasks, leveraging it for agentic tasks that require sequential decision making is challenging -- one must think not only about how to annotate long trajectories at scale and how to select demonstrations, but also what constitutes demonstrations, and when and where to show them. To address this, we first propose an algorithm that l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13109","kind":"arxiv","version":1},"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/2506.13109/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:22:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"82PCx45iSlzfFOFdanSFJ9PVMo2FMAcTH7bclpmnst6UylC2gRNs3OEb6sy1dYMWcrzTeI05OMFC16W47SIBBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T18:11:44.025520Z"},"content_sha256":"47beec150db47bc9b1efb49b4686edf00c754d488dd98a9ec2d93bd95315432d","schema_version":"1.0","event_id":"sha256:47beec150db47bc9b1efb49b4686edf00c754d488dd98a9ec2d93bd95315432d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OCLMPFAXSRX4UL77Q3NBRCWHFW/bundle.json","state_url":"https://pith.science/pith/OCLMPFAXSRX4UL77Q3NBRCWHFW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OCLMPFAXSRX4UL77Q3NBRCWHFW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T18:11:44Z","links":{"resolver":"https://pith.science/pith/OCLMPFAXSRX4UL77Q3NBRCWHFW","bundle":"https://pith.science/pith/OCLMPFAXSRX4UL77Q3NBRCWHFW/bundle.json","state":"https://pith.science/pith/OCLMPFAXSRX4UL77Q3NBRCWHFW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OCLMPFAXSRX4UL77Q3NBRCWHFW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:OCLMPFAXSRX4UL77Q3NBRCWHFW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b8d4b7841e53aca1f21ba5ec0cb826cbea36828e7d542f01acc8d6be0f1fc3cf","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-16T05:37:49Z","title_canon_sha256":"7641c81ac80b22452984850f20a359cae66b4d36f22368488a1d001b41b28c7e"},"schema_version":"1.0","source":{"id":"2506.13109","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.13109","created_at":"2026-07-05T11:22:05Z"},{"alias_kind":"arxiv_version","alias_value":"2506.13109v1","created_at":"2026-07-05T11:22:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13109","created_at":"2026-07-05T11:22:05Z"},{"alias_kind":"pith_short_12","alias_value":"OCLMPFAXSRX4","created_at":"2026-07-05T11:22:05Z"},{"alias_kind":"pith_short_16","alias_value":"OCLMPFAXSRX4UL77","created_at":"2026-07-05T11:22:05Z"},{"alias_kind":"pith_short_8","alias_value":"OCLMPFAX","created_at":"2026-07-05T11:22:05Z"}],"graph_snapshots":[{"event_id":"sha256:47beec150db47bc9b1efb49b4686edf00c754d488dd98a9ec2d93bd95315432d","target":"graph","created_at":"2026-07-05T11:22:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.13109/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In-context learning (ICL) with dynamically selected demonstrations combines the flexibility of prompting large language models (LLMs) with the ability to leverage training data to improve performance. While ICL has been highly successful for prediction and generation tasks, leveraging it for agentic tasks that require sequential decision making is challenging -- one must think not only about how to annotate long trajectories at scale and how to select demonstrations, but also what constitutes demonstrations, and when and where to show them. To address this, we first propose an algorithm that l","authors_text":"Ashish Sabharwal, Ben Bogin, Sameer Singh, Shivanshu Gupta, Tushar Khot","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-16T05:37:49Z","title":"Leveraging In-Context Learning for Language Model Agents"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13109","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:728d5ef2eee10dd3f184d04f013ae7d45b2a1fe503666616ba1782eaabea96eb","target":"record","created_at":"2026-07-05T11:22:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b8d4b7841e53aca1f21ba5ec0cb826cbea36828e7d542f01acc8d6be0f1fc3cf","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-16T05:37:49Z","title_canon_sha256":"7641c81ac80b22452984850f20a359cae66b4d36f22368488a1d001b41b28c7e"},"schema_version":"1.0","source":{"id":"2506.13109","kind":"arxiv","version":1}},"canonical_sha256":"7096c79417946fca2fff86da188ac72db22ce85ff60dea8226307b819c626d6d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7096c79417946fca2fff86da188ac72db22ce85ff60dea8226307b819c626d6d","first_computed_at":"2026-07-05T11:22:05.283115Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:22:05.283115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"t9b/5SGHo88ACxvgQIlXDgKi1sLWtrgvj/aEbKlv0Y7yG+1d5WiL/6tAow1e33Rub01ovPUDNdnGcxFW9zP7Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:22:05.283769Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.13109","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:728d5ef2eee10dd3f184d04f013ae7d45b2a1fe503666616ba1782eaabea96eb","sha256:47beec150db47bc9b1efb49b4686edf00c754d488dd98a9ec2d93bd95315432d"],"state_sha256":"bc212f99c85c4d557e61b29af134a6938005472eed46dda7ff34e11cddb2dced"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"izTr+Q4yHrwbzuhmdrPvQcwMl5WTMQKaAH3pTVcENE9KKTQYyM2dGrFfLPPGCPqb8dcVParzG/xmhu/Ki25pCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T18:11:44.027485Z","bundle_sha256":"dca013fa45aa4aa915741fbe872414da97bfabddffac66a017ccdc6166fd895c"}}