{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:LPDF3Z6EHHHRIVGC327XL5IKIK","short_pith_number":"pith:LPDF3Z6E","canonical_record":{"source":{"id":"2606.08154","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-06T13:09:47Z","cross_cats_sorted":[],"title_canon_sha256":"440b483d16218538220129df967f26c4256a580577ce0cc9f6f7aa686d818402","abstract_canon_sha256":"4d958413decd2f41beae9fb7d190a513db4203400f1b2b637b1b1db50feaa87e"},"schema_version":"1.0"},"canonical_sha256":"5bc65de7c439cf1454c2debf75f50a4280a717d21f4184213e9076eaa31f2409","source":{"kind":"arxiv","id":"2606.08154","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.08154","created_at":"2026-06-09T01:05:28Z"},{"alias_kind":"arxiv_version","alias_value":"2606.08154v1","created_at":"2026-06-09T01:05:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.08154","created_at":"2026-06-09T01:05:28Z"},{"alias_kind":"pith_short_12","alias_value":"LPDF3Z6EHHHR","created_at":"2026-06-09T01:05:28Z"},{"alias_kind":"pith_short_16","alias_value":"LPDF3Z6EHHHRIVGC","created_at":"2026-06-09T01:05:28Z"},{"alias_kind":"pith_short_8","alias_value":"LPDF3Z6E","created_at":"2026-06-09T01:05:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:LPDF3Z6EHHHRIVGC327XL5IKIK","target":"record","payload":{"canonical_record":{"source":{"id":"2606.08154","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-06T13:09:47Z","cross_cats_sorted":[],"title_canon_sha256":"440b483d16218538220129df967f26c4256a580577ce0cc9f6f7aa686d818402","abstract_canon_sha256":"4d958413decd2f41beae9fb7d190a513db4203400f1b2b637b1b1db50feaa87e"},"schema_version":"1.0"},"canonical_sha256":"5bc65de7c439cf1454c2debf75f50a4280a717d21f4184213e9076eaa31f2409","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:28.371098Z","signature_b64":"GOe7B6jEj7rRP81Y6FsiRpTeGgOX68ZGwtb9zNC0l0HoIjFWljC5Xjf9XPCt3kRz7hk/3nwhj9GYl0TxYEPMBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5bc65de7c439cf1454c2debf75f50a4280a717d21f4184213e9076eaa31f2409","last_reissued_at":"2026-06-09T01:05:28.368487Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:28.368487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.08154","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":"2026-06-09T01:05:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6a75mUgP2VwN3lLkus+C3FE8ahXhOlsvhc6nu2UUOdoW4wrT7CKeQ5zN7kxx20nPWThgody2wYIBt+O8BgXmBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T07:51:38.414763Z"},"content_sha256":"1eac1595b9ece7413041e0d3fdf2dddb1e676bfb7a92d3f5ae83a945bc6e1d1c","schema_version":"1.0","event_id":"sha256:1eac1595b9ece7413041e0d3fdf2dddb1e676bfb7a92d3f5ae83a945bc6e1d1c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:LPDF3Z6EHHHRIVGC327XL5IKIK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SynthICL: Scalable In-context Imitation Learning with Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Cheng Qian, Edward Johns, Ruomeng Fan, Yifei Ren, Yilong Wang","submitted_at":"2026-06-06T13:09:47Z","abstract_excerpt":"In-context imitation learning (ICIL) enables robots to learn new tasks from a small number of demonstrations by conditioning a pre-trained policy on task-specific examples, without retraining at test time. Despite this promise, training generalizable and scalable in-context imitation policies remains an open challenge. We present SynthICL, a scalable framework that trains ICIL policies entirely from RGB-only synthetic data. Specifically, we build a data generation pipeline to produce high-fidelity ICIL data and train a flow-matching transformer policy on the resulting dataset. SynthICL avoids "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.08154","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/2606.08154/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":"2026-06-09T01:05:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JHDdCv7NUAkCe9b2kK0f6lHWPzDYUNTYy+nZlrbLxXqjpbR8JCHr4qJ5HPODW734Q+cH+txA2wTDy1TyvKBECg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T07:51:38.415574Z"},"content_sha256":"dda035a9e1ac9b0fd8f55d2408ef75813cbc69666e24c8888ab3e02ac868bc58","schema_version":"1.0","event_id":"sha256:dda035a9e1ac9b0fd8f55d2408ef75813cbc69666e24c8888ab3e02ac868bc58"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LPDF3Z6EHHHRIVGC327XL5IKIK/bundle.json","state_url":"https://pith.science/pith/LPDF3Z6EHHHRIVGC327XL5IKIK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LPDF3Z6EHHHRIVGC327XL5IKIK/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":"2026-06-09T07:51:38Z","links":{"resolver":"https://pith.science/pith/LPDF3Z6EHHHRIVGC327XL5IKIK","bundle":"https://pith.science/pith/LPDF3Z6EHHHRIVGC327XL5IKIK/bundle.json","state":"https://pith.science/pith/LPDF3Z6EHHHRIVGC327XL5IKIK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LPDF3Z6EHHHRIVGC327XL5IKIK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:LPDF3Z6EHHHRIVGC327XL5IKIK","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":"4d958413decd2f41beae9fb7d190a513db4203400f1b2b637b1b1db50feaa87e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-06T13:09:47Z","title_canon_sha256":"440b483d16218538220129df967f26c4256a580577ce0cc9f6f7aa686d818402"},"schema_version":"1.0","source":{"id":"2606.08154","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.08154","created_at":"2026-06-09T01:05:28Z"},{"alias_kind":"arxiv_version","alias_value":"2606.08154v1","created_at":"2026-06-09T01:05:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.08154","created_at":"2026-06-09T01:05:28Z"},{"alias_kind":"pith_short_12","alias_value":"LPDF3Z6EHHHR","created_at":"2026-06-09T01:05:28Z"},{"alias_kind":"pith_short_16","alias_value":"LPDF3Z6EHHHRIVGC","created_at":"2026-06-09T01:05:28Z"},{"alias_kind":"pith_short_8","alias_value":"LPDF3Z6E","created_at":"2026-06-09T01:05:28Z"}],"graph_snapshots":[{"event_id":"sha256:dda035a9e1ac9b0fd8f55d2408ef75813cbc69666e24c8888ab3e02ac868bc58","target":"graph","created_at":"2026-06-09T01:05:28Z","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/2606.08154/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In-context imitation learning (ICIL) enables robots to learn new tasks from a small number of demonstrations by conditioning a pre-trained policy on task-specific examples, without retraining at test time. Despite this promise, training generalizable and scalable in-context imitation policies remains an open challenge. We present SynthICL, a scalable framework that trains ICIL policies entirely from RGB-only synthetic data. Specifically, we build a data generation pipeline to produce high-fidelity ICIL data and train a flow-matching transformer policy on the resulting dataset. SynthICL avoids ","authors_text":"Cheng Qian, Edward Johns, Ruomeng Fan, Yifei Ren, Yilong Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-06T13:09:47Z","title":"SynthICL: Scalable In-context Imitation Learning with Synthetic Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.08154","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":"sha256:1eac1595b9ece7413041e0d3fdf2dddb1e676bfb7a92d3f5ae83a945bc6e1d1c","target":"record","created_at":"2026-06-09T01:05:28Z","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":"4d958413decd2f41beae9fb7d190a513db4203400f1b2b637b1b1db50feaa87e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-06T13:09:47Z","title_canon_sha256":"440b483d16218538220129df967f26c4256a580577ce0cc9f6f7aa686d818402"},"schema_version":"1.0","source":{"id":"2606.08154","kind":"arxiv","version":1}},"canonical_sha256":"5bc65de7c439cf1454c2debf75f50a4280a717d21f4184213e9076eaa31f2409","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5bc65de7c439cf1454c2debf75f50a4280a717d21f4184213e9076eaa31f2409","first_computed_at":"2026-06-09T01:05:28.368487Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:05:28.368487Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GOe7B6jEj7rRP81Y6FsiRpTeGgOX68ZGwtb9zNC0l0HoIjFWljC5Xjf9XPCt3kRz7hk/3nwhj9GYl0TxYEPMBQ==","signature_status":"signed_v1","signed_at":"2026-06-09T01:05:28.371098Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.08154","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1eac1595b9ece7413041e0d3fdf2dddb1e676bfb7a92d3f5ae83a945bc6e1d1c","sha256:dda035a9e1ac9b0fd8f55d2408ef75813cbc69666e24c8888ab3e02ac868bc58"],"state_sha256":"278a74921a71586fa5cd76339027f3e4913f51efe91881e7429e3cd900314b40"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2J1LgSMD8vdxCj7mjATG4IUOIQwt7WjeqI/JfMHygBKE1yBZi4qoomyHRz1DSpGNUJAO0hfl4DSLXHkJ/aEHAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T07:51:38.420836Z","bundle_sha256":"710aa1b8c4bbc942c6933d4136c18f1950575e3b67ae65b0baae6130db44b047"}}