{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:NVK33WXTR6ABTIFZ7QVNXAN7HN","short_pith_number":"pith:NVK33WXT","canonical_record":{"source":{"id":"2505.07877","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NI","submitted_at":"2025-05-10T12:28:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7b21c1db55d402dbbfd22cc01c578989b4376da3315a0850c76193253a3550bc","abstract_canon_sha256":"5c07353ecc3ab9460ec78307f03983d41197b765dc3d5dc659c5b79b23307218"},"schema_version":"1.0"},"canonical_sha256":"6d55bddaf38f8019a0b9fc2adb81bf3b57e1d5c0e58de8ddcc916fe367d4d60f","source":{"kind":"arxiv","id":"2505.07877","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.07877","created_at":"2026-07-05T11:01:58Z"},{"alias_kind":"arxiv_version","alias_value":"2505.07877v1","created_at":"2026-07-05T11:01:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.07877","created_at":"2026-07-05T11:01:58Z"},{"alias_kind":"pith_short_12","alias_value":"NVK33WXTR6AB","created_at":"2026-07-05T11:01:58Z"},{"alias_kind":"pith_short_16","alias_value":"NVK33WXTR6ABTIFZ","created_at":"2026-07-05T11:01:58Z"},{"alias_kind":"pith_short_8","alias_value":"NVK33WXT","created_at":"2026-07-05T11:01:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:NVK33WXTR6ABTIFZ7QVNXAN7HN","target":"record","payload":{"canonical_record":{"source":{"id":"2505.07877","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NI","submitted_at":"2025-05-10T12:28:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7b21c1db55d402dbbfd22cc01c578989b4376da3315a0850c76193253a3550bc","abstract_canon_sha256":"5c07353ecc3ab9460ec78307f03983d41197b765dc3d5dc659c5b79b23307218"},"schema_version":"1.0"},"canonical_sha256":"6d55bddaf38f8019a0b9fc2adb81bf3b57e1d5c0e58de8ddcc916fe367d4d60f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:58.736824Z","signature_b64":"0TjqRmz3ez3SV+LydCCzh1U3ECZWi2r8TqONRvXU0JbaCdi1Uj5ark3TIkSmY7RhebzV6dXo+tfTu0JVnS5cBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d55bddaf38f8019a0b9fc2adb81bf3b57e1d5c0e58de8ddcc916fe367d4d60f","last_reissued_at":"2026-07-05T11:01:58.736288Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:58.736288Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.07877","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":"2026-07-05T11:01:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aTcEXiXDm/lEnuwmrq4ZI56MbOQ7k6rB6uvrvXdkt5EfK/c53sfeVkXQ+Iz1TNXeWjSfY7jxpcnFo+RZeaFoCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T21:08:41.312907Z"},"content_sha256":"a4640a6b0a278340ea7ec641dafea1a7e56f652f8ebbecd406940857a50205d1","schema_version":"1.0","event_id":"sha256:a4640a6b0a278340ea7ec641dafea1a7e56f652f8ebbecd406940857a50205d1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:NVK33WXTR6ABTIFZ7QVNXAN7HN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.NI","authors_text":"Divya Vijay, Heblin Berscilla, Sidhanth Menon, Vignesh Ethiraj","submitted_at":"2025-05-10T12:28:47Z","abstract_excerpt":"General-purpose large language models (LLMs), despite their broad capabilities accrued from open-world data, frequently exhibit suboptimal performance when confronted with the nuanced and specialized demands inherent in real-time telecommunications applications. This investigation addresses this critical limitation through the meticulous fine-tuning of TSLAM-Mini developed by NetoAI, a compact (3.8-billion parameter) causal language model architecturally derived from Phi-4 Mini Instruct 4B. The fine-tuning regimen leverages a bespoke dataset comprising 100,000 samples, strategically engineered"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.07877","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/2505.07877/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":"2026-07-05T11:01:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"20GPmoBmQvH2rZz6cicByeK8gpNr3d8k9kWvTr/i+Hlzk+5HUx6Eoq84EpXG7zET8iB9OtxCwolNt1Tg9ti5Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T21:08:41.313786Z"},"content_sha256":"44671f410662981939a3bcabaa7697b9e0b175678d24569a796630a9c8fae2c5","schema_version":"1.0","event_id":"sha256:44671f410662981939a3bcabaa7697b9e0b175678d24569a796630a9c8fae2c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NVK33WXTR6ABTIFZ7QVNXAN7HN/bundle.json","state_url":"https://pith.science/pith/NVK33WXTR6ABTIFZ7QVNXAN7HN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NVK33WXTR6ABTIFZ7QVNXAN7HN/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":"2026-08-10T21:08:41Z","links":{"resolver":"https://pith.science/pith/NVK33WXTR6ABTIFZ7QVNXAN7HN","bundle":"https://pith.science/pith/NVK33WXTR6ABTIFZ7QVNXAN7HN/bundle.json","state":"https://pith.science/pith/NVK33WXTR6ABTIFZ7QVNXAN7HN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NVK33WXTR6ABTIFZ7QVNXAN7HN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:NVK33WXTR6ABTIFZ7QVNXAN7HN","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":"5c07353ecc3ab9460ec78307f03983d41197b765dc3d5dc659c5b79b23307218","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NI","submitted_at":"2025-05-10T12:28:47Z","title_canon_sha256":"7b21c1db55d402dbbfd22cc01c578989b4376da3315a0850c76193253a3550bc"},"schema_version":"1.0","source":{"id":"2505.07877","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.07877","created_at":"2026-07-05T11:01:58Z"},{"alias_kind":"arxiv_version","alias_value":"2505.07877v1","created_at":"2026-07-05T11:01:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.07877","created_at":"2026-07-05T11:01:58Z"},{"alias_kind":"pith_short_12","alias_value":"NVK33WXTR6AB","created_at":"2026-07-05T11:01:58Z"},{"alias_kind":"pith_short_16","alias_value":"NVK33WXTR6ABTIFZ","created_at":"2026-07-05T11:01:58Z"},{"alias_kind":"pith_short_8","alias_value":"NVK33WXT","created_at":"2026-07-05T11:01:58Z"}],"graph_snapshots":[{"event_id":"sha256:44671f410662981939a3bcabaa7697b9e0b175678d24569a796630a9c8fae2c5","target":"graph","created_at":"2026-07-05T11:01:58Z","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/2505.07877/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"General-purpose large language models (LLMs), despite their broad capabilities accrued from open-world data, frequently exhibit suboptimal performance when confronted with the nuanced and specialized demands inherent in real-time telecommunications applications. This investigation addresses this critical limitation through the meticulous fine-tuning of TSLAM-Mini developed by NetoAI, a compact (3.8-billion parameter) causal language model architecturally derived from Phi-4 Mini Instruct 4B. The fine-tuning regimen leverages a bespoke dataset comprising 100,000 samples, strategically engineered","authors_text":"Divya Vijay, Heblin Berscilla, Sidhanth Menon, Vignesh Ethiraj","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NI","submitted_at":"2025-05-10T12:28:47Z","title":"Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.07877","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:a4640a6b0a278340ea7ec641dafea1a7e56f652f8ebbecd406940857a50205d1","target":"record","created_at":"2026-07-05T11:01:58Z","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":"5c07353ecc3ab9460ec78307f03983d41197b765dc3d5dc659c5b79b23307218","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.NI","submitted_at":"2025-05-10T12:28:47Z","title_canon_sha256":"7b21c1db55d402dbbfd22cc01c578989b4376da3315a0850c76193253a3550bc"},"schema_version":"1.0","source":{"id":"2505.07877","kind":"arxiv","version":1}},"canonical_sha256":"6d55bddaf38f8019a0b9fc2adb81bf3b57e1d5c0e58de8ddcc916fe367d4d60f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6d55bddaf38f8019a0b9fc2adb81bf3b57e1d5c0e58de8ddcc916fe367d4d60f","first_computed_at":"2026-07-05T11:01:58.736288Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:01:58.736288Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0TjqRmz3ez3SV+LydCCzh1U3ECZWi2r8TqONRvXU0JbaCdi1Uj5ark3TIkSmY7RhebzV6dXo+tfTu0JVnS5cBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:01:58.736824Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.07877","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a4640a6b0a278340ea7ec641dafea1a7e56f652f8ebbecd406940857a50205d1","sha256:44671f410662981939a3bcabaa7697b9e0b175678d24569a796630a9c8fae2c5"],"state_sha256":"757160ad0d12ff0439696cddaac76b5cd96f95a401d1b4a3b63372b6f84afbd3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+psXAUnhO1bQ4uw1U7wll2pYCYohX2LCIVLMxtw/flIQko1wH3RQf0fAptN5SDuSSKawHJfdu5hkRuJyGf0SAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T21:08:41.319972Z","bundle_sha256":"b06ecdc65e1dcb1d40c2895a53aecb76bd75b7d7e491659b42ef70028c387743"}}