{"schema_version":"onlylabs.public_signal.v1","title":"Amazon (Nova) Repo: amazon-science/TSFM-Biases","description":"Amazon (Nova) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/94a4f44d-0f4f-46b4-b91c-4eafa42f8620","json_url":"https://onlylabs.fyi/signals/94a4f44d-0f4f-46b4-b91c-4eafa42f8620/signal.json","generated_at":"2026-06-11T02:52:00.926693+00:00","org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/amazon","dossier_json_url":"https://onlylabs.fyi/labs/amazon/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/94a4f44d-0f4f-46b4-b91c-4eafa42f8620","signal_json":"https://onlylabs.fyi/signals/94a4f44d-0f4f-46b4-b91c-4eafa42f8620/signal.json","source":"https://github.com/amazon-science/TSFM-Biases","lab_dossier":"https://onlylabs.fyi/labs/amazon","lab_dossier_json":"https://onlylabs.fyi/labs/amazon/dossier.json","analysis":"https://onlylabs.fyi/analysis/amazon","analysis_json":"https://onlylabs.fyi/analysis/amazon/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/amazon/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Amazon (Nova) published amazon-science/TSFM-Biases (Jupyter Notebook). This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. High-signal details: repo amazon-science/TSFM-Biases · language Jupyter Notebook · Low-stars repo, routine release.. onlylabs links this event to 1 captured evidence page and 6 related repo signals.","signal_desk":"repos","source_context":{"source_url":"https://github.com/amazon-science/TSFM-Biases","source_host":"github.com","occurred_at":"2026-03-18T22:48:34+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source","context":"Jupyter Notebook"},"context_markers":[{"label":"Lab","value":"Amazon (Nova)","source":"signal"},{"label":"Signal desk","value":"repos","source":"signal"},{"label":"Source host","value":"github.com","source":"source"},{"label":"Repository","value":"amazon-science/TSFM-Biases","source":"source"},{"label":"Language","value":"Jupyter Notebook","source":"source"},{"label":"Stars","value":"3","source":"traction"},{"label":"Notability","value":"Low-stars repo, routine release.","source":"signal"},{"label":"Watch term","value":"Eval methodology","source":"evidence"},{"label":"Watch term","value":"Infrastructure","source":"evidence"}],"evidence_coverage":{"target_pages":1,"captured_pages":1,"readable_pages":1,"capture_methods":["plain"],"missing_page_urls":[],"failed_page_urls":[],"blocked_page_urls":[],"page_urls":["https://github.com/amazon-science/TSFM-Biases"],"related_signals":6,"has_source_url":true,"latest_page_fetched_at":"2026-06-11T02:52:00.926693+00:00"},"data_business":{"matches":false,"lanes":[],"matched_terms":[],"score":null,"reason":null},"agent_handoff":{"signal_json":"https://onlylabs.fyi/signals/94a4f44d-0f4f-46b4-b91c-4eafa42f8620/signal.json","dossier_json":"https://onlylabs.fyi/labs/amazon/dossier.json","analysis_json":"https://onlylabs.fyi/analysis/amazon/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/amazon/evidence.json","topic_signals_json":null,"topic_feed":null,"category_signals_json":"https://onlylabs.fyi/signals.json","data_radar_json":null,"opportunities_json":null},"analysis_playbook":{"objective":"Turn new repository signals into early evidence of tooling, eval, infrastructure, model-adjacent, or product work before it appears in polished launch channels.","evidence_focus":["repo name","owner","description","language","stars","source URL","first seen time","data, eval, infra, safety, and product terms"],"extraction_questions":["What technical area does this repository expose?","Does the repo imply eval, data, infrastructure, agent, or deployment work?","Is the repo new evidence for a lab direction that is not yet in writing or releases?","Which related signals should an analyst inspect next?"],"signal_questions":["What does this new repository reveal before a formal announcement exists?","What technical area does this repository expose?","Does the repo imply eval, data, infrastructure, agent, or deployment work?","Do the 6 related repo signals show a repeated pattern?"],"output_fields":["org","repo","technical_theme","data_business_lane","evidence_url"],"data_business_relevance":"New repositories can expose organization build priorities early, especially around internal tooling, eval infrastructure, data systems, deployment, and agent workflows.","required_sources":[{"label":"signal_json","url":"https://onlylabs.fyi/signals/94a4f44d-0f4f-46b4-b91c-4eafa42f8620/signal.json","required":true},{"label":"source","url":"https://github.com/amazon-science/TSFM-Biases","required":true},{"label":"dossier_json","url":"https://onlylabs.fyi/labs/amazon/dossier.json","required":true},{"label":"analysis_evidence_json","url":"https://onlylabs.fyi/analysis/amazon/evidence.json","required":true},{"label":"topic_signals_json","url":null,"required":false},{"label":"data_radar_json","url":null,"required":false}],"expected_output":["one-paragraph source-grounded interpretation","category-specific implication","confidence and missing evidence","recommended next source to inspect"],"prompt_seed":"Using only the linked onlylabs JSON, captured source context, and cited evidence, analyze Amazon (Nova)'s repo signal \"amazon-science/TSFM-Biases\" for frontier lab strategy."},"semantic_triples":[{"subject":"Amazon (Nova)","predicate":"published repo","object":"amazon-science/TSFM-Biases","text":"Amazon (Nova) published repo amazon-science/TSFM-Biases."},{"subject":"amazon-science/TSFM-Biases","predicate":"is classified as","object":"repo signal","text":"amazon-science/TSFM-Biases is classified as repo signal."},{"subject":"amazon-science/TSFM-Biases","predicate":"belongs to","object":"repos desk","text":"amazon-science/TSFM-Biases belongs to repos desk."},{"subject":"amazon-science/TSFM-Biases","predicate":"has context","object":"Jupyter Notebook","text":"amazon-science/TSFM-Biases has context Jupyter Notebook."},{"subject":"amazon-science/TSFM-Biases","predicate":"has evidence coverage","object":"1 captured evidence page","text":"amazon-science/TSFM-Biases has evidence coverage 1 captured evidence page."},{"subject":"amazon-science/TSFM-Biases","predicate":"has captured page count","object":"1","text":"amazon-science/TSFM-Biases has captured page count 1."},{"subject":"amazon-science/TSFM-Biases","predicate":"has readable page count","object":"1","text":"amazon-science/TSFM-Biases has readable page count 1."},{"subject":"amazon-science/TSFM-Biases","predicate":"has related signal count","object":"6","text":"amazon-science/TSFM-Biases has related signal count 6."},{"subject":"amazon-science/TSFM-Biases","predicate":"has analysis playbook objective","object":"Turn new repository signals into early evidence of tooling, eval, infrastructure, model-adjacent, or product work before it appears in polished launch channels.","text":"amazon-science/TSFM-Biases has analysis playbook objective Turn new repository signals into early evidence of tooling, eval, infrastructure, model-adjacent, or product work before it appears in polished launch channels.."},{"subject":"amazon-science/TSFM-Biases","predicate":"has source host","object":"github.com","text":"amazon-science/TSFM-Biases has source host github.com."},{"subject":"amazon-science/TSFM-Biases","predicate":"has lab","object":"Amazon (Nova)","text":"amazon-science/TSFM-Biases has lab Amazon (Nova)."},{"subject":"amazon-science/TSFM-Biases","predicate":"has signal desk","object":"repos","text":"amazon-science/TSFM-Biases has signal desk repos."},{"subject":"amazon-science/TSFM-Biases","predicate":"has source host","object":"github.com","text":"amazon-science/TSFM-Biases has source host github.com."},{"subject":"amazon-science/TSFM-Biases","predicate":"has repository","object":"amazon-science/TSFM-Biases","text":"amazon-science/TSFM-Biases has repository amazon-science/TSFM-Biases."},{"subject":"amazon-science/TSFM-Biases","predicate":"has language","object":"Jupyter Notebook","text":"amazon-science/TSFM-Biases has language Jupyter Notebook."},{"subject":"amazon-science/TSFM-Biases","predicate":"has stars","object":"3","text":"amazon-science/TSFM-Biases has stars 3."},{"subject":"amazon-science/TSFM-Biases","predicate":"has notability","object":"Low-stars repo, routine release.","text":"amazon-science/TSFM-Biases has notability Low-stars repo, routine release.."},{"subject":"amazon-science/TSFM-Biases","predicate":"has watch term","object":"Eval methodology","text":"amazon-science/TSFM-Biases has watch term Eval methodology."}]},"intelligence":{"signal_desk":"repos","answer":"Amazon (Nova) published amazon-science/TSFM-Biases (Jupyter Notebook). This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. High-signal details: repo amazon-science/TSFM-Biases · language Jupyter Notebook · Low-stars repo, routine release.. onlylabs links this event to 1 captured evidence page and 6 related repo signals.","semantic_triples":[{"subject":"Amazon (Nova)","predicate":"published repo","object":"amazon-science/TSFM-Biases","text":"Amazon (Nova) published repo amazon-science/TSFM-Biases."},{"subject":"amazon-science/TSFM-Biases","predicate":"is classified as","object":"repo signal","text":"amazon-science/TSFM-Biases is classified as repo signal."},{"subject":"amazon-science/TSFM-Biases","predicate":"belongs to","object":"repos desk","text":"amazon-science/TSFM-Biases belongs to repos desk."},{"subject":"amazon-science/TSFM-Biases","predicate":"has context","object":"Jupyter Notebook","text":"amazon-science/TSFM-Biases has context Jupyter Notebook."},{"subject":"amazon-science/TSFM-Biases","predicate":"has evidence coverage","object":"1 captured evidence page","text":"amazon-science/TSFM-Biases has evidence coverage 1 captured evidence page."}]},"signal":{"id":"94a4f44d-0f4f-46b4-b91c-4eafa42f8620","url":"https://onlylabs.fyi/signals/94a4f44d-0f4f-46b4-b91c-4eafa42f8620","json_url":"https://onlylabs.fyi/signals/94a4f44d-0f4f-46b4-b91c-4eafa42f8620/signal.json","source_url":"https://github.com/amazon-science/TSFM-Biases","title":"amazon-science/TSFM-Biases","summary":"Amazon (Nova) published a new repository. onlylabs watches repos for tooling, eval, infra, and model-adjacent work.","context":"Jupyter Notebook","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-03-18T22:48:34+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source","evidence_coverage":{"target_pages":1,"captured_pages":1,"readable_pages":1,"capture_methods":["plain"],"missing_page_urls":[],"failed_page_urls":[],"blocked_page_urls":[],"page_urls":["https://github.com/amazon-science/TSFM-Biases"]},"facets":{"repo":"amazon-science/TSFM-Biases","language":"Jupyter Notebook"},"traction":{"github_stars":3,"hn_points":null,"hn_comments":null,"hn_story_id":null,"hf_downloads":null,"hf_likes":null},"data_radar":null},"primary_evidence_page":{"url":"https://github.com/amazon-science/TSFM-Biases","final_url":"https://github.com/amazon-science/TSFM-Biases","title":"amazon-science/TSFM-Biases repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T02:52:00.926693+00:00","bytes":17828,"raw_path":"550f7b24a828a946eb9c8711661b35e2bac5ebc3b325c513cba100464aa499b9.json","content_hash":"b14131603e1e5beea5e653f2871d270e771627781518968c4e062bd227de1e35","excerpt_chars":1200,"truncated":true,"excerpt":"amazon-science/TSFM-Biases Description: Official Implementation of Understanding the Implicit Biases of Design Choices for Time Series Foundation Models Language: Jupyter Notebook License: Apache-2.0 Stars: 3 Forks: 0 Open issues: 0 Created: 2026-03-18T22:48:34Z Pushed: 2026-03-18T23:15:07Z Default branch: main Fork: no Archived: no README: Understanding the Implicit Biases of Design Choices for Time Series Foundation Models This repository is associated with the paper \"Understanding the Implicit Biases of Design Choices for Time Series Foundation Models\" by Annan Yu, Danielle C. Maddix, Boran Han, Xiyuan Zhang, Abdul Fatir Ansari, Oleksandr Shchur, Christos Faloutsos, Andrew Gordon Wilson, Michael W. Mahoney, and Yuyang Wang. It contains codes that are used to investigate the biases one should be aware of when designing time-series foundation models. It is organized as follows. Pretraining Checkpoints * [`hybrid-models`](./hybrid-models/): containing the training scripts of two models formed by combining the design choices of Chronos and Chronos-Bolt. These models are used for ablation to better investigate the role of an isolated design choice. Please refer to the..."},"evidence_pages":[{"url":"https://github.com/amazon-science/TSFM-Biases","final_url":"https://github.com/amazon-science/TSFM-Biases","title":"amazon-science/TSFM-Biases repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T02:52:00.926693+00:00","bytes":17828,"raw_path":"550f7b24a828a946eb9c8711661b35e2bac5ebc3b325c513cba100464aa499b9.json","content_hash":"b14131603e1e5beea5e653f2871d270e771627781518968c4e062bd227de1e35","excerpt_chars":1200,"truncated":true,"excerpt":"amazon-science/TSFM-Biases Description: Official Implementation of Understanding the Implicit Biases of Design Choices for Time Series Foundation Models Language: Jupyter Notebook License: Apache-2.0 Stars: 3 Forks: 0 Open issues: 0 Created: 2026-03-18T22:48:34Z Pushed: 2026-03-18T23:15:07Z Default branch: main Fork: no Archived: no README: Understanding the Implicit Biases of Design Choices for Time Series Foundation Models This repository is associated with the paper \"Understanding the Implicit Biases of Design Choices for Time Series Foundation Models\" by Annan Yu, Danielle C. Maddix, Boran Han, Xiyuan Zhang, Abdul Fatir Ansari, Oleksandr Shchur, Christos Faloutsos, Andrew Gordon Wilson, Michael W. Mahoney, and Yuyang Wang. It contains codes that are used to investigate the biases one should be aware of when designing time-series foundation models. It is organized as follows. Pretraining Checkpoints * [`hybrid-models`](./hybrid-models/): containing the training scripts of two models formed by combining the design choices of Chronos and Chronos-Bolt. These models are used for ablation to better investigate the role of an isolated design choice. Please refer to the..."}],"related_signals":[{"id":"087c32a2-6ad0-4981-9315-11fdd32a0153","url":"https://onlylabs.fyi/signals/087c32a2-6ad0-4981-9315-11fdd32a0153","source_url":"https://github.com/amazon-science/reskill","title":"amazon-science/reskill","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-06-04T02:13:35+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e5701aed-6cd3-48dd-bfa6-ef839031e2e8","url":"https://onlylabs.fyi/signals/e5701aed-6cd3-48dd-bfa6-ef839031e2e8","source_url":"https://github.com/amazon-science/dualkv-flash-attn-for-rl","title":"amazon-science/dualkv-flash-attn-for-rl","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-27T17:38:58+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"8af28f0c-7331-4b08-b517-e18b3555e503","url":"https://onlylabs.fyi/signals/8af28f0c-7331-4b08-b517-e18b3555e503","source_url":"https://github.com/amazon-science/EvoMAS","title":"amazon-science/EvoMAS","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-19T19:23:29+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e3ff8718-7daa-4ebd-a3e6-3d825c538b74","url":"https://onlylabs.fyi/signals/e3ff8718-7daa-4ebd-a3e6-3d825c538b74","source_url":"https://github.com/amazon-science/adaptive-layerwise-perturbation","title":"amazon-science/adaptive-layerwise-perturbation","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-14T17:44:17+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"9afcd328-0124-485c-8ace-9c3ad546e316","url":"https://onlylabs.fyi/signals/9afcd328-0124-485c-8ace-9c3ad546e316","source_url":"https://github.com/amazon-science/temporal-reasoning-dataset","title":"amazon-science/temporal-reasoning-dataset","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-13T13:07:08+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"},{"id":"e19ce80b-3d6a-4aaf-9b1a-82d1b19ab682","url":"https://onlylabs.fyi/signals/e19ce80b-3d6a-4aaf-9b1a-82d1b19ab682","source_url":"https://github.com/amazon-science/PROF-GRPO","title":"amazon-science/PROF-GRPO","context":"Python","kind":{"key":"repo_new","label":"Repo"},"org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab"},"occurred_at":"2026-05-12T19:43:55+00:00","first_seen_at":"2026-06-05T20:58:37.464059+00:00","date_source":"source"}]}