[Yann-CV]

Adds 11 abstract-confirmed papers from the open paper issues.

• Data selection: curation diagnostics, agentic dataset search, group-level synthetic curation, model-aware selection, speech embeddings, OCRia data curation, FACTS, Domino, and gradient-based training-data attribution.
• Training strategy: unified foundation-model training and DISCOVER online prompt discovery/replay.
• Source issues: add Advancement or Acceleration? How Data Curation Improves LLM Training #91, add From Intent to Improvement: Agentic Dataset Search for Better Model Training #90, add Effective Synthetic Data Curation Requires Group-Level Signals #89, add Model-Aware Data Selection from In-and-Out Information Interplay #87, Which Data Matter? Embedding-Based Data Selection for Speech Recognition #84, add Training Foundation Models with a Single Algorithm #82, any data centric cool stuff in https://www.llamaindex.ai/ocria/ocria-1-technical-report.pdf ? #77, add https://peymoon.github.io/discover/static/pdf/DISCOVER.pdf #76, Add FACTS: First Amplify Correlations and Then Slice to Discover Bias #69, add "Domino: Discovering Systematic Errors with Cross-Modal Embeddings" #68, add Select or Project? Evaluating Lower-dimensional Vectors for LLM Training Data Explanations #43. Issue add Effective Synthetic Data Curation Requires Group-Level Signals #89 contains the wrong arXiv link; resolved its title to https://arxiv.org/abs/2610.00779.
• idSCD: Identifying Training Datasets through Semantic Correlation Descriptors #44 remains open: idSCD studies training-dataset membership inference; placement within the current lifecycle taxonomy remains unresolved.

Evidence: canonical arXiv abstracts and author lists; OCRia official technical-report abstract; DISCOVER author-hosted full text and NeurIPS 2026 program. No identifier duplicates found. Notes describe methods without generalizing benchmark-specific numerical claims.

Author follow-up adds 10 researchers and 20 supporting papers. Qualification requires at least three distinct abstract-confirmed relevant papers across topics, including the seed. All ten are new researcher identities in this snapshot; none replaces an existing entry.

Researcher	Verified relevant papers (seed + new)	Topics
Cathy Jiao	3 (1 + 2)	data_selection
Chenyan Xiong	5 (1 + 4)	data_selection
Jiaxing Wang	3 (1 + 2)	data_selection
Judy Hoffman	3 (1 + 2)	data_selection, training_strategy
Viraj Prabhu	3 (1 + 2)	data_selection, training_strategy
James Zou	3 (1 + 2)	data_selection, training_strategy
Christopher Ré	3 (1 + 2)	data_selection, labeling
Yuda Song	3 (1 + 2)	training_strategy
David Grangier	5 (1 + 4)	data_selection, training_strategy
Benjamin Roth	3 (1 + 2)	data_selection, training_strategy

Affiliations verified from current personal/institutional pages on 2026-11-04. In particular, Judy Hoffman is now at UC Irvine, with an adjunct Georgia Tech appointment. Author identity is linked through publication pages listing the qualifying works; full author lists and abstracts were retrieved for all supporting papers. Embedding-based seed papers and CLUE are placed under the embedding subsections.

Coverage: all 12 original paper issues were enumerated; eleven were imported and closed after PR creation, while #44 remains open pending relevance/placement. The 11 imported papers have 73 distinct authors. This follow-up used the default 30-author probe ceiling, prioritizing first/last seed authors and selected coauthors. Ten researchers qualified; twenty other probes remain unresolved or did not establish three relevant papers in the checked evidence. This is not a claim that those authors lack relevant work. Another 43 seed authors and the recursive author queues remain deferred at the author ceiling. The limit can be raised or removed to resume. Depth limit: two; default ceilings: 30 author checks, 200 candidate abstracts, 300 requests. Abstract and request ceilings were not reached.

Examples retained for follow-up include Loris Schoenegger (not reached within the author budget), the other BLADE/TANDEM coauthors, and unresolved Qixia/Qixiang Jiang identity linkage. Below-threshold probes were not imported as bibliography additions.

Validation: git diff --check, balanced HTML blocks, canonical-ID deduplication, researcher name/count checks, and researcher-to-bibliography link consistency. Caller checkout untouched. Worktree: .worktree/add-new-paper-2026-11-04, based on freshly fetched origin/main (08ba697). Local evidence and queues: /tmp/pr92-researcher-additions.json and /tmp/author-paper-evidence.json.

[cubic-dev-ai]

All reported issues were addressed across 2 files

<sub>Reply with feedback, questions, or to request a fix.

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[cubic-dev-ai]

1 issue found across 6 files (changes from recent commits).

Prompt for AI agents (unresolved issues)

Check if these issues are valid — if so, understand the root cause of each and fix them. When an issue isn't valid or won't be fixed in this PR, reply in its thread with the reason and then resolve the thread. If appropriate, use sub-agents to investigate and fix each issue separately.


<file name="training_strategy/BIBLIOGRAPHY.md">

<violation number="1" location="training_strategy/BIBLIOGRAPHY.md:258">
P3: This paper is published in Findings of ACL 2025, so `Origin: arXiv (2024)` misstates its venue. Replace it with the official 2025 Findings of ACL venue.</violation>
</file>

<sub>Reply with feedback, questions, or to request a fix.

View guided diff | Turn on auto-fix | Re-trigger cubic</sub>

[cubic-dev-ai]

P3: This paper is published in Findings of ACL 2025, so Origin: arXiv (2024) misstates its venue. Replace it with the official 2025 Findings of ACL venue.

Prompt for AI agents

Check if this issue is valid — if so, understand the root cause and fix it. When an issue isn't valid or won't be fixed in this PR, reply in its thread with the reason and then resolve the thread. At training_strategy/BIBLIOGRAPHY.md, line 258:

<comment>This paper is published in Findings of ACL 2025, so `Origin: arXiv (2024)` misstates its venue. Replace it with the official 2025 Findings of ACL venue.</comment>

<file context>
@@ -241,6 +250,24 @@
+    <strong>Training Bilingual LMs with Data Constraints in the Targeted Language</strong>
+  </a><br>
+  <small><em>👤 First author: Skyler Seto</em></small><br>
+  <small><em>📍 Origin: arXiv (2024)</em></small><br>
+  📝 Note: studies auxiliary-language data quality and upsampling strategies for language-model training with limited target-language data.
+</div>
</file context>

Suggested change

	<small><em>📍 Origin: arXiv (2024)</em></small><br>
	<small><em>📍 Origin: Findings of the Association for Computational Linguistics: ACL 2025</em></small><br>