{"schema_version":"onlylabs.public_signal.v1","title":"Google (DeepMind / Gemini) Repo: google-deepmind/questbench","description":"Google (DeepMind / Gemini) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/5e01cffe-7c1b-4557-90f4-21bf624591e2","json_url":"https://onlylabs.fyi/signals/5e01cffe-7c1b-4557-90f4-21bf624591e2/signal.json","generated_at":"2026-06-11T04:00:37.736155+00:00","org":{"slug":"google-deepmind","name":"Google (DeepMind / Gemini)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/google-deepmind","dossier_json_url":"https://onlylabs.fyi/labs/google-deepmind/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/5e01cffe-7c1b-4557-90f4-21bf624591e2","signal_json":"https://onlylabs.fyi/signals/5e01cffe-7c1b-4557-90f4-21bf624591e2/signal.json","source":"https://github.com/google-deepmind/questbench","lab_dossier":"https://onlylabs.fyi/labs/google-deepmind","lab_dossier_json":"https://onlylabs.fyi/labs/google-deepmind/dossier.json","analysis":"https://onlylabs.fyi/analysis/google-deepmind","analysis_json":"https://onlylabs.fyi/analysis/google-deepmind/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/google-deepmind/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":"Google (DeepMind / Gemini) published google-deepmind/questbench (Python). 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However, past work has largely assumed that tasks are well-defined. In the real world, queries to LLMs are often underspecified, only solvable through acquiring missing information. We formalize this as a constraint satisfaction problem (CSP) with missing variable assignments. Using a special case of this formalism where only one necessary variable assignment is missing, we can rigorously evaluate an LLM's ability to identify the minimal necessary question to ask and quantify axes of difficulty levels for each problem. We present QuestBench, a set of underspecified reasoning tasks solvable by asking at most one question, which includes: (1) Logic-Q: Logical reasoning tasks with one missing proposition, (2)..."},"evidence_pages":[{"url":"https://github.com/google-deepmind/questbench","final_url":"https://github.com/google-deepmind/questbench","title":"google-deepmind/questbench repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T04:00:37.736155+00:00","bytes":15647,"raw_path":"a35e3b0c02fda3d6e3426ebb65043a85c0b25cf6bbbff6695c9677398803aa9d.json","content_hash":"b4480d629bf208c3d2b3111c9672227b3a5c48c514e77e49110815502f06a9c1","excerpt_chars":1200,"truncated":true,"excerpt":"google-deepmind/questbench Language: Python License: Apache-2.0 Stars: 40 Forks: 6 Open issues: 1 Created: 2025-01-10T20:15:10Z Pushed: 2025-05-15T18:10:02Z Default branch: main Fork: no Archived: no README: [QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?](https://arxiv.org/abs/2503.22674) Recently, a large amount of work has focused on improving large language models' (LLMs') performance on reasoning benchmarks such as math and logic. 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