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Thinking to Recall: How Reasoning Unlocks Parametric Knowledge in LLMs research paper by Google, 2026

Google · Mar 10, 2026 · Reasoning · 12 citations · 76 upvotes · unverified

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What it shows

Reasoning in large language models enhances parametric knowledge recall through computational buffer and factual priming mechanisms, though it carries risks of hallucination that can be mitigated by prioritizing accurate reasoning trajectories.

UnverifiedHugging Face's summary; not yet checked by hand.

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Other reasoning papers

Topic
PaperCitations
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language ModelsDeepSeekMath 7B improves mathematical reasoning through enhanced data pre-training and Group Relative Policy Optimization, achieving high scores on MATH benchmark without external tools.DeepSeek · Feb 2024 · Unverified8,994
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningReinforcement learning taught a model long step-by-step reasoning on par with OpenAI o1.DeepSeek · Jan 20255,694
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code IntelligenceDeepSeek-Coder-V2, a Mixture-of-Experts language model, excels in code-specific tasks by enhancing coding and mathematical reasoning capabilities while expanding language support and context length.DeepSeek · Jun 2024 · Unverified502
MolmoAct: Action Reasoning Models that can Reason in SpaceAction Reasoning Models (ARMs) integrate perception, planning, and control to enable adaptable and explainable robotic behavior, achieving superior performance across various tasks and settings.Ai2 · Aug 2025 · Unverified185
ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent PlanningThinkAct, a dual-system framework, uses reinforced visual latent planning to enable few-shot adaptation, long-horizon planning, and self-correction in embodied AI tasks by bridging high-level reasoning with low-level action execution.NVIDIA · Jul 2025 · Unverified172
WebShaper: Agentically Data Synthesizing via Information-Seeking FormalizationWebShaper, a formalization-driven framework, synthesizes information-seeking datasets using set theory and Knowledge Projections to enhance reasoning structure and achieve top performance in open-sourced benchmarks.Alibaba (Qwen) · Jul 2025 · Unverified113
About this paper
Authors
Zorik Gekhman, Roee Aharoni, Eran Ofek and 3 more
arXiv
2603.09906 · PDF
Venue
arXiv.org
Citations
12, 0 influential · Semantic Scholar
Upvotes
76 · Hugging Face
Lab
Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 12Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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