The Year in AI Papers
The year/Labs/Google DeepMind

Research lab

Google DeepMind

Research from Google and Google DeepMind across foundation models, agents, science, robotics, and safety.

Papers
11
Citations
311
Official code
4

11 papers from Google DeepMind

01

Research paper

Gemma 4 Technical Report

Gemma 4 is a new family of open-weight, natively multimodal language models from Google DeepMind, featuring dense (2.3B, 4.5B, 12B, 31B) and Mixture-of-Experts (26B total, 3.8B active) architectures. Key innovations include a thinking mode for reasoning traces, an encoder-free architecture for the 12B model that processes raw audio and image patches, and…

Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres, et al.
Published
Jul 2026
Citations
18
Code
Not linked
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02

arXiv.org

RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards

RubricEM is a reinforcement learning framework for training deep research agents on open-ended tasks where verifiable rewards are unavailable. It treats rubrics as a shared interface structuring policy execution, judge feedback, and agent memory. The framework decomposes trajectories into four rubric-guided stages (Plan, Research, Review, Answer) and uses…

Gaotang Li, Bhavana Dalvi Mishra, Zifeng Wang, Jun Yan, et al.
Published
May 2026
Citations
3
Code
Not linked
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03

arXiv.org

LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling

The paper introduces AutoTTS, an environment-driven framework for automatically discovering test-time scaling (TTS) strategies for large language models, shifting the human role from hand-crafting heuristics to constructing discovery environments. The framework formulates width-depth TTS as controller synthesis over an offline replay environment built from…

Tong Zheng, Haolin Liu, Chengsong Huang, Huiwen Bao, et al.
Published
May 2026
Citations
0
Code
170 stars
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04

arXiv.org

VGGRPO: Towards World-Consistent Video Generation with 4D Latent Reward

VGGRPO is a framework for geometry-aware post-training of video diffusion models, addressing geometric drift and unstable camera motion. It introduces a Latent Geometry Model (LGM) that stitches video diffusion latents to a geometry foundation model (e.g., Any4D) via a lightweight connector, enabling direct prediction of 4D scene geometry (camera poses,…

Zhaochong An, Orest Kupyn, Théo Uscidda, Andrea Colaco, et al.
Published
Mar 2026
Citations
14
Code
Not linked
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05

arXiv.org

Thinking to Recall: How Reasoning Unlocks Parametric Knowledge in LLMs

The paper investigates why reasoning improves parametric knowledge recall in LLMs for simple, single-hop factual questions. Using hybrid models (Gemini-2.5-Flash, Gemini-2.5-Pro, Qwen3-32B) on SimpleQA-Verified and EntityQuestions, the authors find that enabling reasoning substantially expands the model's capability boundary, as measured by pass@k, with…

Zorik Gekhman, Roee Aharoni, Eran Ofek, Mor Geva, et al.
Published
Mar 2026
Citations
9
Code
Not linked
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06

arXiv.org

LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory

LoGeR (Long-context Geometric Reconstruction) is a novel architecture for scaling feedforward dense 3D reconstruction to extremely long video sequences (up to 19k frames) without post-optimization. It processes video in chunks, using bidirectional attention for intra-chunk reasoning and a hybrid memory module for inter-chunk coherence. The hybrid memory…

Junyi Zhang, Charles Herrmann, Junhwa Hur, Chen Sun, et al.
Published
Mar 2026
Citations
22
Code
609 stars
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07

arXiv.org

Unified Latents (UL): How to train your latents

Unified Latents (UL) is a framework for learning latent representations regularized by a diffusion prior and decoded by a diffusion model. The encoder outputs a deterministic latent, which is noised to a fixed minimum noise level (log-SNR of 5), linking the encoder's output noise to the prior's precision. The training objective combines a diffusion prior…

Jonathan Heek, Emiel Hoogeboom, Thomas Mensink, Tim Salimans
Published
Feb 2026
Citations
15
Code
Not linked
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08

arXiv.org

MARS: Modular Agent with Reflective Search for Automated AI Research

MARS (Modular Agent with Reflective Search) is a framework for automating AI research, specifically addressing the bottleneck of complex machine learning engineering (MLE) tasks. It uses three pillars: Budget-Aware Planning via cost-constrained Monte Carlo Tree Search (MCTS) to balance performance with execution cost, Modular Construction through a…

Jiefeng Chen, Bhavana Dalvi Mishra, Jaehyun Nam, Rui Meng, et al.
Published
Feb 2026
Citations
12
Code
39 stars
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09

arXiv.org

PaperBanana: Automating Academic Illustration for AI Scientists

PaperBanana is an agentic framework that automates the generation of publication-ready academic illustrations, such as methodology diagrams and statistical plots. It uses five specialized agents—Retriever, Planner, Stylist, Visualizer, and Critic—powered by VLMs and image generation models. The Retriever selects relevant reference examples, the Planner…

Dawei Zhu, Rui Meng, Yale Song, Xiyu Wei, et al.
Published
Jan 2026
Citations
19
Code
6.9K stars
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10

arXiv.org

Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning

This paper introduces a method for hierarchical reinforcement learning (RL) in autoregressive models by discovering and using temporally-abstract actions within the model's internal representations. The authors show that autoregressive models pretrained on next-token prediction learn latent representations of subgoals in their residual stream activations.…

Seijin Kobayashi, Yanick Schimpf, Maximilian Schlegel, Angelika Steger, et al.
Published
Dec 2025
Citations
5
Code
Not linked
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11

arXiv.org

Video models are zero-shot learners and reasoners

This paper investigates whether generative video models, like Veo 3, can act as zero-shot learners and reasoners for general-purpose vision tasks, similar to how LLMs transformed NLP. The authors analyzed 18,384 generated videos across 62 qualitative and 7 quantitative tasks, finding that Veo 3 can solve tasks it wasn't explicitly trained for, including…

Thaddäus Wiedemer, Yuxuan Li, Paul Vicol, Shixiang Shane Gu, et al.
Published
Sep 2025
Citations
194
Code
Not linked
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