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Daily Digest - 2026-07-18

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Latest digest: 2026-07-25.

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7 visible entries

huggingface Score 19.4

RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination

2026-07-15 · Haotian Liang, Mingkang Chen, Yufei Huang, Yuchun Guo, Xiaomeng Zhu, Xiangli Shi, Kaixuan Wang, Yunxuan Mao, Weijie Zhou, Ling Chen, Shirong Zeng, Yueyu Long, Yuchen Si, Yajuan Zhu, Xingyu Zhou, Minghui Wang, Wanjia He, Xin Yang, Lingzhu Xiang, Zhiqing Liu, Bohan Ma, Xiran Huang, Tianshuo Yang, Zhiheng Liu, Xuantang Xiong, Zisheng Lu, Ping Luo, Yao Mu, Han Hu, Zhengyou Zhang

General AI

Embodied cognition requires agents to connect high-level task reasoning with the physical states to be achieved. We introduce Hy-Embodied-RxBrain, an embodied cognition foundation model with joint language-visual reasoning and imagination. Unlike vision-language models that emphasize scene understanding and textual dec…

Review
pending
Role
unreviewed
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now
huggingface Score 16.4

Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving

2026-07-15 · Yuan Gao, Wenting Miao, Mattia Piccinini, Haoyu Wang, Qunying Song, Johannes Betz

General AI

Validating autonomous driving systems requires diverse, regulation-compliant test scenarios. In simulation-based testing, scenarios are defined as executable scripts. Yet automatically generating such scripts from regulatory descriptions remains an open challenge, and existing approaches face fundamental trade-offs. Re…

Review
pending
Role
unreviewed
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now
huggingface Score 15.4

Rethinking the Evaluation of Harness Evolution for Agents

2026-07-14 · Yike Wang, Huaisheng Zhu, Zhengyu Hu, Yige Yuan, Zhengyu Chen, Shakti Senthil, Hannaneh Hajishirzi, Yulia Tsvetkov, Pradeep Dasigi, Teng Xiao

General AI

We revisit the evaluation of automatic harness evolution for LLM agents. Existing harness evolution methods use unit test cases to search for harness configurations and then report final performance on the same public benchmark. This protocol raises two fundamental concerns. First, harness evolution is itself an iterat…

Review
pending
Role
unreviewed
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now
huggingface Score 11.4

LongStraw: Long-Context RL Beyond 2M Tokens under a Fixed GPU Budget

2026-07-16 · Changhai Zhou, Kieran Liu, Yuhua Zhou, Qian Qiao, Jun Gao, Harry Zhang, Irvine Lu, Nolan Ho, Lucian Li, Andrew Lei, Cleon Cheng, Steven Chiang, Yihang Zeng, Di Zhang, Rio Yang, Kaijie Chen, Andrew Chen, Pony Ma, Weizhong Zhang, Cheng Jin

General AI

A growing gap separates inference context lengths from RL post-training: inference systems are approaching million-token contexts, while post-training workloads often remain at 256K tokens or below and rely on length generalization at deployment. The gap is especially important for AI agents, whose observations, tool o…

Review
pending
Role
unreviewed
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now
huggingface Score 6.0

On Locality and Length Generalization in Visual Reasoning

2026-07-10 · Pulkit Madan, Sanjay Haresh, Reza Ebrahimi, Sunny Panchal, Apratim Bhattacharyya, Roland Memisevic

General AI

A striking feature of the human visual system is that it ingests visual information through a series of local foveated glimpses, rather than a single global computation. This makes human vision distinctly different from most popular computer vision models in use today, which input images globally and in a single shot. …

Review
pending
Role
unreviewed
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later
huggingface Score 5.4

SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment

2026-07-16 · Saad Ejaz, Miguel Fernandez-Cortizas, Javier Civera, Holger Voos, Jose Luis Sanchez-Lopez

General AI

CAD-to-image alignment aims to estimate an object's 9D pose (rotation, translation, and anisotropic scale) from a single RGB image, enabling applications in robotics and augmented reality. Recent zero-shot methods use visual foundation models to match image regions to CAD models, yet typically their correspondences are…

Review
pending
Role
unreviewed
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later
huggingface Score 5.0

Token Time Continuous Diffusion for Language Modeling

2026-05-07 · Parikshit Bansal, Sujay Sanghavi

General AI

In this paper we introduce token time continuous diffusion (TTCD), a new diffusion language model which (a) operates in continuous space, deterministically mapping Gaussian noise to a final token canvas with no further sampling, and crucially (b) incorporates a new notion of per-token times, with some tokens proceeding…

Review
pending
Role
unreviewed
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later