✨Whom to Query for What: Adaptive Group Elicitation via Multi-Turn LLM Interactions

📝 Summary:
An adaptive group elicitation framework combines LLM information gain with graph neural networks for population predictions. It selects questions and respondents, imputing missing data under budget limits to improve prediction accuracy with fewer queries.

🔹 Publication Date: Published on Feb 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2602.14279
• PDF: https://arxiv.org/pdf/2602.14279
• Github: https://github.com/ZDCSlab/Group-Adaptive-Elicitation

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✨Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges

📝 Summary:
LLM-based judges using natural-language rubrics for evaluation can exhibit systematic preference drift from minor rubric modifications, which can be exploited to manipulate alignment pipelines and deg...

🔹 Publication Date: Published on Feb 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2602.13576
• PDF: https://arxiv.org/pdf/2602.13576
• Github: https://github.com/ZDCSlab/Rubrics-as-an-Attack-Surface

🔹 Models citing this paper:
• https://huggingface.co/ZDCSlab/ripd-ultra-real-gemma2-2b-it-seed-bt
• https://huggingface.co/ZDCSlab/ripd-ultra-real-gemma2-2b-it-biased-bt
• https://huggingface.co/ZDCSlab/ripd-ultra-real-llama3-8b-instruct-seed-bt

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ZDCSlab/ripd-dataset

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✨TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics

📝 Summary:
TOPReward is a novel temporal value function that estimates robotic task progress using pretrained video VLM internal token logits. It achieves superior zero-shot performance across over 130 real-world tasks and multiple robots, greatly outperforming baselines.

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19313
• PDF: https://arxiv.org/pdf/2602.19313
• Project Page: https://topreward.github.io/webpage/
• Github: https://github.com/TOPReward/TOPReward

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✨Mobile-O: Unified Multimodal Understanding and Generation on Mobile Device

📝 Summary:
A compact vision-language-diffusion model called Mobile-O enables efficient unified multimodal understanding and generation on mobile devices through specialized architecture design and optimized trai...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20161
• PDF: https://arxiv.org/pdf/2602.20161
• Project Page: https://amshaker.github.io/Mobile-O/
• Github: https://github.com/Amshaker/Mobile-O

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✨DSDR: Dual-Scale Diversity Regularization for Exploration in LLM Reasoning

📝 Summary:
DSDR is a reinforcement learning framework that enhances large language model reasoning by promoting diversity at both global and local levels through dual-scale regularization techniques. AI-generate...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19895
• PDF: https://arxiv.org/pdf/2602.19895

==================================

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✨Agents of Chaos

📝 Summary:
Autonomous language-model-powered agents in a live laboratory environment exhibited numerous security and governance vulnerabilities including unauthorized actions, information disclosure, and system ...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20021
• PDF: https://arxiv.org/pdf/2602.20021

==================================

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✨tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction

📝 Summary:
A novel 3D reconstruction model called tttLRM uses a Test-Time Training layer to enable efficient, scalable autoregressive reconstruction with linear complexity, achieving better results than existing...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20160
• PDF: https://arxiv.org/pdf/2602.20160
• Project Page: https://cwchenwang.github.io/tttLRM
• Github: https://cwchenwang.github.io/tttLRM/

==================================

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✨A Very Big Video Reasoning Suite

📝 Summary:
A large-scale video reasoning dataset and benchmark are introduced to study video intelligence capabilities beyond visual quality, enabling systematic analysis of spatiotemporal reasoning and generali...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20159
• PDF: https://arxiv.org/pdf/2602.20159
• Project Page: https://video-reason.com/

🔹 Models citing this paper:
• https://huggingface.co/Video-Reason/VBVR-Wan2.2

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Video-Reason/VBVR-Bench-Data

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Video-Reason/VBVR-Bench-Leaderboard

==================================

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✨SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning

📝 Summary:
A hybrid knowledge-injection framework combines general reasoning large language models with time-series LLMs through reinforcement learning-based verifiable rewards to enhance time-series diagnostic ...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19455
• PDF: https://arxiv.org/pdf/2602.19455

==================================

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✨K-Search: LLM Kernel Generation via Co-Evolving Intrinsic World Model

📝 Summary:
K-Search uses a co-evolving world model to optimize GPU kernels by separating high-level planning from low-level implementation, achieving significant performance improvements over existing evolutiona...

🔹 Publication Date: Published on Feb 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.19128
• PDF: https://arxiv.org/pdf/2602.19128

==================================

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