✨RANKVIDEO: Reasoning Reranking for Text-to-Video Retrieval

📝 Summary:
RANKVIDEO is a reasoning-based reranker for text-to-video retrieval that explicitly analyzes query-video pairs for relevance. It uses a multi-objective training approach and a data synthesis pipeline. RANKVIDEO significantly improves retrieval performance by 31 percent on a large benchmark, outpe...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02444
• PDF: https://arxiv.org/pdf/2602.02444
• Github: https://github.com/tskow99/RANKVIDEO-Reasoning-Reranker

🔹 Models citing this paper:
• https://huggingface.co/hltcoe/RankVideo

✨ Datasets citing this paper:
• https://huggingface.co/datasets/hltcoe/RankVideo-Dataset

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

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✨LIVE: Long-horizon Interactive Video World Modeling

📝 Summary:
LIVE is a long-horizon video world model that uses cycle-consistency and diffusion loss to control error accumulation during extended video generation. AI-generated summary Autoregressive video world ...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2602.03747
• PDF: https://arxiv.org/pdf/2602.03747
• Project Page: https://junchao-cs.github.io/LIVE-demo/
• Github: https://junchao-cs.github.io/LIVE-demo/

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

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✨Didactic to Constructive: Turning Expert Solutions into Learnable Reasoning

📝 Summary:
DAIL improves LLM reasoning by converting didactic expert solutions into detailed, in-distribution traces via contrastive learning. This method achieves 10-25% performance gains and 2-4x reasoning efficiency using minimal expert data.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02405
• PDF: https://arxiv.org/pdf/2602.02405
• Github: https://github.com/ethanm88/DAIL

✨ Datasets citing this paper:
• https://huggingface.co/datasets/emendes3/e1-proof

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

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✨Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agents

📝 Summary:
High-quality feedback is essential for effective human-AI interaction. It bridges knowledge gaps, corrects digressions, and shapes system behavior; both during interaction and throughout model develop...

🔹 Publication Date: Published on Feb 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01405
• PDF: https://arxiv.org/pdf/2602.01405

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

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✨Scaling Small Agents Through Strategy Auctions

📝 Summary:
Small language models fail on complex tasks. The paper proposes Strategy Auctions for Workload Efficiency SALE, a marketplace-inspired framework where agents bid strategic plans for task routing and self-improvement. SALE reduces costs by 35% and improves performance, enabling small agents to sca...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02751
• PDF: https://arxiv.org/pdf/2602.02751

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

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✨MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers

📝 Summary:
MemoryLLM decouples feed-forward networks from self-attention in transformers, enabling context-free token-wise neural retrieval memory that improves inference efficiency through pre-computed lookups....

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00398
• PDF: https://arxiv.org/pdf/2602.00398

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

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✨Position: Agentic Evolution is the Path to Evolving LLMs

📝 Summary:
Large language models struggle to adapt to changing real-world environments. Agentic evolution is proposed as a new approach where deployment-time improvement becomes a goal-directed optimization process. This allows for sustained, open-ended adaptation by scaling evolution.

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00359
• PDF: https://arxiv.org/pdf/2602.00359
• Github: https://github.com/ventr1c/agentic-evoluiton

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

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✨MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe

📝 Summary:
MiniCPM-V 4.5 is an 8B multimodal LLM achieving high performance and efficiency. It uses a unified 3D-Resampler, unified learning, and hybrid reinforcement learning. It surpasses larger models like GPT-4o and Qwen2.5-VL with significantly less memory and faster inference.

🔹 Publication Date: Published on Sep 16, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.18154
• PDF: https://arxiv.org/pdf/2509.18154
• Github: https://github.com/OpenBMB/MiniCPM-V

🔹 Models citing this paper:
• https://huggingface.co/openbmb/MiniCPM-V-4_5
• https://huggingface.co/openbmb/MiniCPM-V-4_5-gguf
• https://huggingface.co/openbmb/MiniCPM-V-4_5-AWQ

✨ Datasets citing this paper:
• https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset

✨ Spaces citing this paper:
• https://huggingface.co/spaces/CGQN/MiniCPM-V-4_5-int4-CPU-0
• https://huggingface.co/spaces/CGQN/MiniCPM-V-4_5-from_gpt5
• https://huggingface.co/spaces/CGQN/MiniCPM-V-4_5

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

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✨Privasis: Synthesizing the Largest "Public" Private Dataset from Scratch

📝 Summary:
Privasis is a new million-scale synthetic dataset for AI privacy research. It addresses data scarcity, enabling compact sanitization models that outperform large language models like GPT-5. The diverse dataset and models will be released to the public.

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03183
• PDF: https://arxiv.org/pdf/2602.03183
• Project Page: https://privasis.github.io
• Github: https://github.com/skywalker023/privasis

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

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✨FIRE-Bench: Evaluating Agents on the Rediscovery of Scientific Insights

📝 Summary:
FIRE-Bench evaluates AI agents on rediscovering scientific findings through full research cycles, from hypothesis to conclusions. Agents receive a high-level question and act autonomously. Current agents struggle, showing that reliable AI-driven scientific discovery remains challenging.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02905
• PDF: https://arxiv.org/pdf/2602.02905
• Project Page: https://firebench.github.io/

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

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✨UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning

📝 Summary:
UI-TARS-2 is a native GUI agent model that tackles challenges in data scalability and multi-turn reinforcement learning. It significantly improves over its predecessor and strong baselines on GUI and game benchmarks, demonstrating robust generalization. This advances GUI agents for real-world int...

🔹 Publication Date: Published on Sep 2, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.02544
• PDF: https://arxiv.org/pdf/2509.02544
• Project Page: https://seed-tars.com/showcase/ui-tars-2/
• Github: https://github.com/bytedance/ui-tars

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

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✨SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation

📝 Summary:
SoMA is a 3D Gaussian Splat simulator that enables stable, long-horizon manipulation of soft bodies by coupling deformable dynamics, environmental forces, and robot actions in a unified latent neural ...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02402
• PDF: https://arxiv.org/pdf/2602.02402
• Project Page: https://huggingface.co/collections/SuemH/project-page
• Github: https://city-super.github.io/SoMA/

🔹 Models citing this paper:
• https://huggingface.co/SuemH/SoMA

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

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✨A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces

📝 Summary:
Agentic RAG framework enables models to dynamically adapt retrieval decisions across multiple granularities, outperforming traditional approaches while scaling efficiently with model improvements. AI-...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03442
• PDF: https://arxiv.org/pdf/2602.03442

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

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✨Self-Hinting Language Models Enhance Reinforcement Learning

📝 Summary:
SAGE is an on-policy reinforcement learning framework that enhances GRPO by injecting self-hints during training to increase outcome diversity under sparse rewards, improving alignment of large langua...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.03143
• PDF: https://arxiv.org/pdf/2602.03143
• Github: https://github.com/BaohaoLiao/SAGE

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

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✨Context Learning for Multi-Agent Discussion

📝 Summary:
Multi-Agent Discussion methods suffer from inconsistency due to individual context misalignment, which is addressed through a context learning approach that dynamically generates context instructions ...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02350
• PDF: https://arxiv.org/pdf/2602.02350

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

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✨A2Eval: Agentic and Automated Evaluation for Embodied Brain

📝 Summary:
Agentic automatic evaluation framework automates embodied vision-language model assessment through collaborative agents that reduce evaluation costs and improve ranking accuracy. AI-generated summary ...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01640
• PDF: https://arxiv.org/pdf/2602.01640

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

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✨UI-TARS: Pioneering Automated GUI Interaction with Native Agents

📝 Summary:
UI-TARS, a native GUI agent model using screenshots as input, outperforms commercial models in various benchmarks through enhanced perception, unified action modeling, system-2 reasoning, and iterativ...

🔹 Publication Date: Published on Jan 21, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.12326
• PDF: https://arxiv.org/pdf/2501.12326
• Github: https://github.com/bytedance/UI-TARS

🔹 Models citing this paper:
• https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B
• https://huggingface.co/ByteDance-Seed/UI-TARS-7B-DPO
• https://huggingface.co/ByteDance-Seed/UI-TARS-7B-SFT

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Hcompany/WebClick

✨ Spaces citing this paper:
• https://huggingface.co/spaces/omar0scarf/ui-tars-api
• https://huggingface.co/spaces/bytedance-research/UI-TARS
• https://huggingface.co/spaces/Aheader/gui_test_app

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

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✨Quant VideoGen: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization

📝 Summary:
Quant VideoGen addresses KV cache memory limitations in autoregressive video diffusion models through semantic-aware smoothing and progressive residual quantization, achieving significant memory reduc...

🔹 Publication Date: Published on Feb 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02958
• PDF: https://arxiv.org/pdf/2602.02958

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

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