✨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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📝 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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📝 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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📝 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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📝 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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❤1
✨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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📝 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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arXiv.org
MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and...
Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged as a core...
✨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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📝 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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📝 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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📝 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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📝 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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📝 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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📝 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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📝 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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📝 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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📝 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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arXiv.org
UI-TARS: Pioneering Automated GUI Interaction with Native Agents
This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e.g., keyboard and mouse operations). Unlike prevailing...
✨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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📝 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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✨EgoActor: Grounding Task Planning into Spatial-aware Egocentric Actions for Humanoid Robots via Visual-Language Models
📝 Summary:
EgoActor is a unified vision-language model that translates high-level instructions into precise humanoid robot actions through integrated perception and execution across simulated and real-world envi...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04515
• PDF: https://arxiv.org/pdf/2602.04515
• Github: https://baai-agents.github.io/EgoActor/
==================================
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📝 Summary:
EgoActor is a unified vision-language model that translates high-level instructions into precise humanoid robot actions through integrated perception and execution across simulated and real-world envi...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04515
• PDF: https://arxiv.org/pdf/2602.04515
• Github: https://baai-agents.github.io/EgoActor/
==================================
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✨PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR
📝 Summary:
Search agents trained on scientific paper corpora demonstrate advanced reasoning capabilities for technical question-answering tasks, outperforming traditional retrieval methods through reinforcement ...
🔹 Publication Date: Published on Jan 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18207
• PDF: https://arxiv.org/pdf/2601.18207
• Project Page: https://jmhb0.github.io/PaperSearchQA/
• Github: https://jmhb0.github.io/PaperSearchQA/
✨ Datasets citing this paper:
• https://huggingface.co/datasets/jmhb/PaperSearchQA
• https://huggingface.co/datasets/jmhb/pubmed_bioasq_2022
• https://huggingface.co/datasets/jmhb/bioasq_factoid
==================================
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📝 Summary:
Search agents trained on scientific paper corpora demonstrate advanced reasoning capabilities for technical question-answering tasks, outperforming traditional retrieval methods through reinforcement ...
🔹 Publication Date: Published on Jan 26
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.18207
• PDF: https://arxiv.org/pdf/2601.18207
• Project Page: https://jmhb0.github.io/PaperSearchQA/
• Github: https://jmhb0.github.io/PaperSearchQA/
✨ Datasets citing this paper:
• https://huggingface.co/datasets/jmhb/PaperSearchQA
• https://huggingface.co/datasets/jmhb/pubmed_bioasq_2022
• https://huggingface.co/datasets/jmhb/bioasq_factoid
==================================
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✨Rethinking the Trust Region in LLM Reinforcement Learning
📝 Summary:
DPPO addresses limitations in PPO for LLM fine-tuning by replacing ratio clipping with direct policy divergence constraints, improving training stability and efficiency. AI-generated summary Reinforce...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04879
• PDF: https://arxiv.org/pdf/2602.04879
==================================
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📝 Summary:
DPPO addresses limitations in PPO for LLM fine-tuning by replacing ratio clipping with direct policy divergence constraints, improving training stability and efficiency. AI-generated summary Reinforce...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04879
• PDF: https://arxiv.org/pdf/2602.04879
==================================
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✨Vibe AIGC: A New Paradigm for Content Generation via Agentic Orchestration
📝 Summary:
Vibe AIGC introduces a new generative AI paradigm where users provide high-level aesthetic and functional preferences, which are then orchestrated through multi-agent workflows to bridge the gap betwe...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04575
• PDF: https://arxiv.org/pdf/2602.04575
==================================
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📝 Summary:
Vibe AIGC introduces a new generative AI paradigm where users provide high-level aesthetic and functional preferences, which are then orchestrated through multi-agent workflows to bridge the gap betwe...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.04575
• PDF: https://arxiv.org/pdf/2602.04575
==================================
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