✨Value-Based Pre-Training with Downstream Feedback
📝 Summary:
V-Pretraining reshapes foundation model pretraining objectives by using downstream task gradients. This method improves model capabilities and efficiency for tasks like language reasoning and vision segmentation, using minimal downstream feedback without direct label updates.
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22108
• PDF: https://arxiv.org/pdf/2601.22108
==================================
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📝 Summary:
V-Pretraining reshapes foundation model pretraining objectives by using downstream task gradients. This method improves model capabilities and efficiency for tasks like language reasoning and vision segmentation, using minimal downstream feedback without direct label updates.
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22108
• PDF: https://arxiv.org/pdf/2601.22108
==================================
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✨Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving
📝 Summary:
Drive-JEPA combines V-JEPA video pretraining with multimodal trajectory distillation to achieve state-of-the-art performance in end-to-end autonomous driving. AI-generated summary End-to-end autonomou...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22032
• PDF: https://arxiv.org/pdf/2601.22032
• Project Page: https://github.com/linhanwang/Drive-JEPA
• Github: https://github.com/linhanwang/Drive-JEPA
==================================
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📝 Summary:
Drive-JEPA combines V-JEPA video pretraining with multimodal trajectory distillation to achieve state-of-the-art performance in end-to-end autonomous driving. AI-generated summary End-to-end autonomou...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22032
• PDF: https://arxiv.org/pdf/2601.22032
• Project Page: https://github.com/linhanwang/Drive-JEPA
• Github: https://github.com/linhanwang/Drive-JEPA
==================================
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✨Memorization Dynamics in Knowledge Distillation for Language Models
📝 Summary:
Knowledge distillation reduces training data memorization compared to standard fine-tuning while maintaining performance, with distinct memorization patterns and predictability based on input characte...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15394
• PDF: https://arxiv.org/pdf/2601.15394
==================================
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📝 Summary:
Knowledge distillation reduces training data memorization compared to standard fine-tuning while maintaining performance, with distinct memorization patterns and predictability based on input characte...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15394
• PDF: https://arxiv.org/pdf/2601.15394
==================================
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✨RAPTOR: Ridge-Adaptive Logistic Probes
📝 Summary:
RAPTOR is a ridge-adaptive logistic probe that accurately and stably estimates concept vectors for activation steering in frozen LLMs. It significantly reduces training costs while matching or exceeding baseline accuracy and stability. Theoretical analysis underpins its efficacy.
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00158
• PDF: https://arxiv.org/pdf/2602.00158
==================================
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📝 Summary:
RAPTOR is a ridge-adaptive logistic probe that accurately and stably estimates concept vectors for activation steering in frozen LLMs. It significantly reduces training costs while matching or exceeding baseline accuracy and stability. Theoretical analysis underpins its efficacy.
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00158
• PDF: https://arxiv.org/pdf/2602.00158
==================================
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✨FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents
📝 Summary:
FS-Researcher is a dual-agent framework that scales LLM research tasks beyond context window limits. It uses a file system as persistent external memory, enabling a Context Builder and Report Writer to achieve state-of-the-art report quality and effective test-time scaling.
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01566
• PDF: https://arxiv.org/pdf/2602.01566
==================================
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📝 Summary:
FS-Researcher is a dual-agent framework that scales LLM research tasks beyond context window limits. It uses a file system as persistent external memory, enabling a Context Builder and Report Writer to achieve state-of-the-art report quality and effective test-time scaling.
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01566
• PDF: https://arxiv.org/pdf/2602.01566
==================================
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✨How Well Do Models Follow Visual Instructions? VIBE: A Systematic Benchmark for Visual Instruction-Driven Image Editing
📝 Summary:
Visual Instruction Benchmark for Image Editing introduces a three-level interaction hierarchy for evaluating visual instruction following capabilities in generative models. AI-generated summary Recent...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01851
• PDF: https://arxiv.org/pdf/2602.01851
• Github: https://vibe-benchmark.github.io/
==================================
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📝 Summary:
Visual Instruction Benchmark for Image Editing introduces a three-level interaction hierarchy for evaluating visual instruction following capabilities in generative models. AI-generated summary Recent...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01851
• PDF: https://arxiv.org/pdf/2602.01851
• Github: https://vibe-benchmark.github.io/
==================================
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✨Ebisu: Benchmarking Large Language Models in Japanese Finance
📝 Summary:
A Japanese financial language understanding benchmark named Ebisu is introduced, featuring two expert-annotated tasks that evaluate implicit commitment recognition and hierarchical financial terminolo...
🔹 Publication Date: Published on Feb 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01479
• PDF: https://arxiv.org/pdf/2602.01479
==================================
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📝 Summary:
A Japanese financial language understanding benchmark named Ebisu is introduced, featuring two expert-annotated tasks that evaluate implicit commitment recognition and hierarchical financial terminolo...
🔹 Publication Date: Published on Feb 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01479
• PDF: https://arxiv.org/pdf/2602.01479
==================================
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✨PISCES: Annotation-free Text-to-Video Post-Training via Optimal Transport-Aligned Rewards
📝 Summary:
PISCES is an annotation-free text-to-video generation method that uses dual optimal transport-aligned rewards to improve visual quality and semantic alignment without human preference annotations. AI-...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01624
• PDF: https://arxiv.org/pdf/2602.01624
==================================
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📝 Summary:
PISCES is an annotation-free text-to-video generation method that uses dual optimal transport-aligned rewards to improve visual quality and semantic alignment without human preference annotations. AI-...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01624
• PDF: https://arxiv.org/pdf/2602.01624
==================================
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✨PromptRL: Prompt Matters in RL for Flow-Based Image Generation
📝 Summary:
Flow matching models for text-to-image generation are enhanced through a reinforcement learning framework that addresses sample inefficiency and prompt overfitting by incorporating language models for...
🔹 Publication Date: Published on Feb 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01382
• PDF: https://arxiv.org/pdf/2602.01382
• Github: https://github.com/G-U-N/UniRL
==================================
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📝 Summary:
Flow matching models for text-to-image generation are enhanced through a reinforcement learning framework that addresses sample inefficiency and prompt overfitting by incorporating language models for...
🔹 Publication Date: Published on Feb 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01382
• PDF: https://arxiv.org/pdf/2602.01382
• Github: https://github.com/G-U-N/UniRL
==================================
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✨Adaptive Ability Decomposing for Unlocking Large Reasoning Model Effective Reinforcement Learning
📝 Summary:
Adaptive Ability Decomposing (A²D) enhances reinforcement learning with verifiable rewards by decomposing complex questions into simpler sub-questions, improving LLM reasoning through guided explorati...
🔹 Publication Date: Published on Jan 31
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00759
• PDF: https://arxiv.org/pdf/2602.00759
==================================
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📝 Summary:
Adaptive Ability Decomposing (A²D) enhances reinforcement learning with verifiable rewards by decomposing complex questions into simpler sub-questions, improving LLM reasoning through guided explorati...
🔹 Publication Date: Published on Jan 31
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00759
• PDF: https://arxiv.org/pdf/2602.00759
==================================
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❤1
✨Rethinking LLM-as-a-Judge: Representation-as-a-Judge with Small Language Models via Semantic Capacity Asymmetry
📝 Summary:
Small language models can effectively evaluate outputs by leveraging internal representations rather than generating responses, enabling a more efficient and interpretable evaluation approach through ...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22588
• PDF: https://arxiv.org/pdf/2601.22588
• Github: https://github.com/zhuochunli/Representation-as-a-judge
==================================
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📝 Summary:
Small language models can effectively evaluate outputs by leveraging internal representations rather than generating responses, enabling a more efficient and interpretable evaluation approach through ...
🔹 Publication Date: Published on Jan 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22588
• PDF: https://arxiv.org/pdf/2601.22588
• Github: https://github.com/zhuochunli/Representation-as-a-judge
==================================
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✨WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora
📝 Summary:
WildGraphBench evaluates GraphRAG performance in realistic scenarios using Wikipedia's structured content to assess multi-fact aggregation and summarization capabilities across diverse document types....
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02053
• PDF: https://arxiv.org/pdf/2602.02053
==================================
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📝 Summary:
WildGraphBench evaluates GraphRAG performance in realistic scenarios using Wikipedia's structured content to assess multi-fact aggregation and summarization capabilities across diverse document types....
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02053
• PDF: https://arxiv.org/pdf/2602.02053
==================================
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✨RLAnything: Forge Environment, Policy, and Reward Model in Completely Dynamic RL System
📝 Summary:
RLAnything enhances reinforcement learning for LLMs and agents through dynamic model optimization and closed-loop feedback mechanisms that improve policy and reward model training. AI-generated summar...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02488
• PDF: https://arxiv.org/pdf/2602.02488
• Project Page: https://huggingface.co/collections/Gen-Verse/open-agentrl
• Github: https://github.com/Gen-Verse/Open-AgentRL
🔹 Models citing this paper:
• https://huggingface.co/Gen-Verse/RLAnything-Alf-7B
• https://huggingface.co/Gen-Verse/RLAnything-Alf-Reward-14B
• https://huggingface.co/Gen-Verse/RLAnything-OS-Reward-8B
==================================
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📝 Summary:
RLAnything enhances reinforcement learning for LLMs and agents through dynamic model optimization and closed-loop feedback mechanisms that improve policy and reward model training. AI-generated summar...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02488
• PDF: https://arxiv.org/pdf/2602.02488
• Project Page: https://huggingface.co/collections/Gen-Verse/open-agentrl
• Github: https://github.com/Gen-Verse/Open-AgentRL
🔹 Models citing this paper:
• https://huggingface.co/Gen-Verse/RLAnything-Alf-7B
• https://huggingface.co/Gen-Verse/RLAnything-Alf-Reward-14B
• https://huggingface.co/Gen-Verse/RLAnything-OS-Reward-8B
==================================
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✨Wiki Live Challenge: Challenging Deep Research Agents with Expert-Level Wikipedia Articles
📝 Summary:
Deep Research Agents demonstrate capabilities in autonomous information retrieval but show significant gaps when evaluated against expert-level Wikipedia articles using a new live benchmark and compre...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01590
• PDF: https://arxiv.org/pdf/2602.01590
==================================
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📝 Summary:
Deep Research Agents demonstrate capabilities in autonomous information retrieval but show significant gaps when evaluated against expert-level Wikipedia articles using a new live benchmark and compre...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01590
• PDF: https://arxiv.org/pdf/2602.01590
==================================
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✨Closing the Loop: Universal Repository Representation with RPG-Encoder
📝 Summary:
RPG-Encoder framework transforms repository comprehension and generation into a unified cycle by encoding code into high-fidelity Repository Planning Graph representations that improve understanding a...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02084
• PDF: https://arxiv.org/pdf/2602.02084
• Project Page: https://ayanami2003.github.io/RPG-Encoder/
• Github: https://github.com/microsoft/RPG-ZeroRepo
==================================
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📝 Summary:
RPG-Encoder framework transforms repository comprehension and generation into a unified cycle by encoding code into high-fidelity Repository Planning Graph representations that improve understanding a...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02084
• PDF: https://arxiv.org/pdf/2602.02084
• Project Page: https://ayanami2003.github.io/RPG-Encoder/
• Github: https://github.com/microsoft/RPG-ZeroRepo
==================================
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✨Toward Cognitive Supersensing in Multimodal Large Language Model
📝 Summary:
MLLMs equipped with Cognitive Supersensing and Latent Visual Imagery Prediction demonstrate enhanced cognitive reasoning capabilities through integrated visual and textual reasoning pathways. AI-gener...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01541
• PDF: https://arxiv.org/pdf/2602.01541
• Project Page: https://pediamedai.com/Cognition-MLLM/cogsense/
• Github: https://github.com/PediaMedAI/Cognition-MLLM
==================================
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📝 Summary:
MLLMs equipped with Cognitive Supersensing and Latent Visual Imagery Prediction demonstrate enhanced cognitive reasoning capabilities through integrated visual and textual reasoning pathways. AI-gener...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01541
• PDF: https://arxiv.org/pdf/2602.01541
• Project Page: https://pediamedai.com/Cognition-MLLM/cogsense/
• Github: https://github.com/PediaMedAI/Cognition-MLLM
==================================
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✨Making Avatars Interact: Towards Text-Driven Human-Object Interaction for Controllable Talking Avatars
📝 Summary:
A dual-stream framework called InteractAvatar is presented for generating talking avatars that can interact with objects in their environment, addressing challenges in grounded human-object interactio...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01538
• PDF: https://arxiv.org/pdf/2602.01538
• Github: https://github.com/angzong/InteractAvatar
==================================
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📝 Summary:
A dual-stream framework called InteractAvatar is presented for generating talking avatars that can interact with objects in their environment, addressing challenges in grounded human-object interactio...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01538
• PDF: https://arxiv.org/pdf/2602.01538
• Github: https://github.com/angzong/InteractAvatar
==================================
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✨Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoning
📝 Summary:
Visual metaphor transfer enables creative AI systems to decompose abstract conceptual relationships from reference images and reapply them to new subjects through a multi-agent framework grounded in c...
🔹 Publication Date: Published on Feb 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01335
• PDF: https://arxiv.org/pdf/2602.01335
==================================
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📝 Summary:
Visual metaphor transfer enables creative AI systems to decompose abstract conceptual relationships from reference images and reapply them to new subjects through a multi-agent framework grounded in c...
🔹 Publication Date: Published on Feb 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01335
• PDF: https://arxiv.org/pdf/2602.01335
==================================
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✨Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models
📝 Summary:
Vision-DeepResearch benchmark addresses limitations in evaluating visual-textual search capabilities of multimodal models by introducing realistic evaluation conditions and improving visual retrieval ...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02185
• PDF: https://arxiv.org/pdf/2602.02185
• Project Page: https://osilly.github.io/Vision-DeepResearch/
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📝 Summary:
Vision-DeepResearch benchmark addresses limitations in evaluating visual-textual search capabilities of multimodal models by introducing realistic evaluation conditions and improving visual retrieval ...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02185
• PDF: https://arxiv.org/pdf/2602.02185
• Project Page: https://osilly.github.io/Vision-DeepResearch/
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✨Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models
📝 Summary:
Vision-DeepResearch introduces a multimodal deep-research paradigm enabling multi-turn, multi-entity, and multi-scale visual and textual search with deep-research capabilities integrated through cold-...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22060
• PDF: https://arxiv.org/pdf/2601.22060
• Project Page: https://osilly.github.io/Vision-DeepResearch/
• Github: https://github.com/Osilly/Vision-DeepResearch
==================================
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📝 Summary:
Vision-DeepResearch introduces a multimodal deep-research paradigm enabling multi-turn, multi-entity, and multi-scale visual and textual search with deep-research capabilities integrated through cold-...
🔹 Publication Date: Published on Jan 29
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22060
• PDF: https://arxiv.org/pdf/2601.22060
• Project Page: https://osilly.github.io/Vision-DeepResearch/
• Github: https://github.com/Osilly/Vision-DeepResearch
==================================
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✨Kimi K2.5: Visual Agentic Intelligence
📝 Summary:
Kimi K2.5 is an open-source multimodal agentic model that enhances text and vision processing through joint optimization techniques and introduces Agent Swarm for parallel task execution. AI-generated...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02276
• PDF: https://arxiv.org/pdf/2602.02276
• Project Page: https://huggingface.co/moonshotai/Kimi-K2.5
==================================
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📝 Summary:
Kimi K2.5 is an open-source multimodal agentic model that enhances text and vision processing through joint optimization techniques and introduces Agent Swarm for parallel task execution. AI-generated...
🔹 Publication Date: Published on Feb 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02276
• PDF: https://arxiv.org/pdf/2602.02276
• Project Page: https://huggingface.co/moonshotai/Kimi-K2.5
==================================
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