✨LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference

📝 Summary:
LMCACHE enables efficient KV cache management for large language models by storing caches outside GPU memory, supporting cache reuse across queries and inference engines while achieving significant th...

🔹 Publication Date: Published on Oct 8, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.09665
• PDF: https://arxiv.org/pdf/2510.09665
• Github: https://github.com/LMCache/LMCache

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets

📝 Summary:
This paper introduces an automated framework for high-quality multilingual translation of benchmarks. It uses test-time compute scaling, specifically Universal Self-Improvement and T-RANK, to prevent semantic drift and context loss. This improves LLM evaluation accuracy beyond existing methods.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://huggingface.co/collections/hannayukhymenko/recovered-in-translation-eacl26-mme
• PDF: https://arxiv.org/pdf/2602.22207
• Project Page: https://ritranslation.insait.ai/
• Github: https://github.com/insait-institute/ritranslation

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#LLMEvaluation #MachineTranslation #NLP #AIResearch #Benchmarks

✨InfoNCE Induces Gaussian Distribution

📝 Summary:
InfoNCE objective induces Gaussian distribution in contrastive learning representations. This is supported by theoretical analysis and experimental validation. It explains observed Gaussianity and enables analytical treatment of representations.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24012
• PDF: https://arxiv.org/pdf/2602.24012
• Project Page: https://rbetser.github.io/InfoNCE-induces-Gaussian-distribution/
• Github: https://github.com/rbetser/InfoNCE-induces-Gaussian-distribution

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LongVideo-R1: Smart Navigation for Low-cost Long Video Understanding

📝 Summary:
LongVideo-R1 is an MLLM agent for efficient long video understanding with low computational cost. It uses active reasoning and selective clip navigation, avoiding exhaustive search by focusing on informative segments. This achieves superior accuracy and efficiency.

🔹 Publication Date: Published on Feb 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20913
• PDF: https://arxiv.org/pdf/2602.20913
• Github: https://github.com/qiujihao19/LongVideo-R1

🔹 Models citing this paper:
• https://huggingface.co/ChurchillQAQ/LongVideo-R1-Qwen2.5
• https://huggingface.co/ChurchillQAQ/LongVideo-R1-Qwen3

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ChurchillQAQ/LongVideo-R1-Data

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

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#VideoUnderstanding #MLLM #AI #EfficientAI #DeepLearning

✨LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding

📝 Summary:
LK losses directly optimize speculative decoding acceptance rate, outperforming standard KL divergence training. This boosts speedup, showing consistent gains of up to 10% in average acceptance length across various models and domains with no extra overhead.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23881
• PDF: https://arxiv.org/pdf/2602.23881

🔹 Models citing this paper:
• https://huggingface.co/nebius/MTP-DeepSeek-V3-0324
• https://huggingface.co/nebius/MEDUSA-Llama-3.1-8B-Instruct
• https://huggingface.co/nebius/MLP-Speculator-Llama-3.1-8B-Instruct

✨ Datasets citing this paper:
• https://huggingface.co/datasets/nebius/Llama-3.1-8B-Instruct-Infinity-Instruct-0625
• https://huggingface.co/datasets/nebius/gpt-oss-20b-Infinity-Instruct-0625
• https://huggingface.co/datasets/nebius/DeepSeek-V3-Infinity-Instruct-0625

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

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#SpeculativeDecoding #LLMs #LLMOptimization #DeepLearning #AIResearch

✨Compositional Generalization Requires Linear, Orthogonal Representations in Vision Embedding Models

📝 Summary:
Compositional generalization requires neural representations to decompose linearly into orthogonal per-concept components. This Linear Representation Hypothesis is theoretically grounded and empirically supported in vision models.

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.24264
• PDF: https://arxiv.org/pdf/2602.24264
• Github: https://github.com/oshapio/necessary-compositionality

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

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#CompositionalGeneralization #VisionModels #NeuralNetworks #MachineLearning #AIResearch

✨CL4SE: A Context Learning Benchmark For Software Engineering Tasks

📝 Summary:
CL4SE presents a benchmark for evaluating context learning in software engineering tasks, defining four SE-specific context types. It demonstrates an average 24.7% performance improvement for LLMs across tasks like code generation and review, establishing a standardized evaluation framework.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23047
• PDF: https://arxiv.org/pdf/2602.23047
• Project Page: https://huggingface.co/papers?q=project-specific%20context
• Github: https://github.com/Tomsawyerhu/CodeCL

✨ Datasets citing this paper:
• https://huggingface.co/datasets/tomhu/codecl

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#ContextLearning #SoftwareEngineering #LLMs #CodeGeneration #Benchmarks

✨Fara-7B: An Efficient Agentic Model for Computer Use

📝 Summary:
FaraGen synthesizes high-quality datasets for computer use agents to solve web tasks. This data trains Fara-7B, an efficient model perceiving via screenshots that outperforms larger models on benchmarks. It shows scalable data generation advances small agentic models.

🔹 Publication Date: Published on Nov 24, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19663
• PDF: https://arxiv.org/pdf/2511.19663
• Project Page: https://aka.ms/msaif/fara
• Github: https://github.com/microsoft/fara

🔹 Models citing this paper:
• https://huggingface.co/microsoft/Fara-7B
• https://huggingface.co/XythicK/microsoft_Fara-7B-GGUF

✨ Datasets citing this paper:
• https://huggingface.co/datasets/microsoft/WebTailBench

✨ Spaces citing this paper:
• https://huggingface.co/spaces/2025-ai-timeline/2025-ai-timeline
• https://huggingface.co/spaces/prithivMLmods/CUA-GUI-Operator
• https://huggingface.co/spaces/gouyongxiang/fara-7b

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

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#AIAgents #MachineLearning #EfficientAI #DatasetGeneration #WebAutomation

✨How to Take a Memorable Picture? Empowering Users with Actionable Feedback

📝 Summary:
This paper introduces Memorability Feedback MemFeed, a new task providing actionable natural language guidance to improve photo memorability. Their method, MemCoach, uses MLLMs and a teacher-student strategy, demonstrating that memorability can be taught and instructed.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21877
• PDF: https://arxiv.org/pdf/2602.21877
• Project Page: https://laitifranz.github.io/MemCoach/
• Github: https://laitifranz.github.io/MemCoach/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/laitifranz/MemBench-InternVL3.5-Eval
• https://huggingface.co/datasets/laitifranz/MemBench

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#PhotoMemorability #MLLMs #ComputerVision #AIResearch #HumanComputerInteraction

✨Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution

📝 Summary:
ReMe is a dynamic memory framework for LLM agents that distills, reuses, and refines experiences. It boosts performance, allowing smaller models to outperform larger memoryless ones for efficient lifelong learning.

🔹 Publication Date: Published on Dec 11, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10696
• PDF: https://arxiv.org/pdf/2512.10696
• Project Page: https://reme.agentscope.io/
• Github: https://github.com/agentscope-ai/ReMe

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

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#LLMAgents #LifelongLearning #LLMMemory #ArtificialIntelligence #MachineLearning

✨DUET-VLM: Dual stage Unified Efficient Token reduction for VLM Training and Inference

📝 Summary:
DUET-VLM proposes a dual-stage compression framework for Vision-Language Models. It first reduces visual tokens from the vision encoder, then progressively drops less informative tokens in the language backbone, guided by text. This maintains high accuracy while significantly reducing computation...

🔹 Publication Date: Published on Feb 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18846
• PDF: https://arxiv.org/pdf/2602.18846
• Github: https://github.com/AMD-AGI/DUET-VLM

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#VLM #ModelCompression #AI #DeepLearning #Efficiency

✨Shared Nature, Unique Nurture: PRISM for Pluralistic Reasoning via In-context Structure Modeling

📝 Summary:
LLMs are converging towards a singular 'hivemind,' reducing diversity. PRISM addresses this by equipping models with individualized epistemic trajectories using dynamic on-the-fly epistemic graphs. This enhances creativity, expands diversity, and improves diagnostic accuracy, moving towards a plu...

🔹 Publication Date: Published on Feb 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21317
• PDF: https://arxiv.org/pdf/2602.21317
• Project Page: https://www.prism4research.com/

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

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#LLMs #ArtificialIntelligence #AIDiversity #EpistemicModeling #AIResearch

✨Reinforcement-aware Knowledge Distillation for LLM Reasoning

📝 Summary:
RL-aware distillation RLAD improves knowledge transfer from RL-trained LLMs to smaller students. It addresses distribution mismatch and objective interference by using Trust Region Ratio Distillation TRRD. TRRD replaces the KL regularizer with a likelihood-ratio objective, balancing exploration, ...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22495
• PDF: https://arxiv.org/pdf/2602.22495

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

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#LLMs #KnowledgeDistillation #ReinforcementLearning #NLP #AI

✨Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents

📝 Summary:
This paper proposes that cognitive models and AI algorithms provide templates for designing modular language agents. These agent templates specify roles and functional composition to combine large language models for complex tasks, leading to more effective and interpretable systems.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22523
• PDF: https://arxiv.org/pdf/2602.22523

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Spectral Condition for μP under Width-Depth Scaling

📝 Summary:
This paper presents a unified spectral framework for maximal update parameterization addressing stable feature learning and hyperparameter transfer in deep neural networks scaled in both width and depth. It introduces a spectral condition for weight scaling that unifies existing formulations and ...

🔹 Publication Date: Published on Feb 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00541
• PDF: https://arxiv.org/pdf/2603.00541
• Project Page: https://github.com/ML-GSAI/Width-Depth-muP
• Github: https://github.com/ML-GSAI/Width-Depth-muP

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨CC-VQA: Conflict- and Correlation-Aware Method for Mitigating Knowledge Conflict in Knowledge-Based Visual Question Answering

📝 Summary:
CC-VQA addresses knowledge conflicts in visual question answering by incorporating visual-semantic conflict analysis and correlation-guided encoding-decoding mechanisms without requiring model retrain...

🔹 Publication Date: Published on Feb 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23952
• PDF: https://arxiv.org/pdf/2602.23952
• Github: https://github.com/cqu-student/CC-VQA

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨VGGT-Det: Mining VGGT Internal Priors for Sensor-Geometry-Free Multi-View Indoor 3D Object Detection

📝 Summary:
VGGT-Det enables sensor-geometry-free multi-view indoor 3D object detection. It integrates a Visual Geometry Grounded Transformer, using Attention-Guided Query Generation and Query-Driven Feature Aggregation to leverage VGGT's internal semantic and geometric priors. This approach significantly ou...

🔹 Publication Date: Published on Mar 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.00912
• PDF: https://arxiv.org/pdf/2603.00912
• Github: https://github.com/yangcaoai/VGGT-Det-CVPR2026

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model

📝 Summary:
LLaDA-o is an omni diffusion model that uses a Mixture of Diffusion framework to jointly handle text understanding and visual generation through a shared attention backbone, achieving state-of-the-art...

🔹 Publication Date: Published on Mar 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.01068
• PDF: https://arxiv.org/pdf/2603.01068
• Github: https://github.com/ML-GSAI/LLaDA-o

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Tool-R0: Self-Evolving LLM Agents for Tool-Learning from Zero Data

📝 Summary:
Tool-R0 framework enables training general-purpose tool-calling agents through self-play reinforcement learning without initial datasets, achieving significant performance improvements over base model...

🔹 Publication Date: Published on Feb 24

🔹 Paper Links:
• arXiv Page: https://huggingface.co/collections/emrecanacikgoz/tool-r0
• PDF: https://arxiv.org/pdf/2602.21320
• Project Page: https://emrecanacikgoz.github.io/Tool-R0/
• Github: https://github.com/emrecanacikgoz/Tool-R0

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

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#AI #DataScience #MachineLearning #HuggingFace #Research