✨OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation

📝 Summary:
OmniFlatten is a novel end-to-end GPT model enabling real-time natural full-duplex spoken dialogue. It achieves this by post-training a text LLM with a multi-stage process for speech-text generation, without modifying the original architecture.

🔹 Publication Date: Published on Oct 23, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.17799
• PDF: https://arxiv.org/pdf/2410.17799
• Github: https://github.com/karpathy/nanogpt

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#GPT #VoiceAI #NLP #LLM #DeepLearning

✨olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models

📝 Summary:
olmOCR is an open-source toolkit that uses a fine-tuned vision language model to convert PDFs into clean, structured text. It enables large-scale, cost-effective extraction of trillions of tokens for training language models.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.18443
• PDF: https://arxiv.org/pdf/2502.18443
• Github: https://github.com/allenai/olmocr

✨ Datasets citing this paper:
• https://huggingface.co/datasets/davanstrien/test-olmocr2
• https://huggingface.co/datasets/davanstrien/newspapers-olmocr2
• https://huggingface.co/datasets/stckmn/ocr-output-Directive017-1761355297

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#OCR #VLMs #LLM #DataExtraction #OpenSource

✨MedRAX: Medical Reasoning Agent for Chest X-ray

📝 Summary:
MedRAX is a new AI agent that integrates CXR analysis tools and multimodal large language models. It answers complex medical queries without extra training, achieving state-of-the-art performance.

🔹 Publication Date: Published on Feb 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.02673
• PDF: https://arxiv.org/pdf/2502.02673
• Github: https://github.com/bowang-lab/medrax

✨ Spaces citing this paper:
• https://huggingface.co/spaces/asbamit/MedRAX-main

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#AI #MedicalAI #LLM #Radiology #DeepLearning

✨Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

📝 Summary:
Mem0 is a memory-centric architecture with graph-based memory that enhances long-term conversational coherence in LLMs by efficiently extracting and consolidating information. It outperforms existing memory systems in accuracy, achieving 26% improvement over OpenAI, and significantly reduces comp...

🔹 Publication Date: Published on Apr 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.19413
• PDF: https://arxiv.org/pdf/2504.19413
• Github: https://github.com/mem0ai/mem0

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#AI #LLM #AIAgents #LongTermMemory #GraphMemory

✨WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research

📝 Summary:
WebWeaver is a dual-agent framework addressing open-ended deep research challenges. It uses dynamic planning interleaving evidence acquisition and outline optimization and hierarchical, targeted writing to overcome long-context issues. This approach produces state-of-the-art, high-quality, reliab...

🔹 Publication Date: Published on Sep 16

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/webweaver-structuring-web-scale-evidence-with-dynamic-outlines-for-open-ended-deep-research
• PDF: https://arxiv.org/pdf/2509.13312
• Project Page: https://tongyi-agent.github.io/blog/
• Github: https://tongyi-agent.github.io/blog/

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#AI #Research #AgentSystems #LLM #KnowledgeManagement

✨ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization

📝 Summary:
ReSum enhances LLM-based web agents by overcoming context window limitations through periodic context summarization. This novel paradigm converts interaction histories into compact reasoning states, enabling indefinite exploration for complex tasks. ReSum improves performance by 4.5% over ReAct, ...

🔹 Publication Date: Published on Sep 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.13313
• PDF: https://arxiv.org/pdf/2509.13313
• Project Page: https://tongyi-agent.github.io/blog/
• Github: https://tongyi-agent.github.io/blog/

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#LLM #AI #ContextSummarization #WebAgents #Research

✨WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization

📝 Summary:
WebShaper synthesizes information-seeking datasets to address data scarcity for LLM agents. It uses a formalization-driven framework based on set theory and Knowledge Projections, enabling precise control over reasoning structure. This leads to state-of-the-art performance on open-sourced benchma...

🔹 Publication Date: Published on Jul 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.15061
• PDF: https://arxiv.org/pdf/2507.15061
• Project Page: https://huggingface.co/papers?q=Knowledge%20Projections%20(KP)
• Github: https://github.com/Alibaba-NLP/WebAgent

🔹 Models citing this paper:
• https://huggingface.co/Alibaba-NLP/WebShaper-32B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Alibaba-NLP/WebShaper

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#LLM #AIAgents #DataGeneration #FormalMethods #NLP

✨PokeeResearch: Effective Deep Research via Reinforcement Learning from AI Feedback and Robust Reasoning Scaffold

📝 Summary:
PokeeResearch-7B is a 7B-parameter deep research agent achieving state-of-the-art results using Reinforcement Learning from AI Feedback RLAIF. Its chain-of-thought reasoning scaffold enhances robustness and alignment, producing an efficient, resilient, and research-grade AI.

🔹 Publication Date: Published on Oct 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.15862
• PDF: https://arxiv.org/pdf/2510.15862
• Github: https://github.com/Pokee-AI/PokeeResearchOSS

🔹 Models citing this paper:
• https://huggingface.co/PokeeAI/pokee_research_7b
• https://huggingface.co/Mungert/pokee_research_7b-GGUF

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#AI #ReinforcementLearning #LLM #MachineLearning #AIResearch

✨FAPO: Flawed-Aware Policy Optimization for Efficient and Reliable Reasoning

📝 Summary:
FAPO improves LLM reasoning by penalizing flawed-positive rollouts, which are unreliable reasoning patterns. This secures early gains while shifting optimization toward reliable reasoning later, enhancing correctness and stability.

🔹 Publication Date: Published on Oct 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.22543
• PDF: https://arxiv.org/pdf/2510.22543
• Project Page: https://fapo-rl.github.io/
• Github: https://fapo-rl.github.io

🔹 Models citing this paper:
• https://huggingface.co/dyyyyyyyy/FAPO-32B
• https://huggingface.co/dyyyyyyyy/FAPO-GenRM-4B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/dyyyyyyyy/FAPO-Critic
• https://huggingface.co/datasets/dyyyyyyyy/FAPO-Reasoning-Dataset

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

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#LLM #AI #ReinforcementLearning #DeepLearning #Reasoning

✨MIRIX: Multi-Agent Memory System for LLM-Based Agents

📝 Summary:
MIRIX is a modular multi-agent memory system for LLM-based agents that integrates diverse memory types and a dynamic framework. It significantly enhances memory capabilities for multimodal and long-form conversations. MIRIX achieves superior performance on challenging benchmarks, outperforming ex...

🔹 Publication Date: Published on Jul 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.07957
• PDF: https://arxiv.org/pdf/2507.07957
• Project Page: https://mirix.io/
• Github: https://github.com/Mirix-AI/MIRIX

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#LLM #MultiAgentSystems #AISystems #MemorySystems #AI

✨Cache-to-Cache: Direct Semantic Communication Between Large Language Models

📝 Summary:
C2C enables direct semantic communication between LLMs by projecting and fusing their KV-caches, overcoming text-based communication limits. This method preserves rich semantics, improving accuracy by 3-5% and achieving a 2x speedup over traditional text communication.

🔹 Publication Date: Published on Oct 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.03215
• PDF: https://arxiv.org/pdf/2510.03215
• Project Page: https://fuvty.github.io/C2C_Project_Page/
• Github: https://github.com/thu-nics/C2C

🔹 Models citing this paper:
• https://huggingface.co/nics-efc/C2C_Fuser

✨ Spaces citing this paper:
• https://huggingface.co/spaces/fuvty/C2C_demo
• https://huggingface.co/spaces/nics-efc/C2C_demo

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

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#LLM #SemanticCommunication #AI #DeepLearning #NLP

✨Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents

📝 Summary:
Easy Dataset is a framework that synthesizes LLM fine-tuning data from unstructured documents using a GUI and LLMs. It generates domain-specific question-answer pairs with human oversight. This improves LLM performance in specific domains while retaining general knowledge.

🔹 Publication Date: Published on Jul 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.04009
• PDF: https://arxiv.org/pdf/2507.04009
• Github: https://github.com/ConardLi/easy-dataset

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#LLM #DataSynthesis #FineTuning #AI #NLP

✨TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data

📝 Summary:
TabDSR improves LLM performance on complex tabular numerical reasoning by decomposing queries, sanitizing tables, and using program-of-thoughts reasoning. It achieves state-of-the-art accuracy, consistently outperforming existing methods.

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02219
• PDF: https://arxiv.org/pdf/2511.02219

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#LLM #TabularData #NumericalReasoning #DataScience #AI

✨Let Multimodal Embedders Learn When to Augment Query via Adaptive Query Augmentation

📝 Summary:
M-Solomon is a multimodal embedder that adaptively decides when to augment queries. It uses a Multimodal LLM to generate augmentations for queries that require them, learning to augment only when necessary. This approach improves performance and significantly reduces embedding latency compared to...

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02358
• PDF: https://arxiv.org/pdf/2511.02358

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#MultimodalAI #LLM #Embeddings #MachineLearning #DeepLearning

✨LiveTradeBench: Seeking Real-World Alpha with Large Language Models

📝 Summary:
LiveTradeBench evaluates LLMs in live trading environments with real-time data, multi-asset portfolios, and multiple markets. It reveals that strong static benchmark scores dont predict trading success, and some LLMs can adapt to live market signals. This highlights a gap in current LLM evaluations.

🔹 Publication Date: Published on Nov 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03628
• PDF: https://arxiv.org/pdf/2511.03628
• Project Page: https://trade-bench.live/
• Github: https://github.com/ulab-uiuc/live-trade-bench

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#LLM #AlgorithmicTrading #FinancialAI #QuantitativeFinance #AIResearch

✨The Sequential Edge: Inverse-Entropy Voting Beats Parallel Self-Consistency at Matched Compute

📝 Summary:
Sequential scaling for language model reasoning consistently outperforms parallel self-consistency at matched compute, achieving significant accuracy gains. The paper introduces inverse-entropy weighted voting to further enhance sequential scaling, establishing it as the superior test-time strate...

🔹 Publication Date: Published on Nov 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.02309
• PDF: https://arxiv.org/pdf/2511.02309

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

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#LLM #AIReasoning #SelfConsistency #SequentialScaling #InverseEntropy

✨Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning

📝 Summary:
PaperCoder is a multi-agent LLM framework that automates converting machine learning papers into functional code repositories. It uses planning, analysis, and generation stages with specialized agents. Evaluations show it effectively creates high-quality implementations, outperforming strong base...

🔹 Publication Date: Published on Apr 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.17192
• PDF: https://arxiv.org/pdf/2504.17192
• Project Page: https://huggingface.co/papers/2504.15080
• Github: https://github.com/going-doer/Paper2Code

✨ Datasets citing this paper:
• https://huggingface.co/datasets/iaminju/paper2code

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#CodeGeneration #MachineLearning #LLM #AI #Automation

✨MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model

📝 Summary:
MarS is a financial market simulation engine using LMM, an order-level generative model. It creates realistic, interactive market scenarios for risk-free strategy training and analysis. This offers scalability and strong realism.

🔹 Publication Date: Published on Sep 4, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2409.07486
• PDF: https://arxiv.org/pdf/2409.07486
• Github: https://github.com/microsoft/mars

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#FinancialMarkets #GenerativeAI #Simulation #LLM #FinTech

✨NVIDIA Nemotron Nano V2 VL

📝 Summary:
Nemotron Nano V2 VL is a new hybrid Mamba-Transformer LLM designed for improved document and video understanding. It leverages enhanced architecture and token reduction for higher inference throughput.

🔹 Publication Date: Published on Nov 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.03929
• PDF: https://arxiv.org/pdf/2511.03929

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

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#LLM #MambaTransformer #MultimodalAI #AIResearch #DeepLearning

✨RDMA Point-to-Point Communication for LLM Systems

📝 Summary:
TransferEngine provides a uniform interface for flexible point-to-point communication in LLM systems, overcoming NIC-specific limitations. It bridges different hardware, providing high throughput for disaggregated inference, RL, and MoE tasks. This solution avoids hardware lock-in and complements...

🔹 Publication Date: Published on Oct 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.27656
• PDF: https://arxiv.org/pdf/2510.27656
• Github: https://github.com/perplexityai/pplx-garden

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#RDMA #LLM #HPC #AIInfrastructure #DistributedSystems