✨Stemming Hallucination in Language Models Using a Licensing Oracle

📝 Summary:
This study presents the Licensing Oracle, an architectural solution to eliminate language model hallucinations. It enforces truth constraints via formal validation against structured knowledge graphs, achieving perfect abstention precision and zero false answers where statistical methods fail.

🔹 Publication Date: Published on Nov 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06073
• PDF: https://arxiv.org/pdf/2511.06073

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #AIHallucination #KnowledgeGraphs #NLP #AIResearch

✨Motif 2 12.7B technical report

📝 Summary:
Motif-2-12.7B is an efficient LLM combining Grouped Differential Attention and system-level optimizations. It achieves competitive performance across diverse benchmarks with a smaller model size.

🔹 Publication Date: Published on Nov 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07464
• PDF: https://arxiv.org/pdf/2511.07464

🔹 Models citing this paper:
• https://huggingface.co/Motif-Technologies/optimizer
• https://huggingface.co/Motif-Technologies/Motif-2-12.7B-Instruct
• https://huggingface.co/Motif-Technologies/Motif-2-12.7B-Base

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #AI #DeepLearning #EfficientAI #AttentionMechanisms

✨Superpositional Gradient Descent: Harnessing Quantum Principles for Model Training

📝 Summary:
Superpositional Gradient Descent SGD is a new quantum-inspired optimizer. It uses quantum superposition to enhance gradient updates, leading to faster convergence and lower final loss in LLM training than AdamW.

🔹 Publication Date: Published on Nov 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.01918
• PDF: https://arxiv.org/pdf/2511.01918
• Github: https://github.com/The-Aqua-Labs/Superpositional-Gradient-Descent

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#MachineLearning #AI #LLM #QuantumInspired #Optimization

✨Solving a Million-Step LLM Task with Zero Errors

📝 Summary:
MAKER solves million-step LLM tasks with zero errors. It uses extreme task decomposition for microagents and applies error correction at each step with multi-agent voting. This offers a new scalable approach for complex LLM processes.

🔹 Publication Date: Published on Nov 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09030
• PDF: https://arxiv.org/pdf/2511.09030

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #AI #ErrorCorrection #MultiAgent #TaskDecomposition

✨CC30k: A Citation Contexts Dataset for Reproducibility-Oriented Sentiment Analysis

📝 Summary:
CC30k is a new dataset of 30,000 machine learning paper citation contexts, labeled with reproducibility-oriented sentiments. It enables large language models to better predict paper reproducibility, filling a crucial gap in computational reproducibility studies.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07790
• PDF: https://arxiv.org/pdf/2511.07790

✨ Datasets citing this paper:
• https://huggingface.co/datasets/rochanaro/CC30k

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#MachineLearning #Reproducibility #LLM #SentimentAnalysis #DataScience

✨DiscoX: Benchmarking Discourse-Level Translation task in Expert Domains

📝 Summary:
A new benchmark, DiscoX, and evaluation system, Metric-S, are introduced for discourse-level, expert Chinese-English translation. Findings show advanced LLMs still fall short of human performance, underscoring challenges in professional machine translation.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10984
• PDF: https://arxiv.org/pdf/2511.10984

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#MachineTranslation #NLP #LLM #Benchmarking #AI

✨MarsRL: Advancing Multi-Agent Reasoning System via Reinforcement Learning with Agentic Pipeline Parallelism

📝 Summary:
MarsRL enhances multi-agent reasoning systems by jointly optimizing all agents through reinforcement learning and agentic pipeline parallelism. This novel approach significantly boosts open-source LLM accuracy on complex tasks, even outperforming larger models on benchmarks like AIME2025.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11373
• PDF: https://arxiv.org/pdf/2511.11373
• Github: https://github.com/liushulinle/MarsRL

🔹 Models citing this paper:
• https://huggingface.co/forestliutc/MarsRL

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#ReinforcementLearning #MultiAgentSystems #LLM #AIResearch #MachineLearning

✨Qwen3 Technical Report

📝 Summary:
Qwen3 is a new series of large language models integrating thinking and non-thinking modes for unified performance and efficiency. It achieves state-of-the-art results across diverse tasks and expands multilingual support to 119 languages.

🔹 Publication Date: Published on May 14

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/qwen3-technical-report
• PDF: https://arxiv.org/pdf/2505.09388
• Project Page: https://qwenlm.github.io/blog/qwen3/
• Github: https://github.com/QwenLM/Qwen3

🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct
• https://huggingface.co/Qwen/Qwen3-235B-A22B
• https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct

✨ Spaces citing this paper:
• https://huggingface.co/spaces/modelscope/DocResearch
• https://huggingface.co/spaces/enzostvs/deepsite
• https://huggingface.co/spaces/multimodalart/Eigen-Banana

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #AI #MultilingualAI #NLP #Qwen3

✨MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds

📝 Summary:
MeshCoder reconstructs complex 3D objects from point clouds into editable Blender Python scripts using a multimodal LLM. This enables superior shape-to-code reconstruction, intuitive editing via code, and enhances 3D shape understanding.

🔹 Publication Date: Published on Aug 20

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/meshcoder-llm-powered-structured-mesh-code-generation-from-point-clouds
• PDF: https://arxiv.org/pdf/2508.14879
• Project Page: https://daibingquan.github.io/MeshCoder
• Github: https://daibingquan.github.io/MeshCoder

🔹 Models citing this paper:
• https://huggingface.co/InternRobotics/MeshCoder

✨ Datasets citing this paper:
• https://huggingface.co/datasets/InternRobotics/MeshCoderDataset

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#MeshCoder #LLM #3DReconstruction #PointClouds #ComputerGraphics

✨Experience-Guided Adaptation of Inference-Time Reasoning Strategies

📝 Summary:
Experience-Guided Reasoner EGuR dynamically generates and optimizes complete computational strategies at inference time using accumulated experience. It adapts LLM calls tools and control logic improving accuracy up to 14 percent and reducing costs by up to 111x.

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11519
• PDF: https://arxiv.org/pdf/2511.11519

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #AI #Reasoning #Optimization #MachineLearning

✨From Proof to Program: Characterizing Tool-Induced Reasoning Hallucinations in Large Language Models

📝 Summary:
Tool-augmented LLMs exhibit Tool-Induced Myopia TIM, treating tool outputs as substitutes for true reasoning. This improves final answer accuracy but significantly degrades reasoning quality. A proposed framework realigns these models to use tools as assistive evidence, enhancing both accuracy an...

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10899
• PDF: https://arxiv.org/pdf/2511.10899

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #AIResearch #Reasoning #ToolAugmentation #AIHallucinations

✨MMaDA-Parallel: Multimodal Large Diffusion Language Models for Thinking-Aware Editing and Generation

📝 Summary:
A parallel multimodal diffusion framework, MMaDA-Parallel, enhances cross-modal alignment and semantic consistency in thinking-aware image synthesis by addressing error propagation issues in sequentia...

🔹 Publication Date: Published on Nov 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.09611
• PDF: https://arxiv.org/pdf/2511.09611
• Project Page: https://tyfeld.github.io/mmadaparellel.github.io/
• Github: https://github.com/tyfeld/MMaDA-Parallel

🔹 Models citing this paper:
• https://huggingface.co/tyfeld/MMaDA-Parallel-A
• https://huggingface.co/tyfeld/MMaDA-Parallel-M

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#MultimodalAI #DiffusionModels #ImageSynthesis #LLM #AIResearch

✨WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance

📝 Summary:
WebCoach introduces a self-evolving framework for web agents with persistent cross-session memory. It uses a WebCondenser, External Memory Store, and a Coach to learn from past experiences without retraining. This significantly improves task success and enables smaller models to match larger LLM ...

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12997
• PDF: https://arxiv.org/pdf/2511.12997

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#WebAgents #AI #MachineLearning #LLM #MemoryAI

✨MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling

📝 Summary:
MiroThinker v1.0 is an open-source research agent introducing 'interactive scaling.' It trains models with reinforcement learning for deeper agent-environment interactions, performing up to 600 tool calls per task. This achieves state-of-the-art performance and establishes interaction depth as a ...

🔹 Publication Date: Published on Nov 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.11793
• PDF: https://arxiv.org/pdf/2511.11793
• Project Page: https://dr.miromind.ai/
• Github: https://github.com/MiroMindAI/MiroThinker

🔹 Models citing this paper:
• https://huggingface.co/miromind-ai/MiroThinker-v1.0-72B
• https://huggingface.co/miromind-ai/MiroThinker-v1.0-8B
• https://huggingface.co/miromind-ai/MiroThinker-v1.0-30B

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#MiroThinker #ResearchAgents #ReinforcementLearning #OpenSourceAI #LLM

✨Assessing LLMs for Serendipity Discovery in Knowledge Graphs: A Case for Drug Repurposing

📝 Summary:
SerenQA evaluates LLMs for discovering surprising, valuable serendipitous answers in scientific knowledge graphs, focusing on drug repurposing. It uses a new serendipity metric. Experiments show LLMs struggle with genuine surprising insights.

🔹 Publication Date: Published on Nov 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.12472
• PDF: https://arxiv.org/pdf/2511.12472
• Project Page: https://cwru-db-group.github.io/serenQA
• Github: https://github.com/CWRU-DB-Group/DrugKG

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #KnowledgeGraphs #DrugRepurposing #AI #Serendipity

✨ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning

📝 Summary:
ATLAS is a new, high-difficulty, multidisciplinary benchmark for LLMs, featuring 800 original problems across seven scientific fields. It addresses current benchmark limitations with complex, open-ended answers and aims to differentiate advanced scientific reasoning, serving as a ruler for AGI pr...

🔹 Publication Date: Published on Nov 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14366
• PDF: https://arxiv.org/pdf/2511.14366

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #AGI #AIResearch #ScientificReasoning #Benchmark

✨Think-at-Hard: Selective Latent Iterations to Improve Reasoning Language Models

📝 Summary:
Think-at-Hard TaH improves LLM reasoning by dynamically refining only hard tokens. It uses a neural decider to identify them and LoRA for focused refinement, boosting performance with minimal overhead.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.08577
• PDF: https://arxiv.org/pdf/2511.08577
• Github: https://github.com/thu-nics/TaH

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #AI #MachineLearning #NaturalLanguageProcessing #Reasoning

✨Mitigating Label Length Bias in Large Language Models

📝 Summary:
Large Language Models exhibit a label length bias with multi-token class labels. This paper introduces Normalized Contextual Calibration NCC to mitigate this issue by normalizing and calibrating predictions at the full-label level. NCC significantly improves performance and reliability across div...

🔹 Publication Date: Published on Nov 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14385
• PDF: https://arxiv.org/pdf/2511.14385

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #AI #NLP #BiasInAI #MachineLearning

✨Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework

📝 Summary:
This paper improves Extreme Multi-label Classification XMC by using larger decoder-only models and introduces ViXML, a vision-enhanced framework. ViXML efficiently integrates visual information, significantly outperforming text-only models and achieving new state-of-the-art.

🔹 Publication Date: Published on Nov 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.13189
• PDF: https://arxiv.org/pdf/2511.13189

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #XMC #MultiModalAI #MachineLearning #AIResearch

✨LLM-Powered Fully Automated Chaos Engineering: Towards Enabling Anyone to Build Resilient Software Systems at Low Cost

📝 Summary:
Manual planning and improvement hinder Chaos Engineering adoption. ChaosEater automates the entire Chaos Engineering cycle for Kubernetes using LLMs, handling tasks from requirements to debugging. This enables anyone to build resilient systems quickly and affordably.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07865
• PDF: https://arxiv.org/pdf/2511.07865
• Project Page: https://ntt-dkiku.github.io/chaos-eater/
• Github: https://github.com/ntt-dkiku/chaos-eater

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#ChaosEngineering #LLM #CloudNative #SoftwareResilience #DevOps