✨LongCat-Flash-Thinking-2601 Technical Report

📝 Summary:
LongCat-Flash-Thinking-2601 is a 560B MoE reasoning model that achieves state-of-the-art performance on agentic benchmarks. Its capabilities stem from a unified training framework, robust tool interaction, and a Heavy Thinking mode for complex reasoning.

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16725
• PDF: https://arxiv.org/pdf/2601.16725

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#MoE #ReasoningModels #AgentAI #LLM #AI

✨Endless Terminals: Scaling RL Environments for Terminal Agents

📝 Summary:
Endless Terminals introduces an autonomous pipeline for generating procedural terminal tasks that significantly improves agent performance on both synthetic and human-curated benchmarks through scalab...

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16443
• PDF: https://arxiv.org/pdf/2601.16443
• Github: https://github.com/kanishkg/endless-terminals

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DSGym: A Holistic Framework for Evaluating and Training Data Science Agents

📝 Summary:
DSGym is a standardized framework for evaluating and training data science agents, addressing shortcomings of existing benchmarks. It offers a holistic, data-grounded task suite and enables execution-verified agent training. This allows rigorous measurement of agents' analytical capabilities, dem...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16344
• PDF: https://arxiv.org/pdf/2601.16344

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#DataScience #AI #MachineLearning #AIagents #Research

✨Memory-V2V: Augmenting Video-to-Video Diffusion Models with Memory

📝 Summary:
Memory-V2V enhances multi-turn video editing by adding explicit memory to diffusion models. It ensures cross-consistency using efficient token compression and retrieval. This significantly improves video consistency and performance with low computational cost.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16296
• PDF: https://arxiv.org/pdf/2601.16296
• Project Page: https://dohunlee1.github.io/MemoryV2V

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#VideoEditing #DiffusionModels #GenerativeAI #ComputerVision #MachineLearning

✨SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

📝 Summary:
SWE-Pruner is a self-adaptive context pruning framework for coding agents. It performs task-aware adaptive pruning, guided by explicit agent goals and a neural skimmer, to reduce long context token usage by 23-54 percent with minimal performance loss.

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16746
• PDF: https://arxiv.org/pdf/2601.16746
• Github: https://github.com/Ayanami1314/swe-pruner

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#AIAgents #ContextPruning #LLM #AI #SoftwareEngineering

✨Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification

📝 Summary:
A self-evolving framework improves Deep Research Agents via inference-time, rubric-guided verification. This method iteratively refines outputs without retraining, achieving 8-11% accuracy gains with the DeepVerifier system and releasing a verification dataset.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15808
• PDF: https://arxiv.org/pdf/2601.15808

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#AI #MachineLearning #DeepLearning #Verification #SelfEvolvingAI

✨MeepleLM: A Virtual Playtester Simulating Diverse Subjective Experiences

📝 Summary:
MeepleLM is an AI virtual playtester providing constructive critique for board game design by simulating diverse player experiences. It models subjective feedback via persona-specific reasoning, outperforming commercial AI in critique quality and community alignment.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07251
• PDF: https://arxiv.org/pdf/2601.07251
• Github: https://github.com/leroy9472/MeepleLM

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#AI #GameDesign #BoardGames #Simulation #LLM

✨SALAD: Achieve High-Sparsity Attention via Efficient Linear Attention Tuning for Video Diffusion Transformer

📝 Summary:
SALAD improves video Diffusion Transformers by combining linear and sparse attention with an input-dependent gating mechanism. It achieves 90% sparsity and a 1.72x speedup while maintaining quality and requiring minimal finetuning data.

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16515
• PDF: https://arxiv.org/pdf/2601.16515

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#VideoDiffusion #Transformers #Sparsity #EfficientAI #DeepLearning

✨Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain

📝 Summary:
Mecellem models are a framework for specialized Turkish legal language models. They feature a scratch-trained encoder achieving top retrieval rankings with efficiency, and a continually pre-trained decoder for legal domain adaptation, reducing legal text perplexity.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16018
• PDF: https://arxiv.org/pdf/2601.16018
• Project Page: https://huggingface.co/collections/newmindai/mecellem-models
• Github: https://github.com/newmindai/mecellem-models

🔹 Models citing this paper:
• https://huggingface.co/newmindai/Mursit-Base-TR-Retrieval
• https://huggingface.co/newmindai/Mursit-Base
• https://huggingface.co/newmindai/Mursit-Large-TR-Retrieval

✨ Datasets citing this paper:
• https://huggingface.co/datasets/newmindai/caselaw-retrieval
• https://huggingface.co/datasets/newmindai/contract-retrieval
• https://huggingface.co/datasets/newmindai/regulation-retrieval

✨ Spaces citing this paper:
• https://huggingface.co/spaces/newmindai/Mizan

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#LegalAI #TurkishNLP #LLM #InformationRetrieval #DomainAdaptation

✨Jet-RL: Enabling On-Policy FP8 Reinforcement Learning with Unified Training and Rollout Precision Flow

📝 Summary:
Quantized RL faces instability using FP8 rollout with BF16 training. Jet-RL proposes a unified FP8 precision for both training and rollout. This minimizes numerical mismatch, achieving stable convergence and significant speedups.

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14243
• PDF: https://arxiv.org/pdf/2601.14243

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization

📝 Summary:
Research derives and evaluates prompt optimization guidelines for code generation tasks in software engineering, identifying 10 specific improvement patterns related to input/output specification, con...

🔹 Publication Date: Published on Jan 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13118
• PDF: https://arxiv.org/pdf/2601.13118

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation

📝 Summary:
LLMs struggle to apply new knowledge effectively via SFT alone. PaST combines SFT with injecting a domain-agnostic Skill Vector, derived from RL, to efficiently transfer reasoning skills. This novel framework significantly improves performance in question answering and tool-use tasks.

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11258
• PDF: https://arxiv.org/pdf/2601.11258

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#LLM #ReinforcementLearning #ContinualLearning #AI #MachineLearning

✨Dancing in Chains: Strategic Persuasion in Academic Rebuttal via Theory of Mind

📝 Summary:
RebuttalAgent is a novel AI framework that applies Theory of Mind to academic rebuttal. It models reviewer mental states to formulate strategic, persuasive responses, significantly outperforming existing models.

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15715
• PDF: https://arxiv.org/pdf/2601.15715

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#AI #TheoryOfMind #AcademicRebuttal #NLP #MachineLearning

✨GameTalk: Training LLMs for Strategic Conversation

📝 Summary:
The GameTalk framework trains large language models for strategic multi-turn dialogue, optimizing global objectives using whole-conversation reward signals. This approach significantly outperforms untrained models, showing conversational fine-tuning is a promising path for LLM reasoning and negot...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16276
• PDF: https://arxiv.org/pdf/2601.16276

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#LLMs #ConversationalAI #StrategicDialogue #AITraining #AIReasoning

✨ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch

📝 Summary:
ChartVerse is a framework that synthesizes complex charts and reliable reasoning data for VLMs. It uses a novel metric, Rollout Posterior Entropy, for complexity-aware chart generation and an answer-first QA synthesis to ensure reasoning rigor. This leads to state-of-the-art performance in chart ...

🔹 Publication Date: Published on Jan 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13606
• PDF: https://arxiv.org/pdf/2601.13606
• Project Page: https://chartverse.github.io/
• Github: https://github.com/starriver030515/ChartVerse

🔹 Models citing this paper:
• https://huggingface.co/opendatalab/ChartVerse-Coder
• https://huggingface.co/opendatalab/ChartVerse-2B
• https://huggingface.co/opendatalab/ChartVerse-8B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/opendatalab/ChartVerse-SFT-1800K
• https://huggingface.co/datasets/opendatalab/ChartVerse-SFT-600K
• https://huggingface.co/datasets/opendatalab/ChartVerse-RL-40K

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#AI #VLMs #ChartReasoning #MachineLearning #DataScience

✨VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology

📝 Summary:
VISTA-PATH is an interactive, class-aware foundation model for pathology image segmentation. It integrates visual context, semantic denoscriptions, and expert feedback for precise multi-class segmentation, outperforming existing models. This high-fidelity segmentation supports clinical interpretati...

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16451
• PDF: https://arxiv.org/pdf/2601.16451

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#ComputationalPathology #AIinMedicine #MedicalImaging #FoundationModels #PathologyAI

✨IVRA: Improving Visual-Token Relations for Robot Action Policy with Training-Free Hint-Based Guidance

📝 Summary:
IVRA improves spatial understanding in VLA models by training-free injection of vision encoder affinity signals into language model layers at inference time. This enhances geometric structure and robot action policies. It shows consistent performance gains across diverse 2D and 3D manipulation ta...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16207
• PDF: https://arxiv.org/pdf/2601.16207
• Github: https://jongwoopark7978.github.io/IVRA

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#Robotics #VisionLanguageModels #SpatialAI #RobotLearning #DeepLearning

✨Prometheus: Unified Knowledge Graphs for Issue Resolution in Multilingual Codebases

📝 Summary:
Prometheus is a multi-agent system that uses a unified knowledge graph of code repositories to resolve real-world issues across multiple programming languages. It improves upon existing methods by handling diverse languages and real-world scenarios.

🔹 Publication Date: Published on Jul 26, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.19942
• PDF: https://arxiv.org/pdf/2507.19942
• Github: https://github.com/Pantheon-temple/Prometheus

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#KnowledgeGraphs #MultiAgentSystems #CodeAnalysis #SoftwareEngineering #AI