✨Stochastic CHAOS: Why Deterministic Inference Kills, and Distributional Variability Is the Heartbeat of Artifical Cognition

📝 Summary:
Deterministic inference in LLMs is detrimental, suppressing uncertainty, emergent abilities, and safety awareness by enforcing single-output predictions. This approach misrepresents capabilities and risks. The paper advocates embracing distributional variability as essential for artificial cognit...

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07239
• PDF: https://arxiv.org/pdf/2601.07239

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✨A Rising Tide Lifts All Boats: MTQE Rewards for Idioms Improve General Translation Quality

📝 Summary:
GRPO-style fine-tuning with MTQE models as rewards improves idiom translation by 14 points while enhancing general translation and cross-lingual capabilities. AI-generated summary Non-compositional ex...

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06307
• PDF: https://arxiv.org/pdf/2601.06307

🔹 Models citing this paper:
• https://huggingface.co/ishikaa/Chinese_llama8b-da
• https://huggingface.co/ishikaa/Chinese_llama8b-qe-cons
• https://huggingface.co/ishikaa/Chinese_llama8b-qe-pos

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✨SPINAL -- Scaling-law and Preference Integration in Neural Alignment Layers

📝 Summary:
SPINAL diagnoses how DPO alignment reshapes representations layer by layer, revealing geometric localization of preference gradients in final decoder blocks and enabling practical auditing of alignmen...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06238
• PDF: https://arxiv.org/pdf/2601.06238

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✨Artificial Entanglement in the Fine-Tuning of Large Language Models

📝 Summary:
Using Artificial Entanglement, this paper finds that LLM fine-tuning like LoRA creates distinct internal parameter entanglement. Yet, external attention outputs are robust and similar to full fine-tuning. This no hair property explains LoRAs effectiveness.

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06788
• PDF: https://arxiv.org/pdf/2601.06788

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✨How Do Large Language Models Learn Concepts During Continual Pre-Training?

📝 Summary:
Large language models develop concept circuits during continual pretraining that exhibit learning and forgetting patterns, with semantically similar concepts showing stronger interference and varying ...

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.03570
• PDF: https://arxiv.org/pdf/2601.03570

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✨On the Non-decoupling of Supervised Fine-tuning and Reinforcement Learning in Post-training

📝 Summary:
Supervised fine-tuning SFT and reinforcement learning RL in large language model post-training cannot be decoupled. Separating them causes performance degradation because RL increases SFT loss, and SFT lowers RL reward.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07389
• PDF: https://arxiv.org/pdf/2601.07389

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✨Ministral 3

📝 Summary:
Ministral 3 is a series of parameter-efficient dense language models available in three sizes 3B, 8B, 14B with three variants each. Designed for compute-constrained applications, they are trained via Cascade Distillation and include image understanding capabilities.

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08584
• PDF: https://arxiv.org/pdf/2601.08584

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✨End-to-End Video Character Replacement without Structural Guidance

📝 Summary:
MoCha enables controllable video character replacement using a single frame mask through condition-aware RoPE and a comprehensive data construction pipeline with specialized datasets. AI-generated sum...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08587
• PDF: https://arxiv.org/pdf/2601.08587

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✨JudgeRLVR: Judge First, Generate Second for Efficient Reasoning

📝 Summary:
Reinforcement learning with verifiable rewards is enhanced through a judge-then-generate paradigm that improves both efficiency and accuracy in mathematical problem-solving. AI-generated summary Reinf...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08468
• PDF: https://arxiv.org/pdf/2601.08468

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✨The Confidence Dichotomy: Analyzing and Mitigating Miscalibration in Tool-Use Agents

📝 Summary:
Tool-integrated language model agents exhibit different calibration behaviors based on tool type, with a reinforcement learning framework improving both task accuracy and reliable uncertainty estimati...

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07264
• PDF: https://arxiv.org/pdf/2601.07264

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✨ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

📝 Summary:
Reinforcement learning for large language model agents suffers from discrimination collapse in open-ended tasks due to pointwise scalar scoring, which ArenaRL addresses through relative ranking and pa...

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06487
• PDF: https://arxiv.org/pdf/2601.06487
• Github: https://github.com/Alibaba-NLP/qqr

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

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✨Motion Attribution for Video Generation

📝 Summary:
Motive is a gradient-based data attribution framework that identifies influential video clips for motion improvement in text-to-video models through motion-weighted loss masking. AI-generated summary ...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08828
• PDF: https://arxiv.org/pdf/2601.08828
• Project Page: https://research.nvidia.com/labs/sil/projects/MOTIVE/

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✨SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices

📝 Summary:
An efficient diffusion transformer framework for mobile and edge devices that maintains high-generation quality while reducing computational costs through compact architecture, elastic training, and k...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08303
• PDF: https://arxiv.org/pdf/2601.08303

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✨Aligning Text, Code, and Vision: A Multi-Objective Reinforcement Learning Framework for Text-to-Visualization

📝 Summary:
A reinforcement learning framework for text-to-visualization generation that improves chart quality and code execution by optimizing multiple objectives using post-execution feedback. AI-generated sum...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04582
• PDF: https://arxiv.org/pdf/2601.04582

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✨VLingNav: Embodied Navigation with Adaptive Reasoning and Visual-Assisted Linguistic Memory

📝 Summary:
VLingNav enhances embodied navigation through linguistic-driven cognition with adaptive reasoning and visual-assisted memory, achieving state-of-the-art performance and zero-shot transfer to real robo...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08665
• PDF: https://arxiv.org/pdf/2601.08665
• Project Page: https://wsakobe.github.io/VLingNav-web/

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✨MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences

📝 Summary:
MemGovern framework transforms unstructured GitHub data into structured experiential memory for autonomous software engineering agents, improving bug resolution rates through enhanced experience retri...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06789
• PDF: https://arxiv.org/pdf/2601.06789
• Github: https://github.com/QuantaAlpha/MemGovern

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✨User-Oriented Multi-Turn Dialogue Generation with Tool Use at scale

📝 Summary:
Large reasoning models enable scalable multi-turn dialogue generation through automated task-oriented simulation and user-oriented behavioral modeling for enhanced human-agent interaction datasets. AI...

🔹 Publication Date: Published on Jan 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.08225
• PDF: https://arxiv.org/pdf/2601.08225

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✨Solar Open Technical Report

📝 Summary:
Solar Open presents a 102B-parameter bilingual Mixture-of-Experts language model that addresses data scarcity in underserved languages through synthetic data generation, progressive curriculum coordin...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07022
• PDF: https://arxiv.org/pdf/2601.07022

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✨ShowUI-π: Flow-based Generative Models as GUI Dexterous Hands

📝 Summary:
ShowUI-π is the first flow-based generative model for GUI agents, unifying discrete clicks and continuous drag actions. It achieves smooth, stable trajectories and significantly outperforms prior agents on ScreenDrag, a new benchmark for GUI drag capabilities.

🔹 Publication Date: Published on Dec 31, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24965
• PDF: https://arxiv.org/pdf/2512.24965
• Project Page: https://showlab.github.io/showui-pi
• Github: https://github.com/showlab/showui-pi

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✨KnowMe-Bench: Benchmarking Person Understanding for Lifelong Digital Companions

📝 Summary:
KnowMe-Bench is a new benchmark using long autobiographical narratives to evaluate AI's person understanding, moving beyond simple retrieval. It tests factual recall, subjective states, and principle-level reasoning. Current systems struggle with higher-level inferences despite factual improvemen...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04745
• PDF: https://arxiv.org/pdf/2601.04745
• Github: https://github.com/QuantaAlpha/KnowMeBench

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✨Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking

📝 Summary:
FactArena is a new automated framework for comprehensively benchmarking LLMs across the entire fact-checking pipeline, including claim extraction and evidence retrieval. It reveals significant gaps between claim verification accuracy and overall fact-checking competence, highlighting the need for...

🔹 Publication Date: Published on Jan 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.02669
• PDF: https://arxiv.org/pdf/2601.02669

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