✨RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction

📝 Summary:
RealMem benchmark evaluates memory systems for long-term project-oriented interactions in large language models, revealing challenges in managing dynamic context dependencies. AI-generated summary As ...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06966
• PDF: https://arxiv.org/pdf/2601.06966

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✨Sci-Reasoning: A Dataset Decoding AI Innovation Patterns

📝 Summary:
Sci-Reasoning is a new dataset that maps intellectual synthesis patterns in AI research. It traces key papers to their predecessors, identifying 15 distinct thinking patterns that drive breakthroughs. This dataset enables quantitative study of scientific progress and trains next-generation AI res...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.04577
• PDF: https://arxiv.org/pdf/2601.04577
• Github: https://github.com/AmberLJC/Sci-Reasoning

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✨Does Inference Scaling Improve Reasoning Faithfulness? A Multi-Model Analysis of Self-Consistency Tradeoffs

📝 Summary:
Self-consistency improves reasoning accuracy for some models while potentially sacrificing faithfulness, with varying effects across different language models and problem difficulties. AI-generated su...

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06423
• PDF: https://arxiv.org/pdf/2601.06423

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✨Can Textual Reasoning Improve the Performance of MLLMs on Fine-grained Visual Classification?

📝 Summary:
Multi-modal large language models struggle with fine-grained visual classification, and chain-of-thought reasoning harms performance due to increased reasoning length; a new framework called ReFine-RF...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06993
• PDF: https://arxiv.org/pdf/2601.06993
• Github: https://github.com/jiezhu23/ReFine-RFT

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✨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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