✨Codified Foreshadowing-Payoff Text Generation

📝 Summary:
Large language models struggle with maintaining long-range narrative dependencies, but a new framework called CFPG addresses this by structuring narrative continuity through executable causal predicat...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07033
• PDF: https://arxiv.org/pdf/2601.07033

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

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✨Controllable Memory Usage: Balancing Anchoring and Innovation in Long-Term Human-Agent Interaction

📝 Summary:
This paper presents SteeM, a framework for dynamically regulating memory reliance in LLM agents. It allows users to balance innovation with historical fidelity, overcoming the all-or-nothing problem of memory use. This approach outperforms conventional methods for personalized human-agent interac...

🔹 Publication Date: Published on Jan 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05107
• PDF: https://arxiv.org/pdf/2601.05107

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

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#LLM #AI #HumanAgentInteraction #Memory #MachineLearning

✨DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving

📝 Summary:
DrivingGen is the first comprehensive benchmark for generative driving world models, addressing prior evaluation gaps. It uses diverse datasets and new metrics to assess visual realism, trajectory plausibility, temporal coherence, and controllability. Benchmarking reveals trade-offs between visua...

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01528
• PDF: https://arxiv.org/pdf/2601.01528
• Project Page: https://drivinggen-bench.github.io/
• Github: https://github.com/youngzhou1999/DrivingGen

✨ Datasets citing this paper:
• https://huggingface.co/datasets/yangzhou99/DrivingGen

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

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#AutonomousDriving #GenerativeAI #WorldModels #AIResearch #Benchmarking

✨"TODO: Fix the Mess Gemini Created": Towards Understanding GenAI-Induced Self-Admitted Technical Debt

📝 Summary:
Developers admit technical debt GIST in AI-assisted code, often due to postponed testing, incomplete adaptation, and limited understanding. This debt emerges when incorporating AI-generated code despite developer uncertainty about its behavior or correctness.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07786
• PDF: https://arxiv.org/pdf/2601.07786

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

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✨OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent

📝 Summary:
OS-Symphony is a framework enhancing computer-using agents with robustness and generalization. It features a Reflection-Memory Agent for self-correction and a Multimodal Searcher for visually aligned tutorials. This achieved state-of-the-art results on online benchmarks, including 65.84% on OSWorld.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07779
• PDF: https://arxiv.org/pdf/2601.07779
• Project Page: https://os-copilot.github.io/OS-Symphony
• Github: https://github.com/OS-Copilot/OS-Symphony

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

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✨MHLA: Restoring Expressivity of Linear Attention via Token-Level Multi-Head

📝 Summary:
Multi-Head Linear Attention addresses the performance degradation in linear attention by preserving representational diversity through head-wise token dimension computation, maintaining linear complex...

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07832
• PDF: https://arxiv.org/pdf/2601.07832
• Project Page: https://dagroup-pku.github.io/MHLA/
• Github: https://github.com/DAGroup-PKU/MHLA

🔹 Models citing this paper:
• https://huggingface.co/DAGroup-PKU/MHLA

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models

📝 Summary:
EvoToken-DLM introduces a diffusion-based language modeling approach that uses soft token distributions and continuous trajectory supervision to enable revisable decoding and outperforms existing base...

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07351
• PDF: https://arxiv.org/pdf/2601.07351
• Project Page: https://aim-uofa.github.io/EvoTokenDLM/
• Github: https://github.com/aim-uofa/EvoTokenDLM

🔹 Models citing this paper:
• https://huggingface.co/zhongzero/EvoToken_LLaDA_Instruct_8B_Lora

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

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✨FinForge: Semi-Synthetic Financial Benchmark Generation

📝 Summary:
FinForge presents a scalable semi-synthetic pipeline for creating domain-specific financial evaluation benchmarks using expert curation and language model synthesis, demonstrating significant variatio...

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06747
• PDF: https://arxiv.org/pdf/2601.06747

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving

📝 Summary:
DrivingGen is the first comprehensive benchmark for generative driving world models, addressing prior evaluation gaps. It uses diverse datasets and new metrics to assess visual realism, trajectory plausibility, temporal coherence, and controllability. Benchmarking reveals trade-offs between visua...

🔹 Publication Date: Published on Jan 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.01528
• PDF: https://arxiv.org/pdf/2601.01528
• Project Page: https://drivinggen-bench.github.io/
• Github: https://github.com/youngzhou1999/DrivingGen

✨ Datasets citing this paper:
• https://huggingface.co/datasets/yangzhou99/DrivingGen

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

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#AutonomousDriving #GenerativeAI #WorldModels #AIResearch #Benchmarking

✨Gecko: An Efficient Neural Architecture Inherently Processing Sequences with Arbitrary Lengths

📝 Summary:
Gecko is a neural architecture for efficient processing of arbitrary length sequential data. It improves long range dependency capture with new components like timestep decay normalization and sliding chunk attention. Gecko outperforms Llama2 7B and Megalodon 7B, inherently handling sequences up ...

🔹 Publication Date: Published on Jan 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06463
• PDF: https://arxiv.org/pdf/2601.06463
• Github: https://github.com/XuezheMax/gecko-llm

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

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#Gecko #NeuralNetworks #SequenceModeling #LLM #DeepLearning

✨Forest Before Trees: Latent Superposition for Efficient Visual Reasoning

📝 Summary:
Laser introduces Dynamic Windowed Alignment Learning DWAL for visual reasoning. This method maintains global feature superposition, achieving state-of-the-art performance with significantly reduced computational costs and high efficiency.

🔹 Publication Date: Published on Jan 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.06803
• PDF: https://arxiv.org/pdf/2601.06803

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

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#VisualReasoning #MachineLearning #AIResearch #ComputerVision #EfficientAI

✨FlyPose: Towards Robust Human Pose Estimation From Aerial Views

📝 Summary:
FlyPose is a lightweight, real-time aerial human pose estimation system. It achieves significantly improved accuracy through multi-dataset training and performs efficiently on UAVs. A new challenging dataset, FlyPose-104, is also released.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05747
• PDF: https://arxiv.org/pdf/2601.05747
• Github: https://github.com/farooqhassaan/FlyPose

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

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#HumanPoseEstimation #UAV #ComputerVision #DeepLearning #AI

✨mHC-lite: You Don't Need 20 Sinkhorn-Knopp Iterations

📝 Summary:
mHC-lite proposes a novel reparameterization for Hyper-Connections, explicitly constructing exactly doubly stochastic matrices via convex combinations of permutations. This approach guarantees stability, improves training throughput with native operations, and outperforms prior methods.

🔹 Publication Date: Published on Jan 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.05732
• PDF: https://arxiv.org/pdf/2601.05732
• Github: https://github.com/FFTYYY/mhc-lite

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

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#DeepLearning #MachineLearning #Optimization #Algorithm #AI

✨Benchmarking Small Language Models and Small Reasoning Language Models on System Log Severity Classification

📝 Summary:
Severity classification benchmarks small language models for log understanding and deployability. RAG significantly boosts many models, even tiny ones, but efficiency and RAG integration vary widely, crucial for real-time systems.

🔹 Publication Date: Published on Jan 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07790
• PDF: https://arxiv.org/pdf/2601.07790
• Github: https://github.com/stccenter/Benchmarking-SLMs-and-SRLMs-on-System-Log-Severity-Classification

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

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✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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