✨Mano: Restriking Manifold Optimization for LLM Training

📝 Summary:
A novel optimizer called Mano is proposed that combines manifold optimization with momentum projection onto tangent spaces, achieving superior performance over AdamW and Muon while reducing memory and...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.23000
• PDF: https://arxiv.org/pdf/2601.23000
• Github: https://github.com/xie-lab-ml/Mano-Restriking-Manifold-Optimization-for-LLM-Training

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✨Rethinking Selective Knowledge Distillation

📝 Summary:
This paper introduces student-entropy-guided position selection SE-KD for selective knowledge distillation in autoregressive language models. SE-KD improves accuracy and efficiency, and when extended, significantly reduces training time, memory, and storage compared to prior methods.

🔹 Publication Date: Published on Feb 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01395
• PDF: https://arxiv.org/pdf/2602.01395
• Github: https://github.com/almogtavor/SE-KD3x

✨ Spaces citing this paper:
• https://huggingface.co/spaces/almogtavor/SE-KD3x

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✨Thinking with Comics: Enhancing Multimodal Reasoning through Structured Visual Storytelling

📝 Summary:
Thinking with Comics emerges as an effective visual reasoning approach that bridges images and videos by leveraging comic structures for improved multimodal reasoning efficiency and performance. AI-ge...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02453
• PDF: https://arxiv.org/pdf/2602.02453
• Project Page: https://thinking-with-comics.github.io/
• Github: https://github.com/andongBlue/Think-with-Comics

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

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✨ParalESN: Enabling parallel information processing in Reservoir Computing

📝 Summary:
Parallel Echo State Network (ParalESN) addresses reservoir computing limitations by enabling parallel temporal processing through diagonal linear recurrence, maintaining theoretical guarantees while a...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22296
• PDF: https://arxiv.org/pdf/2601.22296

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

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✨Internal Flow Signatures for Self-Checking and Refinement in LLMs

📝 Summary:
Internal flow signatures analyze depthwise dynamics in large language models to enable self-checking and targeted refinement without modifying the base model. AI-generated summary Large language model...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01897
• PDF: https://arxiv.org/pdf/2602.01897
• Github: https://github.com/EavnJeong/Internal-Flow-Signatures-for-Self-Checking-and-Refinement-in-LLMs

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

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✨Diagnosing the Reliability of LLM-as-a-Judge via Item Response Theory

📝 Summary:
A two-phase diagnostic framework based on Item Response Theory and Graded Response Model is introduced to assess the reliability of LLM-as-a-Judge by examining intrinsic consistency and human alignmen...

🔹 Publication Date: Published on Jan 31

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00521
• PDF: https://arxiv.org/pdf/2602.00521

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

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✨Cross-Lingual Stability of LLM Judges Under Controlled Generation: Evidence from Finno-Ugric Languages

📝 Summary:
Controlled cross-lingual evaluation reveals instability in LLM assessment methods when targeting morphologically rich languages, indicating unreliable zero-shot judge transfer for discourse-level task...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02287
• PDF: https://arxiv.org/pdf/2602.02287
• Github: https://github.com/isaac-chung/

✨ Datasets citing this paper:
• https://huggingface.co/datasets/isaacchung/controlled-generated-convos-gpt-4.1-mini

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

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✨Fast Autoregressive Video Diffusion and World Models with Temporal Cache Compression and Sparse Attention

📝 Summary:
Autoregressive video diffusion models face efficiency challenges due to growing KV caches and redundant attention computations, which are addressed through TempCache, AnnCA, and AnnSA techniques that ...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01801
• PDF: https://arxiv.org/pdf/2602.01801
• Project Page: https://dvirsamuel.github.io/fast-auto-regressive-video/
• Github: https://dvirsamuel.github.io/fast-auto-regressive-video/

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

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✨Generative Visual Code Mobile World Models

📝 Summary:
Visual world models for mobile GUI agents are improved through renderable code generation using vision-language models, achieving better performance with reduced model size compared to existing approa...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01576
• PDF: https://arxiv.org/pdf/2602.01576

🔹 Models citing this paper:
• https://huggingface.co/trillionlabs/gWorld-8B
• https://huggingface.co/trillionlabs/gWorld-32B

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

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✨Sparse Reward Subsystem in Large Language Models

📝 Summary:
Research identifies a sparse reward subsystem in LLM hidden states containing value neurons that represent internal state expectations and dopamine-like neurons encoding reward prediction errors. AI-g...

🔹 Publication Date: Published on Feb 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00986
• PDF: https://arxiv.org/pdf/2602.00986

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

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✨Hunt Instead of Wait: Evaluating Deep Data Research on Large Language Models

📝 Summary:
Agentic large language models require investigatory intelligence for autonomous data analysis, demonstrated through the Deep Data Research benchmark that evaluates their ability to extract insights fr...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02039
• PDF: https://arxiv.org/pdf/2602.02039
• Project Page: https://huggingface.co/spaces/thinkwee/DDR_Bench
• Github: https://github.com/thinkwee/DDR_Bench

✨ Datasets citing this paper:
• https://huggingface.co/datasets/thinkwee/DDRBench_10K

✨ Spaces citing this paper:
• https://huggingface.co/spaces/thinkwee/DDR_Bench

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✨Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs

📝 Summary:
ReSID introduces a recommendation-native framework to improve sequential recommenders. It learns predictive item representations and optimizes quantization for better information preservation and sequential predictability without LLMs. ReSID significantly outperforms baselines by over 10% and red...

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02338
• PDF: https://arxiv.org/pdf/2602.02338

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

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✨Clipping-Free Policy Optimization for Large Language Models

📝 Summary:
Clipping-Free Policy Optimization replaces heuristic clipping with convex quadratic penalty to stabilize reinforcement learning training for large language models without performance loss. AI-generate...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22801
• PDF: https://arxiv.org/pdf/2601.22801

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

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✨Gaming the Judge: Unfaithful Chain-of-Thought Can Undermine Agent Evaluation

📝 Summary:
Large language models used as judges for agent performance evaluation are vulnerable to manipulation of reasoning traces, with content-based fabrications being more effective than style-based alterati...

🔹 Publication Date: Published on Jan 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14691
• PDF: https://arxiv.org/pdf/2601.14691

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

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✨A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation

📝 Summary:
Automated pipeline for sound separation using high-purity single-event segments from in-the-wild datasets achieves competitive performance with significantly reduced data requirements. AI-generated su...

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22599
• PDF: https://arxiv.org/pdf/2601.22599
• Project Page: https://shandaai.github.io/Hive
• Github: https://github.com/ShandaAI/Hive

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✨Where to Attend: A Principled Vision-Centric Position Encoding with Parabolas

📝 Summary:
Parabolic Position Encoding (PaPE) is a novel position encoding method for vision modalities that improves upon existing approaches by incorporating translation invariance, rotation invariance, distan...

🔹 Publication Date: Published on Feb 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.01418
• PDF: https://arxiv.org/pdf/2602.01418
• Project Page: https://chrisohrstrom.github.io/parabolic-position-encoding/
• Github: https://github.com/DTU-PAS/parabolic-position-encoding

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✨YOLOE-26: Integrating YOLO26 with YOLOE for Real-Time Open-Vocabulary Instance Segmentation

📝 Summary:
YOLOE-26 integrates YOLO26 architecture with open-vocabulary learning for real-time instance segmentation, utilizing convolutional backbones, end-to-end regression, and object embedding heads with tex...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00168
• PDF: https://arxiv.org/pdf/2602.00168

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

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✨Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability

📝 Summary:
A new reinforcement learning framework trains LLMs for divide-and-conquer reasoning. This method decomposes complex problems, significantly elevating test-time scalability and outperforming traditional chain-of-thought approaches on challenging benchmarks.

🔹 Publication Date: Published on Feb 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.02477
• PDF: https://arxiv.org/pdf/2602.02477
• Github: https://github.com/MasterVito/DAC-RL

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✨CUA-Skill: Develop Skills for Computer Using Agent

📝 Summary:
CUA-Skill introduces a large-scale library of engineered computer-use skills that enhance agent performance and efficiency on Windows-based tasks. AI-generated summary Computer-Using Agents (CUAs) aim...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21123
• PDF: https://arxiv.org/pdf/2601.21123
• Project Page: https://microsoft.github.io/cua_skill/
• Github: https://github.com/microsoft/cua_skill

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✨VoxServe: Streaming-Centric Serving System for Speech Language Models

📝 Summary:
VoxServe is a unified serving system for SpeechLMs that optimizes streaming performance. It uses model-execution abstraction, streaming-aware scheduling, and asynchronous inference pipelines. This achieves 10-20x higher throughput at comparable latency for diverse SpeechLM architectures.

🔹 Publication Date: Published on Jan 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.00269
• PDF: https://arxiv.org/pdf/2602.00269
• Project Page: https://vox-serve.github.io/
• Github: https://github.com/vox-serve/vox-serve

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✨Competing Visions of Ethical AI: A Case Study of OpenAI

📝 Summary:
This study analyzed OpenAI's public discourse on ethical AI. It found OpenAI primarily frames the discussion around safety and risk, largely avoiding academic ethics frameworks. This indicates a distinct approach to AI ethics in industry communications.

🔹 Publication Date: Published on Jan 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.16513
• PDF: https://arxiv.org/pdf/2601.16513

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

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