✨Exploring Reasoning Reward Model for Agents

📝 Summary:
Agent-RRM, a multi-faceted reward model, provides structured feedback for agentic trajectories through reasoning traces, critiques, and performance scores, with unified feedback integration showing su...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2601.22154
• PDF: https://arxiv.org/pdf/2601.22154
• Github: https://github.com/kxfan2002/Reagent

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✨Beyond Imitation: Reinforcement Learning for Active Latent Planning

📝 Summary:
Active latent planning method improves reasoning accuracy and efficiency by modeling latent token supervision as conditional VAE and using reinforcement learning with coherence rewards. AI-generated s...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21598
• PDF: https://arxiv.org/pdf/2601.21598

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✨Generation Enhances Understanding in Unified Multimodal Models via Multi-Representation Generation

📝 Summary:
UniMRG enhances unified multimodal models by training them to generate multiple visual representations, improving both understanding and generation capabilities through complementary information captu...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21406
• PDF: https://arxiv.org/pdf/2601.21406
• Github: https://github.com/Sugewud/UniMRG

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✨WebArbiter: A Principle-Guided Reasoning Process Reward Model for Web Agents

📝 Summary:
WebArbiter introduces a reasoning-first WebPRM that formulates reward modeling as text generation to improve web navigation through structured justifications and preference verdicts, outperforming exi...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21872
• PDF: https://arxiv.org/pdf/2601.21872

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✨DynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation

📝 Summary:
DynamicVLA addresses dynamic object manipulation challenges through a compact vision-language-action model with temporal reasoning and closed-loop adaptation, supported by a new benchmark for dynamic ...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22153
• PDF: https://arxiv.org/pdf/2601.22153
• Project Page: https://haozhexie.com/project/dynamic-vla
• Github: https://github.com/hzxie/DynamicVLA

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✨MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods

📝 Summary:
A large-scale multimodal reasoning dataset called MMFineReason is introduced to improve vision language models' performance through high-quality reasoning annotations and demonstrates superior paramet...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21821
• PDF: https://arxiv.org/pdf/2601.21821
• Project Page: https://mmfinereason.github.io/

🔹 Models citing this paper:
• https://huggingface.co/OpenDataArena/MMFineReason-8B
• https://huggingface.co/OpenDataArena/MMFineReason-4B
• https://huggingface.co/OpenDataArena/MMFineReason-2B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/OpenDataArena/MMFineReason-1.8M
• https://huggingface.co/datasets/OpenDataArena/MMFineReason-SFT-123K

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✨MAD: Modality-Adaptive Decoding for Mitigating Cross-Modal Hallucinations in Multimodal Large Language Models

📝 Summary:
Multimodal Large Language Models suffer from cross-modal hallucinations where one modality incorrectly influences generation from another, leading to fabricated outputs; this exposes a fundamental def...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21181
• PDF: https://arxiv.org/pdf/2601.21181

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✨Qwen3-ASR Technical Report

📝 Summary:
The Qwen3-ASR family introduces speech recognition models with language identification capabilities and a non-autoregressive forced alignment model, achieving state-of-the-art performance and efficien...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21337
• PDF: https://arxiv.org/pdf/2601.21337

🔹 Models citing this paper:
• https://huggingface.co/Qwen/Qwen3-ASR-1.7B
• https://huggingface.co/Qwen/Qwen3-ASR-0.6B
• https://huggingface.co/Qwen/Qwen3-ForcedAligner-0.6B

✨ Spaces citing this paper:
• https://huggingface.co/spaces/Qwen/Qwen3-ASR
• https://huggingface.co/spaces/prithivMLmods/Qwen3-TTS-Daggr-UI
• https://huggingface.co/spaces/sxjeru/Qwen3-ASR-1.7B

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

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✨ConceptMoE: Adaptive Token-to-Concept Compression for Implicit Compute Allocation

📝 Summary:
ConceptMoE dynamically allocates computation by merging similar tokens into concept representations, improving both performance and efficiency in large language models through adaptive processing and ...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21420
• PDF: https://arxiv.org/pdf/2601.21420
• Github: https://github.com/ZihaoHuang-notabot/ConceptMoE

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✨Self-Improving Pretraining: using post-trained models to pretrain better models

📝 Summary:
A reinforcement learning-based pretraining method improves language model safety, factuality, and quality by evaluating generations through a combination of model rollouts, original suffixes, and rewr...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21343
• PDF: https://arxiv.org/pdf/2601.21343

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✨Scaling Embeddings Outperforms Scaling Experts in Language Models

📝 Summary:
Embedding scaling offers superior sparsity scaling compared to expert scaling in large language models, enabling efficient inference through system optimizations and speculative decoding. AI-generated...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21204
• PDF: https://arxiv.org/pdf/2601.21204

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✨DeepSearchQA: Bridging the Comprehensiveness Gap for Deep Research Agents

📝 Summary:
DeepSearchQA presents a 900-prompt benchmark evaluating agents on complex multi-step information-seeking tasks requiring systematic information collation, deduplication, and reasoning about stopping c...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20975
• PDF: https://arxiv.org/pdf/2601.20975
• Project Page: https://www.kaggle.com/benchmarks/google/dsqa/leaderboard

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✨Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance

📝 Summary:
Neural audio fingerprinting performance varies with segment length, with short segments (0.5-second) generally providing better retrieval accuracy, and large language models showing promise in recomme...

🔹 Publication Date: Published on Jan 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.17690
• PDF: https://arxiv.org/pdf/2601.17690

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✨PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement

📝 Summary:
PRISM leverages design data to create a knowledge base for improving graphic designs based on natural language instructions, achieving superior style alignment compared to existing methods. AI-generat...

🔹 Publication Date: Published on Jan 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11747
• PDF: https://arxiv.org/pdf/2601.11747

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✨WorldBench: Disambiguating Physics for Diagnostic Evaluation of World Models

📝 Summary:
WorldBench is introduced as a video-based benchmark for disentangled evaluation of physical reasoning in generative models, revealing specific failure patterns in current state-of-the-art video world ...

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21282
• PDF: https://arxiv.org/pdf/2601.21282
• Project Page: https://world-bench.github.io/

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✨Idea2Story: An Automated Pipeline for Transforming Research Concepts into Complete Scientific Narratives

📝 Summary:
Offline knowledge construction through structured methodological graphs enables more reliable and scalable autonomous scientific discovery by reducing reliance on real-time literature processing. AI-g...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20833
• PDF: https://arxiv.org/pdf/2601.20833

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✨OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models

📝 Summary:
OCRVerse unifies text and vision-centric OCR into a holistic end-to-end method for diverse visual documents. It uses comprehensive data and a two-stage SFT-RL training with domain-specific rewards to achieve competitive results.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.21639
• PDF: https://arxiv.org/pdf/2601.21639

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✨Everything in Its Place: Benchmarking Spatial Intelligence of Text-to-Image Models

📝 Summary:
Text-to-image models struggle with complex spatial reasoning due to sparse prompts. This paper introduces SpatialGenEval, a new benchmark with dense prompts, showing models struggle with higher-order spatial tasks. A new dataset, SpatialT2I, helps fine-tune models for significant performance gain...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20354
• PDF: https://arxiv.org/pdf/2601.20354
• Github: https://github.com/AMAP-ML/SpatialGenEval

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✨MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

📝 Summary:
Metric Anything introduces a scalable pretraining framework for metric depth using Sparse Metric Prompts to handle diverse, noisy 3D data. It shows clear scaling trends and achieves state-of-the-art performance across various depth estimation and spatial intelligence tasks.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22054
• PDF: https://arxiv.org/pdf/2601.22054
• Project Page: https://metric-anything.github.io/metric-anything-io/
• Github: https://github.com/metric-anything/metric-anything

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✨PLANING: A Loosely Coupled Triangle-Gaussian Framework for Streaming 3D Reconstruction

📝 Summary:
PLANING is an efficient streaming 3D reconstruction framework. It combines explicit geometric primitives and neural Gaussians with decoupled optimization, achieving both high-quality rendering and accurate geometry. It outperforms prior methods in quality and speed.

🔹 Publication Date: Published on Jan 29

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.22046
• PDF: https://arxiv.org/pdf/2601.22046

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✨BMAM: Brain-inspired Multi-Agent Memory Framework

📝 Summary:
BMAM presents a brain-inspired multi-agent memory architecture that decomposes memory into specialized subsystems to address long-term reasoning challenges in language-model-based agents. AI-generated...

🔹 Publication Date: Published on Jan 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.20465
• PDF: https://arxiv.org/pdf/2601.20465
• Github: https://github.com/innovation64/BMAM

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