✨LOCA-bench: Benchmarking Language Agents Under Controllable and Extreme Context Growth
📝 Summary:
LOCA-bench is a new benchmark for evaluating language agents in long context, agentic scenarios with controlled environment state growth. It assesses how models and context management strategies perform as context extends, finding that advanced techniques significantly improve success rates.
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07962
• PDF: https://arxiv.org/pdf/2602.07962
• Github: https://github.com/hkust-nlp/LOCA-bench
==================================
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📝 Summary:
LOCA-bench is a new benchmark for evaluating language agents in long context, agentic scenarios with controlled environment state growth. It assesses how models and context management strategies perform as context extends, finding that advanced techniques significantly improve success rates.
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07962
• PDF: https://arxiv.org/pdf/2602.07962
• Github: https://github.com/hkust-nlp/LOCA-bench
==================================
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✨LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning
📝 Summary:
LatentChem enables chemical reasoning through continuous latent space computations instead of discrete textual tokens, achieving superior performance and efficiency compared to traditional chain-of-th...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07075
• PDF: https://arxiv.org/pdf/2602.07075
==================================
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📝 Summary:
LatentChem enables chemical reasoning through continuous latent space computations instead of discrete textual tokens, achieving superior performance and efficiency compared to traditional chain-of-th...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07075
• PDF: https://arxiv.org/pdf/2602.07075
==================================
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✨AgentCPM-Report: Interleaving Drafting and Deepening for Open-Ended Deep Research
📝 Summary:
AgentCPM-Report presents a lightweight local solution for deep research report generation using a Writing As Reasoning Policy framework and multi-stage agentic training to enhance small models' reason...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06540
• PDF: https://arxiv.org/pdf/2602.06540
• Github: https://github.com/OpenBMB/AgentCPM
🔹 Models citing this paper:
• https://huggingface.co/openbmb/AgentCPM-Report
• https://huggingface.co/openbmb/AgentCPM-Report-GGUF
==================================
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📝 Summary:
AgentCPM-Report presents a lightweight local solution for deep research report generation using a Writing As Reasoning Policy framework and multi-stage agentic training to enhance small models' reason...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.06540
• PDF: https://arxiv.org/pdf/2602.06540
• Github: https://github.com/OpenBMB/AgentCPM
🔹 Models citing this paper:
• https://huggingface.co/openbmb/AgentCPM-Report
• https://huggingface.co/openbmb/AgentCPM-Report-GGUF
==================================
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✨When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning
📝 Summary:
Adaptive test-time framework with world models enables selective visual imagination for spatial reasoning, improving efficiency and reliability by determining when imagination is necessary. AI-generat...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08236
• PDF: https://arxiv.org/pdf/2602.08236
• Project Page: https://adaptive-visual-tts.github.io/
• Github: https://adaptive-visual-tts.github.io/
==================================
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📝 Summary:
Adaptive test-time framework with world models enables selective visual imagination for spatial reasoning, improving efficiency and reliability by determining when imagination is necessary. AI-generat...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08236
• PDF: https://arxiv.org/pdf/2602.08236
• Project Page: https://adaptive-visual-tts.github.io/
• Github: https://adaptive-visual-tts.github.io/
==================================
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✨Recurrent-Depth VLA: Implicit Test-Time Compute Scaling of Vision-Language-Action Models via Latent Iterative Reasoning
📝 Summary:
RD-VLA introduces a recurrent architecture for VLA models, using latent iterative refinement for adaptive compute. It maintains constant memory, boosts success on complex tasks, and offers significant speedups.
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07845
• PDF: https://arxiv.org/pdf/2602.07845
• Project Page: https://rd-vla.github.io/
• Github: https://github.com/rd-vla/rd-vla
==================================
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📝 Summary:
RD-VLA introduces a recurrent architecture for VLA models, using latent iterative refinement for adaptive compute. It maintains constant memory, boosts success on complex tasks, and offers significant speedups.
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07845
• PDF: https://arxiv.org/pdf/2602.07845
• Project Page: https://rd-vla.github.io/
• Github: https://github.com/rd-vla/rd-vla
==================================
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arXiv.org
Recurrent-Depth VLA: Implicit Test-Time Compute Scaling of...
Current Vision-Language-Action (VLA) models rely on fixed computational depth, expending the same amount of compute on simple adjustments and complex multi-step manipulation. While...
✨Demo-ICL: In-Context Learning for Procedural Video Knowledge Acquisition
📝 Summary:
Researchers introduce a new video understanding task and benchmark that evaluates models' ability to learn from few-shot demonstrations, along with a specialized MLLM architecture trained using a two-...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08439
• PDF: https://arxiv.org/pdf/2602.08439
• Github: https://github.com/dongyh20/Demo-ICL
==================================
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📝 Summary:
Researchers introduce a new video understanding task and benchmark that evaluates models' ability to learn from few-shot demonstrations, along with a specialized MLLM architecture trained using a two-...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08439
• PDF: https://arxiv.org/pdf/2602.08439
• Github: https://github.com/dongyh20/Demo-ICL
==================================
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✨QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining
📝 Summary:
Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mini...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07085
• PDF: https://arxiv.org/pdf/2602.07085
• Github: https://github.com/QuantaAlpha/QuantaAlpha
==================================
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📝 Summary:
Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mini...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07085
• PDF: https://arxiv.org/pdf/2602.07085
• Github: https://github.com/QuantaAlpha/QuantaAlpha
==================================
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✨LLaDA2.1: Speeding Up Text Diffusion via Token Editing
📝 Summary:
LLaDA2.1 introduces a novel token-to-token editing approach with speed and quality modes, enhanced through reinforcement learning for improved reasoning and instruction following in large language dif...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08676
• PDF: https://arxiv.org/pdf/2602.08676
• Github: https://github.com/inclusionAI/LLaDA2.X
🔹 Models citing this paper:
• https://huggingface.co/inclusionAI/LLaDA2.1-mini
• https://huggingface.co/inclusionAI/LLaDA2.1-flash
==================================
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📝 Summary:
LLaDA2.1 introduces a novel token-to-token editing approach with speed and quality modes, enhanced through reinforcement learning for improved reasoning and instruction following in large language dif...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08676
• PDF: https://arxiv.org/pdf/2602.08676
• Github: https://github.com/inclusionAI/LLaDA2.X
🔹 Models citing this paper:
• https://huggingface.co/inclusionAI/LLaDA2.1-mini
• https://huggingface.co/inclusionAI/LLaDA2.1-flash
==================================
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✨WorldCompass: Reinforcement Learning for Long-Horizon World Models
📝 Summary:
WorldCompass enhances long-horizon video-based world models through reinforcement learning post-training with clip-level rollouts, complementary rewards, and efficient RL algorithms. AI-generated summ...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09022
• PDF: https://arxiv.org/pdf/2602.09022
• Project Page: https://3d-models.hunyuan.tencent.com/world/
==================================
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📝 Summary:
WorldCompass enhances long-horizon video-based world models through reinforcement learning post-training with clip-level rollouts, complementary rewards, and efficient RL algorithms. AI-generated summ...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09022
• PDF: https://arxiv.org/pdf/2602.09022
• Project Page: https://3d-models.hunyuan.tencent.com/world/
==================================
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arXiv.org
WorldCompass: Reinforcement Learning for Long-Horizon World Models
This work presents WorldCompass, a novel Reinforcement Learning (RL) post-training framework for the long-horizon, interactive video-based world models, enabling them to explore the world more...
✨WildReward: Learning Reward Models from In-the-Wild Human Interactions
📝 Summary:
WildReward demonstrates that reward models can be effectively trained from in-the-wild user interactions using ordinal regression, achieving performance comparable to traditional methods while benefit...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08829
• PDF: https://arxiv.org/pdf/2602.08829
==================================
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📝 Summary:
WildReward demonstrates that reward models can be effectively trained from in-the-wild user interactions using ordinal regression, achieving performance comparable to traditional methods while benefit...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08829
• PDF: https://arxiv.org/pdf/2602.08829
==================================
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✨Reliable and Responsible Foundation Models: A Comprehensive Survey
📝 Summary:
Foundation models including LLMs, MLLMs, and generative models require reliable and responsible development addressing bias, security, explainability, and other critical issues for trustworthy deploym...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08145
• PDF: https://arxiv.org/pdf/2602.08145
==================================
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📝 Summary:
Foundation models including LLMs, MLLMs, and generative models require reliable and responsible development addressing bias, security, explainability, and other critical issues for trustworthy deploym...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08145
• PDF: https://arxiv.org/pdf/2602.08145
==================================
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✨MOVA: Towards Scalable and Synchronized Video-Audio Generation
📝 Summary:
MOVA is an open-source model generating synchronized video-audio content, including lip-synced speech and sound effects. It employs a 32B-parameter Mixture-of-Experts architecture for image-text to video-audio generation, overcoming limitations of previous cascaded and closed-source systems.
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08794
• PDF: https://arxiv.org/pdf/2602.08794
• Project Page: https://mosi.cn/models/mova
• Github: https://github.com/OpenMOSS/MOVA
==================================
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📝 Summary:
MOVA is an open-source model generating synchronized video-audio content, including lip-synced speech and sound effects. It employs a 32B-parameter Mixture-of-Experts architecture for image-text to video-audio generation, overcoming limitations of previous cascaded and closed-source systems.
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08794
• PDF: https://arxiv.org/pdf/2602.08794
• Project Page: https://mosi.cn/models/mova
• Github: https://github.com/OpenMOSS/MOVA
==================================
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✨InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery
📝 Summary:
InternAgent-1.5 is a unified system for autonomous scientific discovery that integrates computational modeling and experimental research through coordinated subsystems for generation, verification, an...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08990
• PDF: https://arxiv.org/pdf/2602.08990
==================================
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📝 Summary:
InternAgent-1.5 is a unified system for autonomous scientific discovery that integrates computational modeling and experimental research through coordinated subsystems for generation, verification, an...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08990
• PDF: https://arxiv.org/pdf/2602.08990
==================================
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✨How2Everything: Mining the Web for How-To Procedures to Evaluate and Improve LLMs
📝 Summary:
A scalable framework for evaluating and improving goal-conditioned procedure generation using large-scale web mining, automated scoring, and reinforcement learning to enhance step-by-step instruction ...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08808
• PDF: https://arxiv.org/pdf/2602.08808
• Github: https://github.com/lilakk/how2everything
==================================
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📝 Summary:
A scalable framework for evaluating and improving goal-conditioned procedure generation using large-scale web mining, automated scoring, and reinforcement learning to enhance step-by-step instruction ...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08808
• PDF: https://arxiv.org/pdf/2602.08808
• Github: https://github.com/lilakk/how2everything
==================================
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✨GISA: A Benchmark for General Information-Seeking Assistant
📝 Summary:
A new benchmark called GISA is introduced for evaluating information-seeking assistants, featuring human-crafted queries with structured answer formats and live updates to prevent memorization. AI-gen...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08543
• PDF: https://arxiv.org/pdf/2602.08543
==================================
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📝 Summary:
A new benchmark called GISA is introduced for evaluating information-seeking assistants, featuring human-crafted queries with structured answer formats and live updates to prevent memorization. AI-gen...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08543
• PDF: https://arxiv.org/pdf/2602.08543
==================================
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✨Rolling Sink: Bridging Limited-Horizon Training and Open-Ended Testing in Autoregressive Video Diffusion
📝 Summary:
Autoregressive video diffusion models suffer from train-test gaps when generating long videos, but a training-free approach called Rolling Sink addresses this by maintaining AR cache and enabling ultr...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07775
• PDF: https://arxiv.org/pdf/2602.07775
• Project Page: https://rolling-sink.github.io/
==================================
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📝 Summary:
Autoregressive video diffusion models suffer from train-test gaps when generating long videos, but a training-free approach called Rolling Sink addresses this by maintaining AR cache and enabling ultr...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07775
• PDF: https://arxiv.org/pdf/2602.07775
• Project Page: https://rolling-sink.github.io/
==================================
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arXiv.org
Rolling Sink: Bridging Limited-Horizon Training and Open-Ended...
Recently, autoregressive (AR) video diffusion models has achieved remarkable performance. However, due to their limited training durations, a train-test gap emerges when testing at longer...
✨Concept-Aware Privacy Mechanisms for Defending Embedding Inversion Attacks
📝 Summary:
SPARSE is a user-centric framework that protects text embeddings from privacy leaks by selectively perturbing sensitive dimensions using differentiable masking and Mahalanobis noise calibration. AI-ge...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07090
• PDF: https://arxiv.org/pdf/2602.07090
==================================
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📝 Summary:
SPARSE is a user-centric framework that protects text embeddings from privacy leaks by selectively perturbing sensitive dimensions using differentiable masking and Mahalanobis noise calibration. AI-ge...
🔹 Publication Date: Published on Feb 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07090
• PDF: https://arxiv.org/pdf/2602.07090
==================================
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✨Aster: Autonomous Scientific Discovery over 20x Faster Than Existing Methods
📝 Summary:
Aster is an AI agent that accelerates scientific discovery by iteratively improving programs, achieving state-of-the-art results across multiple domains including mathematics, biology, and machine lea...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07040
• PDF: https://arxiv.org/pdf/2602.07040
• Project Page: https://www.asterlab.ai/
==================================
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📝 Summary:
Aster is an AI agent that accelerates scientific discovery by iteratively improving programs, achieving state-of-the-art results across multiple domains including mathematics, biology, and machine lea...
🔹 Publication Date: Published on Feb 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07040
• PDF: https://arxiv.org/pdf/2602.07040
• Project Page: https://www.asterlab.ai/
==================================
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✨Weak-Driven Learning: How Weak Agents make Strong Agents Stronger
📝 Summary:
WMSS is a post-training paradigm that uses weak model checkpoints to identify and fill learning gaps, enabling continued improvement beyond conventional saturation points in large language models. AI-...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08222
• PDF: https://arxiv.org/pdf/2602.08222
==================================
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📝 Summary:
WMSS is a post-training paradigm that uses weak model checkpoints to identify and fill learning gaps, enabling continued improvement beyond conventional saturation points in large language models. AI-...
🔹 Publication Date: Published on Feb 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08222
• PDF: https://arxiv.org/pdf/2602.08222
==================================
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✨Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?
📝 Summary:
Current multimodal foundation models show limitations in maintaining coherent spatial beliefs during active exploration, exhibiting gaps between active and passive performance, inefficient exploration...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07055
• PDF: https://arxiv.org/pdf/2602.07055
• Project Page: https://theory-of-space.github.io/
• Github: https://github.com/mll-lab-nu/Theory-of-Space
==================================
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📝 Summary:
Current multimodal foundation models show limitations in maintaining coherent spatial beliefs during active exploration, exhibiting gaps between active and passive performance, inefficient exploration...
🔹 Publication Date: Published on Feb 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07055
• PDF: https://arxiv.org/pdf/2602.07055
• Project Page: https://theory-of-space.github.io/
• Github: https://github.com/mll-lab-nu/Theory-of-Space
==================================
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✨Learning-guided Kansa collocation for forward and inverse PDEs beyond linearity
📝 Summary:
Research explores PDE solvers including neural frameworks for scientific simulations, examining forward solutions, inverse problems, and equation discovery across multi-variable and non-linear systems...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07970
• PDF: https://arxiv.org/pdf/2602.07970
==================================
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📝 Summary:
Research explores PDE solvers including neural frameworks for scientific simulations, examining forward solutions, inverse problems, and equation discovery across multi-variable and non-linear systems...
🔹 Publication Date: Published on Feb 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07970
• PDF: https://arxiv.org/pdf/2602.07970
==================================
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