✨Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns

📝 Summary:
TRC2 introduces a sparse, chunk-parallel architecture for language models to address continual learning challenges. It enables rapid adaptation and prevents catastrophic forgetting, improving the stability-plasticity tradeoff with efficient compute.

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22479
• PDF: https://arxiv.org/pdf/2602.22479
• Project Page: https://trc2lm.github.io

🔹 Models citing this paper:
• https://huggingface.co/akhadangi/trc2

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games

📝 Summary:
AI systems were evaluated across a diverse set of human-designed games to assess general intelligence, revealing significant gaps in performance compared to human players, particularly in complex cogn...

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17594
• PDF: https://arxiv.org/pdf/2602.17594
• Project Page: https://aigamestore.org

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Accelerating Diffusion via Hybrid Data-Pipeline Parallelism Based on Conditional Guidance Scheduling

📝 Summary:
A hybrid parallelism framework for diffusion models that combines condition-based partitioning and adaptive pipeline scheduling to reduce inference latency while maintaining image quality across diffe...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21760
• PDF: https://arxiv.org/pdf/2602.21760
• Github: https://github.com/kaist-dmlab/Hybridiff

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨From Blind Spots to Gains: Diagnostic-Driven Iterative Training for Large Multimodal Models

📝 Summary:
Diagnostic-driven Progressive Evolution enables continuous improvement of large multimodal models through iterative diagnosis and targeted data generation guided by identified weaknesses. AI-generated...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22859
• PDF: https://arxiv.org/pdf/2602.22859
• Github: https://github.com/hongruijia/DPE

🔹 Models citing this paper:
• https://huggingface.co/hongruijia/Qwen3_VL_8B_Instruct_DPE_v3
• https://huggingface.co/hongruijia/Qwen2.5-VL-7B-Instruct_DPE_v3
• https://huggingface.co/hongruijia/Qwen3_VL_8B_Instruct_DPE_v1

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

📝 Summary:
AgentDropoutV2 is a test-time framework that optimizes multi-agent system information flow without retraining. It corrects errors and prunes irreparable agent outputs to prevent error propagation. This approach significantly boosts task performance and offers robust generalization and adaptivity.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23258
• PDF: https://arxiv.org/pdf/2602.23258
• Github: https://github.com/TonySY2/AgentDropoutV2

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

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#MultiAgentSystems #AIResearch #InformationFlow #TestTimePruning #RobustAI

✨Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization

📝 Summary:
SMTL improves long-horizon agentic search by replacing sequential reasoning with parallel evidence acquisition. This framework achieves state-of-the-art performance and reduces reasoning steps by over 70% across diverse benchmarks, addressing efficiency and generalization challenges.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22675
• PDF: https://arxiv.org/pdf/2602.22675

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

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#AgenticSearch #AIResearch #Efficiency #Generalization #MachineLearning

✨MediX-R1: Open Ended Medical Reinforcement Learning

📝 Summary:
MediX-R1 is an open-ended reinforcement learning framework for medical multimodal LLMs. It uses diverse reward signals and LLM-based evaluation to enable clinically grounded, free-form answers, significantly improving reasoning on open-ended tasks.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23363
• PDF: https://arxiv.org/pdf/2602.23363
• Project Page: https://medix.cvmbzuai.com/
• Github: https://github.com/mbzuai-oryx/MediX-R1

🔹 Models citing this paper:
• https://huggingface.co/MBZUAI/MediX-R1-2B
• https://huggingface.co/MBZUAI/MediX-R1-8B
• https://huggingface.co/MBZUAI/MediX-R1-30B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/MBZUAI/medix-rl-data

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

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#MedicalAI #ReinforcementLearning #LLMs #MultimodalAI #AIResearch

✨EmbodMocap: In-the-Wild 4D Human-Scene Reconstruction for Embodied Agents

📝 Summary:
EmbodMocap is a dual-iPhone system for in-the-wild 4D human-scene reconstruction. It unifies human and scene data in a metric world frame, improving accuracy. This supports embodied AI tasks like animation and robot control.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23205
• PDF: https://arxiv.org/pdf/2602.23205
• Project Page: https://wenjiawang0312.github.io/projects/embodmocap/
• Github: https://github.com/WenjiaWang0312/EmbodMocap

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

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#EmbodiedAI #4DReconstruction #ComputerVision #Robotics #Animation

✨Echoes Over Time: Unlocking Length Generalization in Video-to-Audio Generation Models

📝 Summary:
MMHNet uses hierarchical methods and non-causal Mamba for video-to-audio generation. It achieves length generalization, allowing training on short videos to generate over 5 minutes of high-quality audio, outperforming prior models.

🔹 Publication Date: Published on Feb 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20981
• PDF: https://arxiv.org/pdf/2602.20981

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

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#VideoToAudio #LengthGeneralization #Mamba #DeepLearning #AIResearch

✨MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios

📝 Summary:
MobilityBench is a scalable benchmark for evaluating LLM-based route-planning agents using real-world user queries and a deterministic sandbox for reproducible testing. It reveals that current models perform well on basic tasks but struggle with preference-constrained route planning.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22638
• PDF: https://arxiv.org/pdf/2602.22638
• Github: https://github.com/AMAP-ML/MobilityBench

✨ Datasets citing this paper:
• https://huggingface.co/datasets/GD-ML/MobilityBench

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

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#MobilityBench #RoutePlanning #LLM #AI #Benchmarking

✨DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain

📝 Summary:
DLT-Corpus is a large new dataset for Distributed Ledger Technology research from scientific literature, patents, and social media. It reveals technologies originate in science before reaching patents and social media. Scientific and patent activity independently drive economic growth, unaffected...

🔹 Publication Date: Published on Feb 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22045
• PDF: https://arxiv.org/pdf/2602.22045
• Github: https://github.com/dlt-science/DLT-Corpus

🔹 Models citing this paper:
• https://huggingface.co/ExponentialScience/LedgerBERT
• https://huggingface.co/ExponentialScience/LedgerBERT-Market-Sentiment

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ExponentialScience/DLT-Patents
• https://huggingface.co/datasets/ExponentialScience/DLT-Tweets
• https://huggingface.co/datasets/ExponentialScience/DLT-Sentiment-News

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

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#DLT #Dataset #DataScience #Research #Blockchain

✨No One Size Fits All: QueryBandits for Hallucination Mitigation

📝 Summary:
QueryBandits is an adaptive contextual bandit framework that selects optimal query-rewrite strategies to mitigate LLM hallucinations, usable with closed-source models. It outperforms static policies, showing no single rewrite strategy is optimal for all queries and preventing worsened hallucinati...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20332
• PDF: https://arxiv.org/pdf/2602.20332
• Project Page: https://arxiv.org/abs/2602.20332

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

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#LLM #AI #MachineLearning #NLP #HallucinationMitigation

✨What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance

📝 Summary:
This study found that specific linguistic features in user queries correlate with LLM hallucination likelihood. Analyzing over 369000 queries, they identified a risk landscape where features like deep clause nesting increase risk, while clear intention decreases it. This paves the way for better ...

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20300
• PDF: https://arxiv.org/pdf/2602.20300
• Project Page: https://arxiv.org/abs/2602.20300

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

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#LLM #PromptEngineering #NLP #AIResearch #AIHallucination

✨MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data

📝 Summary:
This study demonstrates efficient MEG-based speech decoding using transfer learning. A Conformer model, pre-trained on listening data, significantly improves accuracy and enables reliable cross-task decoding between speech perception and production with limited fine-tuning. Shared neural processe...

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.18253
• PDF: https://arxiv.org/pdf/2602.18253
• Github: https://github.com/hitz-zentroa/meg-phone-decoding

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

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#MEG #TransferLearning #SpeechProcessing #Neuroscience #DeepLearning

✨Retrieve and Segment: Are a Few Examples Enough to Bridge the Supervision Gap in Open-Vocabulary Segmentation?

📝 Summary:
This paper proposes a few-shot retrieval-augmented test-time adapter for open-vocabulary segmentation. It uses learned per-query fusion of textual and visual support features to overcome zero-shot limitations. This approach significantly narrows the performance gap with supervised segmentation wh...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23339
• PDF: https://arxiv.org/pdf/2602.23339
• Github: https://github.com/TilemahosAravanis/Retrieve-and-Segment

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨General Agent Evaluation

📝 Summary:
General-purpose agents lack systematic evaluation. This paper proposes principles, a protocol, and Exgentic framework to assess their versatility. Experiments show these agents generalize across diverse environments, performing well without specific tuning.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.22953
• PDF: https://arxiv.org/pdf/2602.22953
• Project Page: https://www.exgentic.ai
• Github: https://github.com/Exgentic/exgentic

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines

📝 Summary:
A scientific reasoning foundation model pre-trained on diverse scientific data supports multiple tasks and enhances cross-domain generalization and fidelity through specialized training techniques. AI...

🔹 Publication Date: Published on Sep 25, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2509.21320
• PDF: https://arxiv.org/pdf/2509.21320
• Github: https://github.com/open-sciencelab/SciReason

🔹 Models citing this paper:
• https://huggingface.co/SciReason/SciReasoner-8B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/SciReason/SciLM-Instruction_Tuning
• https://huggingface.co/datasets/SciReason/SciLM-CoT_ColdStart

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation

📝 Summary:
MedCLIPSeg adapts CLIP for medical image segmentation using patch-level embeddings and probabilistic attention. This enables data-efficient, generalizable, and uncertainty-aware segmentation, outperforming prior methods with interpretable uncertainty maps.

🔹 Publication Date: Published on Feb 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.20423
• PDF: https://arxiv.org/pdf/2602.20423
• Project Page: https://tahakoleilat.github.io/MedCLIPSeg/
• Github: https://github.com/HealthX-Lab/MedCLIPSeg

🔹 Models citing this paper:
• https://huggingface.co/TahaKoleilat/MedCLIPSeg

✨ Datasets citing this paper:
• https://huggingface.co/datasets/TahaKoleilat/MedCLIPSeg

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨VGG-T^3: Offline Feed-Forward 3D Reconstruction at Scale

📝 Summary:
VGG-T^3 scales 3D reconstruction linearly with input views by distilling variable-length scene representations into fixed-size MLPs through test-time training. This method achieves significant speedup over traditional quadratic approaches while maintaining high accuracy and global scene aggregation.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23361
• PDF: https://arxiv.org/pdf/2602.23361
• Project Page: https://research.nvidia.com/labs/dvl/projects/vgg-ttt/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Overconfident Errors Need Stronger Correction: Asymmetric Confidence Penalties for Reinforcement Learning

📝 Summary:
Reinforcement learning with verifiable rewards suffers from reduced reasoning diversity due to uniform error penalization, which is addressed by a confidence-aware asymmetric error penalty method that...

🔹 Publication Date: Published on Feb 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.21420
• PDF: https://arxiv.org/pdf/2602.21420

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

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#AI #DataScience #MachineLearning #HuggingFace #Research