✨SciLT: Long-Tailed Classification in Scientific Image Domains
📝 Summary:
Scientific long-tailed recognition benefits from a proposed framework that leverages multi-level representations through adaptive feature fusion and dual-supervision learning to achieve balanced perfo...
🔹 Publication Date: Published on Apr 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03687
• PDF: https://arxiv.org/pdf/2604.03687
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Scientific long-tailed recognition benefits from a proposed framework that leverages multi-level representations through adaptive feature fusion and dual-supervision learning to achieve balanced perfo...
🔹 Publication Date: Published on Apr 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03687
• PDF: https://arxiv.org/pdf/2604.03687
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨PLUME: Latent Reasoning Based Universal Multimodal Embedding
📝 Summary:
PLUME introduces a latent reasoning framework for universal multimodal embedding that replaces explicit chain-of-thought reasoning with continuous latent state rollouts, achieving faster inference whi...
🔹 Publication Date: Published on Apr 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02073
• PDF: https://arxiv.org/pdf/2604.02073
• Project Page: https://haoxiangzhao12138.github.io/PLUME/
• Github: https://github.com/haoxiangzhao12138/PLUME
==================================
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#MultimodalAI #LatentReasoning #Embeddings #AIResearch #MachineLearning
📝 Summary:
PLUME introduces a latent reasoning framework for universal multimodal embedding that replaces explicit chain-of-thought reasoning with continuous latent state rollouts, achieving faster inference whi...
🔹 Publication Date: Published on Apr 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02073
• PDF: https://arxiv.org/pdf/2604.02073
• Project Page: https://haoxiangzhao12138.github.io/PLUME/
• Github: https://github.com/haoxiangzhao12138/PLUME
==================================
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#MultimodalAI #LatentReasoning #Embeddings #AIResearch #MachineLearning
✨Adam's Law: Textual Frequency Law on Large Language Models
📝 Summary:
Adam's Law proposes a novel framework to improve LLM performance through textual frequency analysis. It introduces Textual Frequency Law for prompting/fine-tuning, Distillation for estimation, and Curriculum Training. Experiments demonstrate its effectiveness.
🔹 Publication Date: Published on Apr 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02176
• PDF: https://arxiv.org/pdf/2604.02176
• Github: https://github.com/HongyuanLuke/frequencylaw
==================================
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#LLM #TextFrequency #PromptEngineering #NLP #DeepLearning
📝 Summary:
Adam's Law proposes a novel framework to improve LLM performance through textual frequency analysis. It introduces Textual Frequency Law for prompting/fine-tuning, Distillation for estimation, and Curriculum Training. Experiments demonstrate its effectiveness.
🔹 Publication Date: Published on Apr 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02176
• PDF: https://arxiv.org/pdf/2604.02176
• Github: https://github.com/HongyuanLuke/frequencylaw
==================================
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#LLM #TextFrequency #PromptEngineering #NLP #DeepLearning
✨CLEAR: Unlocking Generative Potential for Degraded Image Understanding in Unified Multimodal Models
📝 Summary:
CLEAR improves multimodal models robustness to image degradation. It connects the models generative and reasoning capabilities using supervised fine-tuning, a latent representation bridge, and reinforcement learning. This approach substantially boosts performance on degraded images while maintain...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04780
• PDF: https://arxiv.org/pdf/2604.04780
• Project Page: https://haoxiangzhao12138.github.io/CLEAR/
• Github: https://github.com/haoxiangzhao12138/CLEAR
==================================
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📝 Summary:
CLEAR improves multimodal models robustness to image degradation. It connects the models generative and reasoning capabilities using supervised fine-tuning, a latent representation bridge, and reinforcement learning. This approach substantially boosts performance on degraded images while maintain...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04780
• PDF: https://arxiv.org/pdf/2604.04780
• Project Page: https://haoxiangzhao12138.github.io/CLEAR/
• Github: https://github.com/haoxiangzhao12138/CLEAR
==================================
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✨Paper Espresso: From Paper Overload to Research Insight
📝 Summary:
Paper Espresso is an open-source LLM-powered platform that discovers, summarizes, and analyzes trending arXiv papers. It provides multi-granularity trend analysis, revealing AI research dynamics like a surge in RL for LLM reasoning and topic novelty correlating with community engagement.
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04562
• PDF: https://arxiv.org/pdf/2604.04562
• Project Page: https://mingzhe.space/assets/html/paper-espresso.html
==================================
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#LLM #AIResearch #DataScience #ResearchTools #arXiv
📝 Summary:
Paper Espresso is an open-source LLM-powered platform that discovers, summarizes, and analyzes trending arXiv papers. It provides multi-granularity trend analysis, revealing AI research dynamics like a surge in RL for LLM reasoning and topic novelty correlating with community engagement.
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04562
• PDF: https://arxiv.org/pdf/2604.04562
• Project Page: https://mingzhe.space/assets/html/paper-espresso.html
==================================
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#LLM #AIResearch #DataScience #ResearchTools #arXiv
✨POEMetric: The Last Stanza of Humanity
📝 Summary:
POEMetric evaluates LLM poetry generation across basic, creative, and quality dimensions, revealing significant gaps between human and machine capabilities in poetic expression. AI-generated summary L...
🔹 Publication Date: Published on Apr 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03695
• PDF: https://arxiv.org/pdf/2604.03695
• Github: https://github.com/Bingru-Li/POEMetric
==================================
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#LLM #AIPoetry #AICreativity #NLP #HumanAI
📝 Summary:
POEMetric evaluates LLM poetry generation across basic, creative, and quality dimensions, revealing significant gaps between human and machine capabilities in poetic expression. AI-generated summary L...
🔹 Publication Date: Published on Apr 4
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03695
• PDF: https://arxiv.org/pdf/2604.03695
• Github: https://github.com/Bingru-Li/POEMetric
==================================
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#LLM #AIPoetry #AICreativity #NLP #HumanAI
✨ONE-SHOT: Compositional Human-Environment Video Synthesis via Spatial-Decoupled Motion Injection and Hybrid Context Integration
📝 Summary:
ONE-SHOT enables compositional human-environment video generation through disentangled signals, dynamic positional embeddings, and hybrid context integration for improved control and diversity. AI-gen...
🔹 Publication Date: Published on Apr 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01043
• PDF: https://arxiv.org/pdf/2604.01043
• Project Page: https://martayang.github.io/ONE-SHOT/
==================================
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📝 Summary:
ONE-SHOT enables compositional human-environment video generation through disentangled signals, dynamic positional embeddings, and hybrid context integration for improved control and diversity. AI-gen...
🔹 Publication Date: Published on Apr 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01043
• PDF: https://arxiv.org/pdf/2604.01043
• Project Page: https://martayang.github.io/ONE-SHOT/
==================================
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✨The Geometric Alignment Tax: Tokenization vs. Continuous Geometry in Scientific Foundation Models
📝 Summary:
Foundation models in biology and physics suffer from geometric distortion due to discrete categorical bottlenecks, with continuous objectives showing significantly better preservation of system geomet...
🔹 Publication Date: Published on Apr 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04155
• PDF: https://arxiv.org/pdf/2604.04155
• Github: https://github.com/prashantcraju/geometric-alignment-tax
==================================
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📝 Summary:
Foundation models in biology and physics suffer from geometric distortion due to discrete categorical bottlenecks, with continuous objectives showing significantly better preservation of system geomet...
🔹 Publication Date: Published on Apr 5
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04155
• PDF: https://arxiv.org/pdf/2604.04155
• Github: https://github.com/prashantcraju/geometric-alignment-tax
==================================
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✨Emergent Compositional Communication for Latent World Properties
📝 Summary:
Multi-agent communication systems with Gumbel-Softmax emergently extract compositional representations of latent physical properties from video without supervision. This robust method supports planning and validates on real-world footage.
🔹 Publication Date: Published on Mar 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03266
• PDF: https://arxiv.org/pdf/2604.03266
• Github: https://github.com/TomekKaszynski/emergent-physics-comm
==================================
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📝 Summary:
Multi-agent communication systems with Gumbel-Softmax emergently extract compositional representations of latent physical properties from video without supervision. This robust method supports planning and validates on real-world footage.
🔹 Publication Date: Published on Mar 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03266
• PDF: https://arxiv.org/pdf/2604.03266
• Github: https://github.com/TomekKaszynski/emergent-physics-comm
==================================
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✨Synthetic Sandbox for Training Machine Learning Engineering Agents
📝 Summary:
A multi-agent framework called SandMLE is introduced that generates synthetic machine learning engineering environments from limited seed tasks, enabling efficient on-policy reinforcement learning by ...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04872
• PDF: https://arxiv.org/pdf/2604.04872
==================================
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📝 Summary:
A multi-agent framework called SandMLE is introduced that generates synthetic machine learning engineering environments from limited seed tasks, enabling efficient on-policy reinforcement learning by ...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04872
• PDF: https://arxiv.org/pdf/2604.04872
==================================
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✨Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems
📝 Summary:
Task reformulation and curriculum learning enable reinforcement learning from verifiable rewards to overcome exploration barriers in large language model post-training by transforming complex problems...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04767
• PDF: https://arxiv.org/pdf/2604.04767
• Github: https://github.com/dinobby/Cog-DRIFT
==================================
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📝 Summary:
Task reformulation and curriculum learning enable reinforcement learning from verifiable rewards to overcome exploration barriers in large language model post-training by transforming complex problems...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04767
• PDF: https://arxiv.org/pdf/2604.04767
• Github: https://github.com/dinobby/Cog-DRIFT
==================================
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✨Do Audio-Visual Large Language Models Really See and Hear?
📝 Summary:
AVLLMs exhibit modality bias where visual representations dominate over audio cues during multimodal integration, despite audio semantics being present in intermediate layers. AI-generated summary Aud...
🔹 Publication Date: Published on Apr 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02605
• PDF: https://arxiv.org/pdf/2604.02605
• Project Page: https://ramaneswaran.github.io/avllm_interpretability/
• Github: https://github.com/ramaneswaran/avllm_interpretability
==================================
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📝 Summary:
AVLLMs exhibit modality bias where visual representations dominate over audio cues during multimodal integration, despite audio semantics being present in intermediate layers. AI-generated summary Aud...
🔹 Publication Date: Published on Apr 3
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02605
• PDF: https://arxiv.org/pdf/2604.02605
• Project Page: https://ramaneswaran.github.io/avllm_interpretability/
• Github: https://github.com/ramaneswaran/avllm_interpretability
==================================
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✨Locally Confident, Globally Stuck: The Quality-Exploration Dilemma in Diffusion Language Models
📝 Summary:
Diffusion LLMs struggle with a quality-exploration dilemma; improving single-sample quality often limits reasoning path exploration. This paper explains why existing methods fail and proposes a new Independent Metropolis-Hastings sampler. This approach effectively balances quality and exploration...
🔹 Publication Date: Published on Apr 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00375
• PDF: https://arxiv.org/pdf/2604.00375
==================================
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📝 Summary:
Diffusion LLMs struggle with a quality-exploration dilemma; improving single-sample quality often limits reasoning path exploration. This paper explains why existing methods fail and proposes a new Independent Metropolis-Hastings sampler. This approach effectively balances quality and exploration...
🔹 Publication Date: Published on Apr 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00375
• PDF: https://arxiv.org/pdf/2604.00375
==================================
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✨Type-Checked Compliance: Deterministic Guardrails for Agentic Financial Systems Using Lean 4 Theorem Proving
📝 Summary:
The Lean-Agent Protocol ensures deterministic regulatory compliance for financial AI. It uses Lean 4 theorem proving to auto-formalize policies, verifying agent actions as mathematical conjectures for cryptographic-level certainty, addressing LLM probabilistic nature.
🔹 Publication Date: Published on Apr 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01483
• PDF: https://arxiv.org/pdf/2604.01483
• Project Page: https://axiom.devrashie.space
• Github: https://github.com/arkanemystic/lean-agent-protocol
==================================
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#FormalVerification #AICompliance #FinTech #Lean4 #LLMAgents
📝 Summary:
The Lean-Agent Protocol ensures deterministic regulatory compliance for financial AI. It uses Lean 4 theorem proving to auto-formalize policies, verifying agent actions as mathematical conjectures for cryptographic-level certainty, addressing LLM probabilistic nature.
🔹 Publication Date: Published on Apr 1
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01483
• PDF: https://arxiv.org/pdf/2604.01483
• Project Page: https://axiom.devrashie.space
• Github: https://github.com/arkanemystic/lean-agent-protocol
==================================
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#FormalVerification #AICompliance #FinTech #Lean4 #LLMAgents
❤2
✨Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems
📝 Summary:
This paper introduces LLMA-Mem, a memory framework for LLM multi-agent systems. It finds that scaling is non-monotonic; optimized experience reuse allows smaller teams to outperform larger ones, improving long-term performance and reducing cost.
🔹 Publication Date: Published on Mar 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03295
• PDF: https://arxiv.org/pdf/2604.03295
• Github: https://github.com/ShanglinWu/MAS_lifelong_learning
==================================
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📝 Summary:
This paper introduces LLMA-Mem, a memory framework for LLM multi-agent systems. It finds that scaling is non-monotonic; optimized experience reuse allows smaller teams to outperform larger ones, improving long-term performance and reducing cost.
🔹 Publication Date: Published on Mar 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03295
• PDF: https://arxiv.org/pdf/2604.03295
• Github: https://github.com/ShanglinWu/MAS_lifelong_learning
==================================
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✨BidirLM: From Text to Omnimodal Bidirectional Encoders by Adapting and Composing Causal LLMs
📝 Summary:
BidirLM adapts causal LLMs into bidirectional encoders, overcoming catastrophic forgetting and integrating specialized models. It employs a prior masking phase, weight merging, and data mixture, outperforming alternatives on text, vision, and audio benchmarks.
🔹 Publication Date: Published on Apr 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02045
• PDF: https://arxiv.org/pdf/2604.02045
🔹 Models citing this paper:
• https://huggingface.co/BidirLM/BidirLM-Omni-2.5B-Embedding
• https://huggingface.co/BidirLM/BidirLM-0.6B-Embedding
• https://huggingface.co/BidirLM/BidirLM-1.7B-Embedding
✨ Datasets citing this paper:
• https://huggingface.co/datasets/BidirLM/BidirLM-Contrastive
==================================
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#LLM #MultimodalAI #DeepLearning #AIResearch #ModelAdaptation
📝 Summary:
BidirLM adapts causal LLMs into bidirectional encoders, overcoming catastrophic forgetting and integrating specialized models. It employs a prior masking phase, weight merging, and data mixture, outperforming alternatives on text, vision, and audio benchmarks.
🔹 Publication Date: Published on Apr 2
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02045
• PDF: https://arxiv.org/pdf/2604.02045
🔹 Models citing this paper:
• https://huggingface.co/BidirLM/BidirLM-Omni-2.5B-Embedding
• https://huggingface.co/BidirLM/BidirLM-0.6B-Embedding
• https://huggingface.co/BidirLM/BidirLM-1.7B-Embedding
✨ Datasets citing this paper:
• https://huggingface.co/datasets/BidirLM/BidirLM-Contrastive
==================================
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#LLM #MultimodalAI #DeepLearning #AIResearch #ModelAdaptation
✨Beyond Accuracy: Unveiling Inefficiency Patterns in Tool-Integrated Reasoning
📝 Summary:
The paper introduces PTE Prefill Token Equivalents, a hardware-aware metric for Tool-Integrated Reasoning efficiency. PTE better measures real inference latency than token counts by accounting for KV-Cache inefficiencies and long tool responses. Higher PTE costs often indicate lower reasoning cor...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05404
• PDF: https://arxiv.org/pdf/2604.05404
• Github: https://github.com/sqs-ustc/tool-reasoning-framework-PTE
==================================
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📝 Summary:
The paper introduces PTE Prefill Token Equivalents, a hardware-aware metric for Tool-Integrated Reasoning efficiency. PTE better measures real inference latency than token counts by accounting for KV-Cache inefficiencies and long tool responses. Higher PTE costs often indicate lower reasoning cor...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.05404
• PDF: https://arxiv.org/pdf/2604.05404
• Github: https://github.com/sqs-ustc/tool-reasoning-framework-PTE
==================================
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✨FactReview: Evidence-Grounded Reviews with Literature Positioning and Execution-Based Claim Verification
📝 Summary:
FactReview is an evidence-grounded peer review system for machine learning that analyzes manuscript claims through claim extraction, literature positioning, and execution-based verification to provide...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04074
• PDF: https://arxiv.org/pdf/2604.04074
==================================
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📝 Summary:
FactReview is an evidence-grounded peer review system for machine learning that analyzes manuscript claims through claim extraction, literature positioning, and execution-based verification to provide...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04074
• PDF: https://arxiv.org/pdf/2604.04074
==================================
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✨Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding
📝 Summary:
Video-MME-v2 presents a comprehensive benchmark for evaluating video understanding models through a progressive hierarchy and group-based evaluation to assess robustness and faithfulness. AI-generated...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.05015
• PDF: https://arxiv.org/pdf/2604.05015
• Project Page: https://video-mme-v2.netlify.app/
• Github: https://github.com/MME-Benchmarks/Video-MME-v2
✨ Datasets citing this paper:
• https://huggingface.co/datasets/MME-Benchmarks/Video-MME-v2
==================================
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📝 Summary:
Video-MME-v2 presents a comprehensive benchmark for evaluating video understanding models through a progressive hierarchy and group-based evaluation to assess robustness and faithfulness. AI-generated...
🔹 Publication Date: Published on Apr 6
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.05015
• PDF: https://arxiv.org/pdf/2604.05015
• Project Page: https://video-mme-v2.netlify.app/
• Github: https://github.com/MME-Benchmarks/Video-MME-v2
✨ Datasets citing this paper:
• https://huggingface.co/datasets/MME-Benchmarks/Video-MME-v2
==================================
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✨Learning to Retrieve from Agent Trajectories
📝 Summary:
Retrieval models for agentic search should be trained directly from agent interaction data using a new paradigm that mines supervision from multi-step agent trajectories and incorporates relevance int...
🔹 Publication Date: Published on Mar 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04949
• PDF: https://arxiv.org/pdf/2604.04949
• Project Page: https://yuqi-zhou.github.io/LRAT-homepage/
• Github: https://github.com/Yuqi-Zhou/LRAT
==================================
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📝 Summary:
Retrieval models for agentic search should be trained directly from agent interaction data using a new paradigm that mines supervision from multi-step agent trajectories and incorporates relevance int...
🔹 Publication Date: Published on Mar 30
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04949
• PDF: https://arxiv.org/pdf/2604.04949
• Project Page: https://yuqi-zhou.github.io/LRAT-homepage/
• Github: https://github.com/Yuqi-Zhou/LRAT
==================================
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✨Claw-Eval: Toward Trustworthy Evaluation of Autonomous Agents
📝 Summary:
Claw-Eval addresses limitations in agent benchmarks by providing comprehensive evaluation across multiple modalities with trajectory-aware grading and safety assessments. AI-generated summary Large la...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06132
• PDF: https://arxiv.org/pdf/2604.06132
• Project Page: https://claw-eval.github.io/
• Github: https://github.com/claw-eval/claw-eval
==================================
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📝 Summary:
Claw-Eval addresses limitations in agent benchmarks by providing comprehensive evaluation across multiple modalities with trajectory-aware grading and safety assessments. AI-generated summary Large la...
🔹 Publication Date: Published on Apr 7
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.06132
• PDF: https://arxiv.org/pdf/2604.06132
• Project Page: https://claw-eval.github.io/
• Github: https://github.com/claw-eval/claw-eval
==================================
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