✨Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw

📝 Summary:
A real-world safety analysis of the personal AI agent OpenClaw reveals significant vulnerabilities due to its broad system access. Attacks targeting its Capability, Identity, or Knowledge CIK dimensions drastically increase success rates, and current defenses are insufficient, indicating inherent...

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04759
• PDF: https://arxiv.org/pdf/2604.04759
• Project Page: https://ucsc-vlaa.github.io/CIK-Bench/
• Github: https://github.com/UCSC-VLAA/CIK-Bench

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

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#AISafety #Cybersecurity #AIAgents #Vulnerability #AIsecurity

✨Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing

📝 Summary:
SRPO unifies GRPO and SDPO in reinforcement learning by routing correct samples to GRPO's reward-aligned reinforcement and failed samples to SDPO's targeted logit-level correction. This novel approach achieves superior stability, rapid improvement, and better performance than either baseline.

🔹 Publication Date: Published on Apr 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02288
• PDF: https://arxiv.org/pdf/2604.02288

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#ReinforcementLearning #PolicyOptimization #SampleRouting #MachineLearning #AIResearch

✨LIBERO-Para: A Diagnostic Benchmark and Metrics for Paraphrase Robustness in VLA Models

📝 Summary:
Vision-Language-Action models show significant performance drops when handling paraphrased instructions due to surface-level matching rather than semantic understanding, highlighting the need for bett...

🔹 Publication Date: Published on Mar 30

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.28301
• PDF: https://arxiv.org/pdf/2603.28301
• Project Page: https://cau-hai-lab.github.io/LIBERO-Para/
• Github: https://github.com/cau-hai-lab/LIBERO-Para

✨ Datasets citing this paper:
• https://huggingface.co/datasets/HAI-Lab/LIBERO-Para

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Learning to Learn-at-Test-Time: Language Agents with Learnable Adaptation Policies

📝 Summary:
Meta-TTL formulates adaptation policy discovery as a bi-level optimization problem to improve language agent performance through learned policies rather than hand-crafted ones. AI-generated summary Te...

🔹 Publication Date: Published on Apr 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.00830
• PDF: https://arxiv.org/pdf/2604.00830
• Github: https://github.com/zzzlou/meta-ttl

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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

✨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

✨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

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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

✨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

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research

✨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

✨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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#AI #DataScience #MachineLearning #HuggingFace #Research