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  • EvoTest: Evolutionary Test-Time Learning for Self-Improving Agentic . . .
    A fundamental limitation of current AI agents is their inability to learn complex skills on the fly at test time, often behaving like “clever but clueless interns” in novel environments This severely limits their practical utility To systematically measure and drive progress on this challenge, we first introduce the Jericho Test-Time Learning (J-TTL) benchmark J-TTL is a new evaluation
  • CacheEdit: Efficient Multi-round Image Editing via Adaptive. . .
    All four reviewers gave an overall recommendation rating of Weak Accept (4) for this submission The paper introduces a practical, training-free framework to accelerate multi-round instruction-based image editing by detecting edited regions early and reusing cached token activations for unchanged regions Reviewers appreciate the clever exploitation of Delayed Latent Emergence, its seamless
  • DecompNet: Enhancing Time Series Forecasting Models with Implicit . . .
    TL;DR: We pioneer the idea of implicit decomposition and propose a powerful decomposition-based enhancement framework based on it, which can consistently enhance the forecasting performance of various state-of-the-art time series models
  • CORE: Reducing UI Exposure in Mobile Agents via Collaboration. . .
    The idea of using XML structure for block partitioning is clever, preserving the UI’s logical design in a way that feels intuitive and practical for mobile interfaces Weaknesses: The evaluation is solid but could dig deeper into edge cases, like apps with highly dynamic UIs or tasks needing multiple steps across screens
  • LLMOPT: Learning to Define and Solve General Optimization Problems. . .
    Optimization problems are prevalent across various scenarios Formulating and then solving optimization problems described by natural language often requires highly specialized human expertise
  • Fracture-GS: Dynamic Fracture Simulation with Physics-Integrated. . .
    This paper presents a unified framework for simulating and visualizing dynamic fracture phenomena in extreme mechanical collisions using multi-view image inputs While existing methods primarily
  • Towards Universal Gene Regulatory Network Inference: Unlocking. . .
    As Reviewer naUQ insightfully noted, this uniform setting is "a clever way to reduce dataset-specific scale mismatch " By breaking away from traditional biological input constraints, we adopt a probing strategy similar to those used in LLM research, successfully unlocking the scFM's latent capacity to represent gene relationships
  • Embodied-R1: Reinforced Embodied Reasoning for General Robotic. . .
    The application of RLVR methods in embodied AI is still rare This paper takes a clever approach by not training end-to-end directly; instead, it uses reinforcement learning to train a perception module, and then passes the perception outputs to a motion planner for action execution
  • Text-to-SQL Benchmarks for Enterprise Realities: Under Massive. . .
    Existing Text-to-SQL benchmarks remain overly idealized and differ substantially from enterprise scenarios, which require retrieving tables from massive query scopes, interpreting complex schemas





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