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  • LOSI: Improving Multi-agent Reinforcement Learning via Latent. . .
    In collaborative Multi-Agent Reinforcement Learning (MARL), agents must contend with non-stationarity introduced not only by teammates’ concurrent decisions but also by partially observable and
  • Autoregressive Entity Retrieval - OpenReview
    Strengths: A brand new perspective on entity retrieval Clever use of text generation methods Compared to previous methods, GENRE is memory saving This paper provides comprehensive experiments on 3 important tasks, over 20 datasets, showing the robustness of this method Weaknesses:
  • Roll the dice look before you leap: Going beyond the creative. . .
    For example, authors rarely document their internal thought processes while conceptualizing creative works like a research paper or a clever olympiad problem Given that the creative process is highly complex compared to standard CoT use-cases, (see below!) to what extent can a model produce such traces with originality diversity?
  • GTM: A General Time-series Model for Enhanced Representation. . .
    Despite recent progress in time-series foundation models, challenges persist in improving representation learning and adapting to diverse downstream tasks We introduce a General Time-series Model
  • Sparse but Critical: A Token-Level Analysis of Distributional. . .
    Main claims of the paper are supported, but the execution of experiments might be significantly strenghtened further (see weaknesses) S2 Experiments with cross-sampling and advantage reweighting are interesting and clever and seem to be a promising analysis toolkit; however, see W3 and W4
  • Efficient Edge Inference by Selective Query | OpenReview
    Especially, the routing module is trained via proxy supervision (using oracle), which is a clever way to incorporate all modules in the training Experiments are carefully designed and performed More importantly, actual MCU systems are used for the evaluation “Hybrid accuracy” seems to be a good metric for these two-stage inference systems
  • PINFDiT: Energy-Based Physics-Informed Diffusion Transformers for. . .
    Time series analysis underpins scientific advances While specialized models have advanced various time series tasks, scientific domains face unique challenges: limited samples with complex physical dynamics, missing observations, multi-resolution sampling, and requirements for physical consistency With the increasing demands on generative modeling capabilities, we introduce PINFDiT, a
  • SpecBranch: Speculative Decoding via Hybrid Drafting and. . .
    The idea of modelling speculative decoding as analogous to branch prediction in CPUs is clever, and the introduction of parallel speculative branches is conceptually interesting
  • PaAno: Patch-Based Representation Learning for Time-Series Anomaly . . .
    The paper proposal a method for anomaly detection on time series, based on temporal convolutions to embed overlapping patches, together with multiple other clever design choices, including triplet loss, pretext task, and anomaly scoring using a memory bank of normal patches The paper's methodology is a combination of clever design choices, optimized for maximal benchmark performance The
  • Initialization using Update Approximation is a Silver Bullet for. . .
    TL;DR: We provably optimally approximate full fine-tuning in low-rank subspaces throughout the entire training process using a clever initialization scheme, achieving significant gains in parameter efficiency





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