We propose remote direct code execution (RDX), which elevates the power of RDMA from memory access to code execution. We target runtime extension frameworks such as Wasm filters, BPF programs, and UDF functions, where RDX enables an agentless architecture that unlocks capabilities such as fast extension injection, update consistency guarantees, and minimal resource contention. We outline the roadmap for RDX around a new CodeFlow abstraction, encompassing programming remote extensions, exposing management stubs, remotely validating and JIT compiling code, seamlessly linking code to local context, managing remote extension state, and synchronizing code to targets. The case studies and initial results demonstrate the feasibility of RDX and its potential to spark the next wave of RDMA innovations.

Generating human motion in scenes from text aims to synthesize semantically aligned and scene-aware motions. Existing methods have made significant progress by incorporating spatial reasoning and structured generation strategies to connect text descriptions with human-scene interactions. However, they typically rely on simple textual inputs and struggle to comprehend open-ended instructions. There are three key challenges: (1) difficulty in understanding complex instructions due to limited and templated training text annotations; (2) inability to generate natural motions that align with arbitrary trajectories described in text; (3) lack of motion diversity that matches the intended semantics. To address these challenges, we propose PSMo, which consists of two components: the Semantic Planner and the Scene-Aware Motion Generator. The Semantic Planner leverages a Multimodal Large Language Model (MLLM) to parse open-ended instructions, and plans fine-grained motion states aligned with arbitrary trajectories. The scene-aware motion generator adopts the diffusion model with trajectory constraints and a sequential tiling strategy. To enhance motion diversity, we introduce a retrieval-augmented strategy and Scene-Aware Retrieval Attention, which integrates multi-modal features into the generation process. Extensive experiments demonstrate that our method produces high-quality and natural motions under open-ended instructions in scenes.

Demographic biases and social stereotypes are common in pretrained language models (PLMs), while the fine-tuning in downstream applications can also produce new biases or amplify the impact of the original biases. Existing works separate the debiasing from the fine-tuning procedure, which results in a gap between intrinsic bias and application bias. In this work, we propose a debiasing framework CauDebias to eliminate both biases, which directly combines debiasing with fine-tuning and can be applied for any PLMs in downstream tasks. We distinguish the bias-relevant (non-causal factors) and label-relevant (causal factors) parts in sentences from a causal invariant perspective. Specifically, we perform intervention on non-causal factors in different demographic groups, and then devise an invariant risk minimization loss to trade-off performance between bias mitigation and task accuracy. Experimental results on three downstream tasks show that our CauDebias can remarkably reduce biases in PLMs while minimizing the impact on downstream tasks.

Demographic biases and social stereotypes are common in pretrained language models (PLMs), and a burgeoning body of literature focuses on removing the unwanted stereotypical associations from PLMs.However, when fine-tuning these bias-mitigated PLMs in downstream natural language processing (NLP) applications, such as sentiment classification, the unwanted stereotypical associations resurface or even get amplified.Since pretrain&fine-tune is a major paradigm in NLP applications, separating the debiasing procedure of PLMs from fine-tuning would eventually harm the actual downstream utility.In this paper, we propose a unified debiasing framework Causal-Debias to remove unwanted stereotypical associations in PLMs during fine-tuning.Specifically, Causal-Debias mitigates bias from a causal invariant perspective by leveraging the specific downstream task to identify bias-relevant and labelrelevant factors.We propose that bias-relevant factors are non-causal as they should have little impact on downstream tasks, while labelrelevant factors are causal.We perform interventions on non-causal factors in different demographic groups and design an invariant risk minimization loss to mitigate bias while maintaining task performance.Experimental results on three downstream tasks show that our proposed method can remarkably reduce unwanted stereotypical associations after PLMs are finetuned, while simultaneously minimizing the impact on PLMs and downstream applications. *Corresponding author unwanted stereotypical associations in PLMs.For example, some works (Zmigrod et al., 2019) pretrain a language model using original and counterfactual corpus in order to cancel-out biased associations, some works (Liang et al., 2020) focus on debiasing post-hoc sentence representations, and others (Guo et al., 2022;Cheng et al., 2021) design bias-equalizing objectives to fine-tune PLM's parameters.

Unwanted social biases are usually encoded in pretrained language models (PLMs). Recent efforts are devoted to mitigating intrinsic bias encoded in PLMs. However, the separate fine-tuning on applications is detrimental to intrinsic debiasing. A bias resurgence issue arises when fine-tuning the debiased PLMs on downstream tasks. To eliminate undesired stereotyped associations in PLMs during fine-tuning, we present a mixup-based framework Mix-Debias from a new unified perspective, which directly combines debiasing PLMs with fine-tuning applications. The key to Mix-Debias is applying mixup-based linear interpolation on counterfactually augmented downstream datasets, with expanded pairs from external corpora. Besides, we devised an alignment regularizer to ensure original augmented pairs and gender-balanced counterparts are spatially closer. Experimental results show that Mix-Debias can reduce biases in PLMs while maintaining a promising performance in applications.

Computational notebooks (e.g., Jupyter, Google Colab) are widely used for interactive data science and machine learning. In those frameworks, users can start a session , then execute cells (i.e., a set of statements) to create variables, train models, visualize results, etc. Unfortunately, existing notebook systems do not offer live migration: when a notebook launches on a new machine, it loses its state , preventing users from continuing their tasks from where they had left off. This is because, unlike DBMS, the sessions directly rely on underlying kernels (e.g., Python/R interpreters) without an additional data management layer. Existing techniques for preserving states, such as copying all variables or OS-level checkpointing, are unreliable (often fail), inefficient, and platform-dependent. Also, re-running code from scratch can be highly time-consuming. In this paper, we introduce a new notebook system, Elastic-Notebook, that offers live migration via checkpointing/restoration using a novel mechanism that is reliable, efficient, and platform-independent. Specifically, by observing all cell executions via transparent, lightweight monitoring, ElasticNotebook can find a reliable and efficient way (i.e., replication plan ) for reconstructing the original session state, considering variable-cell dependencies, observed runtime, variable sizes, etc. To this end, our new graph-based optimization problem finds how to reconstruct all variables (efficiently) from a subset of variables that can be transferred across machines. We show that ElasticNotebook reduces end-to-end migration and restoration times by 85%-98% and 94%-99%, respectively, on a variety (i.e., Kaggle, JWST, and Tutorial) of notebooks with negligible runtime and memory overheads of <2.5% and <10%.