The paper proposes a conservative extension of Lustre's clock calculus to facilitate the embedding of ML models in reactive applications.

Omni-Flow is a distributed scheduling framework for multimodal inference that provides a unified abstraction for control flow, data flow, and compute flow.

This paper introduces a domain-specific visual graph editor for designing modular applications in edge and cloud computing environments, enabling users to model kernels, shared memory nodes, and event…

The paper presents Dyserve, a workflow-aware serving layer for agentic AI applications that compiles per-node model and verifier choices into an integer linear program, allowing for efficient model se…

This paper proposes a new paradigm for human-machine cooperation in software engineering, where machines amplify engineers' reasoning through causation.

The paper introduces Hyperparam, a set of lightweight JavaScript libraries designed to enable direct, model-aware querying of unstructured data (like agent traces) within client-side AI applications.

The paper introduces a data-centric optimization pipeline to improve coding agents' ability to interact with a branching lakehouse, showing significant accuracy gains by treating agent evaluation as a…

This paper proposes a four-layer technical architecture for large model inference optimization, including Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion…

The paper proposes $D^3$, a dynamic graph-constrained scheduling framework that optimizes LLM training order by modeling sample interactions as a dynamic influence graph.

The paper introduces BOUNDARY FLOW, an LLVM-based framework that enhances kernel fuzzing and analysis by extracting per-task, state-aware data-flow information (arguments and return values) at functio…

Taurus is a single-machine system for efficient Graph Neural Network (GNN) inference on large-scale graphs that do not fit in RAM, using source-centric broadcasts and a pipelined GPU-CPU-SSD hierarchy…

The paper proposes MLDAS, a framework that dynamically selects the optimal Machine Learning algorithm for Intrusion Detection Systems within Software-Defined Networking to enhance adaptive network sec…

This paper proposes a system called Autogram that uses AI and statistics to discover and validate network invariants.

The paper introduces Post-Deterministic Distributed Systems (PDDS) as a new model to coordinate autonomous infrastructure where participants, including stochastic agents, produce divergent reasoning p…

This paper proposes a decentralized collaborative learning paradigm for Tsetlin Machines using consensus-based inference, allowing heterogeneous agents to maintain their own private models and combine…