AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes IntHQ, a multi-task generative recommender with three components, each alleviating one collapse, and proposes Task-Interactive Modeling (TIM), a multi-task generative recommender with explicit cross-task interaction.
This paper presents FADE, an effective training framework for counterfactual discovery and explanation that is built on an evidence-first, two-stage training paradigm, and demonstrates remarkable robustness when transitioning from constrained MCQs to unconstrained OQA and captioning.
By releasing the compact 7B generator and 2K Refiner, this work seeks to democratize native audio-video generation and provide an accessible foundation for future research in unified audio-video generative modeling.
The results of R2M-Bench support same-rollout relative calibration as a practical way to distinguish revisit-specific consistency from generic temporal stability.
We present \textbf{DreamX-Phi 1.0}, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction, and a prescribed action sequence comprising end-effector poses and gripper states, predicts the resulting future observations. Yet realism alone does not guarantee...
A Multi-dimensional Evaluation-Verification Reward (EVR) decomposes evaluation into distinct visual criteria; for each criterion, an MLLM Evaluator generates multiple candidate hypotheses, and a Verifier grounds each claim in concrete visual evidence to accept or reject it, producing reliable and fine-grained reward si...
This work introduces LoopArena, a benchmark for evaluating how well one model can guide a separate coding agent through a long-running task, and evaluates this ability in three complementary settings that differ in execution scope and cost.
This work reformulate long-horizon execution as a task-state management problem and proposes LongHorizon-Harness, which maintains the task state explicitly outside execution and updates it only with facts independently verified from the environment.
This work proposes a Scale-wise Conditional Decoding mechanism, which projects semantic signals from a frozen vision encoder into scale-specific layers of the VAR backbone, and introduces a Unified Control Alignment strategy (SCALAR-Uni) to handle diverse control modalities within a single projection space.
PhyParam is presented, a physics-guided image-to-video diffusion model that conditions on object-level forces, masses, friction, restitution, and scene-level gravity via a lightweight physical-attention routing mechanism, and further improves motion learning with semantic-structural feature-space supervision.
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