AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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SA-OPD is proposed, a Spurious-Signal-Aware On-Policy Distillation framework that identifies and filters misleading token-level supervision based on input-groundedness and optimization impact, and consistently outperforms Vanilla OPD and competitive selective methods.
This work identifies and formalizes the Distribution-Value Coevolution principle: the training value of data is not intrinsic, but emerges dynamically from the interaction between data characteristics and the model's evolving capability boundary, and operationalizes this principle through a unified framework.
SciToolAgent-Evo, an ontology-aware self-evolving agent for open-world scientific tool acquisition, driven by an evolving memory of skills, experiences, and an ontologized tool graph achieves state-of-the-art performance, validating its robustness and generalization.
EM^2Mem is proposed, an event-centric multimodal memory framework that binds heterogeneous evidence to event anchors during memory construction, enabling compact evidence readout over grounded multimodal events rather than modality-specific fragments.
It is argued that effective knowledge editing must account for the intricate nature of knowledge representation, and three promising research directions are proposed that respect the complexity of knowledge representation in a real-world setting.
The same harness runs across five backend LLMs from three model families, indicating the harness generalizes across backends without tuning, even as different models induce distinct execution styles under the same workflow.
Reinforcement learning from human feedback (RLHF) has become the cornerstone of aligning large language models (LLMs) with human intent. Yet a fundamental question remains unaddressed: how should training data be scheduled when both the model's capabilities and the utility of data are constantly evolving? Current pipel...
SciAtlas is presented, a shared, machine-actionable cross-disciplinary scholarly knowledge infrastructure that integrates evidential, conceptual, disciplinary, expertise, and normative layers under a shared schema and achieves a unified neuro-symbolic retrieval mechanism that grounds heterogeneous research objects, pro...
WorldMind is introduced, a framework that autonomously constructs a symbolic World Knowledge Repository by synthesizing environmental feedback that unifies Process Experience to enforce physical feasibility via prediction errors and Goal Experience to guide task optimality through successful trajectories.
OceanGym is introduced, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding real-world environments, and reveals substantial gaps between state-of-the-art MLLM-driven agents and human experts.
Mechanist is an agentic system that uses AI as a scientific instrument for the autonomous discovery of mechanisms underlying AI intelligence, and develops a mechanism theory of belief, revealing how models represent world knowledge, form beliefs, infer the beliefs of others, and how these mechanisms emerge during pretr...
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