AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Affective computing has progressed from categorical emotion recognition to open-ended affective analysis with large multimodal models. Yet affective science describes emotion as an unfolding process shaped by appraisal, regulation, and social interpretation, which remains underexplored computationally. We propose TRACE...
Tool-using agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by evidence established beforehand. We study where this evidence-to-action chain breaks as agents move from deciding whether to act to executing single actions and dependent workflows....
A preregistered, three-country randomized experiment on out-of-context image misinformation, manipulating a correction's channel affordances (synchronicity, bandwidth) across four conditions shows engagement mechanisms do not substitute for slow AI literacy.
The results establish harness-level capability composition as a practical route to broad, evolvable Omni systems without changing the host agent's reasoning core.
Existing visual emotion understanding methods typically ignore cultural variations in emotional perception. We introduce culture-conditioned visual emotion understanding, a task that predicts the culture-specific emotional perception of a given image and explains the underlying rationale. Although related benchmarks ex...
Althea is introduced, a retrieval-augmented system for user-driven claim evaluation that matches standard pipelines on AVeriTeC while improving supported/refuted discrimination and cautioning against treating AI-delivered verdicts as a source of durable literacy gains.
This work examines what structure is supplied by the game or workflow, what AI learns or produces, which capabilities and artifacts transfer across settings and roles, and what evidence supports the claims and identifies cross-role connections.
QIME is proposed, an ontology-grounded framework for interpretable biomedical text embeddings in which each dimension corresponds to an explicit biomedical-domain yes/no question, providing concise and domain-specific interpretations.
This paper introduces Implicit Social Context Analysis (MoCA), a novel task that systematically models implicit social scenarios along three key dimensions: affection, intent, and stance, and proposes Conflict-Driven Abductive Reasoning (CoDAR), a novel framework that models the discrepancy between observed expressions...
OmniScientist is introduced, an end-to-end, omni-modal AI scientist that conducts multidisciplinary research directly from heterogeneous raw evidence and demonstrates that lifecycle-wide perception is essential for evidence-grounded scientific discovery and provides a practical path toward broadly capable AI scientists...
R3Check is proposed, a rule-guided reinforcement learning framework that enables LLMs to perform iterative retrieval–reasoning for multi-hop fact-checking under weak supervision, and demonstrates the potential of pure reinforcement learning to induce effective retrieval–reasoning behaviors for fact-checking under weak...
This paper proposes UrbanGround, the first sandbox to make this question testable in a physically constrained replica of Hong Kong built from territory-wide 3D geospatial data, and hopes it will support broader study of how far current MLLM agents can explore reliably in complex, open-ended urban environments.
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