An agentic three-stage video annotation pipeline with an MSTED intermediate and top-down domain adaptation produces CoT training data that lifts Cosmos-Reason2 past Gemini on traffic event reasoning.
When single-agent with skills replace multi-agent systems and when they fail
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 9representative citing papers
HMACE deploys Proposer, Generator, Evaluator, and Reflector agents in an evolutionary loop to generate and refine heuristics for NP-hard problems, reporting lower optimality gaps and token costs than baselines on TSP and Online BPP.
SBD is a bilevel optimization framework that learns context-dependent safety weights for runtime task delegation in hierarchical multi-agent systems, with continuous authority transfer alpha and theoretical guarantees on safety monotonicity, policy convergence, and accountability propagation.
The paper defines a bounded reference architecture for LLM-orchestrated hybrid retrieval in dataset search using BM25, dense embeddings, reciprocal rank fusion, and metadata augmentation with pseudo-queries.
ContractSkill converts draft web agent skills into explicit executable contracts that enable deterministic verification, fault localization, and minimal local repair, improving stability on benchmarks like VisualWebArena.
Survey of auto-research systems identifies objective, validation, and acceptance collapses, concluding that workflow closure does not equal scientific closure and advocating non-autonomous epistemic control.
Compiling agentic workflows into LLM weights creates subterranean agents with near-frontier quality at two orders of magnitude less cost, validated empirically on travel booking, Zoom support, and insurance claims tasks.
A metric's 'freedom'—how weakly behavior variation couples to score variation—is claimed to predict skill-distillation benefit, but the proof and validation are flawed.
Agent skills are the emerging standard for giving LLM agents specialized procedures, but the reported 26.1% vulnerability rate in community skills argues for provenance-based, tiered trust governance.
citing papers explorer
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MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks
An agentic three-stage video annotation pipeline with an MSTED intermediate and top-down domain adaptation produces CoT training data that lifts Cosmos-Reason2 past Gemini on traffic event reasoning.
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HMACE: Heterogeneous Multi-Agent Collaborative Evolution for Combinatorial Optimization
HMACE deploys Proposer, Generator, Evaluator, and Reflector agents in an evolutionary loop to generate and refine heuristics for NP-hard problems, reporting lower optimality gaps and token costs than baselines on TSP and Online BPP.
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Safe Bilevel Delegation (SBD): A Formal Framework for Runtime Delegation Safety in Multi-Agent Systems
SBD is a bilevel optimization framework that learns context-dependent safety weights for runtime task delegation in hierarchical multi-agent systems, with continuous authority transfer alpha and theoretical guarantees on safety monotonicity, policy convergence, and accountability propagation.
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A Reference Architecture for Agentic Hybrid Retrieval in Dataset Search
The paper defines a bounded reference architecture for LLM-orchestrated hybrid retrieval in dataset search using BM25, dense embeddings, reciprocal rank fusion, and metadata augmentation with pseudo-queries.
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ContractSkill: Repairable Contract-Based Skills for Multimodal Web Agents
ContractSkill converts draft web agent skills into explicit executable contracts that enable deterministic verification, fault localization, and minimal local repair, improving stability on benchmarks like VisualWebArena.
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Workflow Closure Is Not Scientific Closure in Auto-Research Systems
Survey of auto-research systems identifies objective, validation, and acceptance collapses, concluding that workflow closure does not equal scientific closure and advocating non-autonomous epistemic control.
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Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost
Compiling agentic workflows into LLM weights creates subterranean agents with near-frontier quality at two orders of magnitude less cost, validated empirically on travel booking, Zoom support, and insurance claims tasks.
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From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?
A metric's 'freedom'—how weakly behavior variation couples to score variation—is claimed to predict skill-distillation benefit, but the proof and validation are flawed.
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Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward
Agent skills are the emerging standard for giving LLM agents specialized procedures, but the reported 26.1% vulnerability rate in community skills argues for provenance-based, tiered trust governance.