DIRECT uses a three-level multi-agent framework to solve video mashup creation as a multimodal coherency problem, outperforming baselines on a new benchmark.
InThe twelfth international conference on learning representations
6 Pith papers cite this work. Polarity classification is still indexing.
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GroupGPT decouples intervention timing from response generation via edge-cloud collaboration for multi-user chats, scoring 4.72/5 on the new MUIR benchmark of 2500 segments while cutting token use by up to 3x and adding privacy sanitization.
CUGA introduces a runtime governance architecture that enforces policies at five checkpoints in generalist agent execution pipelines for predictable and compliant behavior.
REA-Coder improves LLM code generation by iteratively aligning requirements with model understanding and verifying outputs against the aligned spec.
A multi-agent SDD framework with phase-level context-grounding hooks improves LLM-judged quality by 0.15 points and SWE-bench Lite Pass@1 by 1.7 percent while preserving near-perfect test compatibility.
VisionClaw couples continuous egocentric vision on smart glasses with speech-driven AI agents to enable hands-free real-world tasks, with lab and field studies showing faster completion and a shift toward opportunistic delegation.
citing papers explorer
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DIRECT: Video Mashup Creation via Hierarchical Multi-Agent Planning and Intent-Guided Editing
DIRECT uses a three-level multi-agent framework to solve video mashup creation as a multimodal coherency problem, outperforming baselines on a new benchmark.
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GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant
GroupGPT decouples intervention timing from response generation via edge-cloud collaboration for multi-user chats, scoring 4.72/5 on the new MUIR benchmark of 2500 segments while cutting token use by up to 3x and adding privacy sanitization.
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Governance by Construction for Generalist Agents
CUGA introduces a runtime governance architecture that enforces policies at five checkpoints in generalist agent execution pipelines for predictable and compliant behavior.
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Bridging the Gap between User Intent and LLM: A Requirement Alignment Approach for Code Generation
REA-Coder improves LLM code generation by iteratively aligning requirements with model understanding and verifying outputs against the aligned spec.
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Spec Kit Agents: Context-Grounded Agentic Workflows
A multi-agent SDD framework with phase-level context-grounding hooks improves LLM-judged quality by 0.15 points and SWE-bench Lite Pass@1 by 1.7 percent while preserving near-perfect test compatibility.
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VisionClaw: Always-On AI Agents through Smart Glasses
VisionClaw couples continuous egocentric vision on smart glasses with speech-driven AI agents to enable hands-free real-world tasks, with lab and field studies showing faster completion and a shift toward opportunistic delegation.