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MAFP extends LLM multi-agent systems beyond task decomposition into genuinely interdependent decision-making, a class of problems the paper shows existing frameworks fail to address.
ProfiLLM demonstrates that an agentic LLM pipeline can move beyond structured numerical features in a live, millisecond-latency industrial dispatcher and produce measurable improvements in real-world GMV and completion rates — validated by a 14-day online A/B test on DiDi's production system.
The work demonstrates that agentic, multi-agent prompt optimization can compound noisy real-world A/B test cycles into statistically robust improvements, offering a practical alternative to gradient-based prompt tuning for open-ended task-oriented dialogue systems.
EARS converts sub-agent silence into structured, coordinator-actionable failure signals, directly raising the production response pass rate from 68.5% to 78.9% in a real enterprise deployment.
MiniPIC removes the requirement for identical prefixes to reuse KV cache entries, enabling efficient caching of recurring structured inputs in retrieval-augmented and agentic workloads without the large server-side code changes or host-to-device transfer overhead of prior PIC approaches.
The framework and dataset directly extend multimodal medical AI to seven major Indian languages, addressing the lack of equitable AI-driven healthcare assistance in multilingual, low-resource settings like rural India that English-centric MLLMs cannot serve.
The evidence-first protocol directly reduces the conversational bias that causes standard LLM assistants to follow misleading user hypotheses, improving diagnostic accuracy over both direct prompting and reasoning-only baselines across multiple LLM backbones.
InterleaveThinker removes the architectural barrier that has prevented existing image generators from producing interleaved text-image sequences, extending a capability previously limited to frontier models like GPT-5 to any image generator via a plug-in multi-agent pipeline.
PhysTool-Bench quantifies a critical and previously underexplored gap between MLLMs' strong digital API performance and their weak physical tool comprehension, pinpointing specific bottlenecks — perception and functional commonsense — that limit the development of practical embodied AI.
TabClaw's combination of transparent, editable execution plans with a self-evolving skill and memory system directly addresses the transparency and adaptability gaps the paper identifies in current LLM-based data-analysis agents.