“Further details of the project are not available in the provided text, but the concept shows great promise.”

“Now there's a planner → checker → revise loop. Plans don't execute until they pass verification.”

“I’m a Full-Stack AI/ML Engineer with strong experience building LLM-powered applications, multi-agent systems, and scalable Python backends.”

“the best-single baseline achieves an 82.5% +- 3.3% win rate, dramatically outperforming the best deliberation protocol(13.8% +- 2.6%)”

“A quote from the content is needed.”

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“By integrating OpenAI models, lightweight tool calling, and a simple internal runbook, […]”

““Plano-Orchestrator decides which agent(s) should handle the request and in what sequence. In other words, it acts as the supervisor agent in a multi-agent system.””

“In this tutorial, we build an advanced yet practical multi-agent system using OpenAI Swarm that runs in Colab. We demonstrate how we can orchestrate specialized agents, such as a triage agent, an SRE agent, a communications agent, and a critic, to collaboratively handle a real-world production incident scenario.”

“MAMAMemeia improves upon the current state-of-the-art by 7.55% in macro-F1 and is established as the new benchmark compared to over 30 methods.”

“The tracking of multiple, unknown targets is formulated as a harmonic extension problem on a cellular sheaf, accommodating nonlinear dynamics and external disturbances for all agents.”

“The paper proposes a joint-state abstraction that compresses the state space while preserving the information necessary to discover strongly coordinated behaviours.”

“AstroReview correctly identifies genuinely accepted proposals with an accuracy of 87% in the meta-review stage, and the acceptance rate of revised drafts increases by 66% after two iterations with the Proposal Authoring Agent.”

“The proposed delay compensation strategy achieves a reduction of over 200,000 infected individuals at the peak.”

“PP-ACDC achieves asymptotic (exact) average consensus on any strongly connected digraph under appropriately chosen quantization parameters.”

“The model improves multi-hop reasoning accuracy by 16.8 percent on HotpotQA, 14.3 percent on 2WikiMultihopQA, and 19.2 percent on MeetingBank, while improving consistency by 21.5 percent.”

“The framework delivers a 3x increase in task processing speed over single-agent baselines, 98.7% structural/style consistency in writing, and a 74.6% test pass rate in coding.”

“MaRCA delivered a 16.67% revenue uplift using existing computation resources.”

“The article focuses on building an advanced, end-to-end multi-agent research workflow using the CAMEL framework.”

“ROAD achieved a 5.6 percent increase in success rate and a 3.8 percent increase in search accuracy within just three automated iterations.”

“SPARK formalizes a persona space defined by role, expertise, task context, and domain, and introduces a Persona Coordinator that dynamically interprets incoming queries to activate the most relevant specialized agents.”

“The proposed method demonstrates superiority over baseline schemes in terms of average sum rate, robustness to CSI imperfection, user mobility, and scalability.”

“The proposed method outperforms zero forcing (ZF) and maximum ratio transmission (MRT) techniques, particularly in high-interference scenarios, while remaining robust to CSI imperfections.”

“The paper highlights that traditional models achieve inflated F1 scores due to label-persistence bias and fail on critical defect-transition cases. The proposed change-aware reasoning and multi-agent debate framework yields more balanced performance and improves sensitivity to defect introductions.”

“BOAD outperforms single-agent and manually designed multi-agent systems. On SWE-bench-Live, featuring more recent and out-of-distribution issues, our 36B system ranks second on the leaderboard at the time of evaluation, surpassing larger models such as GPT-4 and Claude.”

“InfTool transforms a base 32B model from 19.8% to 70.9% accuracy (+258%), surpassing models 10x larger and rivaling Claude-Opus, and entirely from synthetic data without human annotation.”

“The paper suggests a Cross-Agent Multimodal Provenance-Aware Defense Framework whereby all the prompts, either user-generated or produced by upstream agents, are sanitized and all the outputs generated by an LLM are verified independently before being sent to downstream nodes.”

“MoLaCE addresses confirmation bias by mixing experts instantiated as different activation strengths over latent concepts that shape model responses.”

“Experimental outcomes indicate better detection accuracy, shorter mitigation latency and reasonable build-time overhead than rule-based, provenance only and RL only baselines.”

“The study focuses on the behaviour coverage analysis of a multi-agent system simulation designed for autonomous vehicle testing, and provides a systematic approach to measure and assess behaviour coverage within the simulation environment.”

“AKG kernel agent achieves an average speedup of 1.46x over PyTorch Eager baselines implementations.”

“MESA MIG outperforms caption only and single agent baselines in aesthetic quality, semantic consistency, and VA alignment, and achieves competitive emotion regression performance.”

“The results show that although AI4Reading still has a gap in speech generation quality, the generated interpretative scripts are simpler and more accurate.”

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“The paper identifies unreported threats including commercial LLM API model stealing, parameter memorization leakage, and preference-guided text-only jailbreaks.”

“Prompt Choreography significantly reduces per-message latency (2.0--6.2$ imes$ faster time-to-first-token) and achieves substantial end-to-end speedups ($>$2.2$ imes$) in some workflows dominated by redundant computation.”

“The paper defines five types of heterogeneity, proposes a 'heterogeneity distance' for quantification, and demonstrates a dynamic parameter sharing algorithm based on this methodology.”

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“The article likely presents a new algorithm or framework for portfolio management, focusing on improving asset allocation strategies in a multi-agent environment.”

“Reinforcement Networks unify hierarchical, modular, and graph-structured views of MARL, opening a principled path toward designing and training complex multi-agent systems.”

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“Threshold rules produce a distinct non-mean-field universality class with β≈0.75 and a systematic failure of MF-DP dynamical scaling. We show that thresholding acts as a relevant perturbation to DP.”

“We are currently focused on building simulation engines for observing behavior in multi agent scenarios.”

“We are currently focused on building simulation engines for observing behavior in multi agent scenarios.”

“Selective TTS improves insight quality under a fixed compute budget, increasing mean scores from 61.64 to 65.86 while reducing variance.”

“SimVC-CAS significantly improves predictive accuracy while providing interpretable, multiperspective reasoning, for example, approximately 25% relative improvement with respect to average precision@10.”

“The paper proposes three novel metrics for evaluating SANet and achieves performance gains of up to 14.61% while requiring only 44.37% of FLOPs compared to state-of-the-art algorithms.”

“Our 8B-parameter model achieves a Macro F1 of 0.845, outperforming GPT-4o (0.812) by 3.3% while using 20 times fewer parameters.”

“AnalogSAGE achieves a 10$ imes$ overall pass rate, a 48$ imes$ Pass@1, and a 4$ imes$ reduction in parameter search space compared with existing frameworks.”