PaperMind is a new benchmark that evaluates integrated multimodal reasoning and critique over scientific papers through four complementary task families across seven domains.
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AutoGen: Enabling Next-Gen
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MALMAS is a memory-augmented multi-agent LLM system that generates diverse, high-quality features for tabular data via agent decomposition, routing, and iterative memory-guided refinement.
ClawArena-Team is a new execution-based benchmark that isolates and scores an LLM's subagent orchestration skill via a Subagent-Management Score multiplying correctness by privilege and modality factors.
SVR-MAD treats pre-debate signals as priors and debate results as evidence to build a sparser communication graph, cutting token use by up to 61% while preserving or raising accuracy over prior MAD methods.
Nexa learns a response-conditioned policy that starts with parallel agent execution and adds at most one round of sequential message passing via a predicted sparse DAG, strictly subsuming pure parallel mode.
ETI lets LLM agents infer and track partners' psychological traits (warmth and competence) from histories, cutting payoff loss 45-77% in games and boosting performance 3-29% on MultiAgentBench versus CoT baselines.
AgentRevive is a Markov state-aware framework with policy learning and edge optimization that manages Active, Standby, and Terminated agent states to enable resilient multi-agent evolution and reduce token consumption.
RADAR generates query-adaptive multi-agent communication structures via conditional discrete graph diffusion guided by effective graph size, outperforming baselines on accuracy and token consumption across six benchmarks.
ErrorProbe introduces a self-improving pipeline for attributing semantic failures in LLM multi-agent systems to specific agents and steps via anomaly detection, backward tracing, and tool-grounded validation with verified episodic memory.
citing papers explorer
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PaperMind: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs
PaperMind is a new benchmark that evaluates integrated multimodal reasoning and critique over scientific papers through four complementary task families across seven domains.
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Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data
MALMAS is a memory-augmented multi-agent LLM system that generates diverse, high-quality features for tabular data via agent decomposition, routing, and iterative memory-guided refinement.
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ClawArena-Team: Benchmarking Subagent Orchestration and Dynamic Workflows in Language-Model Agents
ClawArena-Team is a new execution-based benchmark that isolates and scores an LLM's subagent orchestration skill via a Subagent-Management Score multiplying correctness by privilege and modality factors.
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SVR-MAD: A Bayesian-Inspired Framework for Posterior-Guided Multi-Agent Debate
SVR-MAD treats pre-debate signals as priors and debate results as evidence to build a sparser communication graph, cutting token use by up to 61% while preserving or raising accuracy over prior MAD methods.
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Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systems
Nexa learns a response-conditioned policy that starts with parallel agent execution and adds at most one round of sequential message passing via a predicted sparse DAG, strictly subsuming pure parallel mode.
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Explicit Trait Inference for Multi-Agent Coordination
ETI lets LLM agents infer and track partners' psychological traits (warmth and competence) from histories, cutting payoff loss 45-77% in games and boosting performance 3-29% on MultiAgentBench versus CoT baselines.
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Taming "Zombie'' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution
AgentRevive is a Markov state-aware framework with policy learning and edge optimization that manages Active, Standby, and Terminated agent states to enable resilient multi-agent evolution and reduce token consumption.
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RADAR: Redundancy-Aware Diffusion for Multi-Agent Communication Structure Generation
RADAR generates query-adaptive multi-agent communication structures via conditional discrete graph diffusion guided by effective graph size, outperforming baselines on accuracy and token consumption across six benchmarks.
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Towards Self-Improving Error Diagnosis in Multi-Agent Systems
ErrorProbe introduces a self-improving pipeline for attributing semantic failures in LLM multi-agent systems to specific agents and steps via anomaly detection, backward tracing, and tool-grounded validation with verified episodic memory.
- Message Passing Enables Efficient Reasoning