A multi-agent pipeline iteratively refines topology optimization outputs to match natural language preferences for branched structures, achieving 60% success rate across replicates in cantilever and phone-stand tasks.
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Agent ai with langgraph: A modular framework for enhancing machine translation using large language models
10 Pith papers cite this work. Polarity classification is still indexing.
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DESBench reveals structural trade-offs among centralized, hierarchical, heterarchical, and holonic coordination in dynamic industrial scheduling that outcome metrics alone miss.
GraphBit is a DAG-based engine-orchestrated framework for agentic LLMs that achieves 67.6% accuracy with zero hallucinations on GAIA benchmarks.
HADES is an agentic AI system that generates mechanistic hypotheses for drug-induced liver injury using molecular, metabolite, and pathway evidence, outperforming prior binary classifiers on the new DILER benchmark while establishing a baseline for hypothesis alignment.
An empirical evaluation of 22 agentic frameworks on BBH, GSM8K, and ARC benchmarks shows stable performance in 12 frameworks but highlights orchestration failures and weaker mathematical reasoning.
A graph-based propagation model for error cascades in LLM multi-agent systems plus a genealogy-graph governance plugin that prevents final infection in at least 89% of runs across tested frameworks.
HEMA is a multi-agent LLM system with analysis, knowledge, and control agents plus a self-consistency router that enables conversational home energy tasks, evaluated via LLM-simulated users on 23 metrics.
Domain-specialized LLM agents for hardware verification close 95-99% coverage using 4-13x fewer tokens and 2-4x faster convergence than general-purpose agents by reallocating tokens toward coverage-directed reasoning.
AgentOpt introduces a framework-agnostic package that uses algorithms like UCB-E to find cost-effective model assignments in multi-step LLM agent pipelines, cutting evaluation budgets by 62-76% while maintaining near-optimal accuracy on benchmarks.
This review synthesizes LLM uses in stock forecasting and catalogs key practical pitfalls from a hedge-fund viewpoint.
citing papers explorer
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TO-Agents: A Multi-Agent AI Pipeline for Preference-Guided Topology Optimization
A multi-agent pipeline iteratively refines topology optimization outputs to match natural language preferences for branched structures, achieving 60% success rate across replicates in cantilever and phone-stand tasks.
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When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling
DESBench reveals structural trade-offs among centralized, hierarchical, heterarchical, and holonic coordination in dynamic industrial scheduling that outcome metrics alone miss.
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GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration
GraphBit is a DAG-based engine-orchestrated framework for agentic LLMs that achieves 67.6% accuracy with zero hallucinations on GAIA benchmarks.
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An explainable hypothesis-driven approach to Drug-Induced Liver Injury with HADES
HADES is an agentic AI system that generates mechanistic hypotheses for drug-induced liver injury using molecular, metabolite, and pathway evidence, outperforming prior binary classifiers on the new DILER benchmark while establishing a baseline for hypothesis alignment.
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Agentic Frameworks for Reasoning Tasks: An Empirical Study
An empirical evaluation of 22 agentic frameworks on BBH, GSM8K, and ARC benchmarks shows stable performance in 12 frameworks but highlights orchestration failures and weaker mathematical reasoning.
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From Spark to Fire: Modeling and Mitigating Error Cascades in LLM-Based Multi-Agent Collaboration
A graph-based propagation model for error cascades in LLM multi-agent systems plus a genealogy-graph governance plugin that prevents final infection in at least 89% of runs across tested frameworks.
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Multi-Agent Home Energy Management Assistant
HEMA is a multi-agent LLM system with analysis, knowledge, and control agents plus a self-consistency router that enables conversational home energy tasks, evaluated via LLM-simulated users on 23 metrics.
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Understanding Inference-Time Token Allocation and Coverage Limits in Agentic Hardware Verification
Domain-specialized LLM agents for hardware verification close 95-99% coverage using 4-13x fewer tokens and 2-4x faster convergence than general-purpose agents by reallocating tokens toward coverage-directed reasoning.
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AgentOpt v0.1 Technical Report: Client-Side Optimization for LLM-Based Agent
AgentOpt introduces a framework-agnostic package that uses algorithms like UCB-E to find cost-effective model assignments in multi-step LLM agent pipelines, cutting evaluation budgets by 62-76% while maintaining near-optimal accuracy on benchmarks.
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A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective
This review synthesizes LLM uses in stock forecasting and catalogs key practical pitfalls from a hedge-fund viewpoint.