FlowAgent models tool chaining as continuous latent trajectory generation with conditional flow matching to deliver global planning, formal utility bounds, and better robustness on long-horizon tasks, plus a new plan-level benchmark.
citation dossier
From llm reasoning to autonomous ai agents: A comprehensive review
18Pith papers citing it
19reference links
cs.AItop field · 9 papers
UNVERDICTEDtop verdict bucket · 17 papers
why this work matters in Pith
Pith has found this work in 18 reviewed papers. Its strongest current cluster is cs.AI (9 papers). The largest review-status bucket among citing papers is UNVERDICTED (17 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
representative citing papers
Backward token warping in ViT-based MLLMs enables reliable reasoning from nearby viewpoints by preserving semantic coherence better than pixel-wise warping or fine-tuning baselines.
A learned orchestration policy for LLM agents that jointly optimizes task decomposition and selective routing to (model, primitive) pairs, delivering 77% macro pass@1 at 10x lower cost than strong baselines across 13 benchmarks.
LATTICE is a scalable LLM-judge benchmark for crypto agent decision support that reveals performance trade-offs among real-world copilots across dimensions and tasks.
QuantClaw dynamically routes precision in agent workflows to cut cost by up to 21.4% and latency by 15.7% while keeping or improving task performance.
A small set of sparse autoencoder features in LLMs drives shifts between generous and selfish allocations in dictator games, with causal patching and steering confirming their role and generalization to other social games.
EvoOR-Agent co-evolves agent architectures as AOE-style networks with graph-mediated recombination and knowledge-base-assisted mutation to outperform fixed LLM pipelines on OR benchmarks.
MemSearch-o1 mitigates memory dilution in agentic LLM search through reasoning-aligned token-level memory growth, retracing with a contribution function, and path reorganization, improving reasoning activation on benchmarks.
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.
AgentComm achieves nearly 50% bandwidth reduction in embodied agent communication via LLM semantic processing, importance-aware transmission, and a task knowledge base, with negligible impact on task completion.
STAR is a failure-aware Markovian router that learns recovery transitions from both successful and unsuccessful execution traces to improve multi-agent performance on spatiotemporal benchmarks.
Researchers developed a fast XGBoost-based detector using 42 runtime features to spot adversarial interaction patterns in LLM agents, running over 9 times faster than LLM detectors on synthetic multi-turn data.
AgentDID is a W3C-compliant decentralized identity system for AI agents enabling self-managed authentication and state verification via challenge-response.
An intention-aware semantic agent system for AI glasses reduces bandwidth by over 50% in simulations while preserving task performance through adaptive preprocessing guided by inferred user intentions.
A survey comparing classical multi-agent systems with large foundation model-enabled multi-agent systems, showing how the latter enables semantic-level collaboration and greater adaptability.
The survey organizes Context Engineering into retrieval, processing, management, and integrated systems like RAG and multi-agent setups while identifying an asymmetry where LLMs handle complex inputs well but struggle with equally sophisticated long outputs.
Agentic RAG embeds agents with reflection, planning, tool use, and collaboration into retrieval pipelines to overcome static RAG limitations, and the survey offers a taxonomy by agent count, control, autonomy, and knowledge representation plus applications and open challenges.
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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Tools as Continuous Flow for Evolving Agentic Reasoning
FlowAgent models tool chaining as continuous latent trajectory generation with conditional flow matching to deliver global planning, formal utility bounds, and better robustness on long-horizon tasks, plus a new plan-level benchmark.
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Token Warping Helps MLLMs Look from Nearby Viewpoints
Backward token warping in ViT-based MLLMs enables reliable reasoning from nearby viewpoints by preserving semantic coherence better than pixel-wise warping or fine-tuning baselines.
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Uno-Orchestra: Parsimonious Agent Routing via Selective Delegation
A learned orchestration policy for LLM agents that jointly optimizes task decomposition and selective routing to (model, primitive) pairs, delivering 77% macro pass@1 at 10x lower cost than strong baselines across 13 benchmarks.
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LATTICE: Evaluating Decision Support Utility of Crypto Agents
LATTICE is a scalable LLM-judge benchmark for crypto agent decision support that reveals performance trade-offs among real-world copilots across dimensions and tasks.
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QuantClaw: Precision Where It Matters for OpenClaw
QuantClaw dynamically routes precision in agent workflows to cut cost by up to 21.4% and latency by 15.7% while keeping or improving task performance.
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Understanding the Mechanism of Altruism in Large Language Models
A small set of sparse autoencoder features in LLMs drives shifts between generous and selfish allocations in dictator games, with causal patching and steering confirming their role and generalization to other social games.
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Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization
EvoOR-Agent co-evolves agent architectures as AOE-style networks with graph-mediated recombination and knowledge-base-assisted mutation to outperform fixed LLM pipelines on OR benchmarks.
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MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search
MemSearch-o1 mitigates memory dilution in agentic LLM search through reasoning-aligned token-level memory growth, retracing with a contribution function, and path reorganization, improving reasoning activation on benchmarks.
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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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AgentComm: Semantic Communication for Embodied Agents
AgentComm achieves nearly 50% bandwidth reduction in embodied agent communication via LLM semantic processing, importance-aware transmission, and a task knowledge base, with negligible impact on task completion.
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STAR: Failure-Aware Markovian Routing for Multi-Agent Spatiotemporal Reasoning
STAR is a failure-aware Markovian router that learns recovery transitions from both successful and unsuccessful execution traces to improve multi-agent performance on spatiotemporal benchmarks.
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A Low-Latency Fraud Detection Layer for Detecting Adversarial Interaction Patterns in LLM-Powered Agents
Researchers developed a fast XGBoost-based detector using 42 runtime features to spot adversarial interaction patterns in LLM agents, running over 9 times faster than LLM detectors on synthetic multi-turn data.
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AgentDID: Trustless Identity Authentication for AI Agents
AgentDID is a W3C-compliant decentralized identity system for AI agents enabling self-managed authentication and state verification via challenge-response.
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Intention-Aware Semantic Agent Communications for AI Glasses
An intention-aware semantic agent system for AI glasses reduces bandwidth by over 50% in simulations while preserving task performance through adaptive preprocessing guided by inferred user intentions.
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Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures
A survey comparing classical multi-agent systems with large foundation model-enabled multi-agent systems, showing how the latter enables semantic-level collaboration and greater adaptability.
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A Survey of Context Engineering for Large Language Models
The survey organizes Context Engineering into retrieval, processing, management, and integrated systems like RAG and multi-agent setups while identifying an asymmetry where LLMs handle complex inputs well but struggle with equally sophisticated long outputs.
-
Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
Agentic RAG embeds agents with reflection, planning, tool use, and collaboration into retrieval pipelines to overcome static RAG limitations, and the survey offers a taxonomy by agent count, control, autonomy, and knowledge representation plus applications and open challenges.
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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.