DSPy compiles short declarative programs into LM pipelines that self-optimize and outperform both standard few-shot prompting and expert-written chains on math, retrieval, and QA tasks.
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MRKL systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
18 Pith papers cite this work. Polarity classification is still indexing.
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The Agent-First Tool API paradigm raises AI agent task success from 64% to 88% and cuts human interventions by 72.7% through semantic phases, structured contracts, and risk governance in a production enterprise system.
Current AI agents achieve only 26% success on SciCrafter's redstone tasks requiring causal discovery and application, indicating the discovery-to-application loop remains challenging with shifting bottlenecks.
Agents improve when they retrieve skills on demand from large corpora, yet current models cannot selectively decide when to load or ignore a retrieved skill.
PaperMind is a new benchmark that evaluates integrated multimodal reasoning and critique over scientific papers through four complementary task families across seven domains.
A practical evaluation protocol for AI pentesting agents that uses validated vulnerability discovery, LLM semantic matching, and bipartite scoring to assess performance in realistic, complex targets.
Compositional selective specificity (CSS) improves overcommitment-aware utility from 0.846 to 0.913 on LongFact while retaining 0.938 specificity by calibrating claim-level backoffs in agentic AI responses.
LLM agents avoid output stalling and reduce generation tokens by 48-72% via deferred template rendering guided by Output Generation Capacity and a Format-Cost Separation Theorem.
COSMO-Agent trains LLMs via tool-augmented RL and a multi-constraint reward to close the CAD-CAE loop, with experiments showing small open-source models outperforming larger ones on feasibility and stability for 25 component categories.
A server-side architecture with policy-aware ingestion and ABAC-based retrieval gating prevents cross-tenant data leakage in multitenant enterprise RAG and agent systems.
A case-based learning framework extracts reusable knowledge from past tasks to improve LLM agents' structured performance on complex real-world tasks, outperforming standard prompting baselines especially as task complexity grows.
A variational language model achieves minimal agentic control by treating internal uncertainty as an operational signal for regulation, checkpoint retention, and inference intervention.
Agent Mentor analyzes semantic trajectories in agent logs to identify undesired behaviors and derives corrective prompt instructions, yielding measurable accuracy gains on benchmark tasks across three agent setups.
A multi-agent SDD framework with phase-level context-grounding hooks improves LLM-judged quality by 0.15 points and SWE-bench Lite Pass@1 by 1.7 percent while preserving near-perfect test compatibility.
UI-TARS-2 reaches 88.2 on Online-Mind2Web, 47.5 on OSWorld, 50.6 on WindowsAgentArena, and 73.3 on AndroidWorld while attaining 59.8 mean normalized score on a 15-game suite through multi-turn RL and scalable data generation.
Proposes a three-layer framework using formal AI reasoning for verification, derivation, and discovery in wireless communications theory.
SciFi is a safe, lightweight agentic AI framework that automates structured scientific tasks with minimal human intervention via isolated environments and layered self-assessing agents.
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.
citing papers explorer
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DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
DSPy compiles short declarative programs into LM pipelines that self-optimize and outperform both standard few-shot prompting and expert-written chains on math, retrieval, and QA tasks.
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Agent-First Tool API: A Semantic Interface Paradigm for Enterprise AI Agent Systems
The Agent-First Tool API paradigm raises AI agent task success from 64% to 88% and cuts human interventions by 72.7% through semantic phases, structured contracts, and risk governance in a production enterprise system.
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Can Current Agents Close the Discovery-to-Application Gap? A Case Study in Minecraft
Current AI agents achieve only 26% success on SciCrafter's redstone tasks requiring causal discovery and application, indicating the discovery-to-application loop remains challenging with shifting bottlenecks.
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Skill Retrieval Augmentation for Agentic AI
Agents improve when they retrieve skills on demand from large corpora, yet current models cannot selectively decide when to load or ignore a retrieved skill.
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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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From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World
A practical evaluation protocol for AI pentesting agents that uses validated vulnerability discovery, LLM semantic matching, and bipartite scoring to assess performance in realistic, complex targets.
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Answer Only as Precisely as Justified: Calibrated Claim-Level Specificity Control for Agentic Systems
Compositional selective specificity (CSS) improves overcommitment-aware utility from 0.846 to 0.913 on LongFact while retaining 0.938 specificity by calibrating claim-level backoffs in agentic AI responses.
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When Agents Go Quiet: Output Generation Capacity and Format-Cost Separation for LLM Document Synthesis
LLM agents avoid output stalling and reduce generation tokens by 48-72% via deferred template rendering guided by Output Generation Capacity and a Format-Cost Separation Theorem.
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COSMO-Agent: Tool-Augmented Agent for Closed-loop Optimization,Simulation,and Modeling Orchestration
COSMO-Agent trains LLMs via tool-augmented RL and a multi-constraint reward to close the CAD-CAE loop, with experiments showing small open-source models outperforming larger ones on feasibility and stability for 25 component categories.
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Securing the Agent: Vendor-Neutral, Multitenant Enterprise Retrieval and Tool Use
A server-side architecture with policy-aware ingestion and ABAC-based retrieval gating prevents cross-tenant data leakage in multitenant enterprise RAG and agent systems.
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Transferable Expertise for Autonomous Agents via Real-World Case-Based Learning
A case-based learning framework extracts reusable knowledge from past tasks to improve LLM agents' structured performance on complex real-world tasks, outperforming standard prompting baselines especially as task complexity grows.
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Agentic Control in Variational Language Models
A variational language model achieves minimal agentic control by treating internal uncertainty as an operational signal for regulation, checkpoint retention, and inference intervention.
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Agent Mentor: Framing Agent Knowledge through Semantic Trajectory Analysis
Agent Mentor analyzes semantic trajectories in agent logs to identify undesired behaviors and derives corrective prompt instructions, yielding measurable accuracy gains on benchmark tasks across three agent setups.
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Spec Kit Agents: Context-Grounded Agentic Workflows
A multi-agent SDD framework with phase-level context-grounding hooks improves LLM-judged quality by 0.15 points and SWE-bench Lite Pass@1 by 1.7 percent while preserving near-perfect test compatibility.
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UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
UI-TARS-2 reaches 88.2 on Online-Mind2Web, 47.5 on OSWorld, 50.6 on WindowsAgentArena, and 73.3 on AndroidWorld while attaining 59.8 mean normalized score on a 15-game suite through multi-turn RL and scalable data generation.
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Rethinking Wireless Communications through Formal Mathematical AI Reasoning
Proposes a three-layer framework using formal AI reasoning for verification, derivation, and discovery in wireless communications theory.
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SciFi: A Safe, Lightweight, User-Friendly, and Fully Autonomous Agentic AI Workflow for Scientific Applications
SciFi is a safe, lightweight agentic AI framework that automates structured scientific tasks with minimal human intervention via isolated environments and layered self-assessing agents.
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The Rise and Potential of Large Language Model Based Agents: A Survey
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.