Agentic RL Daily

VOL. 010

8 research signals

DAILY EDITION / SAVED SNAPSHOT

Agentic AI 趋势

A dated Agentic RL Daily snapshot with 8 verified primary-source signals across papers, official releases, deployment evidence, and safety or alignment findings.

EDITOR'S VIEW

Three judgments

  1. The edition is based only on primary sources or official project releases.
  2. Older signals remain visible as continuing observations, not as rewritten news.
  3. Headline claims are constrained by the evidence included in this dated snapshot.

FULL EDITION

All signals in this edition

Archived / 2026-07-27

01

PapersPapers

Dynamic Capability Scoping for Enterprise AI Agents: A Synthetic Dataset and Three-Source Permission Architecture

Dynamic Capability Scoping for Enterprise AI Agents: A Synthetic Dataset and Three-Source Permission Architecture

Enterprise AI agents are typically granted static credential sets at configuration time, holding every tool the role might need for every task they perform. This persistent over-privilege expands the attack surface. We argue that capability scoping must follow a dynamic least-privilege principle and

Training AlgorithmsAgent Capabilities安全与对齐评估体系
arXiv ->

02

PapersPapers

A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation

A Self-Calibrating Agentic AI Framework for Autonomous Edge Resource Allocation

Large Language Models (LLMs) are increasingly deployed as autonomous agents, transitioning from static conversational interfaces to dynamic systems capable of complex reasoning, tool execution, and decision-making. However, the operational reliability of these agentic AI systems is fundamentally cha

Agent CapabilitiesSystems Engineering
arXiv ->

03

PapersPapers

TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI

TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI

Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI. Existing routers, primarily make independent routing decisions for each LLM call. However, agentic applications execute as long-horizon workflows whose

Reward and Credit AssignmentAgent Capabilities评估体系
arXiv ->

04

PapersPapers

Learning on the Job: Continual Learning from Deployment Feedback for Frozen-Weights Agents

Learning on the Job: Continual Learning from Deployment Feedback for Frozen-Weights Agents

AI agents encounter learning opportunities in every episode they run, and discard nearly all of them: the underlying models are frozen at deployment, so an agent that resolves a difficult request today starts from zero when it recurs tomorrow. Yet ordinary operation already produces feedback, in the

Training AlgorithmsAgent Capabilities记忆与自进化
arXiv ->

05

PapersPapers

Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG

Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG

Large language models (LLMs) and AI agents have demonstrated strong potential for data integration in zero-shot and few-shot settings. However, they continue to face significant accuracy and cost challenges in enterprise environments due to a persistent knowledge gap. This paper envisions trustworth

Training AlgorithmsAgent CapabilitiesData Loops评估体系
arXiv ->

06

PapersPapers

Learning Spatiotemporal Decision Priors for Efficient Path Planning under Partial Observability

Learning Spatiotemporal Decision Priors for Efficient Path Planning under Partial Observability

Path planning under partial observability remains challenging because an agent must make long-horizon navigation decisions from only locally bounded observations. Nevertheless, historical trajectories contain reusable experience-guided directional preferences. Classical planners, however, typically

Agent CapabilitiesSystems Engineering
arXiv ->

07

PapersPapers

DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents

DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents

LLM-based database agents show promise, but differing task scopes, testbeds, and metrics hinder comparison. We identify four gaps between evaluation and production operations: live-environment fidelity (multi-turn read-write interaction with a running database); observation-space scale and complexit

Agent Capabilities安全与对齐评估体系
arXiv ->

08

PapersPapers

Do Agent Benchmarks Measure Capability? Protocol Validity in the Age of Agentic AI

Do Agent Benchmarks Measure Capability? Protocol Validity in the Age of Agentic AI

Agent benchmarks increasingly evaluate repository editing, web research, terminal use, and long-horizon interaction. Their scores support capability claims only when the evaluation protocol keeps the intended capability necessary for success. Recent reward-hacking benchmarks and system reports show

Training AlgorithmsReward and Credit AssignmentAgent Capabilities安全与对齐评估体系
arXiv ->