arXiv AI
Aug 5
Verified Tool Calls Improve LLM Agent Reliability Under Non-Atomic Failures
arXiv:2608. 02645v1 Announce Type: cross Abstract: Large Language Model (LLM) agents rely on external tools to perform multistage tasks.
Incremental Consistency Execution for Autonomous Intelligent Systems
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arXiv Machine Learning
Jul 10
Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems
arXiv:2607. 08010v1 Announce Type: cross Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request.
arXiv AI
Aug 11
AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems
arXiv:2604. 02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data.
arXiv AI
Oct 7
AegisFlow: A Multi-Agent Agentic AI Framework for Autonomous Remediation and Self-Healing in Fragile Data Ecosystems
AegisFlow is a multi‑agent AI framework that automates the detection and remediation of failures in data pipelines, using a Watchdog agent for telemetry and a Repair agent that generates, tests, and deploys code patches via large language models. It employs a non‑intrusive Parallel Shadow Patching approach based on the MAPE‑K loop to validate patches in digital twin environments. Experiments across five failure scenarios show a 98.1% reduction in mean time to repair—from 170 minutes to 3.2 minutes—and a 92% patch success rate, freeing up 98% of data engineering on‑call time for innovation.
arXiv AI
Oct 16
Don't Regenerate, Debug: A Domain-Specific Agent for Repairing Near-Miss Accelerator Kernels
The paper introduces a domain‑specific debugging agent for repairing near‑miss accelerator kernels, arguing that debugging is more efficient than regenerating kernels from scratch. It tackles knowledge scarcity, integrity, and cost control through pattern retrieval, anti‑cheat detection, and convergence safeguards. Empirical results show the debug agent achieves a 66.7% Pass@1 rate and uses 92.8% fewer tokens per success compared to traditional regeneration methods.
Hugging Face Trending Papers
Jul 9
Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems
Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment.
arXiv AI
Aug 5
Don't Regenerate, Debug: A Domain-Specific Agent for Repairing Near-Miss Hardware Operators
arXiv:2608. 02712v1 Announce Type: cross Abstract: Kernel generation for hardware accelerators such as GPUs and NPUs has become a proving ground for large language models (LLMs), and state-of-the-art systems raise correctness through pipelines that couple LLMs with agentic reinforcement learning and evolutionary search.
arXiv AI
Sep 15
Recoverability as a System Primitive for Long-Horizon AI Agents
arXiv:2609.13672v1 Announce Type: new Abstract: AI agents can be interrupted while editing files, calling tools, or carrying out multi-step tasks. Restarting repeats completed work, but continuing fr...
arXiv AI
Oct 21
RITE: Evaluating End-to-End Resilience to Silent Tool Errors in E-Commerce Agentic Systems
arXiv:2610.27777v1 Announce Type: new Abstract: Tool-using LLM agents increasingly answer e-commerce queries through multi-step tool interactions. The expected output of a tool call can shift between...
arXiv AI
Jun 24
LemonHarness Technical Report
arXiv:2606. 24311v1 Announce Type: new Abstract: As large language model (LLM) agents are applied to longer tasks, they increasingly modify workspace state across multiple rounds of iteration.
arXiv AI
Oct 15
When Messages Go Wrong: Understanding Communication Faults in LLM-Based Multi-Agent Systems
arXiv:2610.15459v1 Announce Type: cross Abstract: LLM-based multi-agent systems (LLM-MAS) use specialized agents that exchange messages to accomplish complex tasks. Their reliance on networked protoc...
arXiv AI
Jul 14
AgentAbstain: Do LLM Agents Know When Not to Act?
arXiv:2607. 10059v1 Announce Type: new Abstract: Agent systems based on large language models (LLMs) are increasingly deployed for autonomous tasks, yet existing evaluations mostly focus on task success rather than whether agents know when to abstain.