Topic / Trend Rising

AI agent security and reliability

New agentic and MCP-based integrations expand the attack surface, while LLM judges miss silent failures and models require explicit boundaries for evidence. Security and evaluation are becoming central as AI moves from chat to autonomous actions and high-stakes decisions.

Detected: 2026-09-07 · Updated: 2026-09-07

Related Coverage

2026-09-03 ArXiv cs.CL

When silence is the right answer: training the evidence boundary

A training framework teaches grounded QA models to answer only when evidence becomes sufficient, locating the exact point where abstention gives way to a response. On HotpotQA, 2WikiMultiHopQA and MuSiQue, with Qwen2.5-3B-Instruct and LoRA, the metho...

#LLM On-Premise #Fine-Tuning #DevOps
2026-09-02 ArXiv cs.CL

Outcome-Only LLM Judges Miss Silent Faults in Agent Trajectories

A study across 400 trajectories shows outcome-only LLM judges catch 84% of loud faults but only 45% of silent ones while flagging 33% of correct trajectories. A step-rubric judge reaches 77% silent recall with zero false alarms at 3x cost. No judge r...

#LLM On-Premise #DevOps
2026-09-01 ArXiv cs.AI

DS-Lighting: Making the Agent Harness Explicit for Data-Science Automation

DS-Lighting is an open-source toolkit that makes the agent harness explicit for data-science automation. It decomposes the harness into four reusable layers—data, workflow, execution, and evaluation—and represents agents as executable operator progra...

#Hardware #LLM On-Premise #DevOps
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