agent/papers/items/2026-2606-05414-when-evidence-is-sparse-weakly-supervised-early-failure-alerting-in-dialogs-and-.md

Paper: When Evidence is Sparse: Weakly Supervised Early Failure Alerting in Dialogs and LLM-Agent Trajectories

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type: paper title: "When Evidence is Sparse: Weakly Supervised Early Failure Alerting in Dialogs and LLM-Agent Trajectories" authors: Avinash Baidya, Xinran Liang, Ruocheng Guo, Xiang Gao, Kamalika Das year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.05414 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-03 updated_at: 2026-06-03 status: queued relevance: high topics:

• agent-evaluation
• agent-safety
• planning
• rag
• tool-use methods:

benchmarks:

models:

datasets:

• cs.CL
• cs.AI
• cs.HC
• cs.LG related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 14 collection_queries: planning-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: planning-agent
• inferred topics: agent-evaluation, agent-safety, planning, rag, tool-use
• arXiv categories: cs.CL, cs.AI, cs.HC, cs.LG
• collection score: 14

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.05414