WeSearch

DART: Semantic Recoverability for Structured Tool Agents

·3 min read · 0 reactions · 0 comments · 26 views
#artificial intelligence#machine learning#software engineering
DART: Semantic Recoverability for Structured Tool Agents
TL;DR · WeSearch summary

The paper titled 'DART: Semantic Recoverability for Structured Tool Agents' addresses the challenges faced by structured tool agents during execution failures. It introduces a modular runtime called DART that ensures semantic recoverability by localizing failures and certifying restore points. The results demonstrate that DART successfully recovers from commitment-sensitive cases where traditional recovery methods fail.

Key facts
About this source

arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.

Original article
arXiv cs.AI
Read full at arXiv cs.AI →
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →

Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.

Record

Original publisherarXiv cs.AI
Canonical URLhttps://arxiv.org/abs/2605.23311
Publication timeMon, 25 May 2026 00:00:00 -0400
Retrieval time2026-05-25T04:07:35.648Z
Last seen2026-05-25T04:07:35.648Z
Headline sourcePublisher (no WeSearch rewrite)
Excerpt sourcepublisher body
Excerpt methodFirst ~120 words (~800 chars) of extracted publisher body, fair-use limited.
SummaryWeSearch · cerebras-chat (WeSearch summarizer)
Summary source textcontentText
Citation coverageSummary is a WeSearch-generated derivative; primary citation is the original publisher URL.
ClusterrUakUJ3CRxUr
Cluster logicGrouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison.
Ranking reasonStory pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking.
Publisher visitYes — open original
Substitutes article?No — link-out required for full text

Rights status (four layers)

Publisher-declared
No publisher-confirmed rights record for this source yet.
Machine-readable
No source-specific machine-readable restriction detected beyond the public feed.
WeSearch interpretation
WeSearch declared handling (basis: Derived from the published RSS/Atom feed). This is WeSearch policy, not a legal grant on the publisher's behalf.
Unknown
Retrieval and training permissions are not asserted unless the publisher confirms them.

WeSearch handling by dimension

Indexing May the item be indexed (stored, ranked, made findable)? Allowed
Snippet May a short excerpt of the publisher's text be shown? Allowed
AI summary May WeSearch generate its own short summary of the article? Limited
Retrieval / RAG May the content be exposed for third-party retrieval-augmented generation? Not asserted
Model training May the content be used to train AI models? Not asserted
Commercial reuse May the content be reused commercially? Not permitted

Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

Opening excerpt (first ~120 words) tap to expand

Computer Science > Artificial Intelligence arXiv:2605.23311 (cs) [Submitted on 22 May 2026] Title:DART: Semantic Recoverability for Structured Tool Agents Authors:Ke Yang, Panpan Li, Zonghan Wu, Kejin Xu, Huaxi Huang, Xiaoshui Huang View a PDF of the paper titled DART: Semantic Recoverability for Structured Tool Agents, by Ke Yang and Panpan Li and Zonghan Wu and Kejin Xu and Huaxi Huang and Xiaoshui Huang View PDF HTML (experimental) Abstract:When a structured tool agent fails mid-execution, the runtime faces a dilemma: replaying the entire task is safe but wasteful, while restoring from a local checkpoint is efficient but can leave committed downstream work tied to an upstream history that no longer exists.

Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.

Anonymous · no account needed
Share 𝕏 Facebook Reddit LinkedIn Threads WhatsApp Bluesky Mastodon Email

Discussion

0 comments

More from arXiv cs.AI