CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification

CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/cove-training-interactive-tool-use-agents-via-constraint-guided-verification

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID cove-training-interactive-tool-use-agents-via-constraint-guided-verification | Route /signal-canvas/cove-training-interactive-tool-use-agents-via-constraint-guided-verification

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/cove-training-interactive-tool-use-agents-via-constraint-guided-verification

MCP example

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    "paper_ref": "cove-training-interactive-tool-use-agents-via-constraint-guided-verification",
    "query_text": "Summarize CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification"
  }
}

source_context

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  "mode": "paper",
  "query": "CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification",
  "normalized_query": "2603.01940",
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  "paper_ref": "cove-training-interactive-tool-use-agents-via-constraint-guided-verification",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification

PDF: https://arxiv.org/pdf/2603.01940v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-19T21:31:49.672Z

Signal Canvas receipt window

Watch and verify: CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification

/buildability/cove-training-interactive-tool-use-agents-via-constraint-guided-verification

Watchwatch

Subject: CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
we introduce CoVe (Constraint-Verification), a post-training data synthesis framework designed for training interactive tool-use agents while ensuring both data complexity and correctness.
Implicationpartial
The abstract explicitly introduces CoVe as a 'post-training data synthesis framework designed for training interactive tool-use agents while ensuring both data complexity and correctness.'
Verificationpartial
partial
Evidencepartial
CoVe begins by defining explicit task constraints, which serve a dual role: they guide the generation of complex trajectories and act as deterministic verifiers for assessing trajectory quality.
Implicationpartial
The abstract clearly states the dual role of task constraints within the CoVe framework.
Verificationpartial
partial
Evidencepartial
This enables the creation of high-quality training trajectories for supervised fine-tuning (SFT) and the derivation of accurate reward signals for reinforcement learning (RL).
Implicationpartial
The abstract directly links the output of the constraint-guided process to its utility for SFT and RL.
Verificationpartial
partial
Evidencepartial
Notably, our compact CoVe-4B model achieves success rates of 43.0% and 59.4% in the Airline and Retail domains, respectively
Implicationpartial
This is a specific numerical result reported in the abstract for a particular domain and model.
Verificationpartial
partial
Evidencepartial
Notably, our compact CoVe-4B model achieves success rates of 43.0% and 59.4% in the Airline and Retail domains, respectively
Implicationpartial
This is a specific numerical result reported in the abstract for a particular domain and model.
Verificationpartial
partial
Evidencepartial
its overall performance significantly outperforms strong baselines of similar scale
Implicationpartial
The abstract states this comparative performance advantage.
Verificationpartial
partial
Evidencepartial
and remains competitive with models up to 17× its size.
Implicationpartial
The abstract provides a direct comparison of CoVe-4B's performance against much larger models.
Verificationpartial
partial
Evidencepartial
Potential issues include handling unseen constraints or rapidly changing dataset attributes, which might limit the generalizability of the framework.
Implicationpartial
The 'caveats' section of the analysis explicitly mentions these potential limitations.
Verificationpartial
partial

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CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification

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