For solo builders
A possible application for this research area. Paper-specific feasibility still needs a full-paper review.
An assistant chooses the wrong API or sends arguments your product cannot use.
What you would change
Apply the proposed tool-selection or argument-generation change in a sandbox.
Try the idea on your product
A proposed first test. Set your budget and success criteria before running it.
Apply the proposed tool-selection or argument-generation change in a sandbox.
Compare against
Your current agent with the same tool schemas and task budget.
- Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.
- Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.
- Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.
- Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test.
What to measure
Correct completed actions, invalid calls, retries, latency, and cost per completed task.
When to keep it
Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation.
Download test plan ↓abstract indexedPaperraft next step
Do not allocate compute yet.
This page uses the abstract and public adoption signal only. Request the deep report before relying on its recommendation.
Abstract-level screening only. Risks and infrastructure may not have been evaluated. This recommendation is not a reproduced result.- minimum test setup
- not reported
- published evidence setup
- not reported
- source-linked claims
- 0 of 2
- exposure checks
- 0 of 8
Why it was shortlisted
The technical change, research area, and reason it entered the archive.
- recommendation
- test
- research area
- tool use
- technical change
- Fixed-model harness optimization with failure-surface routing
- published
- 2026-09-04
Harness optimization around a fixed model matches constrained infrastructure directly, but the reported gains are benchmark-specific and the paper's own reliability caveat means the reader must validate lifts on their workload before production adoption.
Central equations
Original equations, typeset for reading. Paperraft explanations connect the mechanism, its aim, and the evidence.
asks how much held-out lift a procedure can reliably obtain under a fixed pre-scorecard search budget. For run , let be the pre-scorecard search cost under a declared accounting convention, covering search-time activity (proposal, candidate evaluation, filtering, repair, and selection) before final scorecard evaluation; the final held-out scorecard evaluation is always excluded, and the components a given convention prices are stated with it (ours are defined in Appendix F). Let be the mean cost over runs. Under budget , the number of affordable independent optimizer runs is . Each run returns a harness and a pre-scorecard selection statistic (gate score, validation score, or the optimizer’s internal pre-scorecard criterion) computed without access to . Under budget we select over the affordable runs, and the held-out lift of the selected harness is . Our budgeted reliable-lift metric is
unnumbered equation · §3 Evaluation Protocol and Metrics · metric
Let be the baseline harness. For run , let be the harness returned before scorecard evaluation. The held-out scorecard lift is
unnumbered equation · §3 Evaluation Protocol and Metrics · baseline
For each benchmark–optimizer pair, we observe run tuples and apply the estimator of Section 3 with 5,000 bootstrap resamples; the resamples reduce Monte Carlo error and do not increase the number of independent optimizer runs beyond the observed. In our implementation, the pre-scorecard statistic , computed identically for every optimizer’s returned harness, orders candidates by gate-split pass rate, breaking ties by reliability:
unnumbered equation · §4 Experiments · proposed method
equations from arXiv HTML · fetched 2026-09-26
Claims and evidence
A claim is source-linked only when the PDF page and matching excerpt are available.
- claim 01Paperraft inference
Read the brief interpretation
The method replaces ad hoc manual prompt and tool-interface tuning with a budgeted search over prompts and guarded tool-boundary middleware edits around a fixed model, requiring no retraining. Costs are optimizer compute and evaluation runs during search, plus ongoing middleware complexity and per-call logging overhead; the paper itself logs cost diagnostics rather than claiming cost reduction. The main failure mode is selecting brittle harness updates that generalize poorly, which is why the protocol mandates worst-condition lift, repeatability, and the conservative RelLift95 metric alongside mean lift.
This is a Paperraft inference. It is not pinned to a verified PDF passage.
Inspect the source - claim 02Paperraft inference
PRISM reports mean held-out lifts of 14.2, 14.9, and 10.1 percentage points on BFCL multi-round, tau2-Retail, and tau2-Telecom, with positive RelLift95 on all three; the ablation attributes the margin mainly to failure-surface routing and the edit-pattern constraint.
This is a Paperraft inference. It is not pinned to a verified PDF passage.
Inspect the source
Exposure map
Author findings, Paperraft interpretations, and the next check. Missing analysis never counts as evidence of safety.
quality
Still unknownWhich tasks improve, and which regress against the baseline?
compute and memory
Still unknownWhat memory, hardware, and total compute does the complete method require?
latency
Still unknownAre startup time, typical response time, and slow requests measured separately?
operations
Still unknownWhat must be monitored, maintained, and rolled back?
software compatibility
Still unknownDoes the implementation support your model, runtime, and hardware?
security
Still unknownWhat new data access, permissions, or trust boundaries does it introduce?
data and training
Still unknownWhat data preparation or training is required, and is it included in the cost?
reproducibility
Still unknownAre code, versions, inputs, and evaluation scripts available?
Continue the investigation
A reading guide for tool use.
Editorial prompts, not findings from this paper or a validated test plan. Detailed analysis is still pending for the topics below.
What to inspect
Which assumptions does the proposed method need, and where might your workload violate them?
Plan a fair comparison
Identify the baseline, metric, dataset, and experimental settings in the paper. Which can you match?
Questions for adoption
What implementation and evaluation artifacts are available, and what would you need to supply?
Inside the paper
Preview the first 3 pages of the original PDF. Click the stack to turn the page.
The downloadable skill includes SKILL.md and evidence.json, with recorded findings and unanswered checks. TRY-IT.md adds a product test plan and a blank results sheet.
A reading aid for your agent. Findings still need validation.
Provisional research brief updated 2026-10-18. Paperraft has not reproduced this experiment.