SANA: What Matters for QA Agents over Massive Data Lakes?
This paper presents SANA (Search Agent Navigation Ablation framework), a diagnostic ablation framework that decomposes failures in exploratory question answering (EQA) over data lakes. By transforming EQA tasks into runtime profiles with gold source sequence, sanitized subquestions, and execution records, SANA constructs idealized search, planning, and data-analysis tools for component ablation, with residual gaps indicating policy failures. Experiments on LakeQA and KramaBench show data analysis as a consistent bottleneck, search as a major limitation in large data lakes, and planning as less critical.
[2606.13904] SANA: What Matters for QA Agents over Massive Data Lakes?
[Submitted on 11 Jun 2026]
Title:SANA: What Matters for QA Agents over Massive Data Lakes?
View a PDF of the paper titled SANA: What Matters for QA Agents over Massive Data Lakes?, by Austin Senna Wijaya and 3 other authors
View PDF HTML (experimental)
Abstract:Exploratory question answering (EQA) over data lakes requires an LLM agent to discover relevant sources, analyze retrieved data, and adapt its actions based on intermediate results. End-to-end accuracy alone cannot distinguish failures in search, planning, data analysis, or the agent's Action Policy: its decisions about what to do next and when to submit an answer. We present SANA (Search Agent Navigation Ablation framework), a diagnostic ablation framework that transforms EQA tasks into runtime profiles containing gold source sequence, sanitized subquestions, and execution records. SANA uses these profiles to construct idealized search, planning, and data-analysis tools, allowing each component to be ablated; the residual gap is diagnostic evidence for policy failures.
To illustrate SANA as a reusable evaluation framework, we adapted two recent EQA benchmarks, LakeQA and KramaBench, and evaluated lightweight and mid-sized agents under fixed prompts, budgets, data lakes, and runtimes. Across both benchmarks, data analysis is a consistent bottleneck while planning is less so. Search is a major limitation in LakeQA's large data-lake setting, but less so for the smaller-scale KramaBench. SANA thus deconstructs end-to-end task accuracies into a diagnosis of where data-lake agents fail, and allows for systematic comparisons of progress in search, planning, data analysis, and agent design.
Comments: 9 pages, 7 figures
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB)
Cite as: arXiv:2606.13904 [cs.CL]
(or arXiv:2606.13904v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.13904
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Austin Senna Wijaya [view email] [v1] Thu, 11 Jun 2026 20:51:44 UTC (617 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled SANA: What Matters for QA Agents over Massive Data Lakes?, by Austin Senna Wijaya and 3 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CL
new | recent | 2026-06
Change to browse by:
cs cs.AI cs.DB
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)