aiqbench analysis
This article introduces Agora, a new benchmark designed to evaluate large language models on archive-grounded reasoning tasks within messy workplace document collections. By forcing agents to deliberately explore over 372M tokens exceeding standard context windows, the study reveals that even top-tier models struggle significantly, achieving only 59.4% accuracy across diverse domains. This work is pivotal for advancing agentic systems capable of handling real-world information retrieval and synthesis rather than relying solely on parametric knowledge.
AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning
arXiv:2606.24526v2 Announce Type: replace Abstract: Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded reasoning: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time conventions, and computing an answer. Existing benchmarks address only parts of this setting and none jointly stresses archive-groundedness, agentic exploration, and cross-domain coverage. We introduce Agora, a benchmark pairing 362 questions with eight domain collections of 9,664 authentic documents and 372M tokens, far exceeding any model's context window, so agents must explore deliberately rather than scan exhaustively. Agora is built by an agentic pipeline combining cross-document task synthesis, leakage-preventing obfuscation, and difficulty filtering. Evaluating eight models, we find the task far from solved: even the strongest reaches only 59.4% accuracy, with notable variation across domains.
Abstract:Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded reasoning: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time conventions, and computing an answer. Existing benchmarks address only parts of this setting and none jointly stresses archive-groundedness, agentic exploration, and cross-domain coverage. We introduce Agora, a benchmark pairing 362 questions with eight domain collections of 9,664 authentic documents and 372M tokens, far exceeding any model's context window, so agents must explore deliberately rather than scan exhaustively. Agora is built by an agentic pipeline combining cross-document task synthesis, leakage-preventing obfuscation, and difficulty filtering. Evaluating eight models, we find the task far from solved: even the strongest reaches only 59.4% accuracy, with notable variation across domains.
Computation and Language (cs.CL)
arXiv:2606.24526 [cs.CL]
(or arXiv:2606.24526v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.24526 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 227 words