arXiv:2606.24526v1 Announce Type: new 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 eigh
As large language models become more sophisticated, the focus is shifting from pure knowledge retrieval to complex, agentic reasoning over diverse and messy real-world data, necessitating new benchmarks.
This benchmark addresses a critical gap in evaluating AI agents' ability to operate effectively in enterprise environments, which is essential for their broader adoption and impact on white-collar work.
The introduction of Agora provides a standardized way to measure and compare the long-range reasoning and contextual understanding capabilities of AI agents, pushing development towards more robust and reliable systems.
- · AI agent developers
- · Enterprises adopting AI agents
- · Generative AI infrastructure providers
- · Routine white-collar tasks
- · Legacy enterprise software systems
Improved AI agents capable of handling complex, real-world document reasoning tasks will emerge.
Accelerated automation of knowledge-intensive work within corporations, leading to shifts in workforce composition.
The development of highly specialized and adaptive AI agents that virtually eliminate many clerical and analytical roles across industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL