Charts can help data agents reason, but plotting access alone is insufficient
A study distinguishes the ability to read a chart from knowing when to create one, then learns reusable analytical and interface-selection experience.
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A study distinguishes the ability to read a chart from knowing when to create one, then learns reusable analytical and interface-selection experience.
The architecture links logs, infrastructure and event sequences, but its abstract describes conceptual benefits rather than completed production benchmarks.
The benchmark combines constrained simulated users with tests after every turn, finding that agent rankings change with the interaction budget.
The framework reports more stable search on MGSM and MMLU, with different accuracy tradeoffs on the two benchmarks.
The study improves an agent’s interface repository rather than only its model, using unseen validation tasks to select changes.
Controlled arithmetic tasks probe whether decision systems follow rules, use changed information and remain jointly correct across related cases.
A diagnostic framework uses model behavior to select capability splits, exposing contrasts that disappear in aggregate benchmark scores.
A runtime-monitoring study separates signal faults, physical inconsistency and model limits, while documenting residual misses under semantic shift.
The model report describes a 35-billion-parameter mixture-of-experts system for orchestrated work and inspectable scientific exploration.
A modified CaseHOLD evaluation tests whether models decline to choose when the correct holding is absent, rather than rewarding forced answers.
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