Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
A Windows-and-Ollama agent harness targets false finishes, repeated tool calls and oversized prompts, with reported gains in a controlled single-machine experiment.
The study reports smaller resident index state at a measurable retrieval-quality cost, with no end-to-end latency claim.
At a fixed two-ply search depth, temporal-difference learning and evolutionary adaptation beat handcrafted evaluations, with no significant head-to-head difference between the.
A simulation-trained Transformer selects readable radar measurements, trading coverage for lower error in two public datasets.
A research prototype stores spatial relationships from VR editing sessions and adjusts retrieval for later scenes, rather than asking users to repeat the same corrections.
PyPottery combines four modules for archaeological pottery documentation. Its case study includes correction and segmentation work; measured processing time, perceived speedup and
VISTA stores original visual observations and provides model-directed inspection tools. Its public-game results and staged ablations show the value of revisiting evidence, with imp
A formal multi-agent study examines missing observations, team-wide inconsistencies and stale proposals. Small controlled experiments distinguish showing agents a shared limit from
CMP randomizes some retrieval slots to distinguish untested memories from unhelpful ones. Its findings support an exposure audit, while leaving deployment-ready pool selection and
ScholarCatalyst uses project authors’ judgments to evaluate research-paper retrieval before a project’s solution is known. Its benchmark separates useful inspiration from topical s
Start with the overview to understand the paper’s main idea, then follow its original paper link for the methods, results and limitations.
Use Submit Research to send the original paper URL and explain why it is useful. An editor reviews the suggestion before publication.
