Explore Franklin AI
Understand new AI research with readable explanations and links to the original papers.
EndoCLIP is a vision-language foundation model designed to bridge the gap between routine colonoscopy reports and the visual data captured during procedures....
Selective Credibility-Limited Belief Update (SCL) is a framework designed to handle belief changes when new information is only partially realizable.
QuantWAMs is a post-training quantization (PTQ) framework designed to make World Action Models (WAMs) efficient for real-robot deployment.
Metaphor Tracer: A Theory-Informed Analysis of Hidden States introduces a training-free instrument designed to visualize how a large language model (LLM) org...
SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination introduces a unified AI framework designed to replace the fragmented, dep...
When Derived Measurements Mislead: Quantifying and Mitigating LLM Over-Trust with Privileged-Modality Reliability Evidence addresses the tendency of large la...
LLM-Guided Evolutionary Search for Constraint Model Reformulation to Improve Solver Efficiency explores how to automate the process of refining constraint mo...
When Specifications Conflict: A Symmetry-Based Framework for Measuring LLM Preferences introduces a method to observe and analyze how large language models (...
MemHarness is a framework designed to improve how large language model (LLM) agents use past experiences to make decisions.
This paper investigates whether AI agents can perform open-ended research, a capability often cited as a prerequisite for explosive progress in AI developmen...
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