Beyond hallucinations, another problem in program synthesis using LLMs is ambiguity in user intent. We illustrate the ambiguity problem in a networking context for LLM-based incremental configuration synthesis of route maps and ACLs. Configuration stanzas frequently overlap in header space, making the relative priority of actions impossible for the LLM to infer without user interaction. Measurements in a large cloud identify complex ACLs with 100s of overlaps, showing ambiguity is a real problem. We propose a prototype system, Clarify, augmenting an LLM with a new module called a Disambiguator that helps elicit user intent. On a small synthetic workload, Clarify incrementally synthesizes routing policies and interactively disambiguates user intent to ensure correctness.

SUMMARY: Lack of data integration remains a significant impediment to cancer research, and many analyses still require customized software to transform and prepare cancer data. We describe a software package to harmonize genetic and clinical cancer data into the GA4GH Phenopacket schema, an ISO standard for representing clinical case data. We integrated demographic, mutation, morphology, diagnosis, intervention, and survival data using case data from the National Cancer Institute for 12 cancer types. The Phenopacket standard provides a foundation for downstream use, including sophisticated statistical and AI/ML analyses. We demonstrate fitness for purpose by using the integrated data to recapitulate a known association between mutations in the gene encoding isocitrate dehydrogenase 1 and survival time in brain cancer patients. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at: https://github.com/monarch-initiative/oncopacket (archived at 10.5281/zenodo.15353125).

We investigate whether Large Language Models (e.g., GPT-4) can synthesize correct router configurations with reduced manual effort. We find GPT-4 works very badly by itself, producing promising draft configurations but with egregious errors in topology, syntax, and semantics. Our strategy, that we call Verified Prompt Programming, is to combine GPT-4 with verifiers, and use localized feedback from the verifier to automatically correct errors. Verification requires a specification and actionable localized feedback to be effective. We show results for two use cases: translating from Cisco to Juniper configurations on a single router, and implementing a no-transit policy on multiple routers. While human input is still required, if we define the leverage as the number of automated prompts to the number of human prompts, our experiments show a leverage of 10X for Juniper translation, and 6X for implementing the no-transit policy, ending with verified configurations.

Coastline detection, and monitoring have different research challenges. Global warming, deforestation, and sea level rising have several adverse effects on coastlines such as, erosion, coastline recession, loss of biodiversity, etc. In the past, various techniques have been proposed to detect, and monitor coastlines. While detecting coastlines, most of the techniques do not consider removing inland water bodies. The objective of this work is to remove inland water bodies, in the process of detection of coastlines. For accomplishing this task, we propose to use difference of Coal Mine Index (CMI) and Normalized Difference Water Index (NDWI) to separate water bodies from other regions. Subsequently, inland water bodies are identified from this set by computing and analyzing their contours, and removed to provide coastlines. Overall, this work presents a novel automated method of coastline detection with the presence of inland water bodies having precision, and recall of 80%, and 82.35%, respectively.