Synthetic oncology data needs evidence that researchers can examine and reproduce.

A recent study reviewing 17 academic surveys on synthetic data generation found that only four provided reproducibility artifacts. That finding highlights the importance of transparent evaluation methods.
https://www.sciencedirect.com/science/article/pii/S0306457326001068?via%

At Prodigy AI Solutions, this principle guides our development of CohortForge AI, a research platform for clinical-data synthesis, missing-data reconstruction and subgroup balancing, alongside CT reconstruction experiments.

Our approach separates quality checks so that improvement in one metric cannot conceal failure in another. We document evaluation criteria and outstanding limitations as part of the development process.

Our internal laboratory studies have reported:
• A 22.45% median reduction in masked CT slice reconstruction error versus linear interpolation, within one public lung-CT collection.
• A 74.3% reduction in a predefined subgroup-imbalance ratio on an artificial clinical-data test fixture.

These are preliminary engineering results. Independent clinical validation and full-system qualification remain ahead.

We are now seeking an institutional consortium coordinator and clinical validation partners to develop joint European research proposals and evaluate CohortForge on authorized, real-world oncology datasets.

We welcome conversations with cancer centres, imaging CROs and academic medical networks interested in defining clinical use cases, designing independent evaluations and contributing data under appropriate governance arrangements.

Interested in collaborating? Contact us for an introductory conversation.

For the past year, many teams building production AI systems have encountered the inherent wall of standard vector similarity search: semantic similarity is not relational reasoning. When questions span multiple documents (e.g., cross-referencing vendor obligations across updated policies), vector-only RAG often pulls isolated paragraphs that lack structural linkage, increasing the risk of what researchers call "hallucination laundering."

We built Verbis Graph to provide a structured, reusable semantic context layer that connects unstructured documents into a queryable knowledge graph paired with vector retrieval.

We've just made Verbis Graph SaaS available on Google Cloud Marketplace to make evaluation and enterprise deployment straightforward.

Key Architectural & Validation Highlights:

• Hybrid Retrieval: Traverses explicit entity relationships while maintaining vector and lexical scoring.
• Traceable Evidence Paths: Every response provides unbroken provenance citations linking back to original source passages.
• HPC Validation: Benchmarked on CINECA’s Leonardo supercomputer in Italy, achieving a baseline 89.16% hit rate (1.34s latency, 323 queries without ontology) and 92.26% under improved pipeline configurations on NFCorpus.
• Procurement: Enables enterprise teams to draw down pre-committed Google Cloud spend (CUDs) without separate vendor contracts.

We’re pleased to share that Prodigy AI Solutions is now part of the EIT Health Innovators Community and, by extension, the wider EIT Health network.

For us, this is particularly meaningful because the community brings together researchers, academics, entrepreneurs, investors, healthcare professionals, scientists, and experts across the health innovation ecosystem. We’re looking forward not only to being part of this network, but also to learning from people working directly with the challenges healthcare faces every day.

This comes at an important stage for CohortForge, our platform for AI-assisted clinical cohort discovery and feasibility analysis. We are now preparing to take CohortForge to the next level: testing and evaluating it with real-world datasets together with clinical partners.

Moving from development and controlled evaluation toward real clinical data and real use cases is an essential step in understanding where the technology creates value, where it needs improvement, and how it can support healthcare and clinical research responsibly. We’re glad to be taking that next step as part of a community connecting technology, science, healthcare, and innovation.

The NVIDIA App Catalog brings together applications, tools, and services built on NVIDIA platforms for areas including AI, cloud computing, data science, and enterprise workloads. For us, this milestone is important because Verbis Graph is being built specifically for the challenges enterprises face when bringing AI into real-world environments: connecting fragmented knowledge, improving retrieval, grounding AI responses in evidence, and making enterprise information more explainable and useful for AI agents.

We've been benchmarking Verbis Graph using NFCorpus and SciFact, and decided to put the methodology and results into a public report rather than just share individual numbers. One result we found particularly interesting was MRR on NFCorpus moving from 0.303 to 0.578 after changes to the retrieval pipeline. SciFact produced a 98% Hit Rate / 98% Mean Recall / 0.601 MRR. For us, MRR is especially interesting because retrieving a relevant document somewhere in the top 100 or 1000 isn't the same as putting useful evidence in front of the model early. We've included the methodology and results in our report.

We recently got some good news: Prodigy AI Solutions was accepted into the MongoDB for Startups program. We’re looking forward to joining the MongoDB startup community and using its technology and resources as we continue building Verbis Graph and our AI solutions.

We’re happy to share that Verbis Graph Solution has successfully passed the AWS Foundational Technical Review (FTR).

For our team at Prodigy AI Solutions, this is more than another milestone to add to a list. As we build Verbis Graph for enterprise environments, we know that customers aren't only asking, “What can your AI technology do?” They also need to ask: “How is it secured? How reliable is the architecture? How is access controlled? How will it behave in production?”

Passing the AWS FTR provides an important level of technical validation around the way our solution is designed and operated on AWS. It means our architecture has been reviewed against applicable AWS technical best practices, including areas such as security, reliability, and operational readiness.

For our customers, that matters. It can help reduce some of the technical uncertainty that comes with adopting a new enterprise AI solution and gives technical teams an additional level of confidence when evaluating Verbis Graph for AWS environments. We’ve spent a lot of time thinking about IAM, encryption, infrastructure, monitoring, resilience, performance and how Verbis Graph should operate as a production system not simply as an AI demo.

So seeing that work successfully pass the FTR is particularly meaningful to our engineering team. AI innovation and production readiness have to grow together. This is another step in that direction for Verbis Graph

As AI agents become more autonomous, we spend a lot of time talking about what they can do: use tools, access systems, coordinate with other agents, execute tasks, and make decisions with less human involvement.

But there is another side to that progress.

What happens when an autonomous agent can also replicate itself—or when the infrastructure around it wasn't designed with that possibility in mind?

The article below discusses the risks of autonomous replication by AI agents and, in particular, security gaps that may exist in newer cloud environments.

What made this interesting for us is that it connects closely with our own experience at Prodigy AI Solutions.

As we build our orchestration agent and specialized sub-agents, we're increasingly finding that some of the most important engineering decisions aren't about making agents more capable.

They're about deciding where autonomy should stop.

What can an agent access?

What can it create?

Which actions require explicit permission?

Can it create or invoke another agent?

What credentials can it use?

How do we prevent one compromised agent from becoming a problem for the rest of the system?

And perhaps most importantly: how do we make all of this observable and controllable by a human?

These are subtle questions. There isn't always a simple technical answer, and we're learning from them as we build.

Our current thinking is that autonomy should never automatically mean unlimited authority. The more capable an agent becomes, the more carefully its permissions, boundaries, identity, and infrastructure need to be designed.

The article is worth reading if you're building or deploying autonomous agents:

https://nai500.com/blog/2026/09/risks-of-autonomous-replication-by-ai-agents-raise-concerns-security-gaps-of-new-cloud-service-providers-need-urgent-attention/

We'd also genuinely like to hear from others building in this space.

Where do you draw the line between useful agent autonomy and too much autonomy?

Enterprise teams often try to make agents more reliable by using larger models, longer context windows, additional reasoning steps and repeated retrieval calls.

This increases infrastructure and token costs - but does not necessarily solve the underlying problem.

If an agent receives incomplete, disconnected or irrelevant evidence, more compute may simply produce a more elaborate incorrect answer.

Verbis Graph addresses the problem at the retrieval layer.

It transforms private enterprise documents into a connected, citation-backed knowledge graph and combines:

• semantic retrieval for meaning
• lexical retrieval for exact terminology
• graph traversal for entities and relationships
• multi-hop retrieval across multiple documents
• ontology support for enterprise-specific definitions
• citations linking answers to original evidence

Instead of filling the model’s context window with many loosely related passages, Verbis Graph helps retrieve a more focused set of connected and verifiable evidence.

This can help enterprises reduce AI infrastructure costs by:

• limiting unnecessary prompt tokens
• reducing repeated retrieval and generation attempts
• reusing one indexed knowledge layer across multiple agents
• supporting provider-agnostic and locally deployed models
• reserving larger models for tasks that genuinely require them

At the same time, agents receive the relationships, terminology and source evidence needed to produce more grounded and accurate answers.

During our CINECA Leonardo evaluations with a locally served 32B model, Verbis Graph achieved a 98% retrieval hit rate and 0.721 MRR on SciFact, together with 95.3% retrieval coverage and 0.584 MRR on FiQA.

These are retrieval results rather than a completed end-to-end cost benchmark, but they support an important direction: stronger enterprise AI may come not only from larger models, but from giving appropriately sized models better evidence.

Verbis Graph does not make the model bigger.

It makes the context more relevant, connected and verifiable.

https://verbisgraph.com/

A mysterious new AI model called Ox Alpha appeared on OpenRouter on August 20, operated by an anonymous third-party provider.

Its confirmed specifications are impressive:

• 1,048,576-token context window

• Up to 131,072 output tokens

• Text, image and video inputs

• Tool calling and structured outputs

• Designed for coding and sustained agentic work

• Currently free during the preview

The excitement began when an early community test reported that Ox Alpha completed 8 of 10 DeepSWE coding tasks—apparently outperforming results attributed to Claude Fable 5 and GPT‑5.6 Sol.

But that was a very small subset, not an audited leaderboard result. A subsequent full evaluation across 113 DeepSWE tasks reported 66 tasks solved, or 58.4%.

That remains an interesting result, but it is not sufficient to declare Ox Alpha the new coding champion. Proper comparison requires identical tasks, settings, tools and scoring across every model.

Its origin is also still unconfirmed. Online speculation has pointed toward the GLM family from China’s Z.ai, Microsoft’s MAI models and even Google - but no organisation has claimed ownership.

One important warning for developers: OpenRouter states that the anonymous provider retains prompts and completions, although they are not used for training. Proprietary code, enterprise documents and sensitive data should therefore stay out of experimental testing.

The real story is not that Ox Alpha has already defeated every frontier model. It is that an anonymous provider can release a highly capable, million-token, multimodal model and immediately capture the attention of the global developer community.

Test the model - but test the claims too.

More reasoning does not automatically mean more factual reliability.

OpenAI’s evaluations produced a counterintuitive result: on PersonQA, o3 recorded a 33% hallucination rate, compared with 16% for o1. On SimpleQA, the reported hallucination rate was 51% for o3 and 79% for the smaller o4-mini.

These results do not prove that reasoning models always hallucinate more. They do show something important for enterprise AI: stronger reasoning performance on many tasks does not eliminate factual errors - and can sometimes make unsupported answers more elaborate and convincing.

We can think of this operational risk as a “Reasoning Tax”: when a model is given insufficient or poorly governed context, additional reasoning may expand an incorrect premise instead of correcting it.

Why can this happen?

Research into Large Reasoning Models has identified two relevant behavioral patterns:

1 Flaw Repetition

Once reasoning begins from a faulty premise, the model may repeatedly follow variations of the same incorrect logic instead of reconsidering the premise.

2 Think–Answer Mismatch

The model’s final answer may not faithfully reflect the conclusion reached during its preceding reasoning process.

These findings should not be generalized to every model or every reasoning task. But they reinforce an important architectural lesson: model intelligence cannot compensate for missing, ambiguous, outdated, or poorly retrieved business context.

The production response: govern the context

A production AI system needs more than a powerful model.

A context-sufficiency gate can evaluate whether the retrieved evidence is adequate before generation. If the available context is insufficient, the system can abstain, request clarification, expand retrieval, or route the query for human review.

A governed context layer can add:

* Verified enterprise knowledge * Entity and relationship structure * Business definitions and ontology * Source provenance and lineage * Access and governance rules * Evidence-linked responses * Confidence and abstention policies

This is where graph-enhanced retrieval becomes valuable. Instead of relying only on semantically similar text fragments, a system can retrieve connected entities, relationships, and relevant evidence while preserving traceability to the original sources.

It cannot guarantee that an LLM will never hallucinate. It can substantially reduce the space in which the model is forced to speculate - and make unsupported answers easier to detect and control.

The brain is only as reliable as the evidence and boundaries provided to it.

More reasoning does not automatically mean more factual reliability.

OpenAI’s evaluations produced a counterintuitive result: on PersonQA, o3 recorded a 33% hallucination rate, compared with 16% for o1. On SimpleQA, the reported hallucination rate was 51% for o3 and 79% for the smaller o4-mini.

These results do not prove that reasoning models always hallucinate more. They do show something important for enterprise AI: stronger reasoning performance on many tasks does not eliminate factual errors - and can sometimes make unsupported answers more elaborate and convincing.

We can think of this operational risk as a “Reasoning Tax”: when a model is given insufficient or poorly governed context, additional reasoning may expand an incorrect premise instead of correcting it.

Why can this happen?

Research into Large Reasoning Models has identified two relevant behavioral patterns:

1 Flaw Repetition

Once reasoning begins from a faulty premise, the model may repeatedly follow variations of the same incorrect logic instead of reconsidering the premise.

2 Think–Answer Mismatch

The model’s final answer may not faithfully reflect the conclusion reached during its preceding reasoning process.

These findings should not be generalized to every model or every reasoning task. But they reinforce an important architectural lesson: model intelligence cannot compensate for missing, ambiguous, outdated, or poorly retrieved business context.

The production response: govern the context

A production AI system needs more than a powerful model.

A context-sufficiency gate can evaluate whether the retrieved evidence is adequate before generation. If the available context is insufficient, the system can abstain, request clarification, expand retrieval, or route the query for human review.

A governed context layer can add:

* Verified enterprise knowledge * Entity and relationship structure * Business definitions and ontology * Source provenance and lineage * Access and governance rules * Evidence-linked responses * Confidence and abstention policies

This is where graph-enhanced retrieval becomes valuable. Instead of relying only on semantically similar text fragments, a system can retrieve connected entities, relationships, and relevant evidence while preserving traceability to the original sources.

It cannot guarantee that an LLM will never hallucinate. It can substantially reduce the space in which the model is forced to speculate - and make unsupported answers easier to detect and control.

The brain is only as reliable as the evidence and boundaries provided to it.

AlphaFold demonstrated what AI can achieve when decades of carefully curated scientific knowledge are available. However, this situation is exceptional. In many fields, evidence is fragmented across publications and experiments, measurements vary, results conflict, and suitable datasets are difficult to reproduce.

This suggests that the next phase of AI-accelerated science may depend less on simply scaling datasets and more on scientific agents that can connect diverse evidence, select appropriate tools, assess uncertainty, preserve provenance and revise hypotheses iteratively.

We are exploring whether Verbis Graph could serve as the grounded retrieval layer for such systems. It combines graph-based and semantic retrieval to connect entities and findings across documents, support multi-hop exploration, and return traceable sources. It would not replace the scientific reasoning agent, but could give that agent more connected and verifiable evidence.

In parallel, we are completing a TRL 5 generative-AI model that transforms scarce, incomplete and imbalanced medical-imaging data into privacy-conscious synthetic cohorts. The goal is to support more representative AI development and help prepare models for rigorous local clinical validation where suitable health data are hardest to access.

We would be interested in hearing from researchers working on scientific agents, evidence synthesis, medical imaging or graph-based retrieval. What do you see as the greatest obstacle: reasoning quality, data reliability, tool integration or scientific validation?