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At a glance

• Today’s AI agents don’t remember past interactions. They must repeatedly be fed relevant information or retrieve it from external sources, which becomes less efficient as they handle longer and more complex tasks. To scale agent capabilities, we need a more efficient way to retain and access information over time.
• Memora is a scalable memory system that dramatically increases agent productivity on long-horizon tasks by decoupling what is stored (rich memory content) from how it’s retrieved (lightweight abstractions and cue anchors), balancing abstraction and specificity.
• Memora sets new state-of-the-art on LoCoMo and LongMemEval, outperforming Mem0, RAG, and full-context inference while using up to 98% fewer context tokens.
• Memora paper (opens in new tab) is published at ICML 2026. Memora code is available at https://github.com/microsoft/Memora (opens in new tab).

Imagine a workplace AI assistant helping you run a multi-month project. Over weeks of conversations, you share constraints, agree on milestones, revise deadlines, and surface dozens of stakeholder preferences. When you later ask it to draft an update for a colleague, it should recall not just the latest decision but the journey that got you there: what was tried, what was ruled out, who weighed in. Today’s AI agents struggle with this. Modern large language models (LLMs) are powerful reasoners, but they are effectively stateless: every session starts from zero, every long conversation forces the model to re-read its entire history, and every new piece of information is either stored as raw text (fragmented and noisy) or compressed into a vague summary (precise details lost). As AI assistants and autonomous agents move into long-horizon deployments, such as copilots that track a project for many months or even research agents that build up domain expertise with long horizon usage, the absence of principled memory system has become the critical bottleneck.

A growing line of work has begun to fill this gap. Systems like Mem0 extract atomic facts from conversations; retrieval-augmented (RAG) approaches index raw text fragments for later recall; and graph-based memory systems such as Zep and GraphRAG impose structure through entity relations. Each represents real progress, yet each runs into the same wall: existing designs force an unavoidable tradeoff between specificity (preserving fine-grained detail) and abstraction (organizing memory efficiently as it grows). Memora is built to give agents both.

What is Memora

Memora is an agentic memory framework designed for long-horizon AI agents. Memora’s central insight is to decouple what is stored from how it is retrieved. Memory content can remain rich and expressive, such as a project timeline, a multi-turn discussion about constraints, while a separate, lightweight structural layer handles indexing and retrieval. The result is a memory system that scales: it consolidates related information into stable units, surfaces fine-grained details when they matter, and lets the agent navigate its own history without re-reading everything. On standard long-conversation benchmarks, Memora sets new state-of-the-art performance while using up to 98% fewer tokens than would be consumed by dumping the full history into context.

Why this is hard: the abstraction–specificity tension

Existing memory systems fall into two extremes. Content-fragmentation systems, such as RAG and Mem0, embed extracted facts or text fragments directly. This preserves detail but produces brittle, isolated entries that lose narrative coherence. Coarse-abstraction systems compress experience into compact summaries. They are efficient, but summarization strips away the constraints, edge cases, and numeric details that make memory useful in the first place. Graph-based systems add structure on top of content, yet still rely on the content itself for retrieval and typically require rigid ontologies that don’t generalize across domains. None of these resolves the underlying tension between abstraction (which keeps memory efficient) and specificity (which gives memory utility).

Figure 1: Architecture overview of Memora. How Memora works

Memora resolves this tension through a harmonic organization. Each memory entry has two components: a primary abstraction, which a short phrase (6–8 words) that captures what the memory is fundamentally about, and a memory value holding the rich content itself. Crucially, only the primary abstraction is embedded for similarity search; the value is never directly retrieved through its own content. This separation means new information about an evolving topic merges into the existing memory entry under the same primary abstraction, rather than fragmenting into a chain of partial duplicates. Complementing primary abstractions, cue anchors are short, context-aware tags extracted from each memory’s value, providing alternative access paths to the same memory. They function as flexible, organically-generated metadata.

To make this concrete: suppose a user says, “Dave and Sarah agreed to push the prototype to April 1, the pilot to May 2, and the MVP to May 30.” A knowledge-graph system would need predefined entity types and relation schemas: Person → agreed_on → Milestone → has_date → Date, and any new relation type would require schema extension. In Memora, the primary abstraction Updated Project Orion timeline agreed by Dave and Sarah serves as the canonical access point, while cue anchors like Dave Project Orion update, Project Orion prototype schedule, and Project Orion pilot timeline provide alternative retrieval paths — all without committing to an ontology. A later query about Dave’s recent contributions, or the prototype schedule, or pilot timing can all route to the same underlying memory through different cues, with the full detail preserved in the memory value.

On top of this representation, Memora introduces a policy-guided retriever that treats memory access as an active reasoning process. Rather than returning the top-k semantically similar items in a single shot, the policy retriever iteratively refines its query, expands through cue anchors to surface related-but-not-similar memories, and decides when to stop. This lets the agent navigate to relevant non-local context that pure semantic search would miss, chasing multi-hop dependencies the way a human would when recalling connected events. The retrieval policy can be either hand-prompted with a strong LLM or distilled into a much smaller model via reinforcement learning.

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We evaluate Memora on two long-context benchmarks: LoCoMo, where dialogues average 600 turns, and LongMemEval, with 115,000-token contexts. Memora achieves new state-of-the-art performance on both: 86.3% LLM-judge accuracy on LoCoMo and 87.4% on LongMemEval, outperforming RAG, Mem0, Nemori, Zep, LangMem, and even full-context inference. The gap is largest on multi-hop reasoning, where Memora’s ability to traverse cue anchors pays the biggest dividends. The efficiency story is just as striking: Memora stores roughly half the memory entries per conversation that Mem0 does (344 vs. 651) and reduces token consumption by up to 98% relative to full-context inference. Less to read, less to store, better answers.

Looking forward

Memora’s design has implications beyond benchmark performance. We see this work as a step toward AI agents that can sustain long-term collaboration with users and accumulate organizational knowledge over months and years, not just within a single session. Building on this foundation, we are pursuing several complementary directions. MemLoop explores how memory systems can learn from retrieval and task failures, attribute errors to specific stages of the memory pipeline, and improve themselves over time. Deferred Memory investigates when memory construction should be postponed until sufficient context, evidence, or future utility becomes available, rather than committing prematurely to what should be stored. Group Memory examines how knowledge can be shared across teams and agents while preserving provenance, access boundaries, ownership, and sensitive context. We release our code alongside the paper and invite the community to build on this representation and explore what becomes possible when AI agents are no longer stateless.

Acknowledgements

We would like to thank Shantanu Dixit (Research Fellow) Paramaguru Harimurugan (Research Fellow), Rujia Wang, Victor Rühle, and Robert Sim for contributing to this project.

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At a glance

• LLM-based models can predict the human brain’s responses to language with high accuracy. But what drives that performance is essentially unreadable: a vast collection of learned parameters, not scientific theories anyone can read.
• Generative causal testing (GCT), developed in a collaboration between Microsoft Research, the University of California, Berkeley, the University of California, San Francisco, and Columbia University, distills these brain-prediction models into short verbal explanations of what each patch of cortex responds to: phrases like “food preparation” or “location names.”
• GCT then closes the loop: an LLM writes new stories designed to activate a targeted brain area, subjects hear them in the scanner, and the region lights up only if the explanation is right.
• In experiments, GCT confirmed known selectivity, teased apart neighboring place-processing regions long thought interchangeable, and revealed tiny prefrontal “micro-regions” tuned to specific concepts like dialogue, clock times, and measurements.

The explainability problem in language neuroscience

Over the past decade, LLMs have become the most accurate tools we have for predicting how the human brain responds to language. Feed an LLM the same story a person hears in an fMRI scanner, and the model’s internal representations can predict the activity of individual patches of cortex with remarkable fidelity. But this success comes with a catch: nobody can read these models. They are millions of inscrutable parameters that can’t be directly translated into interpretations. A model that predicts brain activity tells us that a region responds to language, but not what it is actually picking up on, whether it’s food, places, numbers, or something else entirely. As black-box models spread, the gap between prediction and understanding has become one of the central problems in computational neuroscience.

Turning black boxes into testable theories

In a new paper accepted in Nature Neuroscience, Microsoft Research scientists, in collaboration with scientists at the University of California, Berkeley, University of California, San Francisco, and Columbia University, introduce a framework to overcome this explainability crisis: generative causal testing (GCT). GCT distills brain-prediction models into short, readable accounts of what each patch of cortex responds to, then tests those claims. An LLM writes new stories engineered to activate a specific brain area, subjects hear them in the scanner, and if the explanation is correct, the targeted region lights up. The result is a method that translates uninterpretable predictive models back into the currency of science: concise hypotheses that can be confirmed or refuted in a follow-up experiment. An LLM writes new stories engineered to activate a specific brain area, subjects hear them in the scanner, and if the explanation is correct, the targeted region lights up. The result is a method that translates uninterpretable predictive models back into the currency of science: concise hypotheses that can be confirmed or refuted in a follow-up experiment.

Figure 1. The two steps of generative causal testing (GCT). In Step 1, the phrases that most strongly drive a brain region’s predictive model are summarized by an LLM into a short candidate explanation, such as “food preparation.” In Step 2, an LLM writes new stories designed to match that explanation, and the region’s response to these “driving” stories is measured in the scanner and compared against baseline.  How GCT works

GCT has two steps: explanation, then verification. To generate an explanation, the method starts from a predictive model for a single voxel or region and identifies the short phrases that most strongly drive its predicted response. An LLM then summarizes those words into a concise verbal explanation, often a single phrase such as “food preparation” or “location names.”

The crucial second stage closes the loop. To build trust in the explanation, GCT uses an LLM to write new stories in which each paragraph is carefully constructed to drive a brain region according to its explanation. Three subjects returned to the scanner to read these synthetic stories. If a region’s activity to its “driving” paragraphs was significantly greater than to baseline text, the explanation passed a genuine causal test, not just a correlational one.

Across all three subjects, the core approach held up: the synthetic stories reliably drove their target regions above baseline, confirming that GCT’s short explanations capture something the cortex genuinely responds to. The explanations were also most trustworthy where the underlying brain-prediction models were strongest (the more stable the model, the more reliably its explanation could be confirmed in the scanner). With the method validated on regions whose selectivity was already known, the researchers turned GCT on harder questions.

Figure 2. Brain response maps to GCT stories for different topics. Some maps recover well-established findings: the explanation “Locations” produces strong responses in the place areas RSC, OPA, and PPA. Others independently confirm newer hypotheses: “Food Preparation” activates a region in ventral occipital cortex near the fusiform face area (FFA). Some like (“Birthdays”) do not map cleanly onto any known result, pointing toward directions for future research.

GCT also proved sharp enough to settle long-standing ambiguities. Three neighboring regions involved in processing places have often been treated as functionally similar: the retrosplenial cortex (RSC), the parahippocampal place area (PPA), and the occipital place area (OPA). At first, stories written for one region also activated the others. But by generating differential stimuli (stories designed to switch one region on while keeping its neighbors quiet), GCT teased the three apart. For example, RSC responds more strongly to proper noun location names, like Tokyo or Connecticut, rather than general location. This is the kind of nuanced, region-specific theory that a raw predictive model cannot provide on its own.

Beyond known regions, the authors discovered new prefrontal “micro-regions.” By scanning a grid of candidate locations and keeping only the most stable ones, GCT surfaced these previously unmapped regions tuned to remarkably specific concepts: one selective for dialogue between people (words like “said” or “told”), one for mentions of clock times (“one o’clock”), and one for numeric measurements (“50 feet”). These are distinctions no one had gone looking for; they emerged because the method could propose a hypothesis and immediately test it.

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Listen now Opens in a new tab Implications and looking forward

The significance of GCT reaches well beyond neuroscience. Researchers increasingly face the same dilemma: a model that predicts beautifully but explains nothing. GCT shows that a data-driven model need not be the end of inquiry; it can be distilled into a readable, experimentally testable theory, and that theory can be checked against reality by generating new experiments on demand.

For neuroscience specifically, GCT points toward a faster, more hypothesis-rich way of mapping the cortex—one where an AI system proposes what a brain region might encode and a closed-loop experiment confirms or rejects it within a single study. The same generate-and-verify philosophy could extend to other domains where powerful predictive models have outrun our ability to understand them. The broader lesson is hopeful: the rise of black-box models in science does not necessarily mean the retreat of human-readable theory. With the right framework, the two can advance together.

Acknowledgements

This work was a collaboration across Microsoft Research, UC Berkeley (Alex Huth, Bin Yu, Sihang Guo, and Aliyah Hsu), Columbia University (RJ Antonello, co-lead), and UCSF (Shailee Jain). We also thank the study participants and the broader language-neuroscience community whose tools and datasets made this research possible.

Read the paper (opens in new tab): “Generative causal testing to bridge data-driven models and scientific theories in language neuroscience,” accepted in Nature Neuroscience and the code on Github (opens in new tab).

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The post Understanding the brain with AI-driven explanations and experiments appeared first on Microsoft Research.

At a glance

• LLM-based models can predict the human brain’s responses to language with high accuracy. But what drives that performance is essentially unreadable: a vast collection of learned parameters, not scientific theories anyone can read.
• Generative causal testing (GCT), developed in a collaboration between Microsoft Research, the University of California, Berkeley, the University of California, San Francisco, and Columbia University, distills these brain-prediction models into short verbal explanations of what each patch of cortex responds to: phrases like “food preparation” or “location names.”
• GCT then closes the loop: an LLM writes new stories designed to activate a targeted brain area, subjects hear them in the scanner, and the region lights up only if the explanation is right.
• In experiments, GCT confirmed known selectivity, teased apart neighboring place-processing regions long thought interchangeable, and revealed tiny prefrontal “micro-regions” tuned to specific concepts like dialogue, clock times, and measurements.

The explainability problem in language neuroscience

Over the past decade, LLMs have become the most accurate tools we have for predicting how the human brain responds to language. Feed an LLM the same story a person hears in an fMRI scanner, and the model’s internal representations can predict the activity of individual patches of cortex with remarkable fidelity. But this success comes with a catch: nobody can read these models. They are millions of inscrutable parameters that can’t be directly translated into interpretations. A model that predicts brain activity tells us that a region responds to language, but not what it is actually picking up on, whether it’s food, places, numbers, or something else entirely. As black-box models spread, the gap between prediction and understanding has become one of the central problems in computational neuroscience.

Turning black boxes into testable theories

In a new paper accepted in Nature Neuroscience, Microsoft Research scientists, in collaboration with scientists at the University of California, Berkeley, University of California, San Francisco, and Columbia University, introduce a framework to overcome this explainability crisis: generative causal testing (GCT). GCT distills brain-prediction models into short, readable accounts of what each patch of cortex responds to, then tests those claims. An LLM writes new stories engineered to activate a specific brain area, subjects hear them in the scanner, and if the explanation is correct, the targeted region lights up. The result is a method that translates uninterpretable predictive models back into the currency of science: concise hypotheses that can be confirmed or refuted in a follow-up experiment. An LLM writes new stories engineered to activate a specific brain area, subjects hear them in the scanner, and if the explanation is correct, the targeted region lights up. The result is a method that translates uninterpretable predictive models back into the currency of science: concise hypotheses that can be confirmed or refuted in a follow-up experiment.

Figure 1. The two steps of generative causal testing (GCT). In Step 1, the phrases that most strongly drive a brain region’s predictive model are summarized by an LLM into a short candidate explanation, such as “food preparation.” In Step 2, an LLM writes new stories designed to match that explanation, and the region’s response to these “driving” stories is measured in the scanner and compared against baseline.  How GCT works

GCT has two steps: explanation, then verification. To generate an explanation, the method starts from a predictive model for a single voxel or region and identifies the short phrases that most strongly drive its predicted response. An LLM then summarizes those words into a concise verbal explanation, often a single phrase such as “food preparation” or “location names.”

The crucial second stage closes the loop. To build trust in the explanation, GCT uses an LLM to write new stories in which each paragraph is carefully constructed to drive a brain region according to its explanation. Three subjects returned to the scanner to read these synthetic stories. If a region’s activity to its “driving” paragraphs was significantly greater than to baseline text, the explanation passed a genuine causal test, not just a correlational one.

Across all three subjects, the core approach held up: the synthetic stories reliably drove their target regions above baseline, confirming that GCT’s short explanations capture something the cortex genuinely responds to. The explanations were also most trustworthy where the underlying brain-prediction models were strongest (the more stable the model, the more reliably its explanation could be confirmed in the scanner). With the method validated on regions whose selectivity was already known, the researchers turned GCT on harder questions.

Figure 2. Brain response maps to GCT stories for different topics. Some maps recover well-established findings: the explanation “Locations” produces strong responses in the place areas RSC, OPA, and PPA. Others independently confirm newer hypotheses: “Food Preparation” activates a region in ventral occipital cortex near the fusiform face area (FFA). Some like (“Birthdays”) do not map cleanly onto any known result, pointing toward directions for future research.

GCT also proved sharp enough to settle long-standing ambiguities. Three neighboring regions involved in processing places have often been treated as functionally similar: the retrosplenial cortex (RSC), the parahippocampal place area (PPA), and the occipital place area (OPA). At first, stories written for one region also activated the others. But by generating differential stimuli (stories designed to switch one region on while keeping its neighbors quiet), GCT teased the three apart. For example, RSC responds more strongly to proper noun location names, like Tokyo or Connecticut, rather than general location. This is the kind of nuanced, region-specific theory that a raw predictive model cannot provide on its own.

Beyond known regions, the authors discovered new prefrontal “micro-regions.” By scanning a grid of candidate locations and keeping only the most stable ones, GCT surfaced these previously unmapped regions tuned to remarkably specific concepts: one selective for dialogue between people (words like “said” or “told”), one for mentions of clock times (“one o’clock”), and one for numeric measurements (“50 feet”). These are distinctions no one had gone looking for; they emerged because the method could propose a hypothesis and immediately test it.

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Start now Opens in a new tab Implications and looking forward

The significance of GCT reaches well beyond neuroscience. Researchers increasingly face the same dilemma: a model that predicts beautifully but explains nothing. GCT shows that a data-driven model need not be the end of inquiry; it can be distilled into a readable, experimentally testable theory, and that theory can be checked against reality by generating new experiments on demand.

For neuroscience specifically, GCT points toward a faster, more hypothesis-rich way of mapping the cortex—one where an AI system proposes what a brain region might encode and a closed-loop experiment confirms or rejects it within a single study. The same generate-and-verify philosophy could extend to other domains where powerful predictive models have outrun our ability to understand them. The broader lesson is hopeful: the rise of black-box models in science does not necessarily mean the retreat of human-readable theory. With the right framework, the two can advance together.

Acknowledgements

This work was a collaboration across Microsoft Research, UC Berkeley (Alex Huth, Bin Yu, Sihang Guo, and Aliyah Hsu), Columbia University (RJ Antonello, co-lead), and UCSF (Shailee Jain). We also thank the study participants and the broader language-neuroscience community whose tools and datasets made this research possible.

Read the paper (opens in new tab): “Generative causal testing to bridge data-driven models and scientific theories in language neuroscience,” accepted in Nature Neuroscience and the code on Github (opens in new tab).

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The post Understanding the brain with AI-driven explanations and experiments appeared first on Microsoft Research.

At a glance

• Talos is an open-source tool for automated, iterative reanalysis of genomic data in rare disease. It efficiently re-examines stored sequencing data as scientific knowledge evolves and flags variants with newly actionable evidence.
• Talos is tuned for a low false-positive rate: across a validation set of nearly 1,100 patients, it recovered 90% of in-scope diagnoses while flagging only 1.3 candidate variants per patient for expert review. This is essential to making reanalysis sustainable at scale.
• Deployed across a prospective cohort of almost 5,000 undiagnosed patients, Talos delivered 241 new diagnoses (5.1% additional yield). An average of only 32 days passed between supporting evidence becoming public and the resultant diagnosis.
• On monthly iterative cycles, analysts only needed to review one new variant per 200 patients, demonstrating that frequent, systematic reanalysis can be run sustainably.

Why genome reanalysis matters

Genomic testing has transformed the diagnosis of rare disease, but even with this advancement, more than half of patients remain undiagnosed after their first test. This is because our knowledge of the genome is still incomplete. Researchers are learning more every day about the function of specific genes and how they relate to disease.

However, unlike most diagnostic investigations, genomic data has a unique property: it can be stored and reexamined indefinitely. Because our understanding of the genome improves constantly, simply rerunning the analysis later can yield a diagnosis that was impossible to make the first time. This is because there are hundreds of new gene–disease associations and thousands of new variant classifications reported every year.

Reanalysis of the genomes of undiagnosed patients is the solution; a meta-analysis of nearly 9,500 undiagnosed patients found that reanalysis lifted diagnostic yield by about 10% over roughly two years. However, the problem is that reanalysis today is overwhelmingly manual. It depends on motivated clinicians, scarce laboratory staff, and inconsistent reimbursement, so the vast majority of stored genomes are never revisited and the data keep accumulating. Automation has long been proposed as the answer, but the developers of automated machinery must navigate hard trade-offs between sensitivity, specificity, how many candidate variants a human must review, and how often the analysis is rerun.

Talos (opens in new tab), developed through a collaboration spanning the Centre for Population Genomics, Australian Genomics, the Broad Institute, and Microsoft, was built to resolve those trade-offs and to demonstrate, at international scale, that systematic reanalysis is both feasible and valuable. We have recently published a journal article (opens in new tab) detailing how Talos functions and evaluating its performance on multiple rare disease cohorts.

How Talos works

Talos re-interprets a patient’s existing variant calls against the latest community knowledge each time it runs. It draws on two continuously updated public resources: PanelApp Australia (opens in new tab) for gene–disease relationships and modes of inheritance, and ClinVar (opens in new tab) for variant-level pathogenicity. It then applies a variant-prioritization algorithm designed to surface variants most likely to meet ACMG/AMP criteria for clinical reporting.

Figure 1 – Talos overview. Talos operates in multiple stages, first collecting unchanging information about genetic variants and the patients who possess them, then applying up to date knowledge to filter and prioritize variants that are likely to be clinically relevant, then finally surfacing those variants to clinicians alongside supporting evidence. 

The pipeline uses newly discovered information to tag and filter variants, then refines the candidate set using family structure (for example, mode of inheritance and de novo status) and, when available, the patient’s phenotype. Talos can be used to interpret single-nucleotide variants, small insertions/deletions, copy number variants, and large structural variants from exome or genome data.

Two design choices distinguish Talos. First, it is deliberately conservative, optimized to return a small set of high confidence variants rather than a long ranked list, because in real-world genomic reanalysis the limiting factor is human review time, not algorithmic recall. Second, on repeat runs, Talos returns only variants whose supporting evidence has changed since the previous cycle, allowing clinicians to focus exclusively on findings that aregenuinely new.

Validated against expert manual analysis

We benchmarked Talos on two independent cohorts that had already undergone careful manual analysis: the Australian Acute Care Genomics (ACG) cohort of critically ill infants and children, and the U.S.-based Rare Genomes Project (RGP) cohort of families with prior uninformative testing. This included 1,089 probands in total.

On ACG trios, Talos recovered 90% of in-scope diagnoses while returning a median of just 1.3 candidate variants per family. The diagnoses it missed were largely a direct consequence of its conservative strategy, for example, recessive variants lacking ClinVar support that human analysts had classified using trans configuration or functional studies.

Crucially, Talos held the same operating point on the very different RGP cohort, agroup of families who had previously had uninformative clinical testing, with probands ranging up to 82 years of age. On RGP trios, it recovered 87% of in-scope diagnoses (47 of 54) at a median of 1.3 candidate variants per trio, showing generalizability across cohorts.

We then benchmarked head-to-head against Exomiser, a widely used prioritization tool. Talos matched its overall sensitivity for small variants, but at a very different operating point: Exomiser ranks and returns a broad list, while Talos returns a short, highly specific one. In a paired comparison, the two tools were statistically indistinguishable when all of Exomiser’s ranked variants were reviewed, but Talos came out significantly ahead once review was limited to a realistic budget—the top five (p = 0.017) or top one (p < 0.0001) ranked variants. Notably, the two tools surfaced different variants, so they are complementary and should ideally be used together in diagnostic workflows.

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The experiment we were most excited about was a tested-but-undiagnosed cohort of 4,735 individuals, drawn from Australian Genomics research studies and a single diagnostic laboratory. Most patients were singletons with neurodevelopmental, cardiac, renal, and/or neurological indications.

Talos produced 241 new diagnoses in 238 individuals—a 5.1% additional yield, with every single likely-causative variant subsequently confirmed as pathogenic or likely pathogenic by accredited labs.

The sources of those diagnoses illustrate why reanalysis is such a powerful paradigm:

• 32% came from new gene–disease relationships discovered since the original test,
• 22% came from new variant-level evidence (reclassifications), and
• 45% came from improved filtering and analysis—including variant types such as CNVs and structural variants not examined originally, phenotype filters that had been set too narrowly, and other sources.

Yield was consistent across clinical areas (roughly 5–6% for neurodevelopmental, cardiac, and renal indications) but the reasons differed: new gene associations and CNVs dominated neurodevelopmental diagnoses, while variant reclassification drove most cardiac ones. Genome data outperformed exome (6.1% vs 4.8%), partly by reaching non-coding diagnoses such as RNU4-2 and a deep-intronic MRPL39 variant. A recurring theme was the lag in conventional knowledge bases: 59% of the new gene–disease diagnoses were not yet curated in OMIM at the time of reanalysis, underscoring the value of drawing on a rapidly updated resource like PanelApp Australia.

From a one-off event to a continuous program

We then ran Talos for 29 monthly iterative cycles. Most diagnoses (92%) came on a cohort’s first pass, but the iterative design proved its value on two fronts. First, it demonstrated the scalability of ongoing reanalysis: because later cycles return only newly actionable evidence, they surfaced an average of just one variant per 200 cases over the program. Second, it showed how quickly we can move from scientific discovery to diagnosis: on average just 32 days passed between new knowledge appearing in a public database and a patient receiving a diagnosis, with the fastest case turning around in a single day. Figure 2 provides timelines for three example patients showing how continual reanalysis can bring answers to families within weeks of new scientific findings. The whole pipeline is cheap enough to run continuously: annotating 1,000 genomes cost about $11, and a monthly reanalysis pass ran for a few cents per cohort.

Figure 2 – Diagnostic odyssey for three example patients. Each patient spent years after genetic sequencing waiting for a diagnosis. For Patient 1, the scientific discovery enabling their diagnosis happened one month after their testing, but no diagnosis was made until the first time their genetic data was reanalyzed using Talos. For patients 2 and 3, diagnoses were made within a month of the relevant scientific findings because the patients were already in the reanalysis pipeline.  Looking ahead

Talos reframes genomic reanalysis from a rare, labor-intensive event into a continuous, automated program that can keep pace with the science. By optimizing for specificity, it respects the real bottleneck of expert reviewer time, and by drawing on openly shared, frequently updated resources like PanelApp Australia and ClinVar, it turns the global community’s accumulating knowledge into diagnoses for individual patients, often within weeks.

We believe we’ve established a foundational capability, and we’re excited to see how the community builds on it. In particular, as more advanced AI models for understanding and predicting the consequences of genetic variation become available, we’re looking forward to leveraging them in the reanalysis of unsolved rare disease cases.

Talos is open source and straightforward to deploy in cloud environments like Azure. Our results offer a practical blueprint for health systems aiming to deliver frequent, scalable reanalysis to the many patients still searching for diagnoses.

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