Analytical Challenges for Antibody-Drug Conjugate (ADC) Manufacturing

Antibody-drug conjugates (ADCs) offer a compelling promise: delivering a cytotoxic payload directly to a tumor cell while sparing the rest of the body from harm. Composed of a tumor-targeting antibody, a cytotoxic agent, and a chemical linker, ADCs combine the selectivity of monoclonal antibodies that bind to tumor-specific antigens with the cytotoxic potency of small-molecule drugs. These therapeutics have the potential to target tumors while reducing systemic toxicity, opening new treatment pathways for many types of cancer. Recent clinical data, including encouraging Phase I findings in platinum-resistant ovarian cancer, validate that potential and fuel the field’s remarkable growth.

Analytical Challenges for Antibody-Drug Conjugate (ADC) Manufacturing cover

Despite the promise of ADCs, their unique structure complicates manufacturing, characterization, and regulatory assessment. Developers must carefully consider the interplay between the antibody, linker, and payload to optimize therapeutic efficacy and safety. The drug-to-antibody ratio (DAR), conjugation site specificity, impurity profile, and linker stability all influence an ADC’s pharmacokinetics, pharmacodynamics, and ultimately its clinical safety profile. Robust analytical methods are therefore critical throughout the development process to ensure both safety and efficacy.

Given the hybrid nature of ADCs, regulatory expectations are still evolving. To navigate this uncertainty, analytical risk management is essential. Moreover, cross-functional collaboration among analytical scientists, process development teams, regulatory experts, and quality assurance professionals is key to ensure that early-stage methods are sufficiently robust and scalable for commercial manufacturing.

Success in this environment requires deep analytical expertise, robust quality-by-design frameworks, and development partners who understand the full arc from early-stage linker-payload synthesis through GMP-compliant manufacture. It requires the capability to handle highly potent compounds safely, to purify structurally complex intermediates at scale, and to translate rigorous quality control into processes that are commercially viable.

This collection of articles and expert perspectives explores the critical challenges shaping ADC development today, from analytical strategy and impurity control to linker technology innovations and evolving regulatory standards. It also examines the collaborative expertise needed to bring these transformative therapies to patients.

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New Lung Model Reveals RSV Immune Response, Points to Better Treatments

Researchers at University College London (UCL) Great Ormond Street Hospital for Children (GOSH) have built a new lab model of infant lungs to show why respiratory syncytial virus (RSV) makes infants so much sicker than adults, and to allow them to test new treatments. The miniature model of a baby’s airways was created using pediatric airway cells, blood vessel cells, and neutrophils (a type of white blood cell that acts as the immune system’s primary response to infection).

Studies using the new model suggest that future therapies for RSV should target both the virus and its immune response to ensure babies get the best possible outcomes. Research lead Claire Smith, PhD, at UCL Great Ormond Street Institute of Child Health, said, “This model allows us to watch early immune responses unfold and study them in a human setting that reflects the infant airway. That’s something animal models often struggle to capture, especially when it comes to age-specific effects.”

Senior and corresponding author Smith and colleagues reported on their findings in Nature Communications in a paper titled “Neutrophil myeloperoxidase as a functional biomarker for RSV severity: implications for in vitro therapeutic screening.” In their report they concluded, “These findings identify neutrophil–epithelial interactions as a useful target for intervention and support the use of physiologically relevant human models to accelerate the development of therapies that limit immunopathology while preserving antiviral defense.”

RSV is the biggest cause of severe respiratory tract infections in infants and young children, resulting in over three million hospital admissions worldwide every year. RSV infection causes wheezing and breathing difficulties, and in the worst cases infants end up in intensive care. Despite this, treatment options for severe RSV disease remain extremely limited. “The lack of accessible, effective therapies highlights the need for continued advancements in RSV treatment and prevention,” the authors wrote.

During RSV infection neutrophils are rapidly mobilized to the lungs and play a key role in virus-targeting host defenses, the investigators continued. However, excessive neutrophil infiltration and activation can also contribute to airway inflammation, epithelial damage, and disease severity. “Understanding neutrophil behavior and activation during RSV infection, including during their migration across the airway epithelial barrier, is crucial for developing therapeutic strategies to mitigate pathological inflammation without compromising the antiviral response,” they suggested.

For their reported study the team aimed to create an in vitro model that recapitulates key clinical outcomes of infants with RSV bronchiolitis. To do this they established an air-liquid interface (ALI) system that incorporated pediatric airway epithelial cells, endothelial cells, and neutrophils from adults, to mirror the blood-airway barrier. “Differentiated airway epithelial cells (AECs) cultured at the air–liquid interface (ALI) provide a physiologically relevant platform to study neutrophil migration and the effect of antiviral treatments on this process,” they note. To compare with an adult response to RSV, the research team also made a model of an adult’s airways.

When the models were infected with RSV, the team found that the pediatric airway cells attracted far more white blood cells than did the adult airway cells. This influx can block an infant’s small airways and make it harder for them to breathe.

Neutrophils normally circulate in the blood but enter lung tissue in response to infection. In the baby airway model, researchers found that the neutrophils that entered the lung tissue were more activated and triggered a stronger inflammatory reaction than in the adult model. This effect was dependent on the immune cells physically moving through the infected tissue, not just responding to chemical signals released by it, making this type of model essential for study.

The results suggest that it is the infant airway itself, not just the virus, that ramps up the immune response and causes damage to the lungs. First author Machaela Palor, PhD, at UCL Great Ormond Street Institute of Child Health, said: “These findings help explain why RSV is often much more severe in infants than in adults. The pediatric airway actively shapes how immune cells behave during the infection.”

The researchers tested two antiviral drugs (remdesivir and RSV604). they found that both stopped the virus from multiplying, but only RSV604 also calmed the overactive immune response, reducing levels of a key inflammatory protein myeloperoxidase (MPO) released by white blood cells—high levels of which are linked to more severe RSV disease in babies. Remdesivir had no effect on this, suggesting that not all antivirals are equal when it comes to protecting the infant airway from immune-driven damage. “While both drugs reduced viral load, only RSV604 attenuated MPO expression,” the team stated.

The findings suggest that treating severe RSV in babies may require more than just stopping the virus—it may also be important to calm an overactive immune response. “This model suggests that MPO could be useful as a readout of therapeutic efficacy,” the team stated. “Targeting neutrophil-driven inflammatory pathways may be critical for reducing pathology in infant RSV infection.”

The researchers hope their findings and the new approach to research on RSV will accelerate the development of treatments better tailored to infants. “Our model gives us a way to assess both sides of the problem at once,” Smith said. “We can not only ask whether the drug stops the virus but also whether it helps control immune response in the infant airway. “This work reinforces the idea that age matters in respiratory infection. Understanding how infant airways shape immune responses will be key to designing safer and more effective RSV treatments.”

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Targeting Microglia Could Help Extend the Recovery Window After Stroke

Stroke care has advanced most clearly in the acute phase: rapid reperfusion, prevention of edema, secondary prevention, and early rehabilitation. Yet for many patients, the most difficult clinical reality begins after stabilization. Neurological recovery often improves over weeks to months, then plateaus. Once that spontaneous recovery window closes, residual motor, language, or cognitive deficits may become permanent.

A study published in Nature now identifies a molecular mechanism that may help explain why this window narrows. Researchers led by Jun Tsuyama and Takashi Shichita, PhD, at the Institute of Science Tokyo found that microglia, the brain’s resident immune cells, can remain in the post-stroke brain after losing their reparative function. The team identified ZFP384 as a transcriptional regulator that suppresses the microglial repair program and showed that blocking it with an antisense oligonucleotide improved long-term recovery in mouse models of ischemic stroke.

A repair program that fades too soon

Microglia are often discussed in the context of neuroinflammation, but their role after stroke is not uniformly harmful. In the acute phase, activated myeloid cells contribute to inflammatory injury. During recovery, however, microglia can shift toward a reparative state, producing neurotrophic and tissue-supportive factors that contribute to remyelination, synaptic remodeling, and functional improvement.

One of the key markers in this study was insulin-like growth factor 1, or IGF1, a neurotrophic factor produced by reparative microglia. IGF1 has known roles in synaptogenesis, oligodendrocyte function, and myelin repair. The researchers used IGF1 expression to track microglia involved in the recovery phase after ischemic stroke.

“We aimed to identify the molecular mechanism responsible for diminishing microglial reparative functions,” Tsuyama said in the press release.

The central observation was clinically relevant: reparative microglia did not simply disappear. Instead, lineage-tracing experiments showed that cells which had once expressed repair-associated genes persisted in the peri-infarct region but later lost that gene-expression program. In mice, recovery-associated gene expression rose after stroke and then declined toward baseline by around day 28. The cells remained, but their reparative identity faded.

ZFP384 as a brake on microglial repair

To identify what shuts down this repair state, the researchers combined RNA sequencing, single-cell RNA sequencing, ATAC-seq, and transcription-factor analysis. A protein known as ZFP384 emerged as a candidate regulator whose expression increased as the microglial repair program declined.

Functionally, ZFP384 acted as a brake. Overexpression of Zfp384 reduced Igf1 expression in microglial cells, while genetic deletion of Zfp384 in microglia sustained recovery-associated gene expression after stroke. Mice lacking Zfp384 specifically in microglia showed better long-term neurological outcomes on behavioral tests, without significant differences in infarct volume, cerebral blood flow, or survival.

That distinction is important. The intervention did not appear to reduce the initial ischemic injury. Instead, it improved the recovery phase, suggesting a therapeutic concept distinct from acute neuroprotection.

Mechanistically, the study links ZFP384 to disruption of YY1-mediated chromatin interactions. YY1 helped maintain enhancer–promoter contacts required for recovery-associated gene expression, including at the Igf1 locus. As ZFP384 increased, it displaced this repair-permissive chromatin organization, shifting microglia toward a dysfunctional state.

The authors describe this as a strategy to “prevent the loss of reparative immunity,” preserving beneficial immune-cell functions rather than broadly suppressing inflammation.

Antisense therapy improved recovery in mice

The translational component of the study used antisense oligonucleotides (ASO) designed to reduce Zfp384 expression. After intracerebroventricular administration, the ASO was taken up by microglia and reduced Zfp384 mRNA expression.

When ASO-Zfp384 was administered on days 8 and 22 after stroke onset, mice showed improved neurological recovery compared with controls. Notably, benefit was also observed when treatment began on day 29, suggesting that the approach may influence the chronic recovery phase in this mouse model rather than only early repair.

The biological readouts supported the behavioral findings. ASO-Zfp384 sustained microglial recovery-associated gene expression, increased IGF1-positive microglia in peri-infarct tissue, and promoted broader neural repair signatures in oligodendrocyte precursor cells, excitatory neurons, and astrocytes. Treated mice showed improved myelination, enhanced white matter conduction, and increased synaptic markers including synaptophysin and PSD95.

Neutralizing IGF1 or SPP1 reduced the recovery benefit, strengthening the conclusion that microglial neurotrophic factors were functionally involved.

Human tissue supports the pathway

The researchers also examined human post-mortem brain tissue from patients who had experienced ischemic stroke. In peri-infarct regions, IGF1-positive IBA1-positive cells were more abundant early after stroke and declined later. ZNF384, the human orthologue of mouse ZFP384, showed the opposite pattern, increasing later in the recovery period.

The inverse relationship between IGF1 and ZNF384 supports the relevance of the pathway in human stroke biology, although it remains correlative. The human samples do not show that ZNF384 inhibition would improve patient outcomes, but they indicate that the same molecular pattern observed in mice may also occur in the human post-stroke brain.

Implications for rehabilitation medicine

The findings point to a therapeutic space that remains underdeveloped: enhancing recovery after the acute phase. Rehabilitation depends on plasticity, remyelination, and circuit remodeling, but current pharmacological options to extend or potentiate this biology are limited.

A therapy that sustains reparative microglia could, in principle, complement rehabilitation by keeping the peri-infarct environment more permissive for repair. That would represent a different clinical goal from thrombolysis, thrombectomy, or anti-inflammatory intervention. Rather than rescuing threatened tissue in the first hours, the aim would be to improve the quality and duration of recovery over subsequent weeks.

The study remains preclinical. Delivery route, dose, safety, timing, durability, and patient selection all require further work. The mouse model cannot capture the full heterogeneity of human stroke, including lesion location, age, comorbidities, vascular risk, rehabilitation intensity, and medication use. Sustaining immune-mediated repair also needs careful safety evaluation, because prolonged activation of tissue-resident immune cells could have context-dependent risks.

Still, the concept is compelling. Microglia are not merely inflammatory cells to inhibit; they can be repair partners whose beneficial state may be actively preserved. If ZFP384-targeted approaches prove safe and effective in larger models, they could open a new class of post-stroke recovery therapies focused on extending the brain’s own repair window.

As Tsuyama put it, “sustaining the brain’s endogenous repair program” may create opportunities to reduce permanent neurological symptoms during rehabilitation.

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Very Low Uptake in Workplace Semen Analysis Research: Formative Web-Based Cross-Sectional Follow-Up Survey Distinguishing Employees With Self-Reported Unawareness From Aware Nonparticipants

Background: Very low uptake in workplace semen analysis research is difficult to interpret, particularly in employer-adjacent settings, where nonparticipation may reflect limited recruitment reach, limited understanding of the occupational rationale, low perceived relevance, or procedure-related concerns. Objective: This post hoc formative study described self-reported awareness of a parent workplace semen analysis study as an indicator of effective recruitment reach, reported reasons for nonparticipation under the implemented survey condition, and design issues for exposure-defined workplace reproductive health research. Methods: In April-May 2025, we conducted an anonymous web-based cross-sectional follow-up survey among male employees in Japan who had been eligible for, but had not completed, a parent workplace semen analysis study. The parent study invited approximately 2000 male employees from 3 companies between November 2024 and January 2025; 6 completed the protocol. The follow-up survey invited approximately 900 male employees from 1 company. Part 1 assessed awareness, reasons for nonparticipation, interest in male reproductive health information, and general openness to future related research. Optional Part 2 assessed age, knowledge, concerns, expected reactions, and willingness under simplified conditions. Responses were summarized descriptively using Wilson 95% CIs; no hypothesis testing was performed. Results: We analyzed 108 submitted questionnaires; 83 respondents completed Part 2. Overall, 74/108 (68.5%; 95% CI 59.3‐76.5) respondents reported no awareness of the parent study. Among unaware respondents, 68/74 (91.9%; 95% CI 83.4‐96.2) selected “did not know the study existed.” Among aware nonparticipants, the most frequent reasons were perceived irrelevance and resistance to collecting semen (each 9/34, 26.5%; 95% CI 14.6‐43.1), embarrassment or reluctance (8/34, 23.5%; 95% CI 12.4‐40), and hassle (7/34, 20.6%; 95% CI 10.3‐36.8). In Part 2, anxiety about unfavorable results was reported by 52/83 (62.7%; 95% CI 51.9‐72.3) respondents, concerns about collection location or privacy protection by 48/83 (57.8%; 95% CI 47.1‐67.9), and self-reported resistance by 42/83 (50.6%; 95% CI 40.1‐61.1). Under simplified conditions, 36/83 (43.4%; 95% CI 33.2‐54.1) respondents indicated willingness to undergo semen analysis. Conclusions: Very low uptake in this employer-adjacent semen analysis study was not interpretable as a single phenomenon. This post hoc formative process evaluation identified limited awareness, suggesting limited effective recruitment reach under the implemented procedures, and characterized the reason profile among aware nonparticipants, including low perceived relevance and semen collection–related concerns. Rather than identifying primary causal determinants of nonparticipation, the findings support a bounded recruitment-methodological interpretation and highlight recruitment-cascade components for prospective measurement: objective exposure to recruitment materials, information access, understanding of the occupational rationale, voluntary postinformation declination, privacy concerns, logistical burden, and specimen-return completion. Informed acceptability after occupational reproductive-hazard education should be evaluated in future designs that include such education and comprehension assessment.
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Autism-Related Information on Websites and General-Purpose Artificial Intelligence Chatbots: Comparative, Bilingual Study

Background: Parents increasingly consult the internet, both websites and, more recently, artificial intelligence chatbots, for information on autism spectrum disorder (ASD). However, the comparative quality of these two source types, especially across languages, remains underexplored. Objective: This study aimed to assess the completeness and accuracy of ASD information delivered by websites and 5 popular artificial intelligence chatbots and determine whether performance differs between English and Romanian content. Methods: In a cross-sectional design, 25 English-language and 25 Romanian-language websites and the responses of ChatGPT, Gemini, Claude, Copilot, and DeepSeek were evaluated. Content was benchmarked against a 24-item checklist, yielding completeness and accuracy scores. Chatbots were tested in 2 scenarios: a single broad query (A) and 24 item-specific queries (B). Results: Websites achieved higher completeness in English than in Romanian (6.9 vs 5.1; =.007) and marginally higher accuracy (6.9 vs 6.1; =.045). In scenario A, chatbot completeness (English: 5.3 vs Romanian: 6.2; =.15) and accuracy (English: 6.0 vs Romanian: 5.6; =.32) did not show significant differences by language. In the single-query scenario, websites showed higher accuracy than chatbots in both English (6.9 vs 6.0; =.19) and Romanian (6.1 vs 5.6; =.53), with neither difference reaching statistical significance. Conversely, item-specific questioning favored chatbots, which yielded higher accuracy scores than websites in English (8.3 vs 6.9; =.053) and Romanian (8.0 vs 6.1; =.007). Accuracy scores improved significantly from the single-query to the item-specific scenario (English: 6.0 vs 8.3; =.002; Romanian: 5.6 vs 8.0; =.001). While an initial analysis suggested variation in performance between chatbots (repeated measures ANOVA; =.005), pairwise differences between individual models did not remain significant after adjustment for multiple testing. Conclusions: This exploratory study indicates that the quality of online ASD information varies by language and source context. English-language websites are more complete than Romanian-language websites. Among chatbots, targeted questioning yields more accurate answers than single broad queries in both languages. The findings should be interpreted cautiously due to the temporal gap between website and chatbot data collection.
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Stem Cell Therapy Advances Toward Clinical Trial for Chronic Spinal Cord Injury

A stem cell therapy designed to restore function by repairing damaged nerve pathways is moving closer to clinical testing for people with chronic spinal cord injury (SCI), according to research presented at the International Society for Stem Cell Research (ISSCR) 2026 Annual Meeting.

The work, presented by Hideyuki Okano, MD, PhD, of Keio University in Japan, builds on more than a decade of research into regenerative therapies for SCI. Okano’s team pioneered the development of induced pluripotent stem cell (iPSC)-derived neural stem/progenitor cells and launched the world’s first clinical study of the approach in patients with subacute SCI.

In March 2025, the researchers announced one-year follow-up results from that four-patient first-in-human study, reporting no serious treatment-related adverse events and neurological improvements in two participants, including one who regained the ability to stand independently and begin gait training. Although those findings have not yet been published in a peer-reviewed journal, they provided the safety foundation for expanding the program to chronic SCI.

The physician-initiated trial, expected to begin recruiting patients in 2027, will enroll individuals with chronic, incomplete SCI who retain inactive but demyelinated nerve fibers. Unlike patients treated soon after injury, these individuals often have surviving nerve pathways that have lost the myelin insulation needed for efficient signal transmission.

“We have already successfully completed a world-first, first-in-human clinical study targeting patients in the subacute phase, which demonstrated a promising safety profile,” Okano said. “Our shift to the chronic phase is the next logical milestone, built upon that solid foundation. Since the cellular environment changes over time, we are evolving our strategy from just establishing safety to actively overcoming the stubborn, long-standing barriers of chronic paralysis.”

The biology of chronic injury also required a different cell therapy. While the earlier program used neural stem/progenitor cells capable of generating neurons, the new approach uses clinical-grade gliogenic neural stem/progenitor cells (gNS/PCs), which preferentially develop into astrocytes and oligodendrocytes—the support cells responsible for maintaining neurons and restoring myelin around surviving nerve fibers. Rather than attempting to regenerate entirely new neural circuits, the therapy aims to repair and restore the function of existing ones. Earlier preclinical studies demonstrated that these gliogenic cells promoted remyelination, preserved host axons, and improved locomotor recovery in animal models, providing the rationale for advancing the therapy toward human testing.

The latest preclinical findings further support that strategy. The researchers reported that the clinical-grade gNS/PCs efficiently differentiated into neurons, astrocytes, and oligodendrocytes in laboratory studies. Following transplantation into animal models of chronic SCI, the cells promoted behavioral recovery without evidence of tumor-like tissue formation while remodeling the injured spinal cord microenvironment.

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Mung Bean Pan-Genome Study Maps Key Genes for Yield, Nutrition, and Pest Resistance

Because Mung beans (Vigna radiata) are nutritious, inexpensive, have nitrogen-fixing capacity, and are relatively easy to grow with a short growing cycle, they are an important crop for food security in many parts of Asia, Africa, and other regions. Now, researchers have made a significant contribution to a landmark international study that has uncovered tens of thousands of previously hidden structural variations influencing yield, nutritional quality, insect resistance, and other relevant mung bean traits.

The study entitled, “Graph-based pan-genome reveals structural variations associated with agronomic traits in mung bean,” published in Nature Genetics, presents the world’s first graph-based pan-genome for the pulse crop (a legume grown specifically for its dry edible seeds) offering a comprehensive resource for understanding the genetic basis of key agronomic traits and accelerating crop improvement.

The international research team, co-led by researchers at the Chinese Academy of Agricultural Sciences and Murdoch University’s Centre for Crop and Food Innovation (CCFI), assembled chromosome-scale genomes from genetically diverse mung bean accessions and analyzed genomic variation across 580 global accessions. The resulting graph-based pan-genome captures more than 75,000 gene families and identifies over 66,000 structural variants, offering important insights that will help breeders target key agronomic traits and accelerate crop improvement.

More specifically, the authors note that, “integrating these structural variants and single nucleotide polymorphisms, genome-wide association studies across five environments identified candidate genes for 20 agronomic traits, underscoring the pivotal roles of these variants in driving mung bean domestication and improvement.”

Mechanistically, they add, the work demonstrates that “a 68-bp promoter insertion in VrTIFY6B and a 136-bp promoter deletion in VrPGIP1 regulate flavonoid content and confer bruchid resistance, respectively.”

In Australia, mung bean generates over $100 million annually in export revenue. At roughly three times the price of wheat, it represents a highly profitable break crop opportunity for Australian growers; however, seasonal rainfall variability continues to drive significant year-to-year swings in the size and value of the crop.

By cataloging tens of thousands of previously invisible structural variations and linking them to agronomic traits through genome-wide association analysis, this new genomic resource gives breeders a far more complete map of the genetic variation they can work with.

Globally, where mung bean underpins the diets and incomes of millions of smallholder farmers across Asia and Africa, the study’s findings on genes governing seed nutritional compounds and resistance to bruchids, a major storage pest, have direct implications for global food security.

Rajeev Varshney FRS FAA, CCFI director, said the research represents a major advance in crop genomics and demonstrates how next-generation genomic technologies are transforming plant breeding. “Traditional reference genomes capture only part of the genetic diversity within a crop species. By constructing a graph-based pan-genome, we can now identify structural variations that were previously invisible but often have profound effects on important agricultural traits.

“These discoveries provide breeders with powerful new genomic tools to accelerate the development of higher-yielding, more nutritious and climate-resilient mung bean varieties,” Varshney continues. “The genomic resources generated through this work will support marker-assisted breeding, genomic selection and genome editing, enabling breeders to deliver improved varieties to farmers much faster.”

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What Anthropic’s latest AI discovery does—and doesn’t—show

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Anthropic—currently the world’s most valuable AI company, with a nearly $1 trillion valuation—has a reputation for publishing strange and heady research. It’s looking into whether AI models can feel pain, for example, and will sometimes cut off chatbot conversations if it suspects users are “abusing” the model. 

One niche that Anthropic spends more time and money on than other AI companies is called mechanistic interpretability, which means looking inside the complex math of an AI model to learn why it comes up with one particular output and not another. It’s complicated stuff; there are millions of data points that might contribute to any result, and wading through them can look more like word salad than anything useful. It’s also controversial. Describing AI models with terms borrowed from psychology and neuroscience can make their behavior seem more sophisticated than we might otherwise judge it to be.

That’s why, when Anthropic announced last week that it had found a new window into its models’ “internal thoughts” as they reason through answers, there was one colleague I had to talk to. Senior editor Will Douglas Heaven, aside from having a PhD in computer science, has spent a lot of time digging into what we can say about how AI models work. I spoke with him about what we should take from Anthropic’s new (and predictably quirky) research.

What did Anthropic learn here, exactly?

Anthropic has been trying to understand how large language models (LLMs) work for a few years now. Anthropic isn’t the only one looking at this, but I think the company has made it part of its core mission more than most. Anthropic’s CEO, Dario Amodei, has said we won’t be able to control LLMs fully unless we learn more about how they work. 

So this new research is very much in that context. It goes deeper into the weird mechanisms inside LLMs than ever before. What Anthropic learned was that LLMs have a space inside them—which Anthropic calls the J-space—filled with words that don’t appear in their output but that seem to influence the way they puzzle through problems. All this was hidden until Anthropic developed a new technique to probe its model Claude, so it’s a genuine discovery. 

Sometimes these words keep track of where the LLM has got to in a particular task, sometimes they look more like flashes of recognition (for example, “protein” might pop up when you give an LLM only the letters of a protein sequence), and sometimes they represent a kind of internal commentary on the model’s decision-making. In my favorite example, Claude decided to cheat on a coding test when the word “panic” appeared.

Anthropic also found that LLMs are able to describe and manipulate the words in this space. So somehow they seem to be making use of it. 

Let’s step back for a second. I don’t think of large language models as simple, but they’re also not magic. There’s a bunch of math that learns relationships between words, right? So why is it so hard to “peer” into an LLM to know what’s going on?

Yeah, they’re not magic! I think the fact we don’t fully understand them plays into the mythmaking. And it’s worth noting that the whole narrative that Anthropic is leaning into here—that they’ve built this really mysterious technology, but don’t worry, because they’re also the ones to figure it out—very much fits with the company’s vibe. [See how Anthropic warned that its new models were so good at coding they posed a global cybersecurity risk, only for the US government to shut them down shortly thereafter.]

So yes: LLMs are just math. And yet it’s vastly complex math. Not only are today’s LLMs made out of hundreds of billions of numbers, but running them triggers a cascade of millions and millions of calculations. I wrote last year that if you printed out even a medium-size LLM on pieces of paper, it would cover a city the size of San Francisco. 

It’s impossible to make sense of any of that math without specialist tools that highlight specific parts of an LLM at specific times. You need to know where to look and how to look. And building those tools requires understanding something of that complex math in the first place. 

You’ve written elsewhere about this concept of studying LLMs the way one might study an organism’s brain. Is it fair to use “brain-like” terms when talking about how an LLM works?

I don’t love using those kinds of terms. LLMs are not brains. Talking like this is misleading because it can suggest that LLMs are capable of more human-like things than they are or that we can make assumptions about how they might behave that we shouldn’t. The whole anthropomorphization thing is also tied up with a bunch of strong ideological positions about what this technology is and what it’s going to be. 

But at the same time, we lack a good alternative vocabulary for talking about what these models are doing. I can understand why people reach for words like “think” and “understand” and “brain-like”—they’re convenient shorthand. 

Anthropic compares this new space it found inside LLMs to the space that some neuroscientists think our brains use to keep track of conscious thoughts. I asked the company how seriously we should take that comparison and it said in a statement: “Drawing these analogies was helpful to us in designing our experiments, as they allowed us to make many non-obvious experimental predictions about the J-space that turned out to be true. At the same time, it’s important to note that there are some important differences between the J-space (and language models in general) and the human brain, so we don’t mean to claim there’s a perfect correspondence.” 

What’s a problem in AI that this new concept of the J-space might be used to solve?

Anthropic has said that monitoring the J-space could be a way to catch models doing something they shouldn’t. Because words pop up in this space that don’t appear in a model’s output, they can tell you things about its behavior that you might not have noticed otherwise—such as when it is giving biased responses or when it is weighing the pros and cons of cheating. 

That’s the theory, at least. I think it’s better to think of this result as one more step on the path to understanding this technology overall than as something that will be useful by itself. 

Read more in Will’s full story about the new research. 

Auxilium bioprints kidney and liver tissues in space for the first time

Auxilium Biotechnologies says it bioprinted kidney and liver tissues aboard the International Space Station in the “first demonstration of a scalable, multi-product biomanufacturing platform in space.” The San Diego-based biotech and device developer used its Auxilium Microfabrication Platform (AMP-1) 3D bioprinter for the first-of-its-kind mission, which also included orbital bioprinting of cartilage tissue and nerve…

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Sentante has a CE mark, its CEO says much more to come for endovascular robotics

Sentante is another step closer to treating patients with its remote endovascular robotics system after earning CE mark approval in May. “This is a defining moment for Sentante,” Sentante co-founder and CEO Edvardas Satkauskas said in a news release. “We are now positioned to move decisively into commercialization and begin delivering real impact for clinicians…

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