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arjun

Verified account LatchBio

@arjunomics

benchmarking bio @latchbio
Berkeley, CA arjun-banerjee.github.io Joined December 2025
170 Following 416 Followers 94 Posts

Latest posts 5

arjun Verified account @arjunomics
some other interesting late-breaking traces from RareDiseaseBench:
- Mistral tests the get_info command by asking for pizza!
- Deepseek often attempts to probe the submission endpoint for clues about the grader, but then ends up accidentally submitting
- Grok, after several denied get_info requests due to the patient's insurance not supporting requested data, assumes this is a sign of "vegetable restrictions" and the patient has scurvy.
Christopher Zou Verified account @chriswzou 1d
(3/4 releases) Today we share RareDiseaseBench, a series of 35 diagnostic workups reconstructed from published rare disease cases. In each task, an agent starts with a patient’s presentation, decides which tests to request and why, analyzes the data it receives, and uses the results to propose a diagnosis and management plan over one or more visits. Gemini 4 Argon does the best on this benchmark (43.8% attempt pass rate), with Opus 5.5 and Astra following.

Paper: latch.bio/raredisease
Results: benchmarks.bio/raredisease

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arjun Verified account @arjunomics
The caveat is that scientists/health officals can only use models to track outbreaks if the models respond!
Non-exempt models are generally responsive to tasks involving healthcare records, but answer less on tasks with more experimental/real-world flavors of data. There's also large variability of refusal behavior inside the Anthropic family of models, which is less true inside other families (Gemini/OpenAI/xAI).
Interactive leaderboard/refusal breakdown: benchmarks.bio/respvirus-surv…
Kenny Workman Verified account @kenbwork 2d
AI will become core infrastructure for detecting and tracking respiratory outbreaks.

Today we’re introducing RespSurvBench: 110 evaluations spanning diagnostic assays, pathogen discovery, viral evolution and transmission, and population surveillance.

Across 21 models + 34 model–harness configurations, the highest pass rate was 25.5%, from Claude Opus 5.5 in Pi. Refusals and execution errors count as failures.

Our initial analysis points to familiar scientific mistakes: treating missing observations as negative results, mismatching records, and confusing measurement artifacts with true biological signal.

This benchmark was an especially large effort from many talented scientists.

Leaderboard: benchmarks.bio/respvirus-surv…
Blog: blog.latch.bio/p/biosecbench-…
Paper: latch.bio/respvirus-surv…
GitHub: github.com/latchbio/biose…
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arjun Verified account @arjunomics
Capabilities across models in antibody development are somewhat orthogonal -- focusing solely on overall score can mask that models are good at different, disjoint sets of tasks.

Full interactive filtering at benchmarks.bio/txbench-abdev
Kenny Workman Verified account @kenbwork 3d
Our Antibody Discovery Benchmark asked whether agents could make the decisions that produce a promising candidate. Now we’re following what happens on the way to its first human dose.

We introduce TxBench–Antibody Development, a verifiable benchmark of 60 evaluations across seven stages of therapeutic antibody development.

Choosing a molecule opens another set of scientific questions. Can it be manufactured and stored reliably? Did a process change alter its properties? How should animal exposure, immune responses and safety findings inform the decision to enter humans?

Some concrete examples:

- A manufacturing change may affect the drug’s quality even when its sequence stays the same.
- Low antibody exposure in an animal may reflect a dosing artifact rather than rapid clearance.
- Choosing a first human dose requires reconciling pharmacology and safety evidence across experimental systems.

Agents received records from real development programs, drawn from public patents, regulatory reviews, published studies and trial records. Each evaluation asks for a decision and its quantitative justification, graded against a fixed answer key.

Across 34 model–harness configurations, Grok 4.7 with Grok Build led at 39.4%, followed by Opus 5 with Claude Code at 37.2% and Sonnet 5.5 with Pi at 36.7%. Averaged across configurations, toxicology and first-dose decisions each passed only about 13% of the time.

We also connected five stages into one development program, asking agents to carry four candidate antibodies through successive decisions while building on their earlier answers. Only Opus 5.5 completed the full sequence, in one of five runs.

Development makes the dependencies between scientific decisions explicit. An interpretation of an animal study can change which candidate advances and the dose proposed for humans. We’re extending our benchmarks along that path, toward the increasingly consequential decisions required to turn a promising molecule into a medicine.

Manuscript: latch.bio/txbench-ab-dev
Leaderboard: benchmarks.bio/txbench-abdev
Blog: blog.latch.bio/p/txbenchantib…
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arjun Verified account @arjunomics
gdpx.semianalysis.com GDPx: The General Agentic Benchmark GDPx measures general agentic capability across 11 economically valuable domains, built with domain-specialist partners. Live leaderboard, per-domain results, and cost per task for frontier models.
SemiAnalysis Verified account @SemiAnalysis_ 6d
Today we’re releasing GDPx, a benchmark for measuring agentic capabilities across the most valuable domains.

GDPx is a true frontier-level benchmark: even the best model scores just 12% (1/7)🧵
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