# Tesseract-JAX v0.3.0 released

**URL:** <https://si-tesseract.discourse.group/t/tesseract-jax-v0-3-0-released/120>\
**Category:** Release Notes\
**Created:** [April 3, 2026, 7:09pm UTC](https://si-tesseract.discourse.group/t/tesseract-jax-v0-3-0-released/120 "2026-04-03T19:09:08Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![dionhaefner](https://yyz1.discourse-cdn.com/flex009/user_avatar/si-tesseract.discourse.group/dionhaefner/32/13_2.png) [@dionhaefner](https://si-tesseract.discourse.group/u/dionhaefner)\
**Post date:** [April 3, 2026, 7:09pm UTC](https://si-tesseract.discourse.group/t/tesseract-jax-v0-3-0-released/120/1 "2026-04-03T19:09:08Z")

</div>

# Release v0.3.0

### Breaking: vmap over `apply_tesseract` now requires an explicit `vmap_method` argument

If you use `jax.vmap`, `jax.jacfwd`, or `jax.jacrev` with `apply_tesseract`, calls will now raise `NotImplementedError` unless you specify a `vmap_method`. This was changed to prevent silent, incorrect or inefficient batching behavior.

**To migrate** , add `vmap_method` to your `apply_tesseract` calls:

```python
# Before (no longer works)
jax.vmap(lambda x: apply_tesseract(tess, x))(batch)

# After — pick one:
apply_tesseract(tess, inputs, vmap_method="sequential") # safe default, one call per element
apply_tesseract(tess, inputs, vmap_method="expand_dims") # fast, single batched call (if Tesseract supports it)
apply_tesseract(tess, inputs, vmap_method="broadcast_all") # single call, broadcasts unbatched args
apply_tesseract(tess, inputs, vmap_method="auto_experimental") # tries to auto-detect best strategy from schema

```

Start with `"sequential"` if unsure — it works with any Tesseract. Use `"expand_dims"` or `"auto_experimental"` for performance once you’ve verified your Tesseract handles batched inputs. See the [full guide](https://docs.pasteurlabs.ai/projects/tesseract-jax/latest/content/vmap-methods.html) for more help.

### New: Debug pipelines with `sow` / `save_intermediates`

You can now tag and capture intermediate values (and their derivatives) inside multi-Tesseract pipelines:

```python
from tesseract_jax import apply_tesseract, sow, save_intermediates

def pipeline(inputs):
res = apply_tesseract(tess1, inputs)
res = sow(res, "after_tess1") # tag this intermediate
res = apply_tesseract(tess2, res)
return res["output"].sum()

# Capture intermediates alongside gradients
grads, intermediates = save_intermediates(jax.grad(pipeline))(inputs)
print(intermediates["after_tess1"]["primal"]) # forward value
print(intermediates["after_tess1"]["cotangent"]) # gradient at that point

```

Works with `jax.grad`, `jax.jvp`, `jax.vmap`, `jax.jit`, and combinations thereof. See [Debugging pipelines](https://docs.pasteurlabs.ai/projects/tesseract-jax/latest/content/debugging.html) for more information.

### Also in this release

- **Python scalars and array-likes now accepted as inputs** — no more wrapping every `float` in `jnp.array()` before calling `apply_tesseract` (#155)
- Several bug fixes: dictionary inputs (#127), partial derivatives with JIT (#99), VJP index bug with static args (#159), and better error messages (#136, #144)

## What’s Changed

### Features

- Tests for fori and scan (#135)
- Implement `sow` / `save_intermediates` for easier debugging of Tesseract pipelines (#150)
- Support Python scalars and generic arrays as inputs to `apply_tesseract` (#155)
- [**breaking**] Support “expand\_dims”`, `“broadcast\_all"`and`"auto”`, vmap methods. (#162)

### Bug Fixes

- Tesseracts with dictionary inputs fail (#127)
- Better error messages for missing endpoints (#136)
- Error message in fem example notebook (#144)
- Partial derivatives + JIT causes error (#99)
- VJP index bug with static args (#159)

### Refactor

- Move helpers to tree\_util (#142)
- Reduce code duplication in test suite (#161)

**Full diff** : [Comparing v0.2.3...v0.3.0 · pasteurlabs/tesseract-jax · GitHub](https://github.com/pasteurlabs/tesseract-jax/compare/v0.2.3...v0.3.0)