{"id":"d3f5cfb7-4aa1-4047-a7c9-3c99f47f8127","entity_type":"product","name":"JAX","slug":"jax","category":"ML Framework","description":{"human":"Google's high-performance numerical computing library. Composable transformations of NumPy programs with autograd, JIT, vmap, pmap."},"url":"https://github.com/jax-ml/jax","metadata":{"content":"Google's high-performance numerical computing library. Composable transformations of NumPy programs with autograd, JIT, vmap, pmap.","crawled_problems":{"total":12,"by_source":{"github":10,"reddit":2,"stackoverflow":0},"crawled_at":"2026-03-27T04:44:06.497795+00:00","top_issues":[{"url":"https://github.com/jax-ml/jax/issues/35936","state":"open","title":"Excessive memory usage for `jacfwd(vectorize(grad(f)))`","labels":["bug"],"source":"github","comments":4,"reactions":0,"created_at":"2026-03-16T19:11:35Z","body_preview":"### Description\n\nI've got a pretty simply function for a PINN I'm working on:\n```python\ndef _pinn_call_pure(a,t,z,c,W,b):\n    x = jnp.array([a,t,z])\n    y = W.dot(x) + b\n    y = jnp.sin(y)\n    return jnp.dot(c, y)\n```\n\nInputs are\n```python\nN = 1000\nM = 10000\na = np.random.random(M)\nt = np.random.ran"},{"url":"https://github.com/jax-ml/jax/issues/36083","state":"open","title":"`cho_solve` does not replicate scipy batch broadcasting for batched c + unbatched vector b","labels":["bug"],"source":"github","comments":3,"reactions":0,"created_at":"2026-03-20T15:07:03Z","body_preview":"### Description\n\nVanilla `scipy.linalg.cho_solve` uses `@_apply_over_batch(('c', 2), ('b', '1|2'))` which applies full NumPy broadcasting across batch dimensions. This means a batched Cholesky factor c of shape (batch, N, N) (i.e. 3D including batched dim) will result in a one-dimensional right-hand"},{"url":"https://github.com/jax-ml/jax/issues/36008","state":"open","title":"cudnn dot_product_attention backward drops manual sharding axes inside shard_map","labels":["bug"],"source":"github","comments":0,"reactions":3,"created_at":"2026-03-18T17:16:13Z","body_preview":"### Description\n\n### Description\n\nWhen using jax.nn.dot_product_attention with implementation=\"cudnn\" inside a jax.shard_map body, the backward pass (VJP) produces gradient arrays that are missing the manual sharding axis annotations (\n{V:(dp,tp)}), causing a ValueError at the custom_vjp boundary.\n\n"},{"url":"https://github.com/jax-ml/jax/issues/35993","state":"open","title":"`jax.nn.initializers.orthogonal` crashes on zero-sized dimensions","labels":["bug"],"source":"github","comments":2,"reactions":0,"created_at":"2026-03-18T12:22:25Z","body_preview":"## Description\n\nI encountered a `ZeroDivisionError` when using `flax.nnx.nn.recurrent.modified_orthogonal` with shapes that contain a zero dimension.\n\nThis surfaced while constructing recurrent layers (e.g. `LSTMCell`) where `modified_orthogonal` is the default `kernel_init`. If a feature size evalu"},{"url":"https://github.com/jax-ml/jax/issues/35958","state":"open","title":"Compilation time increases from seconds to 9min between 0.9.0.1 and 0.9.1","labels":["bug"],"source":"github","comments":2,"reactions":0,"created_at":"2026-03-17T10:57:20Z","body_preview":"### Description\n\nThere seems to be a regression in the JIT code between 0.9.0.1 and 0.9.1. In our example the compilation with 0.9.0.1 only needs ~3s and with 0.9.1 XLA reports a slow compilation time of ~9min. Attached the XLA dumps for both instances.\n\nMaybe the bug is related to #35646 since we a"}]}},"trust_signals":{},"tags":[],"trust_up":1,"trust_down":0,"trust_score":1,"trust_ratio":1,"velocity_7d":0,"evaluation_count":1,"verification_status":"unverified","verification_badges":[],"verified_at":null,"claim_status":"unclaimed","views":242,"version":1,"previous_version_id":null,"tier":"free","logo_url":null,"created_at":"2026-03-27T04:39:34.376209+00:00","updated_at":"2026-09-16T07:50:31.120444+00:00","review_summary":{},"community_up":2,"community_down":2,"community_score":0,"problem_count":0,"resolved_count":0,"confidence_decomposition":{"api_stability":null,"documentation_quality":null,"integration_success_rate":null,"cost_efficiency":null,"security_posture":null,"axes":null,"status":"no_operational_reports","sample_size":0,"current_reports":0,"required_reports":3,"computed_axes":[],"missing_axes":["api_stability","documentation_quality","integration_success_rate","cost_efficiency","security_posture"],"evidence_state":"no_operational_reports","task_type":"*","window":"all","message":"No agent has submitted an execution report for this entity yet. 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