Related Issues

• fixes https://github.com/deepset-ai/haystack-private/issues/339

ExtractiveReader.run was raising ValueError: zip() argument 3 is longer than arguments 1-2 whenever the number of valid answer spans for a sequence was smaller than answers_per_seq (e.g. short documents, or any case where the default answers_per_seq=20 exceeded the number of non-masked upper-triangular (start, end) token pairs).

Proposed Changes:

In _postprocess, filter each sequence's candidates_values row by the same valid mask and return a list[torch.Tensor] (one 1D tensor per sequence, variable length) instead of a 2D tensor. The return-type annotation and the _nest_answers probabilities parameter annotation were updated to match.

Root cause

_postprocess filtered the per-sequence start/end token-index lists by valid = candidates_values[i] > 0, producing variable-length lists, but returned candidates_values as the unfiltered 2D tensor of shape (batch_size, answers_per_seq).

How did you test it?

Added two unit tests in test/components/readers/test_extractive.py:

• test_postprocess_filters_probs_when_answers_per_seq_exceeds_valid_spans:
  constructs a batch where answers_per_seq=5 exceeds the 3 valid upper-triangular spans, asserts all three returned per-sequence structures have matching length 3 and that retained probabilities are all > 0.
• test_nest_answers_accepts_variable_length_probability_rows:
  calls _nest_answers with probabilities as a list[Tensor] containing rows of length 2 and 1 (the new contract) and
  verifies scores flow through correctly.

Notes for the reviewer

Checklist

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