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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Chess MCVS - Zone Guided AI

Advanced Monte-Carlo Value Search (MCVS) engine for the game Chess (8x8), powered by a novel Displacement-based ABC Model and Weighted Adjacency Matrices with Hilbert-ordered Zone Guidance.

This repository implements a complete zone-guided reinforcement learning system, including self-play training, neural networks, and comparative tournaments against classic UCT.

Core Idea

The engine uses:

  • Displacement-based ABC Model with homogeneous coordinates
  • Dynamic Weighted Adjacency Matrices W = A ⊙ S ⊙ F
  • Hilbert curve ordering for efficient zone retrieval
  • A learned Zone Database that stores winning/losing position patterns
  • Zone Guidance (λ-PUCT) to bias search toward promising zones

For more information please refer to the paper at: https://doi.org/10.13140/RG.2.2.18795.09764

Files Overview

File Purpose
chess_zone_db.npz Main implementation: Winning, Loosing and Drawing positions in matrix form.
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