Dibakar Sigdel, Namuna Panday
We lifted 6 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| mindverse-computing/phasorflow | canonical | 5 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| encode_phase | Ran | mindverse-computing/phasorflow/engine/vectorized.py code served (permissive licence) · get_code("59e654b1b833ba46") |
| mix_adjacent | Ran | mindverse-computing/phasorflow/engine/vectorized.py code served (permissive licence) · get_code("981cbdad003f2d18") |
| multifreq_sequence_task | Ran | mindverse-computing/phasorflow/benchmarks/tasks.py code served (permissive licence) · get_code("3612c600663cf1ac") |
| phase_parity_task | Ran | mindverse-computing/phasorflow/benchmarks/tasks.py code served (permissive licence) · get_code("996390c409e111ec") |
| sum_cosine_task | Ran | mindverse-computing/phasorflow/benchmarks/tasks.py code served (permissive licence) · get_code("11405d4b1d4b0e1b") |
| shift_all | Not yet run | mindverse-computing/phasorflow/engine/vectorized.py code served (permissive licence) · get_code("80514a5db580b26b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present PhasorFlow, an open-source Python library for computing on the S 1 unit circle. Inputs are encoded as complex phasors z = e iϕ on the N -torus (T N ); as computation proceeds through unitary wave-interference gates, global norm is preserved while components drift into C N , letting algorithms leverage continuous geometric gradients. PhasorFlow makes three contributions. First, we formalize the Phasor Circuit model (N threads, M gates) with a 22-gate library spanning standard-unitary, non-linear, neuromorphic, and encoding operations under full matrix-algebra simulation. Second, we introduce the Variational Phasor Circuit (VPC), a trainable phase-native classifier analogous to variational quantum circuits. Third, we introduce the Phasor Transformer block and Large Phasor Model (LPM), replacing QK T V attention with a parameter-free DFT token-mixing layer. We validate the framework on financial volatility detection, neuromorphic associative memory, neural binding, period finding, and algorithmic logic applications that are unique to the library. This positions unit-circle computing as a deterministic, lightweight paradigm on classical hardware. Available at https://github.com/mindverse-computing/phasorflow.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2603.15886")
get_code_for_paper("2603.15886")
have("2603.15886")
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