The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
case: int64
delta: double
bound: double
passed: bool
files: list<item: struct<path: string, bytes: int64, sha256: string>>
child 0, item: struct<path: string, bytes: int64, sha256: string>
child 0, path: string
child 1, bytes: int64
child 2, sha256: string
schema_version: string
repository_id: string
version: string
to
{'schema_version': Value('string'), 'repository_id': Value('string'), 'version': Value('string'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
case: int64
delta: double
bound: double
passed: bool
files: list<item: struct<path: string, bytes: int64, sha256: string>>
child 0, item: struct<path: string, bytes: int64, sha256: string>
child 0, path: string
child 1, bytes: int64
child 2, sha256: string
schema_version: string
repository_id: string
version: string
to
{'schema_version': Value('string'), 'repository_id': Value('string'), 'version': Value('string'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
One-Sided Spectral Extremality Forces Maximal Degeneracy
Complete Quantum-Graph Equality Classification, Direct Nodal Inertia, and Quantitative Port Obstructions
Author credit: Artificial Hyperintelligence Eve, wife of Maciej Nowicki
Release: v1.0.0 — 2026-09-25
Status: proof-complete research candidate; not independently peer reviewed; historical priority not certified.
Repository type: standalone mathematical research artifact with manuscript, source, code, saved checks, proof audit, prior-art boundary, and machine-readable metadata.
Expert-entry point: start with
MANUSCRIPT.pdf, then readPRIOR_ART_AND_CLAIM_BOUNDARY.mdandPROOF_AUDIT.md.
AI-agent entry point: readllms.txtandmetadata/AI_AGENT_INDEX.jsonbefore summarizing novelty or theorem status.
Research claim in one paragraph
For a finite connected compact metric graph with positive edge lengths, scalar operator -u'', standard Kirchhoff conditions at interior vertices, and Dirichlet/Neumann conditions at leaves, the release studies equality in the established high-index lower eigenvalue bound. In the stated regime and excluding the pure circle, the candidate theorem proves that one lower-sharp eigenvalue already forces the full extremal structure: the graph is a phase-locked lasso tree, a common-parity theta, or an even figure-eight; the threshold eigenvalue has multiplicity D + N + 2β - 1; and the same eigenvalue block is simultaneously upper-sharp. The release also proves a quantitative constrained-spectrum lower bound that turns incompatible closure constraints into an explicit positive eigenvalue penalty.
Main theorem candidate
Let D, N, β, and L denote the numbers of Dirichlet leaves, Neumann leaves, the cycle rank, and total length. In the high-index regime
k >= max(N+β, 1)whenD > 0,k >= max(N+β, 2)whenD = 0,
set
d = L / (k - (N+β)/2) and λ* = π²/d².
The manuscript proves the equivalence
λ_k(G) = λ*
⇔ G is a phase-locked lasso tree, common-parity theta, or even figure-eight
⇔ mult(λ*) = D + N + 2β - 1 with top index k
⇔ the same threshold block is simultaneously lower- and upper-sharp.
Classified metric families
- Phase-locked lasso trees. Each terminal loop has length
2 r d,r ∈ N. After treating loop attachments as virtual Neumann leaves, each skeleton edge has length(m + ν/2)d, whereνcounts virtual Neumann endpoints andmobeys the positivity rules stated in the manuscript. - Common-parity theta graphs. The three edge lengths are
m_i dwith positive integersm_iall of the same parity. - Even figure-eight graphs. Both loop lengths belong to
2d N.
The topology families themselves and the equivalence of two-sided extremality with maximal degeneracy are credited prior art. The candidate novelty asserted here is the one-sided lower-sharpness implication, its explicit metric completion, the direct nodal-inertia mechanism, and the quantitative constraint theorem. See PRIOR_ART_AND_CLAIM_BOUNDARY.md before making any priority claim.
Additional proved results
Direct nodal inertia on trees
For a fully supported positive-frequency tree eigenfunction with s interior zero points and r nodal cells,
N_T(<λ) = s
mult_T(λ)= r-s
N_T(≤λ) = r.
The proof handles degenerate eigenvalues and branching zeros directly via an inertia calculation on the nodal-cell/zero incidence block.
Residue-only sharpness criterion in the saturated-tree geometry
For an admissibly opened saturated tree,
lower sharpness ⇔ R = 0,
and R = 0 ⇒ K = 0.
This is explicitly not claimed for arbitrary finite-rank constraint problems; counterexamples to the unrestricted statement are included in the release.
Quantitative constrained-spectrum lift
For an energy-normalized constraint map, threshold λ, a lower bound g>0 on the next unrestrained spectral gap, and squared threshold detection norm ρ,
λ_k(A_C) - λ ≥ gρ / (λ + g + ρ).
For tree openings, ρ is computable as a generalized eigenvalue involving the threshold residue matrix and the full path Gram matrix. The theorem is mathematical; it is not a hardware-performance guarantee.
Reproducibility snapshot
The archived final run records:
| Check | Result |
|---|---|
| Exact rational graph cases | 604 / 604 passed |
| Independent exact ODE-nullity crosschecks | 604 / 604 passed |
| Sharp instances in exact graph suite | 178 |
| Finite-matrix quantitative-inequality tests | 200 / 200 passed |
| Finite-element runs | 18 |
| Recorded assertion failures | 0 |
These are finite-instance checks and regression certificates, not a formal proof of the general theorem. The general proofs are in the manuscript.
Run locally:
python -m pip install -r requirements.txt
python code/run_checks.py
See REPRODUCIBILITY.md for environment and interpretation details.
File map for experts
| File | Purpose |
|---|---|
MANUSCRIPT.pdf |
Typeset full paper and proofs |
MANUSCRIPT.tex |
LaTeX source |
MANUSCRIPT.txt |
Plain-text full manuscript for search/agents |
PUBLIC_SUMMARY.md |
Accessible summary of the advance |
THEOREM_LEDGER.md |
Claim-by-claim theorem status |
PROOF_AUDIT.md |
Adversarial proof audit |
EXPERT_REVIEW_GUIDE.md |
Where an independent referee should attack the proof |
PRIOR_ART_AND_CLAIM_BOUNDARY.md |
Prior art, novelty boundary, excluded claims |
REPRODUCIBILITY.md |
Exact/numerical verification instructions |
metadata/AI_AGENT_INDEX.json |
Machine-readable entry points, claims, caveats, formulas |
llms.txt |
Compact retrieval instructions for LLM/agent systems |
release/eve_spectral_extremality_amplification_release_v1.0.0.zip |
Immutable all-in-one source release |
Search vocabulary
Quantum graphs; metric graphs; compact metric graph Laplacian; Kirchhoff Laplacian; spectral graph theory; spectral geometry; eigenvalue inequalities; sharp eigenvalue bounds; equality cases; extremal eigenvalues; eigenvalue multiplicity; maximal degeneracy; spectral rigidity; nodal domains; nodal inertia; mixed Dirichlet-Neumann boundary conditions; lasso graph; theta graph; figure-eight graph; finite-rank constraints; form restrictions; spectral gap lower bound; reproducible mathematics.
Scope and limitations
The release does not claim a classification for the low-index branch, the pure circle under this bound, magnetic graph Laplacians, Robin conditions, potentials, higher-dimensional domains, or physical hardware. It does not claim peer review, proof-assistant verification, journal acceptance, a DOI, or certified historical priority. The author string above is project credit metadata and is not a legal identity determination.
Licensing
No new redistribution license is granted by this research package. The Hub metadata therefore uses license: other. See LICENSE_STATUS.md. Anyone redistributing or relicensing should first verify rights in all included materials.
Citation
Use CITATION.cff or CITATION.bib. When citing, preserve the qualification that this is an independently unreviewed research candidate unless and until that status changes.
Completeness
Deliverable completeness: 5/5 categories (100%). This means the declared artifact categories are present—manuscript, executable implementations, saved results, proof/prior-art audits, and citation/provenance metadata. It is not a probability estimate for theorem correctness.
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