Start with coverage and measured results; open a run only when you need evidence or technical details.
0/6
claims supported by evidence
0
Supported
0
Challenged or mixed
0
Contradicted
0
Inconclusive
6
Not assessed
Each row is one recorded execution. Commands, logs, hashes, and signatures are available in its details.
No reproduction runs yet
Runs will appear here as the paper's experiment plans are executed.
See each experiment's execution state, blocker, recovery action, and evidence destination while keeping operations separate from scientific conclusions.
A successful execution does not by itself validate a paper claim
Target state says whether the platform completed the work. The scientific conclusion is determined only by immutable evidence and Assessments. Resource shortages and platform failures are never presented as scientific contradictions.
This task predates target-level records. The states below are conservative projections; no attempts, resource decisions, or evidence links are invented.
The architecture ablation table reports that removing ContextNorm fails to converge, while additive fusion, auxiliary-loss budget control, and removing P-mask reduce the reported recall average relative to LoGo.
Optional independent architecture-ablation reconstruction
An experiment plan exists, but the current processing task has no matching execution target.
On the paper's 32k-checkpoint short-context recall aggregate, LoGo is reported as competitive with the other methods and scores 57.65 percentage points across six recall tasks.
Optional independent short-context recall reconstruction
An experiment plan exists, but the current processing task has no matching execution target.
In the controlled 1.5B comparison, LoGo reports the best language-modeling loss and Lambada perplexity among the listed full-attention and matched-budget static hybrid variants, with a higher commonsense-reasoning average than the full Transformer.
Optional independent matched-budget reconstruction
An experiment plan exists, but the current processing task has no matching execution target.
LoGo preserves the reported scaling behavior of full-attention Transformers from 200M through 3.3B parameters, matching or improving the baseline on the listed language-modeling and commonsense metrics.
Optional independent scaling-direction reconstruction
An experiment plan exists, but the current processing task has no matching execution target.
The paper reports that its query-sparse Triton kernel reaches a 1.99x forward-plus-backward speedup over the dense Triton baseline at 64k sequence length and a 0.5 attention budget.
This claim has no executable experiment plan yet.
LoGo reports higher average needle-style RULER recall than the three comparison paradigms at both the 32k and 128k context-extension stages, with averages of 83.0 and 65.4 percentage points.
Optional independent long-range recall reconstruction
An experiment plan exists, but the current processing task has no matching execution target.
