benchmark pass/fail result
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
benchmark pass/fail result has 23 facts recorded in Dontopedia across 8 references, with 3 live disagreements.
23 facts·17 predicates·8 sources·3 in dispute
Mostly:rdf:type(4), contains language metric(2), reports runtime(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (3)
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hasPerformanceMetricHas Performance Metric(1)
- Fed Sym System
ex:fed-sym-system
isNotJustIs Not Just(1)
- Phase Collapse Risk
ex:phase-collapse-risk
likesSoundOfLikes Sound of(1)
- Lisamegawatts
ex:lisamegawatts
Other facts (22)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
22 facts
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Measurement | [4] |
| Rdf:type | Performance Outcome | [5] |
| Rdf:type | Binary Outcome | [7] |
| Rdf:type | Benchmark Outcome | [8] |
| Contains Language Metric | Ocaml Metric | [3] |
| Contains Language Metric | Python Metric | [3] |
| Reports Runtime | 3.5 | [4] |
| Reports Runtime | 160.7 | [4] |
| Changes Interpretation | Accuracy Edge | [1] |
| Computes | 655 million 2D point-steps | [2] |
| Demonstrates Efficiency | true | [2] |
| Emphasizes Performance | true | [2] |
| Takes Time | 63 seconds | [2] |
| Reported by | Rolandnsharp7643 | [3] |
| Has Calculated Speedup | ~3.9x | [3] |
| Has Value | beat all tested competitors | [5] |
| Has Method | G8 Analytical | [6] |
| Tokens Per Second Before | 48000 | [6] |
| Tokens Per Second After | 85000 | [6] |
| Has Speedup Factor | 1.8 | [6] |
| Has True Outcome | Print True | [7] |
| Has False Outcome | Print False | [7] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
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changesInterpretationblah/watt-activation/part-308
ex:accuracy-edge
—
computesblah/watt-activation/part-533
655 million 2D point-steps
—
demonstratesEfficiencyblah/watt-activation/part-533
true
—
emphasizesPerformanceblah/watt-activation/part-533
true
—
takesTimeblah/watt-activation/part-533
63 seconds
—
reportedByblah/vidya/2
ex:rolandnsharp7643
—
containsLanguageMetricblah/vidya/2
ex:ocaml-metric
—
containsLanguageMetricblah/vidya/2
ex:python-metric
—
hasCalculatedSpeedupblah/vidya/2
~3.9x
—
typeblah/watt-activation/18
ex:Measurement
—
reportsRuntimeblah/watt-activation/18
3.5
—
reportsRuntimeblah/watt-activation/18
160.7
—
typeblah/watt-activation/459
ex:PerformanceOutcome
—
hasValueblah/watt-activation/459
beat all tested competitors
—
hasMethodblah/watt-activation/474
ex:g8-analytical
—
tokensPerSecondBeforeblah/watt-activation/474
48000
—
tokensPerSecondAfterblah/watt-activation/474
85000
—
hasSpeedupFactorblah/watt-activation/474
1.8
—
typebeam/1fa70fe7-abc5-4650-aa84-5baafcb016d6
ex:BinaryOutcome
—
labelbeam/1fa70fe7-abc5-4650-aa84-5baafcb016d6
benchmark pass/fail result
—
hasTrueOutcomebeam/1fa70fe7-abc5-4650-aa84-5baafcb016d6
ex:print-true
—
hasFalseOutcomebeam/1fa70fe7-abc5-4650-aa84-5baafcb016d6
ex:print-false
—
typebeam/8c1b3b89-a29c-4d7d-a956-9a7531ea0ef6
ex:BenchmarkOutcome
References (8)
8 references
ctx:discord/blah/watt-activation/part-308ctx:discord/blah/watt-activation/part-533ctx:discord/blah/vidya/2- full textvidya-2text/plain3 KB
doc:agent/vidya-2/c8624315-9110-4e57-94b2-3fa2c01cec46Show excerpt
[2026-02-20 05:28] rolandnsharp7643: oland@cube:~/code/flow$ ./microgpt_ref num docs: 16478 vocab size: 80 num params: 5888 step 1000 / 1000 | loss 1.6514 --- inference (new, hallucinated text) --- sample 1: in there angerer sample 2: ara…
ctx:discord/blah/watt-activation/18- full textwatt-activation-18text/plain3 KB
doc:agent/watt-activation-18/812b86dc-f424-4d37-b858-94cd90ca9fbfShow excerpt
[2026-03-04 05:47] xenonfun: feat: Complete training system with optimizations - 68x speedup with optimized MLX conv1d - Parallel tokenizer with caching - From-scratch training with best checkpoints - Async checkpoint saving - Learning rate…
ctx:discord/blah/watt-activation/459- full textwatt-activation-459text/plain2 KB
doc:agent/watt-activation-459/5a24a4b5-2967-488c-8337-25c0e0516514Show excerpt
[2026-03-21 16:37] xenonfun: ``` FedSym Port: symbiogenesis → HarmonicRust Context The Python symbiogenesis project (github.com/MonumentalSystems/symbiogenesis) implements a population-based neural architecture evolution framework. Neur…
ctx:discord/blah/watt-activation/474- full textwatt-activation-474text/plain2 KB
doc:agent/watt-activation-474/367f85bd-8740-4ca7-98b3-b2e3fb89cd49Show excerpt
[2026-03-21 20:17] xenonfun: ``` ⏺ There we go. 85K tok/s (up from 48K pre-rayon) — the parallel loss computation and per-group backward are giving 1.8× speedup. The per-token forward is still sequential (correct), and the coarse-grained …
ctx:claims/beam/1fa70fe7-abc5-4650-aa84-5baafcb016d6- full textbeam-chunktext/plain1 KB
doc:beam/1fa70fe7-abc5-4650-aa84-5baafcb016d6Show excerpt
# Simulate the log ingestion process time.sleep(0.1) logging.info(message) # Define the benchmarking function def benchmark_ingestion(): # Define the number of events num_events = 5000 # Define the target ingestion…
ctx:claims/beam/8c1b3b89-a29c-4d7d-a956-9a7531ea0ef6- full textbeam-chunktext/plain1 KB
doc:beam/8c1b3b89-a29c-4d7d-a956-9a7531ea0ef6Show excerpt
- Use libraries like `scikit-learn` or `TensorFlow` for training and deploying models. - **Continuous Improvement**: - Continuously collect and analyze data to refine your rules and heuristics. - Regularly update your language detect…
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