pith:OV6P2JTV
Unified Deployment-Aware Evaluation of Open Reasoning Language Models
Accuracy-efficiency tradeoffs in reasoning LLMs depend jointly on architecture, prompting protocol, and task rather than sparse activation alone.
arxiv:2604.07035 v2 · 2026-04-08 · cs.CL
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Claims
These results show that sparse activation alone does not guarantee the best practical operating point: observed accuracy-efficiency tradeoffs depend jointly on architecture, prompting protocol, and task composition.
That the four chosen benchmarks and three prompting strategies are representative enough of real-world reasoning workloads to support general claims about accuracy-efficiency tradeoffs.
Gemma-4-E4B with few-shot chain-of-thought reaches the highest weighted accuracy of 0.675 at 14.9 GB VRAM, while the larger Gemma-4-26B-A4B MoE model scores 0.663 but uses 48.1 GB.
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| First computed | 2026-05-20T01:05:12.591934Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/OV6P2JTV7LJPILFH6UXU4QI52A \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 757cfd2675fad2f42ca7f52f4e411dd01459177b3dd47ff7c13db0f7d27b9898
Canonical record JSON
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