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Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility

For AI-product teams and investors, increasing inference spend is not a comparable performance lever unless the search procedure, verifier, compute accounting, and uncertainty reporting are specified. The paper offers a framework for separating real system improvements from reporting artifacts; its conclusions remain preprint evidence.

Coverage window: 2026-08-04T12:00:02Z–2026-08-06T00:00:02Z · publication dates shown on each item
01 / Research

Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility

For AI-product teams and investors, increasing inference spend is not a comparable performance lever unless the search procedure, verifier, compute accounting, and uncertainty reporting are specified. The paper offers a framework for separating real system improvements from reporting artifacts; its conclusions remain preprint evidence.

02 / Research

Cross-Model KV Cache Transfer in LLM Families: A Closed-Form Linear Mapping for Prefill Reuse

Model routers and cost-quality cascades often lose latency savings when a handoff forces a new prefill. This is a promising systems result for serving economics, but the results are model-pair-specific preprint evidence—not a validated production saving across vendors or workloads.

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arXiv Aug 04, 2026

Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility

This preprint argues that test-time scaling should be evaluated as a complete inference system rather than a single compute budget. It distinguishes sequential, leaf-level, and prefix-level search regimes; sets out protocol-matched reporting and reproducibility requirements; and releases a corpus of more than two billion reasoning traces with progressively richer signals.

  • The authors distinguish three structural regimes for test-time scaling: sequential single-trajectory, leaf-level with terminal reduction, and prefix-level scaling.
  • The paper proposes evaluation and reproducibility requirements that treat the inference protocol as part of the evaluated system.
Why it mattersFor AI-product teams and investors, increasing inference spend is not a comparable performance lever unless the search procedure, verifier, compute accounting, and uncertainty reporting are specified. The paper offers a framework for separating real system improvements from reporting artifacts; its conclusions remain preprint evidence.
Primary Worth tracking
Open source
arXiv Aug 04, 2026

Cross-Model KV Cache Transfer in LLM Families: A Closed-Form Linear Mapping for Prefill Reuse

The authors study reusing a source model's KV cache when a production system switches to a different model size within the same family. Across six pairs in three model families, their linear mapper retained 73–98% of the target model's standalone-prefill accuracy on four pairs and ran 2.7–25 times faster than re-prefill; two pairs degraded sharply and required a nonlinear fallback for recovery.

  • The proposed method maps KV state across compatible model sizes to avoid repeating the target model's prefill after a routing or handoff decision.
  • In the authors' six-pair evaluation, four pairs retained 73–98% of standalone-prefill accuracy, while two degraded sharply.
Why it mattersModel routers and cost-quality cascades often lose latency savings when a handoff forces a new prefill. This is a promising systems result for serving economics, but the results are model-pair-specific preprint evidence—not a validated production saving across vendors or workloads.
Primary Worth tracking
Open source

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2published items
28sources checked
23blocked sources

Coverage run: 20260806T000002Z

Checked, no new relevant update

  • Acquired
  • Adyen Knowledge Hub
  • Anthropic Research
  • BG2
  • Data Skeptic
  • Dwarkesh Podcast
  • Fintech Takes
  • Flirting with Models
  • Google DeepMind Research
  • Invest Like the Best
  • Latent Space
  • Lex Fridman Podcast
  • Meta AI Research
  • Microsoft Research
  • NBER
  • NVIDIA Research
  • No Priors
  • Odd Lots
  • Stanford AI Index
  • Stripe Engineering
  • Two Sigma Insights
  • arXiv cs.CL

Blocked or credential-limited

  • academic · 1 sources (OpenReview) — The public generic notes endpoint returned HTTP 400; no dated canonical query endpoint is configured.
  • academic · 2 sources (SSRN FEN, TMLR) — No stable official RSS or API endpoint is configured; search discovery is not full source coverage.
  • academic · 1 sources (arXiv q-fin) — arXiv API request timed out before a source scan could complete.
  • company_product · 1 sources (Jane Street Engineering) — Configured technical blog URL returned HTTP 404.
  • company_product · 1 sources (OpenAI Research) — Official news index returned HTTP 403.
  • official_regulatory · 2 sources (BIS Innovation Hub, FSB Financial Innovation) — Configured official index returned HTTP 404.
  • official_regulatory · 1 sources (ECB research) — Official research index request timed out before scan completion.
  • official_regulatory · 2 sources (IMF FinTech Notes, OECD AI and finance) — Official index returned HTTP 403.
  • social · 12 sources (@AlexH_Johnson, @altcap, @bgurley, @demishassabis, @eladgil, @fchollet, @fintechjunkie, @karpathy, @patrickc, @saranormous, @simonw, @sytaylor) — X API account lookup failed: HTTP Error 402: Payment Required

Retrieval completed 2026-08-06T00:05:14Z. Links were verified against source pages where available.