CHANGELOG

Workspace lab notebook for long-running or resumable research work.

Use this file to track chronology, not release notes. Keep entries short, factual, and operational.

Entry template

Objective: Set up a deep research workflow for scaling laws.
Changed: Created plan artifact at outputs/.plans/scaling-laws.md; defined 4 disjoint researcher dimensions and acceptance criteria.
Verified: Read CHANGELOG.md and checked prior memory for related plan scaling-laws-implications.
Failed / learned: No prior run-specific changelog entries existed beyond the template.
Blockers: Waiting for user confirmation before launching researcher round 1.
Next: On confirmation, spawn 4 parallel researcher subagents and begin evidence collection.

Objective: Complete T4 on inference/test-time scaling and reasoning-time compute, scoped to 2023–2026.
Changed: Wrote notes/scaling-laws-research-inference.md; updated outputs/.plans/scaling-laws.md to mark T4 done and log the inference-scaling verification pass.
Verified: Cross-read 13 primary/official sources covering Tree-of-Thoughts, PRMs, repeated sampling, compute-optimal test-time scaling, provable laws, o1, DeepSeek-R1, s1, verifier failures, Anthropic extended thinking, and OpenAI reasoning API docs.
Failed / learned: OpenAI blog fetch for learning-to-reason-with-llms returned malformed content, so the note leans on the o1 system card and API docs instead of that blog post.
Blockers: T2 and T5 remain open before final synthesis; no single unified law for inference-time scaling emerged from public sources.
Next: Complete T5 implications synthesis, then reconcile T3/T4 with foundational T2 before drafting the cited brief.

Objective: Synthesize the four research notes into a single user-facing draft brief for the scaling-laws workflow.
Changed: Wrote outputs/.drafts/scaling-laws-draft.md with an executive summary, curated reading list, qualitative meta-analysis, core-paper comparison table, explicit training-vs-inference distinction, and numbered inline citations with direct-URL sources.
Verified: Cross-checked the draft against notes/scaling-laws-research-foundations.md, notes/scaling-laws-research-revisions.md, notes/scaling-laws-research-inference.md, and notes/scaling-laws-research-implications.md to ensure the brief explicitly states the literature is too heterogeneous for a pooled effect-size estimate.
Failed / learned: The requested temp-run context.md and plan.md were absent, so the synthesis used outputs/.plans/scaling-laws.md plus the four note files as the working context.
Blockers: Citation/claim verification pass still pending; this draft should be treated as pre-verification.
Next: Run verifier/reviewer passes, then promote the draft into the final cited brief and provenance sidecar.

Objective: Deliver a paper guide and qualitative meta-analysis on AI scaling laws.
Changed: Finalized outputs/scaling-laws.md and sidecar outputs/scaling-laws.provenance.md; rendered preview PDF at outputs/scaling-laws.pdf; updated plan ledger and verification log in outputs/.plans/scaling-laws.md.
Verified: Ran a reviewer pass recorded in notes/scaling-laws-verification.md; spot-checked key primary papers via alpha-backed reads for Kaplan 2020, Chinchilla 2022, and Snell 2024; confirmed PDF render output exists.
Failed / learned: A pooled statistical meta-analysis would be misleading because the literature mixes heterogeneous outcomes, scaling axes, and evaluation regimes; final deliverable uses a qualitative meta-analysis instead.
Blockers: None for this brief.
Next: If needed, extend into a narrower sub-survey (e.g. only pretraining laws, only inference-time scaling, or only post-Chinchilla data-quality revisions).