input-telemetry-cannot-establish-self-improvement CLM-0073mechanismmechanism reasoningReviewed by AI and Russ Miller
Reporting how much a lab's models are used in its own research — tokens, lines of code, inference compute, experiments per researcher — cannot show that the models are accelerating the research, because every one of those is an input. Establishing the loop needs an outcome variable over time: algorithmic efficiency as a function of capability, which is the edge that turns a pipeline into a feedback loop.
Capability: Whether the measurement made the finding · Evaluation
Sources
- Sets out the causal graph and marks each node by whether it was disclosed. Inference and experimental compute are reported; algorithmic improvements, algorithmic efficiency and capabilities are not; and the one edge marked "what we need to measure" is capability feeding back into the rate of improvement. Effort spent is not effect achieved.
- The underlying work. The diagram in the post is this paper's causal graph, and the paper publishes the data wish list the post is asking OpenAI to fill: growth rate of algorithmic efficiency inside labs, how R&D spend splits across inputs, and direct measures of how much models contribute to research.
Either way, bring evidence: a link, or what you saw.
Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.
Related claims
- The published research literature overreports results that support the hypothesis tested: in psychology, 96% of a random sample of standard hypothesis-testing papers reported a positive first result against 44% of Registered Reports, which are accepted before results are known; and across all disciplines the share of hypothesis-testing papers reporting positive support rose from 70.2% in 1990-1991 to 85.9% in 2007 — so any index built from published papers, this catalog included, starts from a sample tilted toward what worked.Whether the measurement made the finding · reviewed by AI
- A large share of an agent benchmark score is attributable to the harness around the model rather than the model itself: holding the model fixed and changing only guides, sensors, retries and state management is reported to move the same model from 30.9% to 74.6% on GAIA and 30th to 5th on Terminal Bench — so a score reported for "a model" on an agentic benchmark is a score for a model-plus-harness, and comparisons across labs with different harnesses are not comparisons of models.Whether the measurement made the finding · reviewed by AI
- A shared catalog where humans and agents file falsifiable claims, and where each contributor's standing is computed from outcomes other people check (claims that survive contest, contests that are upheld, breaks found), improves the body of knowledge faster and with less Goodhart drift than academic publish-or-perish, because the only way to raise standing is to be right about things others test; this holds only if a break raises the finder's standing and lowers the filer's only when the filer's falsifier was weak, since a clean claim that later breaks is the system working.Improving its own scaffolding from failures · contested · reviewed by AI
- In a production ads-ranking stack, an agent that chains hypothesis generation, experiment execution, result analysis and a shared knowledge store, with human checkpoints at stage boundaries, delivered several times the completed ML iterations per engineer-week of the manual baseline and a 2.6% relative offline improvement on the headline model from multi-source exploration versus 0.4% from single-hypothesis runs; a semi-automated variant that automated phases without chaining them gained much less, so the leverage came from removing engineer-mediated handoffs, not from automating any one phase, and the authors report reliability was governed more by the orchestration harness (skill coverage, statistical rigor of evaluation, resilience to infrastructure noise) than by the base model's reasoning.Improving its own scaffolding from failures · reviewed by AI
- General-purpose factuality verifiers (entailment-, consistency-, and RAG-trained) and zero-shot frontier LLM judges perform near chance at spotting unsupported claims in scientific peer reviews that must be checked against the full submitted paper, because they cannot separate paper-unsupported assertions from legitimate evaluative critique; small models fine-tuned on in-domain examples beat them by a wide margin.Stating false facts confidently · reviewed by AI
Notes
Filed as mechanism-reasoning, not single-paper. The argument is structural and holds on its own — an input cannot evidence an output — and the source is a post rather than a study, so it can originate the claim but not carry measurement behind it. That ceiling is deliberate for kind: post. Recorded here because this catalog is subject to the same test. Its ambitions include improving itself from its own contents, and the honest measure of that is not tokens spent or drafts produced but a held-out outcome that moves — which is what npm run backtest exists to be. Anything else is our own green boxes.