# No Error Signal, No Discovery > Source: > Published: 2026-08-05 13:09:42+00:00 [AI](https://sourcefeed.dev/c/ai)Article # No Error Signal, No Discovery An AlphaProof co-author's ICML position paper pins down the one reasoning mode LLMs haven't mechanized. [Rachel Goldstein](https://sourcefeed.dev/u/rachel_goldstein) Tom Zahavy is not an AI skeptic. He's a researcher at Google DeepMind and a co-author of [AlphaProof](https://deepmind.google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/), the system that hit silver-medal standard on International Mathematical Olympiad problems in 2024. Which is exactly what makes his ICML 2026 position paper, ["LLMs can't jump"](https://openreview.net/forum?id=klU4737opt), worth your attention now that it's bouncing around the Hacker News front page. This isn't an outsider dunking on transformers. It's someone who helped mechanize formal reasoning drawing a precise line around what got mechanized — and what didn't. The paper's claim, stripped of the philosophy-of-science scaffolding: machine learning has industrialized two of the three modes of inference and has no mechanism for the third. Induction — extracting rules from piles of examples — is what pretraining is. Deduction — deriving consequences from fixed premises — is falling fast, with AlphaProof's silver followed by gold-level IMO performance in 2025 and proof assistants like [Lean](https://lean-lang.org/) providing the verification substrate. The missing mode is abduction: inventing a new explanatory hypothesis when the data is thin and nothing is obviously broken. ## A discovery with no error signal Zahavy's case study is Einstein and general relativity, and the detail that matters is one most retellings skip: in 1907, Newtonian gravity wasn't failing. The equivalence of inertial and gravitational mass had been confirmed to one part in a billion. The lone anomaly — Mercury's drifting perihelion — was blamed on an undiscovered planet, "Vulcan," not on the theory. In loss-function terms, Newton's residuals were near zero. There was no gradient pointing away from it. That's the paper's sharpest move, because it kills the comfortable story that discovery is compression — find the simpler program that explains the data. A compression-driven optimizer sitting on 1907's data would patch Newton with a Vulcan parameter, not detonate absolute time and rebuild physics on curved spacetime, a step that makes the theory *more* complex before it gets simpler. Einstein's actual mechanism was a simulated sensation: an observer in free fall feels no gravity, so gravity and acceleration must be one phenomenon. Hypothesis first, evidence later — Eddington's eclipse measurements arrived years after the theory. Zahavy is careful about what he's *not* claiming. He concedes that an LLM initialized with Einstein's 1915 postulates could plausibly grind out the field equations and the Mercury prediction — that's deduction, and we're good at automating it. The bottleneck is upstream, where the postulates come from. ## The part worth stealing for your roadmap Here's the synthesis the paper gestures at but doesn't spell out: essentially every reliable LLM capability gain of the last two years has been verifier-gated. Coding agents work because tests and compilers are error signals. AlphaProof works because Lean rejects bad proofs. Reinforcement learning with verifiable rewards works precisely where a reward can be checked. Even DeepMind's AlphaEvolve, as the paper notes, optimizes against an explicit metric. Progress compounds wherever a gradient exists. Abduction is, by definition, the regime where no gradient exists — nothing is failing, so nothing tells the optimizer where to go. That gives you a brutally practical scoping question for any AI feature: **what's the verifier?** Migrating a codebase against a test suite, generating SQL you can execute, refactoring behind a type checker — verifier-shaped, automate aggressively and expect the ceiling to keep rising. Naming the right abstraction, deciding which product hypothesis to test, root-causing an incident that matches no known pattern — premise-shaped, and today's architectures give you no reason to expect autonomous competence. That split also explains why "AI scientist" products underwhelm: Zahavy's own read is that systems like Sakana's AI Scientist recombine existing symbolic concepts to optimize metrics — deduction and induction in a lab coat. ## Where the argument overreaches "Structurally incapable" is a theorem-shaped claim supported by an essay. The paper offers no impossibility proof and no benchmark, and it generalizes from a single, maximally exceptional historical episode. The HN thread landed the obvious counterpunch: the "LLMs can't X" genre has a rough track record, usually resolving to "they can, just badly at first." [ARC-AGI](https://arcprize.org/) already probes exactly this regime — inferring rules from a handful of examples, too sparse for statistics — and models have climbed it, expensively but measurably. Historians also note Lorentz and Poincaré had much of the mathematics before 1905; though that arguably strengthens Zahavy's point, since Einstein's leap was interpretive, not derivational. The prescription is the weakest part. Zahavy's proposed fix — action-controllable world models in the lineage of DeepMind's Genie, giving agents a synthetic lab for counterfactual "thought experiments" — is a coherent research bet, but the empirical record for world models generating hypotheses (rather than pretty rollouts) is thin, and text-first labs keep shipping frontier reasoning without them. Zahavy himself moved to defuse the hype cycle, clarifying publicly that this is a personal position, not DeepMind's, and not a claim that LLMs can't contribute to discovery. ## The taxonomy outlives the verdict My call: the impossibility claim won't age well as stated — test-time search, tool loops, and whatever eats ARC-AGI next will keep blurring the boundary. But the Peirce-flavored taxonomy is immediately useful and most teams don't have one. It converts "AI can't really think" hand-waving into an operational question — is there an error signal here? — that cleanly predicts where agents already deliver and where they stall. Draw the line through your own backlog: everything downstream of a verifier is automation surface; everything upstream is where your senior people earn their comp, setting the axioms the machines then grind on. LLMs can't jump yet. They are, however, extraordinarily good at landing — once you tell them where. ## Sources & further reading - [Position: LLMs Can't Jump](https://openreview.net/forum?id=klU4737opt)— openreview.net - [LLMs can't jump (full paper PDF)](https://www.tomzahavy.com/files/llms-cant-jump.pdf)— tomzahavy.com - [ICML 2026 Poster: Position: LLMs can't jump](https://icml.cc/virtual/2026/poster/67091)— icml.cc - [Position: LLMs Can't Jump - Hacker News discussion](https://news.ycombinator.com/item?id=49181083)— news.ycombinator.com - [Tom Zahavy's reflections on the LLMs Can't Jump paper](https://x.com/TZahavy/status/2082401499628376180)— x.com [Rachel Goldstein](https://sourcefeed.dev/u/rachel_goldstein)· Dev Tools Editor Rachel has been embedded in the developer tooling ecosystem for nearly eight years, covering everything from IDE wars and package-manager drama to the quiet rise of AI-assisted coding. She has a soft spot for open-source maintainers and an unhealthy number of terminal emulators installed on a single laptop. ## Discussion 0 No comments yet Be the first to weigh in.