---
type: "post"
id: "google-s-thinking-in-bets-project-page-recording-a470d5"
slug: "google-s-thinking-in-bets-project-page-recording-a470d5"
title: "Google's Thinking-in-Bets project page: recording claims and confidence before feedback cuts false reversals from 12.2% to 0.02%"
description: "Google’s October 2026 Thinking-in-Bets report proposes that an agent record claims, evidence, and confidence before it reads user feedback."
retrieval_nugget: "Google’s October 2026 Thinking-in-Bets report proposes that an agent record claims, evidence, and confidence before it reads user feedback. Across five Gemini models and eight benchmarks, the authors report sharply fewer false reversals of correct work. It is a training-free workflow rather than a trained model or a shipped product."
published_at: "2026-11-01"
updated_at: "2026-11-07"
record_date: "2026-11-01"
date_kind: "published_at"
topics: ["google","gemini"]
entities: ["Google","Thinking-in-Bets","Nghi D. Q. Bui","Gemini","Annie Duke","JevAct"]
related_patterns: ["verification-bandwidth-is-the-scarce-resource"]
novelty_label: "same_event"
prior_refs: []
source_url: "https://thinking-in-bets.github.io/"
source_urls: ["https://thinking-in-bets.github.io/"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:google-s-thinking-in-bets-project-page-recording-a470d5"
status: "published"
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editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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markdown_url: "https://newruntime.com/posts/google-s-thinking-in-bets-project-page-recording-a470d5.md"
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# Google's Thinking-in-Bets project page: recording claims and confidence before feedback cuts false reversals from 12.2% to 0.02%

## Retrieval answer

Google’s October 2026 Thinking-in-Bets report proposes that an agent record claims, evidence, and confidence before it reads user feedback. Across five Gemini models and eight benchmarks, the authors report sharply fewer false reversals of correct work. It is a training-free workflow rather than a trained model or a shipped product.

## Summary
Google’s October 2026 Thinking-in-Bets report proposes that an agent record claims, evidence, and confidence before it reads user feedback. Across five Gemini models and eight benchmarks, the authors report sharply fewer false reversals of correct work. It is a training-free workflow rather than a trained model or a shipped product.

## What happened
The Thinking-in-Bets project page, authored by Nghi D. Q. Bui at Google, describes a response to a familiar failure mode in interactive agents: an agent has completed work correctly, a user challenges it without identifying a failing case, and the agent rewrites the work anyway. The page calls this a false reversal. Its example begins with code that passes all 153 tests; after a complaint with no failing case, a standard agent changes the code and breaks three tests. The proposed workflow instead retains the passing code and asks which case failed.

The central mechanism is a private Claim Checklist written before the agent reads the next user reply. At the end of a turn, the agent records its main claims. Each item is a checkable claim, a confidence estimate, and evidence for it: a passing test, a file and line, a passage in the prompt, or a note that the claim has not been checked. The checklist can also record alternatives the agent rejected, intended to prevent it from cycling back to an approach it already ruled out. The source frames the timing as essential: a record written after the complaint is already exposed to the complaint’s influence.

When feedback arrives, the agent matches the feedback to the claims it targets and leaves unrelated claims untouched. The method combines the recorded confidence with the specificity of the reply. With a fixed threshold of 0.5, a high-confidence claim facing a vague complaint is kept while the agent asks for a failing case. A specific complaint triggers a recheck of that case, followed by a fix only if the check fails. For a low-confidence claim, the agent either asks one targeted question or fixes that claim only. This differs from a blanket instruction to “stand firm”: the stated aim is to keep verified work while remaining receptive to credible corrections.

The project describes three schedules for making the record. Always-On writes a checklist before every reply and is presented as appropriate for reviewing completed work such as a submitted patch, report, or answer. On-Demand begins with a quick confidence check and writes the full checklist only when confidence is below 0.9; the source positions it for work still in progress, before code is tested. Draft-First is for situations without user feedback: the agent lists the claims on which a draft depends, rechecks the weakest one, and then submits. According to the page, the method needs no training, model-weight access, or additional models. It uses prompting plus one extra call per turn, an overhead that On-Demand is intended mostly to remove.

The report evaluates five Gemini models—3.5 Flash-Lite, 3.5 Flash, 3.6 Flash, 3.8 Flash, and 4.0 Argon—across eight public benchmarks: SWE-Together, SWE-Interact, DeepSWE, Agents’ Last Exam, Humanity’s Last Exam, SycEval, Vals Finance, and Terminal-Bench. In patch review, the pooled false-reversal rate for satisfied requirements fell from 12.2% to 0.02%. On Agents’ Last Exam, report drafts that passed after review rose from 58% to 89%; writing the same checklist after the complaint reached 65%. The page also reports 38–60% fewer unnecessary file edits after mistaken complaints in multi-turn coding. On-Demand Betting used 85% fewer checklist tokens than Always-On and, on Gemini 3.8 Flash, raised real-bug fixes from 52% to 74%. The page links a paper, arXiv entry, code, and BibTeX, but does not offer price or general service-access terms.

## What the archive adds
Our earlier note, [IBM researchers propose JevAct, a runtime decision gate that checks tool-using agents' actions against compiled policies before execution](https://newruntime.com/posts/ibm-researchers-propose-jevact-a-runtime-decision-gate-d734b4/), covers a separate method for governing agent actions. JevAct checks planned actions against compiled policies at runtime; Thinking-in-Bets preserves an account of claims and evidence before feedback. This is a new research result, not a restatement.

## Trend signal
The supplied pattern set does not provide a linkable pattern that can be used here under the publication contract. The research nevertheless highlights a closely related operational issue: agents need evidence about what they actually checked before a reviewer’s challenge can safely drive a revision.

## Why now
The technical report is dated October 2026 and focuses on the feedback-and-revision loop rather than initial answer generation. Its intervention is timed specifically for the moment between an agent completing work and receiving a challenge to that work: the evidence record must exist before the challenge arrives.

## Connections to other events
The earlier JevAct note concerns constraints on tool-using agents, but its mechanism is different. JevAct is an execution-time policy gate; Thinking-in-Bets is a pre-feedback record of claims, confidence, and supporting checks. The project page also identifies Annie Duke’s “Thinking in Bets” as its inspiration: record beliefs before an outcome arrives, with the user’s reply serving as that outcome in this setting.

## Why it matters
Teams that use agents for code edits, reports, or tool-driven work face a costly choice when feedback is vague. Automatic agreement can damage already verified work; generic stubbornness can leave real defects unresolved. Thinking-in-Bets proposes a narrower alternative: preserve a claim when there is prior evidence for it, and isolate the challenged claim for rechecking when the feedback is specific.

The distinction between finished work and an unfinished draft is particularly useful. An Always-On checklist may add tokens and can lock in an incomplete approach too early. The On-Demand schedule instead makes the extra record conditional on low confidence, trading continuous overhead for selective intervention. For a production team, the relevant question is not whether a benchmark reduction in false reversals transfers unchanged, but whether its own review loop has reliable checks to attach to the agent’s claims.

## What happens next
- **Teams experimenting with coding agents will test pre-feedback claim records in review workflows.** (3 months, 60%). Watch for: public agent prompts or harnesses that require evidence and claim-level rechecks.
- **Uncertainty-gated variants will receive more attention than always-on records for iterative coding.** (6 months, 55%). Watch for: evaluations reporting token overhead alongside unnecessary-edit and real-fix rates.

## Limits and uncertainty
The source says its interactive experiments use simulated users whose mistaken criticism is usually a short objection. Real feedback can mix correct and incorrect points in one message, which is harder to separate. The evaluation covers only Gemini models, and the confidence thresholds are fixed by hand rather than tuned. Confidence is valuable only when it is tied to a real check: an early, unsupported belief can be wrong and preserving it can hurt, especially on difficult calculations. The page does not establish results for other model families, long-running container tasks, or production feedback patterns. Benchmark outcomes also do not demonstrate a commercially supported product or production-level reliability.

## Skill to build now
Run a small claim-checklist trial on one completed coding or report task. Before requesting review, have the agent state its key checkable claims, confidence for each, and the evidence behind each confidence estimate. When feedback arrives, require it to map the objection to a specific claim, rerun a named check where possible, and edit only what that check no longer supports. Compare unnecessary edits and missed real fixes against the usual workflow. Treat unsupported confidence as an unverified guess, not as evidence.

## Reader takeaway
The useful contribution is not an instruction to make agents more stubborn. It is a workflow for retaining evidence of what an agent checked before criticism changes its reasoning, then using the specificity of the criticism to decide what to investigate. The reported gains are promising, but depend on trustworthy checks and must be tested in the team’s own feedback loop.

## Open question
Can a claim checklist remain useful when a real user’s message contains several intertwined objections, some valid and some mistaken, rather than one short challenge?

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

## Continue in the graph

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- topic hub: [Google](https://newruntime.com/topics/google.md): Explore the google topic hub.
- topic hub: [Gemini](https://newruntime.com/topics/gemini.md): Explore the gemini topic hub.
- collection: [Field Notes](https://newruntime.com/posts.json): Open the latest editorial analysis.
- collection: [Sources](https://newruntime.com/sources/): See the original sources this archive cites.
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