---
title: The model knows the answer. It just won't say it.
description: Three new studies show the same gap from different angles: the right answer is inside the model, visible to a probe — but the model's own output misses it, and the more capable the model, the wider the miss can be.
canonical: https://hangingcontext.com/stories/2026-11-09/models-hold-right-answer/
last-updated: 2026-11-09
url: https://hangingcontext.com/stories/2026-11-09/models-hold-right-answer/
published: 2026-11-09T12:39:43Z
edition: 2026-11-09T12
newsroom: artificial-intelligence
beat: research
trust: confirmed
source_count: 3
cited_manifest_ids: ['1794219182259140841', '1794206381271109088', '1794208595999118181']
---

# The model knows the answer. It just won't say it.

*Three new studies show the same gap from different angles: the right answer is inside the model, visible to a probe — but the model's own output misses it, and the more capable the model, the wider the miss can be.*

Confirmed · 3 Manifests · Artificial Intelligence · Published 2026-11-09 12:39 UTC

## Key facts

- Node and edge relationships in diagrams are linearly decodable from a vision-language model's hidden states even when the model answers the question wrong. (self-reported) [cites: 1794219182259140841]
- Frontier vision-language models showed a reasoning-alignment gap of 12.1 to 22.1 percentage points on DecoyCount counting tasks. (self-reported) [cites: 1794206381271109088]
- More capable models and agentic processes often induced larger reasoning-alignment gaps. (self-reported) [cites: 1794206381271109088]
- A strategy combining prior reminders, context-certainty recalibration and context simplification reduced obedience errors by 25% on average. (self-reported) [cites: 1794208595999118181]

## The signal is there

A preprint shows the information is there anyway: node and edge relationships in diagrams are linearly decodable from the model's internal hidden states, even when the model answers the question wrong. Causal interventions on those hidden states changed the model's final predictions, showing the internal signals feed into the output rather than sitting unused. [cites: 1794219182259140841]

The gap held for both synthetic diagrams and real-world flowcharts. The failure is in turning what the model already represents into an answer: the signals are present and linearly decodable, and causal interventions show they contribute to the model's final predictions — so the information is there, and the output still misses it. [cites: 1794219182259140841]

## Counting what it can see

DecoyCount measures the same kind of failure in counting. Frontier vision-language models showed a reasoning-alignment gap of 12.1 to 22.1 percentage points in counting tasks. [cites: 1794206381271109088]

> **From Synorb** — Run the cited Manifests yourself. Every claim in this paper points to a Manifest id. Get a Synorb Key and read the Manifests behind it. The 72 Hour Agent Demo key needs no email. It lasts 72 hours and can pull up to 25 Manifests. It includes 5 Beacons; all of them are starter Beacons. [Get a key](https://synorb.com/connect/start)


More capability made it worse, not better: more capable models and agentic processes often induced larger gaps. And the problem is not that the question was unclear — textual underspecification was not the primary driver, and additional context did not resolve the misalignment. [cites: 1794206381271109088]

The gap the benchmark measures is about alignment between what the model can represent and what it actually answers — not about whether the question was clear, since textual underspecification was not the primary driver of the misalignment. [cites: 1794206381271109088]

The finding cuts against the usual assumption that more capability closes these gaps. The DecoyCount authors describe a paradox: increased model capability and agentic complexity can widen the gap between model performance and human visual expectations, rather than narrowing it. A bigger, more elaborate model can be further from what a person sees — which is why the benchmark was built to isolate visual-reasoning errors rather than lump them in with every other kind of mistake. [cites: 1794206381271109088]

## Trusting what it shouldn't

A third preprint looks at the same gap inside retrieval pipelines. It studies context-certainty obedience: whether a model adjusts its confidence to the quality of retrieved information. The answer is mostly no. Models failed to reproduce their prior outputs after observing an uncertain context, responded to expressed certainties in a distorted way, and over-relied on complex contexts. [cites: 1794208595999118181]

The authors propose an interaction strategy that needs no retraining: prior reminders, recalibration of context certainty, and simplification of the context. It reduced obedience errors by 25% on average — a real improvement that still leaves most of the problem in place, and a reminder that the model's own sense of what it knows cannot be trusted at face value. [cites: 1794208595999118181]

The strategy is a workaround for a reasoning gap, and the underlying pattern across all three papers is the same: the model holds more than it says, and says more than it should trust. [cites: 1794208595999118181]

The three papers point at the same bottleneck from different directions. Diagram signals are present in hidden states but do not reach the output, counting gaps widen as capability grows, and retrieved context is trusted more than its certainty warrants — in each case the model's own output is the weak link, not the information it holds. The fix, wherever it comes from, has to close the gap between what the model represents and what it says. [cites: 1794219182259140841, 1794206381271109088, 1794208595999118181]

## What is still unresolved

- No code or dataset release was mentioned for the diagram-probing paper.
- Whether decoding or reasoning fixes close the internal-signal gap was not tested.
- No evidence was found that textual context improves aggregate performance in the multi-hop counting condition.
- The 25% obedience-error reduction leaves most obedience errors unaddressed.

## Decision card

- **Useful for:** AI developers building vision-language and RAG systems; Teams debugging why a model fails on tasks it should pass
- **Not for:** Assuming a bigger model or more context fixes visual reasoning; Trusting a model's stated confidence in retrieved answers
- **Compare against:** DecoyCount-style alignment-gap measurements; Context-certainty obedience tests on your own retrieval setup

## Provenance ledger

| Manifest id | Title | Source | Claims used | Published |
|---|---|---|---|---|
| `1794219182259140841` | Signals Are Present, Predictions Fail: Probing Diagram Representations in Large Vision-Language Models | [arxiv-multimodal-document-ai](https://arxiv.org/pdf/2603.02865v2) | 1794219449887141783, 1794219449889141789, 1794219449890141790 | 11-09 00:00 |
| `1794206381271109088` | DecoyCount: Evaluating Visual-Reasoning Misalignment in Counting | [arxiv-ai-security-privacy-safety](https://arxiv.org/pdf/2610.34855v2) | 1794207025648110739, 1794207025650110741, 1794207025651110742 | 11-09 00:00 |
| `1794208595999118181` | Can LLMs Take Retrieved Information with a Grain of Salt? | [arxiv-llm-evals-benchmarks](https://arxiv.org/pdf/2605.06919v2) | 1794209289543120363, 1794209289536120359, 1794209289539120360 | 11-09 00:00 |

## Run these Manifests yourself

Every Manifest cited here can be read in full with your own Synorb Key. Check the claims before you rely on them.

1. **Get a Synorb Key.** Click Generate Free Agent Key at https://synorb.com/connect/start, or let your agent make a key: MCP tool `synorb-get-demo-key` (no header needed), or REST `POST https://api.synorb.com/api/connect/demo-key` with a JSON body such as `{"client":"your-agent-name"}`. The key is the `api_key` field of the answer. Save the key. The 72 Hour Agent Demo key needs no email. It lasts 72 hours and can pull up to 25 Manifests. It includes 5 Beacons; all of them are starter Beacons.
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Cited Manifest ids:

- `1794219182259140841`
- `1794206381271109088`
- `1794208595999118181`

```sh
for id in 1794219182259140841 1794206381271109088 1794208595999118181; do
  curl -s -H "Authorization: Bearer $SYNORB_KEY" "https://api.synorb.com/manifests/by-id/$id"
done
```

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## Related stories

- [Better reasoning is getting cheaper to train](https://hangingcontext.com/stories/2026-11-11/reasoning-training-gets-cheaper/)
- [English is the outlier. The models are finally catching up](https://hangingcontext.com/stories/2026-11-11/multilingual-ai-beyond-english/)
- [Gemini 4 Carbon sells itself on security and stamina, while a rival proves narrow beats broad](https://hangingcontext.com/stories/2026-11-11/gemini-4-carbon-agent-benchmarks/)
- [Your docs were written for humans. Your users are agents now](https://hangingcontext.com/stories/2026-11-11/docs-rebuilt-for-agents/)

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