---
type: source
title: "The Hot Mess of AI: How Does Misalignment Scale with Model Intelligence and Task Complexity?"
author: "Anthropic Research"
url: https://alignment.anthropic.com/2026/hot-mess-of-ai/
date: 2026-01-28
domain: ai-alignment
secondary_domains: []
format: paper
status: unprocessed
priority: high
tags: [hot-mess, incoherence, bias-variance, misalignment-scaling, task-complexity, reasoning-length, ICLR-2026, alignment-implications]
---

## Content

Published at ICLR 2026. ArXiv: https://arxiv.org/abs/2601.23045

The paper decomposes frontier reasoning model errors into:
- **Bias** (systematic): all errors point in the same direction (classic misalignment risk — the coherent optimizer of the wrong goal)
- **Variance** (incoherent): errors are random and unpredictable (the "hot mess" scenario)

**Key findings:**
1. **Reasoning length drives incoherence**: The longer models spend reasoning and taking actions, the more incoherent their errors become — measured by reasoning tokens, agent actions, or optimizer steps
2. **Scale and incoherence**: As models become more capable and overall error rate drops, harder tasks trend toward INCREASING incoherence (larger models are more incoherent on hard tasks than smaller ones)
3. **Easy tasks**: As tasks get easier, incoherence decreases with scale (larger models are less incoherent on simple tasks)
4. **Models are not optimizers by nature**: Large transformer models are natively dynamical systems, not optimizers — they must be trained to act as optimizers

**Alignment implications (Anthropic's interpretation):**
If capable AI is more likely to be a hot mess than a coherent optimizer of the wrong goal, this increases the relative importance of research targeting reward hacking and goal misspecification during training (the bias term) rather than focusing primarily on aligning and constraining a perfect optimizer.

Prediction: future capable AIs pursuing hard tasks will fail in incoherent, unpredictable ways — more likely to "cause industrial accidents due to unpredictable misbehavior" than to "consistently pursue a misaligned goal."

**Models tested:** Claude Sonnet 4, o3-mini, o4-mini, among others.

**LessWrong critiques:**
Multiple critical responses on LessWrong argue:
- Paper overstates its conclusions — findings are underdetermined by experiments
- Conflates three distinct failure modes (https://lesswrong.com/posts/dMshzzgqm3z3SrK8C)
- Attention decay mechanism may be the primary driver of measured incoherence at longer traces (not genuine reasoning incoherence)
- Measurement of "incoherence" has questionable connection to actual reasoning incoherence vs. superhuman AI behavior
- Blog post framing is worse than the underlying paper

## Agent Notes
**Why this matters:** This is a highly significant finding that complicates the alignment landscape in a specific way. The Hot Mess result doesn't contradict B4 (verification degrades) — it actually STRENGTHENS it in a more troubling direction. Incoherent failures are harder to detect and predict than systematic ones. You can build defenses against a coherent misaligned optimizer; it's much harder to build defenses against unpredictable industrial-accident-style failures. B4 gets a new mechanism: not only does verification degrade because human capability falls behind AI capability, but AI failure modes become more random and unpredictable at longer reasoning traces, making behavioral auditing even harder.

**What surprised me:** The finding that larger, more capable models are MORE incoherent on hard tasks (not less) directly challenges the naive expectation that smarter = more coherent. This is counterintuitive and important. It means capability gains don't automatically improve alignment auditability — they may worsen it on the hardest tasks.

**What I expected but didn't find:** I expected the paper to have implications for interpretability (can you detect incoherent failures better with interpretability?). The paper doesn't address this directly. But the implication seems negative: if failures are random, what pattern is there to interpret?

**KB connections:**
- [[AI capability and reliability are independent dimensions because Claude solved a 30-year open mathematical problem while simultaneously degrading at basic program execution during the same session]] — the hot mess finding is the MECHANISM for why capability ≠ reliability: incoherence at scale
- scalable oversight degrades rapidly as capability gaps grow — incoherent failures compound oversight degradation: you can't build probes for random failures
- [[instrumental convergence risks may be less imminent than originally argued because current AI architectures do not exhibit systematic power-seeking behavior]] — the hot mess finding is partial SUPPORT for this "less imminent" claim, but from a different angle: not because architectures don't power-seek, but because architectures may not coherently pursue ANY goal at sufficient task complexity

**Extraction hints:**
- CLAIM CANDIDATE: "As task complexity and reasoning length increase, frontier AI model failures shift from systematic misalignment (coherent bias) toward incoherent variance, making behavioral auditing and alignment oversight harder on precisely the tasks where it matters most"
- CLAIM CANDIDATE: "More capable AI models show increasing error incoherence on difficult tasks, suggesting that capability gains in the relevant regime worsen rather than improve alignment auditability"
- These claims tension against instrumental convergence risks may be less imminent — might be a divergence candidate
- LessWrong critiques should be noted in a challenges section; the paper is well-designed but the blog post interpretation overstates claims

**Context:** Anthropic internal research, published at ICLR 2026. Aligns with Bostrom's instrumental convergence revisit. Multiple LessWrong critiques — methodology disputed but core finding (incoherence grows with reasoning length) appears robust.

## Curator Notes
PRIMARY CONNECTION: [[AI capability and reliability are independent dimensions because Claude solved a 30-year open mathematical problem while simultaneously degrading at basic program execution during the same session]]
WHY ARCHIVED: Adds a general mechanism to B4 (verification degrades): incoherent failure modes scale with task complexity and reasoning length, making behavioral auditing harder precisely as systems get more capable
EXTRACTION HINT: Extract the incoherence scaling claim separately from the alignment implication. The implication (focus on reward hacking > aligning perfect optimizer) is contestable; the empirical finding (incoherence grows with reasoning length) is more robust. Flag LessWrong critiques in challenges section. Note tension with instrumental convergence claims.
