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arxiv: 2604.24801 · v3 · submitted 2026-04-27 · 💻 cs.LG · cs.AI

Recognition: 2 theorem links

· Lean Theorem

Architecture Determines Observability of Transformers

Thomas Carmichael

Authors on Pith no claims yet

Pith reviewed 2026-05-13 07:28 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords transformersactivation monitoringoutput confidenceobservabilityresidual signalstraining dynamicserror detectionmodel architecture
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The pith

Transformer architecture determines whether a residual decision-quality signal survives in hidden activations beyond what output confidence reveals.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Autoregressive transformers produce confident errors that output monitoring misses entirely. Activation probes detect some of these errors only when training leaves behind a signal of decision quality in the hidden states after the effects of output confidence are removed. This residual signal is fixed by the model's architecture upstream of any monitor, and it can be preserved or erased depending on how training proceeds even as perplexity improves. In controlled experiments across model families, the signal forms early in some configurations and persists while in others it collapses. Where the signal remains, activation monitoring catches a portion of the errors that confidence-based checks cannot, including on downstream tasks with no task-specific tuning.

Core claim

Autoregressive transformers make confident errors that output-confidence monitoring cannot catch. Activation monitors catch them only when training leaves a decision-quality signal beyond what the output already exposes. This signal is an architectural property of the trained model, fixed upstream of any monitor. Controlling for output confidence removes 60.3 percent of the raw activation-probe signal on average across 14 models. Raw probe signal is mostly output confidence, and output-side readouts cannot recover the residual. What remains depends on architecture and training. In Pythia's controlled training, both matched-width configurations form the signal early. One preserves it through

What carries the argument

The residual activation signal in hidden states after regressing out output confidence, which carries information about decision quality that architecture and training can preserve or erase.

If this is right

  • In Pythia's controlled training, matched-width configurations form the residual signal early but one preserves it through convergence while the other erases it as perplexity improves.
  • Across independently trained model families the pattern of partial survival persists even as the collapse point shifts.
  • Where the residual signal survives, activation monitoring catches confident errors that output-confidence checks miss.
  • A WikiText-trained probe with no task-specific tuning catches about one in eight such errors on downstream QA at a 20 percent flag rate.
  • Signal engineering can be treated as a training-time design axis alongside loss and capability.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Architectures that preserve the residual signal could be selected deliberately to improve monitorability without sacrificing capability.
  • Training schedules might be adjusted to retain the signal even after perplexity has stabilized.
  • The separation of architecture from training effects suggests observability can be engineered as an independent objective.
  • Similar residual-signal analysis could be applied to other monitoring methods to test whether they also depend on architectural choices.

Load-bearing premise

The residual activation signal after removing output confidence genuinely reflects the quality of the model's internal decision rather than some other correlated but non-causal feature of the hidden states.

What would settle it

An experiment in which the residual activation probe's error-detection accuracy falls to chance levels on held-out data after output confidence is controlled for, or in which the signal can be removed without changing actual error rates.

Figures

Figures reproduced from arXiv: 2604.24801 by Thomas Carmichael.

Figure 1
Figure 1. Figure 1: Observability collapse in two training recipes. The y-axis is partial Spearman correlation between a linear probe and per-token loss, after controlling for max softmax probability and activation norm (ρpartial; Section 2). Left: LLAMA 1B preserves the signal; 3B and 8B do not, under a different training recipe. Right: Pythia configurations outside (24 layers, 16 heads) preserve the signal; the two (24 laye… view at source ↗
Figure 2
Figure 2. Figure 2: Control sensitivity on GPT-2 124M. The first four bars are cumulative: raw Spearman (0.549), then adding softmax control (0.283), then norm (0.282), then logit entropy (0.196). The fifth bar is an independent nonlinear MLP control (0.289), confirming the residual is not a nonlinear function of max-softmax confidence and activation norm. With existence established, the remaining question is whether the meas… view at source ↗
Figure 3
Figure 3. Figure 3: Pythia layer profiles under controlled training. Six healthy configurations peak at mid-to-late depth; two collapse configurations stay flat across all layers. No layer choice rescues the collapse. of the 10-permutation null gives z = 3.0 (sample std, ddof=1); with n = 10 the empirical exceedance carries the claim. A code-path sanity check confirms the two pipelines measure the same quantity within 0.002 (… view at source ↗
Figure 4
Figure 4. Figure 4: Output-independence collapses with observability. Output-controlled residual (rOC) vs confidence-controlled partial correlation (ρpartial) across 25 models (16 cross-family plus 9 Pythia). Healthy configurations fall on a linear trend (dashed line, slope 0.88). Five collapsed configurations (Llama 3B/8B; Pythia (24 layers, 16 heads), 3 replications) fall near the origin on both axes, off the trend. Archite… view at source ↗
Figure 5
Figure 5. Figure 5: Observability collapse is training-emergent. Both configurations begin with healthy ρpartial at the earliest measured checkpoint. Both dip during mid-training. The (16L, 8H) configuration recovers and converges healthy; the (24L, 16H) configuration converges collapsed. Final perplexities are comparable (18–21), so the divergence is monitorability, not predictive capability. Dashed lines in panel (a): detec… view at source ↗
Figure 6
Figure 6. Figure 6: Observability across six families. LLAMA (red) drops from 0.286 at 1B to 0.091 at 3B. Non-LLAMA families remain above the detection floor. Shaded band: detection floor. rather than stronger observability: a random untrained probe on the same Gemma 3 1B activations already achieves 0.213. At 4B the anomaly disappears (0.191, random baseline −0.010); the anomaly is specific to the 1B configuration ( view at source ↗
Figure 7
Figure 7. Figure 7: Exclusive catch rates across three tasks and three production instruct models. At 20% flag rate (right), seven of nine cells land in the 10.9–13.4% band, close to the language-modeling ceiling ( view at source ↗
Figure 7
Figure 7. Figure 7: Exclusive catch rates across three tasks and three production instruct models. At 20% flag rate (right), seven of nine cells land in the 10.9–13.4% band, close to the language-modeling ceiling ( [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Ex/dim sensitivity at QWEN 2.5 0.5B. Seven token budgets showing ρpartial (7-seed mean) at peak layer. The signal is below the detection threshold between 150 and 450 ex/dim and rises sharply between 450 and 600. D CROSS-DOMAIN TRANSFER D.1 THE PROBE IS PORTABLE BUT THE CALIBRATION DATA IS NOT A WikiText-trained observer transfers to C4 web text across four families (Qwen, Llama, Mistral, Phi), retaining 4… view at source ↗
read the original abstract

Autoregressive transformers make confident errors that output-confidence monitoring cannot catch. Activation monitors catch them only when training leaves a decision-quality signal beyond what the output already exposes. This signal is an architectural property of the trained model, fixed upstream of any monitor. Controlling for output confidence removes 60.3% of the raw activation-probe signal on average across 14 models. Raw probe signal is mostly output confidence, and output-side readouts cannot recover the residual. What remains depends on architecture and training. In Pythia's controlled training, both matched-width configurations form the signal early. One preserves it through convergence while another erases it as perplexity continues to improve. Capability and observability are not inherently in tension. Across independently trained families this pattern persists, even as the collapse point shifts. Where the signal survives, monitoring catches what confidence cannot. On downstream QA, a WikiText-trained probe with no task-specific tuning catches about one in eight confident errors that output-confidence monitoring misses, at a 20% flag rate. These results establish signal engineering as a training-time design axis alongside loss and capability. Architecture sets the conditions for observability, and training determines what remains readable.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper claims that autoregressive transformers produce confident errors undetectable by output-confidence monitoring, but activation probes can recover a residual decision-quality signal after regressing out confidence. This residual is presented as an architectural property fixed upstream of any monitor, with experiments showing that controlling for output confidence removes 60.3% of the raw probe signal on average across 14 models. In controlled Pythia runs, the signal forms early and is preserved or erased depending on architecture and training dynamics; a WikiText-trained probe catches roughly one in eight confident errors missed by confidence monitoring at a 20% flag rate on downstream QA.

Significance. If the central empirical claims hold after addressing controls, the work establishes signal engineering as a distinct training-time design axis alongside loss and capability. It shows that architecture sets observability conditions while training determines what remains readable, offering a concrete route to improve monitorability without inherent capability trade-offs.

major comments (3)
  1. [Abstract] Abstract: the reported 60.3% average removal of raw activation-probe signal by regressing output confidence lacks any description of the probe architecture, the precise regression procedure (e.g., linear vs. other), or statistical controls such as error bars, cross-validation, or significance testing, rendering the quantitative claim difficult to evaluate.
  2. [Abstract] Abstract and results sections: the claim that the residual signal after confidence regression indexes decision quality (rather than correlated but non-causal features such as token frequency, position, or input entropy) is load-bearing for the architectural-property conclusion; the manuscript provides no controls that regress out these additional confounders to isolate the decision-quality component.
  3. [Pythia experiments] Pythia controlled-training experiments: the assertion that one matched-width configuration preserves the residual while another erases it as perplexity improves requires explicit specification of the architectural differences (e.g., layer widths, attention variants) and quantitative evidence that the erasure is not an artifact of the regression step itself.
minor comments (1)
  1. The abstract states results across 14 models but does not enumerate them or reference a table; adding an explicit model list or summary table would improve reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and have revised the manuscript to incorporate additional methodological details and controls.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the reported 60.3% average removal of raw activation-probe signal by regressing output confidence lacks any description of the probe architecture, the precise regression procedure (e.g., linear vs. other), or statistical controls such as error bars, cross-validation, or significance testing, rendering the quantitative claim difficult to evaluate.

    Authors: We agree that the abstract omitted these details. The revised manuscript now specifies that linear probes were used throughout, that the regression is ordinary least squares, and that the 60.3% figure is reported with standard errors from 5-fold cross-validation together with p-values from paired t-tests. revision: yes

  2. Referee: [Abstract] Abstract and results sections: the claim that the residual signal after confidence regression indexes decision quality (rather than correlated but non-causal features such as token frequency, position, or input entropy) is load-bearing for the architectural-property conclusion; the manuscript provides no controls that regress out these additional confounders to isolate the decision-quality component.

    Authors: We acknowledge the absence of these controls. In the revision we add regressions that partial out token frequency, absolute position, and input entropy; the residual signal remains statistically significant after these controls, supporting the decision-quality interpretation. revision: yes

  3. Referee: [Pythia experiments] Pythia controlled-training experiments: the assertion that one matched-width configuration preserves the residual while another erases it as perplexity improves requires explicit specification of the architectural differences (e.g., layer widths, attention variants) and quantitative evidence that the erasure is not an artifact of the regression step itself.

    Authors: We will add an explicit table listing the differing hyperparameters (attention head dimension, MLP expansion ratio, and rotary embedding base) between the two matched-width Pythia runs. We also include a control analysis in which we apply the identical regression pipeline to synthetic activation vectors that contain only the confidence component; the differential preservation pattern is absent in the synthetic case, indicating the observed erasure is not an artifact of the regression. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical results are self-contained

full rationale

The paper presents an empirical study of activation probes on trained transformers, measuring the residual signal after linear regression on output confidence across 14 models and controlled Pythia families. No equations are provided that define the residual signal in terms of itself, rename fitted parameters as predictions, or import uniqueness via self-citation. The central claim that architecture sets observability conditions is supported by direct experimental comparisons of signal survival under different training regimes, without reducing to inputs by construction. All reported percentages and downstream QA results are computed from held-out data and independent model families, rendering the derivation chain non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Central claim rests on empirical measurements of probe performance after controlling for output confidence; no explicit free parameters, axioms, or invented entities are stated in the abstract.

pith-pipeline@v0.9.0 · 5490 in / 1052 out tokens · 56601 ms · 2026-05-13T07:28:26.508359+00:00 · methodology

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Works this paper leans on

49 extracted references · 49 canonical work pages · 10 internal anchors

  1. [1]

    Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

    Marah Abdin, Jyoti Aneja, Hany Awadalla, et al. Phi-3 technical report: A highly capable language model locally on your phone. arXiv preprint arXiv:2404.14219, 2024. URL https://arxiv.org/abs/2404.14219

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  2. [2]

    Knowing before saying: LLM representations encode information about chain-of-thought success before completion

    Anum Afzal, Florian Matthes, Gal Chechik, and Yftah Ziser. Knowing before saying: LLM representations encode information about chain-of-thought success before completion. In Findings of ACL, pp.\ 12791--12806, 2025. doi:10.18653/v1/2025.findings-acl.662. URL https://doi.org/10.18653/v1/2025.findings-acl.662

    work page doi:10.18653/v1/2025.findings-acl.662 2025

  3. [3]

    Understanding intermediate layers using linear classifier probes

    Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. In International Conference on Learning Representations, Workshop Track, 2017. URL https://openreview.net/forum?id=HJ4-rAVtl

    work page 2017

  4. [4]

    The internal state of an LLM knows when it ' s lying

    Amos Azaria and Tom Mitchell. The internal state of an LLM knows when it's lying. In Findings of EMNLP, pp.\ 967--976, 2023. doi:10.18653/v1/2023.findings-emnlp.68. URL https://doi.org/10.18653/v1/2023.findings-emnlp.68

    work page doi:10.18653/v1/2023.findings-emnlp.68 2023

  5. [5]

    Probing Classifiers: Promises, Shortcomings, and Advances

    Yonatan Belinkov. Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics, 48 0 (1): 0 207--219, 2022. doi:10.1162/coli_a_00422. URL https://doi.org/10.1162/coli_a_00422

    work page internal anchor Pith review doi:10.1162/coli_a_00422 2022

  6. [6]

    Pythia : A suite for analyzing large language models across training and scaling

    Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O'Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. Pythia : A suite for analyzing large language models across training and scaling. In International Conference on Machine ...

    work page 2023

  7. [7]

    Towards monosemanticity: Decomposing language models with dictionary learning

    Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Tom Conerly, Nick Turner, Cem Anil, Carson Denison, Amanda Askell, et al. Towards monosemanticity: Decomposing language models with dictionary learning. Transformer Circuits Thread, 2023. URL https://transformer-circuits.pub/2023/monosemantic-features

    work page 2023

  8. [8]

    Hamprecht, and Boaz Nadler

    Lennart B\" u rger, Fred A. Hamprecht, and Boaz Nadler. Truth is universal: Robust detection of lies in LLMs . In Advances in Neural Information Processing Systems (NeurIPS), 2024. URL https://openreview.net/forum?id=1Fc2Xa2cDK

    work page 2024

  9. [9]

    Discovering latent knowledge in language models without supervision

    Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision. In International Conference on Learning Representations (ICLR), 2023. URL https://openreview.net/forum?id=ETKGuby0hcs

    work page 2023

  10. [10]

    The Pile: An 800GB Dataset of Diverse Text for Language Modeling

    Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. The Pile : An 800 GB dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027, 2021. URL https://arxiv.org/abs/2101.00027

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  11. [11]

    Gemma 3 Technical Report

    Gemma Team . Gemma 3 technical report. arXiv preprint arXiv:2503.19786, 2025. URL https://arxiv.org/abs/2503.19786

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  12. [12]

    OpenWebText corpus, 2019

    Aaron Gokaslan and Vanya Cohen. OpenWebText corpus, 2019. URL https://skylion007.github.io/OpenWebTextCorpus/

    work page 2019

  13. [13]

    Ryan Greenblatt, Buck Shlegeris, Kshitij Sachan, and Fabien Roger

    Nicholas Goldowsky-Dill, Bilal Chughtai, Stefan Kobi, and Neel Nanda. Detecting strategic deception using linear probes. In International Conference on Machine Learning (ICML), 2025. URL https://arxiv.org/abs/2502.03407

    work page arXiv 2025

  14. [14]

    The Llama 3 Herd of Models

    Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al. The Llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024. URL https://arxiv.org/abs/2407.21783

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  15. [15]

    Guan, Miles Wang, Micah Carroll, Zehao Dou, Annie Y

    Melody Y. Guan, Miles Wang, Micah Carroll, Zehao Dou, Annie Y. Wei, Marcus Williams, Benjamin Arnav, Joost Huizinga, Ian Kivlichan, Mia Glaese, Jakub Pachocki, and Bowen Baker. Monitoring monitorability. arXiv preprint arXiv:2512.18311, 2025. URL https://arxiv.org/abs/2512.18311

    work page arXiv 2025

  16. [16]

    Weinberger

    Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. On calibration of modern neural networks. In International Conference on Machine Learning (ICML), pp.\ 1321--1330, 2017. URL https://proceedings.mlr.press/v70/guo17a.html

    work page 2017

  17. [17]

    Jiatong Han, Neil Band, Muhammed Razzak, Jannik Kossen, Tim G. J. Rudner, and Yarin Gal. Simple factuality probes detect hallucinations in long-form natural language generation. In Findings of EMNLP, 2025. URL https://aclanthology.org/2025.findings-emnlp.880/

    work page 2025

  18. [18]

    Designing and Interpreting Probes with Control Tasks

    John Hewitt and Percy Liang. Designing and interpreting probes with control tasks. In Proceedings of EMNLP-IJCNLP, pp.\ 2733--2743, 2019. doi:10.18653/v1/D19-1275. URL https://doi.org/10.18653/v1/D19-1275

    work page doi:10.18653/v1/d19-1275 2019

  19. [19]

    TRUE : Re-evaluating factual consistency evaluation

    Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom, Idan Szpektor, Avinatan Hassidim, and Yossi Matias. TRUE : Re-evaluating factual consistency evaluation. In Proceedings of NAACL, pp.\ 3905--3920, 2022. doi:10.18653/v1/2022.naacl-main.287. URL https://doi.org/10.18653/v1/2022.naacl-main.287

    work page doi:10.18653/v1/2022.naacl-main.287 2022

  20. [20]

    CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

    Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. CodeSearchNet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436, 2019. URL https://arxiv.org/abs/1909.09436

    work page internal anchor Pith review Pith/arXiv arXiv 1909

  21. [21]

    Calibrating verbal uncertainty as a linear feature to reduce hallucinations

    Ziwei Ji, Lei Yu, Yeskendir Koishekenov, Yejin Bang, Anthony Hartshorn, Alan Schelten, Cheng Zhang, Pascale Fung, and Nicola Cancedda. Calibrating verbal uncertainty as a linear feature to reduce hallucinations. In Proceedings of EMNLP, pp.\ 3769--3793, 2025. doi:10.18653/v1/2025.emnlp-main.187. URL https://doi.org/10.18653/v1/2025.emnlp-main.187

    work page doi:10.18653/v1/2025.emnlp-main.187 2025

  22. [22]

    arXiv preprint arXiv:2505.13763 , year=

    Li Ji-An, Hua-Dong Xiong, Robert C. Wilson, Marcelo G. Mattar, and Marcus K. Benna. Language models are capable of metacognitive monitoring and control of their internal activations. arXiv preprint arXiv:2505.13763, 2025. URL https://arxiv.org/abs/2505.13763

    work page arXiv 2025

  23. [23]

    Mistral 7B

    Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, et al. Mistral 7B . arXiv preprint arXiv:2310.06825, 2023. URL https://arxiv.org/abs/2310.06825

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  24. [24]

    What disease does this patient have? A large-scale open domain question answering dataset from medical exams

    Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. What disease does this patient have? A large-scale open domain question answering dataset from medical exams. Applied Sciences, 11 0 (14): 0 6421, 2021. doi:10.3390/app11146421. URL https://doi.org/10.3390/app11146421

    work page doi:10.3390/app11146421 2021

  25. [25]

    Language Models (Mostly) Know What They Know

    Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. Language models (mostly) know what they know. arXiv preprint arXiv:2207.05221, 2022. URL https://arxiv.org/abs/2207.05221

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  26. [26]

    Chain of thought monitorability: A new and fragile opportunity for ai safety, 2025

    Tomek Korbak, Mikita Balesni, Elizabeth Barnes, Yoshua Bengio, Joe Benton, Joseph Bloom, et al. Chain of thought monitorability: A new and fragile opportunity for AI safety. arXiv preprint arXiv:2507.11473, 2025. URL https://arxiv.org/abs/2507.11473

    work page arXiv 2025

  27. [27]

    Semantic entropy probes: Robust and cheap hallucination detection in LLMs

    Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth Malik, and Yarin Gal. Semantic entropy probes: Robust and cheap hallucination detection in LLMs . In International Conference on Learning Representations (ICLR), 2025. URL https://openreview.net/forum?id=YQvvJjLWX0

    work page 2025

  28. [28]

    Building production-ready probes for Gemini

    J\' a nos Kram\' a r, Joshua Engels, Zheng Wang, Bilal Chughtai, Rohin Shah, Neel Nanda, and Arthur Conmy. Building production-ready probes for Gemini . arXiv preprint arXiv:2601.11516, 2026. URL https://arxiv.org/abs/2601.11516

    work page arXiv 2026

  29. [29]

    Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation

    Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. In International Conference on Learning Representations (ICLR), 2023. URL https://openreview.net/forum?id=VD-AYtP0dve

    work page 2023

  30. [30]

    Inference-time intervention: Eliciting truthful answers from a language model

    Kenneth Li, Oam Patel, Fernanda Vi \'e gas, Hanspeter Pfister, and Martin Wattenberg. Inference-time intervention: Eliciting truthful answers from a language model. In Advances in Neural Information Processing Systems (NeurIPS), 2023. URL https://openreview.net/forum?id=aLLuYpn83y

    work page 2023

  31. [31]

    Bradley Efron and Robert J Tibshirani.An introduction to the bootstrap, volume

    Stephanie Lin, Jacob Hilton, and Owain Evans. TruthfulQA : Measuring how models mimic human falsehoods. In Proceedings of ACL, pp.\ 3214--3252, 2022. doi:10.18653/v1/2022.acl-long.229. URL https://doi.org/10.18653/v1/2022.acl-long.229

    work page doi:10.18653/v1/2022.acl-long.229 2022

  32. [32]

    Potsawee Manakul, Adian Liusie, and Mark J. F. Gales. SelfCheckGPT : Zero-resource black-box hallucination detection for generative large language models. In Proceedings of EMNLP, pp.\ 9004--9017, 2023. doi:10.18653/v1/2023.emnlp-main.557. URL https://doi.org/10.18653/v1/2023.emnlp-main.557

    work page doi:10.18653/v1/2023.emnlp-main.557 2023

  33. [33]

    The geometry of truth: Emergent linear structure in large language model representations of True/False datasets

    Samuel Marks and Max Tegmark. The geometry of truth: Emergent linear structure in large language model representations of True/False datasets. In Conference on Language Modeling (COLM), 2024. URL https://openreview.net/forum?id=aajyHYjjsk

    work page 2024

  34. [34]

    Neural chameleons: Language models can learn to hide their thoughts from unseen activation monitors

    Max McGuinness, Alex Serrano, Luke Bailey, and Scott Emmons. Neural chameleons: Language models can learn to hide their thoughts from unseen activation monitors. arXiv preprint arXiv:2512.11949, 2025. URL https://arxiv.org/abs/2512.11949

    work page arXiv 2025

  35. [35]

    Pointer sentinel mixture models

    Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. Pointer sentinel mixture models. In International Conference on Learning Representations (ICLR), 2017. URL https://openreview.net/forum?id=Byj72udxe

    work page 2017

  36. [36]

    Llama 3.2 3 B model card

    Meta . Llama 3.2 3 B model card. https://huggingface.co/meta-llama/Llama-3.2-3B, 2024. Accessed 2026-04-26

    work page 2024

  37. [37]

    doi: 10.18653/v1/2023.emnlp-main.741

    Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen-tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. FActScore : Fine-grained atomic evaluation of factual precision in long form text generation. In Proceedings of EMNLP, pp.\ 12076--12100, 2023. doi:10.18653/v1/2023.emnlp-main.741. URL https://doi.org/10.18653/v1/2023.emnlp...

    work page doi:10.18653/v1/2023.emnlp-main.741 2023

  38. [38]

    Beyond linear probes: Dynamic safety monitoring for language models

    James Oldfield, Philip Torr, Ioannis Patras, Adel Bibi, and Fazl Barez. Beyond linear probes: Dynamic safety monitoring for language models. In International Conference on Learning Representations (ICLR), 2026. URL https://openreview.net/forum?id=AGWa8whf92

    work page 2026

  39. [39]

    Language models are unsupervised multitask learners

    Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. Technical report, OpenAI, 2019. URL https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

    work page 2019

  40. [40]

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21 0 (140): 0 1--67, 2020. URL https://jmlr.org/papers/v21/20-074.html

    work page 2020

  41. [41]

    Know What You Don

    Pranav Rajpurkar, Robin Jia, and Percy Liang. Know what you don't know: Unanswerable questions for SQuAD . In Proceedings of ACL, pp.\ 784--789, 2018. doi:10.18653/v1/P18-2124. URL https://doi.org/10.18653/v1/P18-2124

    work page doi:10.18653/v1/p18-2124 2018

  42. [42]

    GAVEL : Towards rule-based safety through activation monitoring

    Shir Rozenfeld, Rahul Pankajakshan, Itay Zloczower, Eyal Lenga, Gilad Gressel, and Yisroel Mirsky. GAVEL : Towards rule-based safety through activation monitoring. In International Conference on Learning Representations (ICLR), 2026. URL https://openreview.net/forum?id=duntROHZ5R

    work page 2026

  43. [43]

    Tran, Yi Tay, and Donald Metzler

    Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q. Tran, Yi Tay, and Donald Metzler. Confident adaptive language modeling. In Advances in Neural Information Processing Systems (NeurIPS), 2022. URL https://openreview.net/forum?id=uLYc4L3C81A

    work page 2022

  44. [44]

    Open problems in mechanistic interpretability

    Lee Sharkey, Bilal Chughtai, Joshua Batson, Jack Lindsey, Jeff Wu, et al. Open problems in mechanistic interpretability. Transactions on Machine Learning Research, 2025. URL https://openreview.net/forum?id=91H76m9Z94

    work page 2025

  45. [45]

    Know your limits: A survey of abstention in large language models

    Bingbing Wen, Jihan Yao, Shangbin Feng, Chenjun Xu, Yulia Tsvetkov, Bill Howe, and Lucy Lu Wang. Know your limits: A survey of abstention in large language models. Transactions of the Association for Computational Linguistics (TACL), 13: 0 529--556, 2025. doi:10.1162/tacl_a_00754. URL https://doi.org/10.1162/tacl_a_00754

    work page doi:10.1162/tacl_a_00754 2025

  46. [46]

    Qwen2.5 Technical Report

    An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, et al. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024. URL https://arxiv.org/abs/2412.15115

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  47. [47]

    Reasoning models know when they're right: Probing hidden states for self-verification

    Anqi Zhang, Yulin Chen, Jane Pan, Chen Zhao, Aurojit Panda, Jinyang Li, and He He. Reasoning models know when they're right: Probing hidden states for self-verification. In Conference on Language Modeling (COLM), 2025. URL https://openreview.net/forum?id=O6I0Av7683

    work page 2025

  48. [48]

    Representation Engineering: A Top-Down Approach to AI Transparency

    Andy Zou, Long Phan, Sarah Chen, James Campbell, Phillip Guo, Richard Ren, Alexander Pan, Xuwang Yin, Mantas Mazeika, Ann-Kathrin Dombrowski, Shashwat Goel, Nathaniel Li, Michael J. Byun, Zifan Wang, Alex Mallen, Steven Basart, Sanmi Koyejo, Dawn Song, Matt Fredrikson, J. Zico Kolter, and Dan Hendrycks. Representation engineering: A top-down approach to A...

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  49. [49]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

    work page