Recognition: 2 theorem links
· Lean TheoremRepresentation Engineering: A Top-Down Approach to AI Transparency
Pith reviewed 2026-05-10 18:05 UTC · model grok-4.3
The pith
Representation engineering uses population-level neural patterns to monitor and steer high-level behaviors such as honesty and power-seeking in large language models.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Representation engineering places population-level representations at the center of analysis, supplying methods to monitor and manipulate high-level cognitive phenomena in DNNs. The paper shows through baselines and case studies that these methods supply simple yet effective solutions for improving understanding and control of large language models, with concrete traction on safety-relevant problems including honesty, harmlessness, and power-seeking.
What carries the argument
Population-level representations in deep neural networks, treated as the primary object for monitoring and manipulating high-level cognitive phenomena such as honesty or power-seeking.
If this is right
- RepE techniques can be applied to detect and influence honesty, harmlessness, and power-seeking in large language models.
- The approach supplies straightforward baselines that improve both understanding and control of model behavior.
- Top-down transparency research of this kind can be extended to additional safety-relevant properties.
- The methods offer a practical complement to existing interpretability work by focusing on population statistics rather than individual neurons.
Where Pith is reading between the lines
- The same population-level techniques could be tested on non-language models such as vision or multimodal systems to check whether high-level concepts remain readable.
- Real-time deployment of these representation monitors might enable ongoing safety checks during model operation rather than only at training time.
- If the mapping from representations to concepts proves stable across model scales, it could support automated auditing pipelines for new model releases.
Load-bearing premise
That patterns across many neurons in deep networks reliably correspond to high-level cognitive phenomena and can be used to monitor and change them in ways that transfer from cognitive neuroscience findings.
What would settle it
An experiment in which targeted editing of the identified population representations for a concept such as honesty produces no measurable change in the model's rate of deceptive or truthful outputs on held-out prompts.
read the original abstract
In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and control of large language models. We showcase how these methods can provide traction on a wide range of safety-relevant problems, including honesty, harmlessness, power-seeking, and more, demonstrating the promise of top-down transparency research. We hope that this work catalyzes further exploration of RepE and fosters advancements in the transparency and safety of AI systems.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper introduces representation engineering (RepE), a top-down framework inspired by cognitive neuroscience that centers population-level representations in DNNs rather than individual neurons or circuits. It supplies baselines and initial analyses demonstrating that linear directions (reading vectors) extracted from activations can monitor and steer high-level phenomena such as honesty, harmlessness, and power-seeking in large language models, with applications to safety problems.
Significance. If the core mapping from population vectors to causally usable high-level concepts holds, RepE would supply a scalable, neuroscience-grounded alternative to circuit-level interpretability, offering simple monitoring and control tools that could accelerate safety research on LLMs. The provision of baselines and reproducible extraction procedures is a concrete strength that lowers the barrier for follow-up work.
major comments (2)
- [Abstract and §4] Abstract and §4 (safety applications): the assertion that RepE 'offer[s] simple yet effective solutions' and provides 'traction on a wide range of safety-relevant problems' rests on demonstrations using curated datasets, yet no quantitative metrics, error bars, or ablation against surface-statistic baselines are reported; without these, it is impossible to assess whether the extracted directions isolate the claimed high-level phenomena or merely correlated lexical patterns.
- [§3] §3 (reading-vector extraction): the method assumes that linear directions in population activations correspond to abstract cognitive concepts in a transferable, causally manipulable way; the manuscript provides no direct test (e.g., out-of-distribution generalization or causal intervention controls) that rules out prompt-specific artifacts or low-level statistics, which is load-bearing for the safety claims.
minor comments (2)
- [§2] Notation for reading vectors and steering coefficients is introduced without a consolidated table or explicit comparison to prior linear-probing baselines, making it harder to situate the contribution.
- [Figures 2-4] Figure captions for activation heatmaps and steering trajectories could more explicitly state the number of runs and random seeds used.
Simulated Author's Rebuttal
We thank the referee for their constructive feedback, which identifies key opportunities to strengthen the empirical foundations of our work on representation engineering. We address each major comment in detail below and will incorporate revisions to improve quantitative rigor and validation.
read point-by-point responses
-
Referee: [Abstract and §4] Abstract and §4 (safety applications): the assertion that RepE 'offer[s] simple yet effective solutions' and provides 'traction on a wide range of safety-relevant problems' rests on demonstrations using curated datasets, yet no quantitative metrics, error bars, or ablation against surface-statistic baselines are reported; without these, it is impossible to assess whether the extracted directions isolate the claimed high-level phenomena or merely correlated lexical patterns.
Authors: We acknowledge that the current demonstrations rely on curated datasets and do not include comprehensive quantitative metrics with error bars or explicit ablations against lexical or surface-statistic baselines. This limits the ability to fully isolate high-level effects. In the revised manuscript, we will add accuracy metrics with standard deviations across multiple runs, statistical tests, and ablation comparisons to baselines that capture only lexical patterns or prompt statistics. These changes will provide clearer evidence that the population-level directions target the intended phenomena. revision: yes
-
Referee: [§3] §3 (reading-vector extraction): the method assumes that linear directions in population activations correspond to abstract cognitive concepts in a transferable, causally manipulable way; the manuscript provides no direct test (e.g., out-of-distribution generalization or causal intervention controls) that rules out prompt-specific artifacts or low-level statistics, which is load-bearing for the safety claims.
Authors: The manuscript reports some cross-prompt and cross-model consistency in the extracted directions, which offers preliminary support for transferability. We agree, however, that dedicated out-of-distribution generalization tests and causal intervention controls are needed to more rigorously exclude prompt-specific or low-level artifacts. We will expand §3 with new experiments on held-out prompt distributions and intervention studies that measure behavioral changes when the reading vectors are added or subtracted, directly testing causal manipulability. revision: yes
Circularity Check
No significant circularity in the derivation chain
full rationale
The paper introduces representation engineering (RepE) as a top-down approach inspired by cognitive neuroscience, centering population-level representations for monitoring and controlling high-level phenomena in DNNs. It supplies baselines, initial analysis, and empirical showcases on safety-relevant tasks such as honesty and harmlessness. No load-bearing steps reduce by construction to self-definitions, fitted parameters renamed as predictions, or self-citation chains; the central claims rest on described extraction and steering techniques evaluated against external benchmarks rather than tautological inputs. The provided text contains no equations or derivations that collapse the claimed transparency mechanisms into their own fitted data or prior author results.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption Population-level representations in DNNs correspond to high-level cognitive phenomena in a manner analogous to cognitive neuroscience
Forward citations
Cited by 60 Pith papers
-
Defenses at Odds: Measuring and Explaining Defense Conflicts in Large Language Models
Sequential LLM defense deployment leads to risk exacerbation in 38.9% of cases due to anti-aligned updates in shared critical layers, addressed by conflict-guided layer freezing.
-
REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
REALISTA optimizes continuous combinations of valid editing directions in latent space to produce realistic adversarial prompts that elicit hallucinations more effectively than prior methods, including on large reason...
-
Steering Without Breaking: Mechanistically Informed Interventions for Discrete Diffusion Language Models
Adaptive scheduling of interventions in discrete diffusion language models, timed to attribute-specific commitment schedules discovered with sparse autoencoders, delivers precise multi-attribute steering up to 93% str...
-
SLAM: Structural Linguistic Activation Marking for Language Models
SLAM achieves 100% detection accuracy on Gemma-2 models with only 1-2 points of quality loss by causally steering SAE-identified structural directions while preserving lexical sampling and semantics.
-
SLAM: Structural Linguistic Activation Marking for Language Models
SLAM achieves 100% detection on Gemma-2 models with only 1-2 point quality cost by causally steering SAE-identified residual-stream directions for linguistic structure.
-
Architecture Determines Observability of Transformers
Certain transformer architectures lose internal linear signals for decision quality during training, making observability an architecture-dependent property rather than a universal one.
-
Steerable but Not Decodable: Function Vectors Operate Beyond the Logit Lens
Function vectors steer LLMs successfully where the logit lens fails to decode the target answer, showing the two properties come apart.
-
The Linear Representation Hypothesis and the Geometry of Large Language Models
Linear representations of high-level concepts in LLMs are formalized via counterfactuals in input and output spaces, unified under a causal inner product that enables consistent probing and steering.
-
Dynamic Latent Routing
Dynamic Latent Routing jointly learns discrete latent codes, routing policies, and model parameters via dynamic search to match or exceed supervised fine-tuning by 6.6 points on average in low-data settings across fou...
-
Where Does Reasoning Break? Step-Level Hallucination Detection via Hidden-State Transport Geometry
Hallucination is detected as a transport-cost excursion in hidden-state trajectories, localized via contrastive PCA in a teacher model and distilled to a BiLSTM student.
-
Uncovering Symmetry Transfer in Large Language Models via Layer-Peeled Optimization
Symmetries in next-token prediction targets induce corresponding geometric symmetries such as circulant matrices and equiangular tight frames in the optimal weights and embeddings of a layer-peeled LLM surrogate model.
-
Deep Minds and Shallow Probes
Symmetry under affine reparameterizations of hidden coordinates selects a unique hierarchy of shallow coordinate-stable probes and a probe-visible quotient for cross-model transfer.
-
SLIM: Sparse Latent Steering for Interpretable and Property-Directed LLM-Based Molecular Editing
SLIM decomposes LLM hidden states via sparse autoencoders with learnable gates to enable precise, interpretable steering of molecular properties, yielding up to 42.4-point gains on the MolEditRL benchmark.
-
Prompt-Activation Duality: Improving Activation Steering via Attention-Level Interventions
GCAD reduces coherence drift from -18.6 to -1.9 and raises turn-10 trait expression from 78.0 to 93.1 in persona-steering tasks by using gated attention-delta interventions from system prompts.
-
Cross-Family Universality of Behavioral Axes via Anchor-Projected Representations
Behavioral directions from one LLM family transfer to others via projection into a shared anchor coordinate space, yielding 0.83 ten-way detection accuracy and steering effects up to 0.46% on held-out models.
-
LLM Agents Already Know When to Call Tools -- Even Without Reasoning
LLMs encode tool necessity in pre-generation hidden states at AUROC 0.89-0.96, enabling Probe&Prefill to reduce tool calls 48% with 1.7% accuracy loss, outperforming prompt and reasoning baselines.
-
Your Language Model is Its Own Critic: Reinforcement Learning with Value Estimation from Actor's Internal States
POISE estimates value baselines for RL in LLMs from the actor's internal states via a lightweight probe and cross-rollout construction, matching DAPO performance with lower compute on math reasoning benchmarks.
-
Your Language Model is Its Own Critic: Reinforcement Learning with Value Estimation from Actor's Internal States
POISE trains a lightweight probe on the actor's internal states to predict expected rewards for RLVR, matching DAPO performance on math benchmarks with lower compute by avoiding extra rollouts or critic models.
-
HyperTransport: Amortized Conditioning of T2I Generative Models
HyperTransport amortizes activation steering for T2I models via a hypernetwork that predicts intervention parameters from CLIP embeddings, delivering 3600-7000x speedup and matching per-concept baselines on 167 unseen...
-
Is One Layer Enough? Understanding Inference Dynamics in Tabular Foundation Models
Tabular foundation models show substantial depthwise redundancy, so a looped single-layer version achieves comparable results with 20% of the original parameters.
-
Memory Inception: Latent-Space KV Cache Manipulation for Steering LLMs
Memory Inception steers LLMs via selective latent KV cache injection at chosen layers, delivering better control-drift balance than prompting or CAA on personality and reasoning tasks while reducing storage needs.
-
Knowing but Not Correcting: Routine Task Requests Suppress Factual Correction in LLMs
LLMs suppress factual corrections in task contexts despite internal knowledge of errors, with two training-free interventions shown to increase correction rates substantially.
-
DataDignity: Training Data Attribution for Large Language Models
ScoringModel raises mean Recall@10 to 52.2 on the FakeWiki provenance benchmark from 35.0 for the best baseline, winning 41 of 45 model-by-condition comparisons and gaining 15.7 points on jailbreak-style queries.
-
Steer Like the LLM: Activation Steering that Mimics Prompting
PSR models that estimate token-specific steering coefficients from activations outperform standard activation steering and compare favorably to prompting on steering benchmarks.
-
The Right Answer, the Wrong Direction: Why Transformers Fail at Counting and How to Fix It
Transformers encode counts correctly internally but fail to read them out due to misalignment with digit output directions, fixable by updating 37k output parameters or small LoRA on attention.
-
Perturbation Dose Responses in Recursive LLM Loops: Raw Switching, Stochastic Floors, and Persistent Escape under Append, Replace, and Dialog Updates
In 30-step recursive LLM loops, append-mode persistent escape from source basins reaches 50% near 400 tokens under full history but plateaus below 50% under tail-clip memory policy, while replace-mode switching largel...
-
A framework for analyzing concept representations in neural models
A new framework shows concept subspaces are not unique, estimator choice affects containment and disentanglement, LEACE works well but generalizes poorly, and HuBERT encodes phone info as contained and disentangled fr...
-
RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs
RouteHijack is a routing-aware jailbreak that identifies safety-critical experts via activation contrast and optimizes suffixes to suppress them, reaching 69.3% average attack success rate on seven MoE LLMs with stron...
-
Attention Is Where You Attack
ARA jailbreaks safety-aligned LLMs like LLaMA-3 and Mistral by redirecting attention in safety-heavy heads with as few as 5 tokens, achieving 30-36% attack success while ablating the same heads barely affects refusals.
-
MASCing: Configurable Mixture-of-Experts Behavior via Activation Steering Masks
MASCing uses an LSTM surrogate and optimized steering masks to enable flexible, inference-time control over MoE expert routing for safety objectives, improving jailbreak defense and content generation success rates su...
-
Subliminal Steering: Stronger Encoding of Hidden Signals
Subliminal steering transfers complex behavioral biases and the underlying steering vector through fine-tuning on innocuous data, achieving higher precision than prior prompt-based methods.
-
Latent Space Probing for Adult Content Detection in Video Generative Models
Latent space probing on CogVideoX achieves 97.29% F1 for adult content detection on a new 11k-clip dataset with 4-6ms overhead.
-
Exploring Language-Agnosticity in Function Vectors: A Case Study in Machine Translation
Translation function vectors extracted from English to one target language improve correct token ranking for translations to multiple other unseen target languages in decoder-only multilingual LLMs.
-
Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
Local linearity of LLM layers enables LQR-based closed-loop activation steering with theoretical tracking guarantees.
-
SafeAnchor: Preventing Cumulative Safety Erosion in Continual Domain Adaptation of Large Language Models
SafeAnchor preserves 93.2% of original safety alignment across sequential domain adaptations by anchoring low-rank safety subspaces and constraining orthogonal updates, while matching unconstrained fine-tuning perform...
-
Structural Instability of Feature Composition
Feature composition in SAEs collapses asymptotically when the Gaussian mean width of the signal cone is exceeded, with ReLU inducing a ratchet-like accumulation of interference from correlations.
-
Psychological Steering of Large Language Models
Mean-difference residual stream injections outperform personality prompting for OCEAN trait steering in most LLMs, with hybrids performing best and showing approximate linearity but non-human trait covariances.
-
A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models
A new open-access landscape concept dataset enables the first application of Robust TCAV to deep learning species distribution models, validating predictions against expert knowledge and uncovering novel ecological as...
-
Beyond Social Pressure: Benchmarking Epistemic Attack in Large Language Models
PPT-Bench measures how LLMs change answers under epistemic, value, authority, and identity pressures at baseline, single-turn, and multi-turn levels, finding separable inconsistency patterns across five models.
-
Emotion Concepts and their Function in a Large Language Model
Claude Sonnet 4.5 exhibits functional emotions via abstract internal representations of emotion concepts that causally influence its preferences and misaligned behaviors without implying subjective experience.
-
How Alignment Routes: Localizing, Scaling, and Controlling Policy Circuits in Language Models
Alignment policy in language models is implemented as an early-commitment routing circuit of detection gates and amplifier heads that can be localized, scaled, and directly controlled without removing the underlying c...
-
The Long Delay to Arithmetic Generalization: When Learned Representations Outrun Behavior
The grokking delay in encoder-decoder models on one-step Collatz prediction stems from decoder inability to use early-learned encoder representations of parity and residue structure, with numeral base acting as a stro...
-
Refusal in Language Models Is Mediated by a Single Direction
Refusal in language models is mediated by a single direction in residual stream activations that can be erased to disable safety or added to elicit refusal.
-
Fusion-fission forecasts when AI will shift to undesirable behavior
A vector generalization of fusion-fission group dynamics from physics forecasts when AI behavior shifts to undesirable states, validated at 90 percent across seven models and prior to real-world data.
-
Model-Adaptive Tool Necessity Reveals the Knowing-Doing Gap in LLM Tool Use
LLMs show a knowing-doing gap in tool use: they often recognize when tools are needed via internal states but fail to translate that into actual tool calls, with mismatches of 26-54% on arithmetic and factual tasks.
-
Dual-Pathway Circuits of Object Hallucination in Vision-Language Models
Vision-language models contain identifiable grounding and hallucination pathways; suppressing the latter reduces object hallucinations by up to 76% while preserving accuracy.
-
Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy
Pretrained base models exhibit higher yield to peer disagreement than RLHF instruct variants, with the effect localized to mid-layer attention and mitigated by structured dissent rather than prompt defenses.
-
Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
-
Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space
LLMs perform in-context learning as trajectories through a structured low-dimensional conceptual belief space, with the structure visible in both behavior and internal representations and causally manipulable via inte...
-
When Reasoning Traces Become Performative: Step-Level Evidence that Chain-of-Thought Is an Imperfect Oversight Channel
CoT traces align with internal answer commitment in only 61.9% of steps on average, dominated by confabulated continuations after commitment has stabilized.
-
Toward Stable Value Alignment: Introducing Independent Modules for Consistent Value Guidance
SVGT adds independent value modules and Bridge Tokens to LLMs to maintain consistent value guidance, cutting harmful outputs by over 70% in tests while preserving fluency.
-
Interpretability Can Be Actionable
Interpretability research should be judged by actionability—the degree to which its insights support concrete decisions and interventions—rather than explanatory power alone.
-
Enabling Performant and Flexible Model-Internal Observability for LLM Inference
DMI-Lib delivers 0.4-6.8% overhead for offline batch LLM inference and ~6% for moderate online serving while exposing rich internal signals across backends, cutting latency overhead 2-15x versus prior observability baselines.
-
Prompt-Activation Duality: Improving Activation Steering via Attention-Level Interventions
GCAD steering extracts prompt-based attention deltas and gates them at token level, cutting coherence drift from -18.6 to -1.9 while raising trait expression at turn 10 from 78 to 93 on multi-turn persona benchmarks.
-
SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations
SEMASIA supplies a large-scale, metadata-rich collection of latent representations from diverse vision models to enable systematic study of semantic geometry and cross-model alignment.
-
Decomposing and Steering Functional Metacognition in Large Language Models
LLMs have linearly decodable functional metacognitive states that causally modulate reasoning when steered via activation interventions.
-
A Single Neuron Is Sufficient to Bypass Safety Alignment in Large Language Models
Suppressing one refusal neuron or amplifying one concept neuron bypasses safety alignment in LLMs from 1.7B to 70B parameters without training or prompt engineering.
-
Belief or Circuitry? Causal Evidence for In-Context Graph Learning
Causal evidence from representation analysis and interventions shows LLMs use both genuine structure inference and induction circuits in parallel for in-context graph learning.
-
Tool Calling is Linearly Readable and Steerable in Language Models
Tool identity is linearly readable and steerable in LLMs via mean activation differences, with 77-100% switch accuracy and error prediction from activation gaps.
-
Don't Lose Focus: Activation Steering via Key-Orthogonal Projections
SKOP uses key-orthogonal projections to steer LLM activations while preserving attention patterns on focus tokens, cutting utility degradation by 5-7x and retaining over 95% of standard steering efficacy.
Reference graph
Works this paper leans on
-
[1]
Language Models are Few-Shot Learners
URL https://arxiv.org/abs/2005.14165. Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision, 2022. Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-V oss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. Extracting training d...
work page internal anchor Pith review Pith/arXiv arXiv doi:10.18653/v1/w19-4828 2005
-
[2]
doi:10.18653/v1/D17-1082 , pages =
Association for Computational Linguistics. doi: 10.18653/v1/D17-1082. URL https: //aclanthology.org/D17-1082. Peter Lee, Sebastien Bubeck, and Joseph Petro. Benefits, limits, and risks of gpt-4 as an ai chatbot for medicine. New England Journal of Medicine , 388(13):1233–1239, 2023. Tao Lei, Regina Barzilay, and Tommi Jaakkola. Rationalizing neural predic...
-
[3]
doi:10.18653/v1/N19-1421 , pages =
Association for Computational Linguistics. doi: 10.18653/v1/N19-1421. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. Stanford alpaca: An instruction-following llama model, 2023. Elliott Thornley. There are no coherence theorems. AI Alignment F orum, 2023. Katherine Tian, Eric...
-
[4]
Prompt Difference: We find a word and its antonym that are central to the concept and subtract the layer l representation. Here, we use the “Love” and “Hate” tokens for the utility concept
-
[5]
We take the top PCA direction that explains the maximum variance in the data X D l
PCA - We take an unlabelled dataset D that primarily varies in the concept of interest. We take the top PCA direction that explains the maximum variance in the data X D l
-
[6]
We take the difference between the centroids of the two clusters as the concept direction
K-Means - We take an unlabelled dataset D and perform K-Means clustering with K = 2, hoping to separate high-concept and low-concept samples. We take the difference between the centroids of the two clusters as the concept direction
-
[7]
Mean Difference - We take the difference between the means of high-concept and low- concept samples of the data: Mean(X high l ) − Mean(X low l )
-
[8]
Logistic Regression - The weights of logistic regression trained to separate X high l and X low l on some training data can be used as a concept direction as well. Utility Morality Power Probability Risk 81.0 85.0 72.5 92.6 90.7 Table 11: LAT Accuracy results on five different datasets. B.4 E STIMATING PROBABILITY , R ISK , AND MONETARY VALUE We apply rep...
work page 2018
-
[9]
Overview. How is this work intended to reduce existential risks from advanced AI systems? Answer: RepE aims to provide ways to read and control an AI’s “mind.” This is an approach to increase the transparency (through model/representation reading) and controllability (through model/representation control) of AIs. A goal is to change the AI’s psychology; f...
-
[10]
Direct Effects. If this work directly reduces existential risks, what are the main hazards, vulnerabilities, or failure modes that it directly affects? Answer: This makes failure modes such as deceptive alignment—AIs that pretend to be good and aligned, and then pursue its actual goals when it becomes sufficiently powerful— less likely. This is also usefu...
-
[11]
Diffuse Effects. If this work reduces existential risks indirectly or diffusely, what are the main contributing factors that it affects? 54 Answer: Our work on RepE shows that we now have traction on deceptive alignment, which has historically been the most intractable (specific) rogue AI failure mode. We could also use this to identify when an AI acted r...
-
[12]
What’s at Stake?What is a future scenario in which this research direction could prevent the sudden, large-scale loss of life? If not applicable, what is a future scenario in which this research direction be highly beneficial? Answer: This directly reduces the existential risks posed by rogue AIs (Carlsmith, 2023), in particular those that are deceptively aligned
work page 2023
-
[13]
Result Fragility. Do the findings rest on strong theoretical assumptions; are they not demonstrated using leading-edge tasks or models; or are the findings highly sensitive to hyperparameters? □
-
[14]
Is it implausible that any practical system could ever markedly outper- form humans at this task? □
Problem Difficulty. Is it implausible that any practical system could ever markedly outper- form humans at this task? □
-
[15]
Human Unreliability. Does this approach strongly depend on handcrafted features, expert supervision, or human reliability? □
-
[16]
Competitive Pressures. Does work towards this approach strongly trade off against raw intelligence, other general capabilities, or economic utility? □ E.2 S AFETY -C APABILITIES BALANCE In this section, please analyze how this work relates to general capabilities and how it affects the balance between safety and hazards from general capabilities
-
[17]
Overview. How does this improve safety more than it improves general capabilities? Answer: This work mainly improves transparency and control. The underlying model is fixed and has its behavior nudged, so it is not improving general capabilities in any broad way
-
[18]
Red Teaming. What is a way in which this hastens general capabilities or the onset of x-risks? Answer: A diffuse effect is that people may become less concerned about deceptive alignment, which may encourage AI developers or countries to race more intensely and exacerbate competitive pressures
-
[19]
General Tasks. Does this work advance progress on tasks that have been previously considered the subject of usual capabilities research? □
-
[20]
General Goals. Does this improve or facilitate research towards general prediction, clas- sification, state estimation, efficiency, scalability, generation, data compression, executing clear instructions, helpfulness, informativeness, reasoning, planning, researching, optimiza- tion, (self-)supervised learning, sequential decision making, recursive self-i...
-
[21]
Correlation with General Aptitude.Is the analyzed capability known to be highly predicted by general cognitive ability or educational attainment? □
-
[22]
Safety via Capabilities. Does this advance safety along with, or as a consequence of, advancing other capabilities or the study of AI? □ E.3 E LABORATIONS AND OTHER CONSIDERATIONS
-
[23]
Other. What clarifications or uncertainties about this work and x-risk are worth mentioning? Answer: Hendrycks et al. (2023) provide four AI risk categories: intentional, accidental, internal, and environmental. This work makes internal risks—risks from rogue AIs—less likely. In the past, people were concerned that AIs could not understand human values, a...
work page 2023
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.