§ Reading · Field Radar
Field Radar.
What’s worth reading right now in AI reward hacking, specification & evaluation gaming, and mechanistic interpretability — an auto-scored radar over public discussion, refreshed a few times a day.
How this list is made
This page is generated, not hand-picked. A few times a day a script checks LessWrong, Hacker News, and a handful of subreddits for posts about reward hacking, specification gaming, evaluation gaming, and mechanistic interpretability, then scores each one on how on-topic it is, how recently the conversation actually moved, and whether there’s still room to get a word in — as opposed to a thread that already has two hundred comments. Higher scores float to the top. Every title links out to the original; I’m pointing at other people’s work, not reproducing it.
The score is a crude weighted sum, and like any harness it pins down what I bothered to measure and silently lets everything else vary. So read this as one opinionated filter, not a survey of the field — it will miss things, and when it surfaces something dull that’s the weights, not the author.
Sources — LessWrong: ok (9 on-topic) · Hacker News: ok (3 stories) · Reddit: ok (25 posts) — some subreddits rate-limited
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- 2026-07-10 14:00 ET
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- 0.72LessWrong2wReward Hacking Without Egregious Misalignment in an RL-Only Setting
This work was done as part of the MATS fellowship by Joey Yudelson and Vladimir Ivanov. It was mentored by Ryan Greenblatt. Thanks to Aghyad Deeb and Anders Woodruff for comments on this post. Thanks to Monte…
why score 0.723
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.01 ×0.25 0.002 contributability 0.96 ×0.15 0.144 venue 0.77 ×0.10 0.077 direct 1.00 ×0.20 0.200 tier-1: reward hacking; 1 matching tag(s)
- 0.71LessWrong11dDeployment Awareness Matters More Than Evaluation Awareness
TL;DR Evaluation awareness — an AI recognizing it's being evaluated — is a widely discussed concept in AI safety. But there is a closely related concept that we claim is more important: deployment awareness, the AI's…
why score 0.707
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.02 ×0.25 0.006 contributability 0.86 ×0.15 0.130 venue 0.71 ×0.10 0.071 direct 1.00 ×0.20 0.200 tier-1: evaluation awareness; 1 matching tag(s)
- 0.60LessWrong3hnewFree will as a model parameter
The most popular take on the standard free will debate is that you are the algorithm. Your preferences and reasoning that determine your actions IS free will. But this resolution leaves me not entirely satisfied because…
why score 0.600
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.95 ×0.25 0.237 contributability 0.12 ×0.15 0.017 venue 0.45 ×0.10 0.045 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.59LessWrong2dA global workspace in language models
[This is the blog post for our new paper Verbalizable Representations Form a Global Workspace in Language Models Readers might also be interested in: the Public commentary, Github and Neuronpedia] As you read this…
why score 0.588
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.50 ×0.25 0.126 contributability 0.41 ×0.15 0.062 venue 1.00 ×0.10 0.100 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.59LessWrong3dBounding eval awareness of ~human-level AI across the safe-to-dangerous shift
In our last post, we argued that measuring evaluation awareness is fundamentally challenging because of the safe-to-dangerous distributional shift: we cannot directly measure the evaluation awareness of a model without…
why score 0.585
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.32 ×0.25 0.080 contributability 0.57 ×0.15 0.085 venue 0.70 ×0.10 0.070 direct 1.00 ×0.20 0.200 tier-1: evaluation awareness
- 0.54LessWrong1dnewPersistent Latent Misalignment, a new dimension of misalignment?
A new paper was released at ICML that I'm worried will open an entire new dimension of alignment problems: Latent Collaboration in Multi-Agent Systems (LatentMAS) TLDR: they show that multiagent systems can communicate…
why score 0.544
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.62 ×0.25 0.154 contributability 0.27 ×0.15 0.040 venue 0.50 ×0.10 0.050 direct 0.00 ×0.20 0.000 3 matching tag(s)
- 0.54LessWrong2wEval-Awareness Steering detects the Test, Not the Sabotage
Produced as part of independent research Huge thanks to Apollo Research (org) for open-sourcing the deception-detection harness which proved to be foundational in this work. Prior work by Devbunova (2026), the…
why score 0.541
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.01 ×0.25 0.002 contributability 0.02 ×0.15 0.003 venue 0.37 ×0.10 0.037 direct 1.00 ×0.20 0.200 tier-1: sandbagging; 3 matching tag(s)
- 0.54LessWrong2dTie training can make DPO/RLHF-trained AIs generalize better
This post covers our recent ICML paper: Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training. TL;DR Our theorems and experiments suggest that DPO and RLHF…
why score 0.538
signal value weight points topic 0.75 ×0.30 0.225 liveness 0.48 ×0.25 0.121 contributability 0.79 ×0.15 0.118 venue 0.74 ×0.10 0.074 direct 0.00 ×0.20 0.000 tier-2: feature; 1 matching tag(s)
- 0.53LessWrong1dnewWhen is misalignment just a bug?
Cross-posted from The Foretellix CTO Blog. Introduction and epistemic status: This is the first post in a planned series, “Alignment as a verification problem”. I co-originated coverage-driven verification (CDV), which…
why score 0.533
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.71 ×0.25 0.177 contributability 0.02 ×0.15 0.003 venue 0.53 ×0.10 0.053 direct 0.00 ×0.20 0.000 3 matching tag(s)
- 0.51LessWrong1dnewModels are blind outside the J-space. NLAs aren't.
TLDR: On Llama-3.3-70B, I found thoughts it cannot see that are actively steering its behavior; and Anthropic's released NLA (Natural Language Autoencoder) reads them anyway. When asked if it sees a hidden thought, the…
why score 0.510
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.55 ×0.25 0.138 contributability 0.12 ×0.15 0.017 venue 0.55 ×0.10 0.055 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.51LessWrong1dnewYour Prompt-Injection Defense Metric Might Be Lying to You
Indirect Prompt Injection at present day, is one of the main reasons for agentic failures deployed in personal systems as well as enterprise grade applications/systems. The agent reads untrusted content from numerous…
why score 0.508
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.72 ×0.25 0.179 contributability 0.02 ×0.15 0.003 venue 0.26 ×0.10 0.026 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.50LessWrong17hnewHow robust are natural language autoencoders to initialization?
Natural language autoencoders are meant to take in an LLM's activation vector and describe in plain text what the model is thinking. However, its training data collection involves asking Claude to guess what a model…
why score 0.502
signal value weight points topic 0.75 ×0.30 0.225 liveness 0.79 ×0.25 0.197 contributability 0.02 ×0.15 0.003 venue 0.78 ×0.10 0.078 direct 0.00 ×0.20 0.000 tier-2: activation; 1 matching tag(s)
- 0.50LessWrong11dDo LLMs Have Desires?
Work conducted with Yujun Zhou (yzhou25@nd.edu) and supported by SPAR TL;DR: In paired-choice paradigms, LLMs report consistent preferences over outcomes (e.g., types and number of lives saved, types of policies…
why score 0.498
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.03 ×0.25 0.006 contributability 0.79 ×0.15 0.118 venue 0.73 ×0.10 0.073 direct 0.00 ×0.20 0.000 3 matching tag(s)
- 0.49LessWrong2wBrittle model organisms obstructs deception elicitation work
This work was done by Advik Raj Basani with Daniel Tan and Chloe Li as part of SPAR Spring 2026. tl;dr: Finetuning-based auditing methods for model organisms may unintentionally erase the deceptive behavior we are…
why score 0.487
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.00 ×0.25 0.001 contributability 0.86 ×0.15 0.130 venue 0.56 ×0.10 0.056 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.48LessWrong1dData filtering works a lot worse than you would expect
This work was largely done during Neel Nanda's MATS 10.0 Exploration Phase. J Rosser and Dohun Lee are co-first authors for this post with equal contribution. Josh Engels and Neel Nanda supervised the project, and…
why score 0.476
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.55 ×0.25 0.137 contributability 0.79 ×0.15 0.118 venue 0.70 ×0.10 0.070 direct 0.00 ×0.20 0.000 1 matching tag(s)
- 0.46LessWrong2dSub-agent delegation chaining
Epistemic status: pretty confident in the validity of the core proposal, not that confident in specific implementation details TL;DR: we should cryptographically verify that sub-agent instances/sessions are downstream…
why score 0.456
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.38 ×0.25 0.096 contributability 0.96 ×0.15 0.144 venue 0.66 ×0.10 0.066 direct 0.00 ×0.20 0.000 1 matching tag(s)
- 0.44LessWrong5dWe need 3rd party Training-Run Assessments
Training-run assessments conducted by a 3rd party should become a standard part of frontier AI safety. By a Training-Run Assessment, or TRA, I mean an in-depth analysis of the post-training pipeline and dynamics leading…
why score 0.441
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.18 ×0.25 0.046 contributability 0.02 ×0.15 0.003 venue 0.92 ×0.10 0.092 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.44LessWrong2dOpen-source LLMs administer maximum electric shocks in a Milgram-like obedience experiment
By Roland Pihlakas and Jan Llenzl Dagohoy This post is a slightly updated copy of our Arxiv preprint available at https://arxiv.org/abs/2605.21401 . The tables are converted to images in order to preserve cell…
why score 0.439
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.38 ×0.25 0.095 contributability 0.02 ×0.15 0.003 venue 0.41 ×0.10 0.041 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.44LessWrong2dCalibrating alignment evals
Currently, alignment evaluation works by constructing a situation, observing the model's behavior and scoring it. We put a lot of thought into designing these benchmarks, and tuning them for our requirement. We are now…
why score 0.439
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.37 ×0.25 0.092 contributability 0.02 ×0.15 0.003 venue 0.43 ×0.10 0.043 direct 0.00 ×0.20 0.000 5 matching tag(s)
- 0.43LessWrong2dProbing is not enough; a validity audit for any probe
tl;dr A probe can have excellent AUROC and yet fail as a safety signal. I audited 3 probes: a monitoring awareness probe leakage example, a refusal direction as positive control, and Apollo's deception probe as a…
why score 0.434
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.37 ×0.25 0.092 contributability 0.02 ×0.15 0.003 venue 0.39 ×0.10 0.039 direct 0.00 ×0.20 0.000 tier-2: probe; 4 matching tag(s)
- 0.43LessWrong5dSuccess Per Tokens
Work smart more than hard, to expand the pareto frontier (but also work hard) A Pareto Frontier is a set of nondominated (optimal) solutions in multi-objective optimization. In 2 dimensions, this traces out a curve on…
why score 0.432
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.15 ×0.25 0.038 contributability 0.02 ×0.15 0.003 venue 0.41 ×0.10 0.041 direct 1.00 ×0.20 0.200 1 matching tag(s)
- 0.42LessWrong10dRole confusion: sounding like the cause is indistinguishable from being it.
A replication of Prompt Injection as Role Confusion (2026) and why the mechanistic story of prompt injection is harder to pin down than it looks. ---------------------------------------- Epistemic status: I reproduced…
why score 0.420
signal value weight points topic 0.75 ×0.30 0.225 liveness 0.03 ×0.25 0.009 contributability 0.86 ×0.15 0.130 venue 0.56 ×0.10 0.056 direct 0.00 ×0.20 0.000 tier-2: activation; 1 matching tag(s)
- 0.41LessWrong12dCan we use steering vectors to suppress reward-hacking? Somewhat
Can steering vectors drive gradient routing? Yes, but not in realistic reward hacking environments, they are not precise enough classifiers of hacky vs clean solutions. Instead can we use a steering vector to initialise…
why score 0.406
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.02 ×0.25 0.004 contributability 0.02 ×0.15 0.003 venue 0.50 ×0.10 0.050 direct 1.00 ×0.20 0.200 tier-1: reward hacking
- 0.40LessWrong2wResearch note on negated reward hacking
This work was done as part of the BlueDot's Technical AI Safety Project Sprint and should be treated as an informal report of preliminary results done over a couple of days. The code is available on GitHub, the negated…
why score 0.396
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.01 ×0.25 0.002 contributability 0.02 ×0.15 0.003 venue 0.41 ×0.10 0.041 direct 1.00 ×0.20 0.200 tier-1: reward hacking
- 0.39LessWrong2wA misalignment taxonomy
I am going to discuss five kinds of inner misalignment and two kinds of outer misalignment, which create a simple taxonomy of alignment failure modes. When I talk about a kind of misalignment here, I am talking about a…
why score 0.390
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.00 ×0.25 0.000 contributability 0.27 ×0.15 0.040 venue 0.50 ×0.10 0.050 direct 0.00 ×0.20 0.000 2 matching tag(s)