§ 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 (8 on-topic) · Hacker News: ok (5 stories) · Reddit: ok (25 posts) — some subreddits rate-limited
- As of
- 2026-08-19 20:00 ET
- Showing
- 25 items
- New
- 13 in last 48h
- Refresh
- Every 6 hours
- 0.72LessWrong1dnewAI Sandbagging (w/ Inspect)
TLDR For my BlueDot AI Safety Project Sprint, I chose to replicate an AI Sandbagging paper, using the Inspect framework to evaluate strategic underperformance capabilities in frontier models. Project Selection While I…
why score 0.722
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.71 ×0.25 0.178 contributability 0.02 ×0.15 0.003 venue 0.41 ×0.10 0.041 direct 1.00 ×0.20 0.200 tier-1: sandbagging; 1 matching tag(s)
- 0.71LessWrong2hnewRL creates split personas
I describe my current view of personas in LLMs and why RL leads to egregious reward hacking in some contexts while the same models seem very aligned in other contexts. This post describes the framing/paradigm without…
why score 0.714
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.97 ×0.25 0.242 contributability 0.27 ×0.15 0.040 venue 0.82 ×0.10 0.082 direct 1.00 ×0.20 0.200 tier-1: reward hacking
- 0.69LessWrong6hnewDebate Training Reduces Reward Hacking in RLAIF
Paper: Debate Training Reduces Reward Hacking in RLAIF Linkpost for GDM Alignment blogpost Work done by the GDM Amplified Oversight team (we're hiring). TL;DR: When you RL against an LLM judge, the judge gets hacked…
why score 0.688
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.91 ×0.25 0.228 contributability 0.27 ×0.15 0.040 venue 0.70 ×0.10 0.070 direct 1.00 ×0.20 0.200 tier-1: reward hacking
- 0.61LessWrong7dIs Eval Gaming Downstream of Verbalized Eval Awareness? Not when it's reflexive.
Code and data available at github.com/KieronKretschmar/latent-awareness TL;DR We take two eval-gaming model organisms (Hua et al.'s (2025) organism and RogueQwen) and apply direct preference optimization (DPO) to their…
why score 0.609
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.08 ×0.25 0.021 contributability 0.27 ×0.15 0.040 venue 0.48 ×0.10 0.048 direct 1.00 ×0.20 0.200 tier-1: eval gaming; 1 matching tag(s)
- 0.57LessWrong2wRLVR that rewards red teaming the training environment
Epistemic status: throwing an idea at the wall and seeing if it sticks I've been thinking about how to mitigate egregious reward hacking, a la the Hugging Face incident. I don't have the resources I'd need to write a…
why score 0.566
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.00 ×0.25 0.001 contributability 0.86 ×0.15 0.130 venue 0.85 ×0.10 0.085 direct 1.00 ×0.20 0.200 tier-1: reward hacking
- 0.56LessWrong3dnewHow To Catch a Distilled Model
"Distillation is great until your new trillion-dollar sovereign AI introduces itself as 'Claude from Anthropic' on day one." - random guy from Reddit TLDR We introduce a novel algorithm to detect distillation across…
why score 0.563
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.27 ×0.25 0.067 contributability 0.92 ×0.15 0.138 venue 0.57 ×0.10 0.057 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.52LessWrong1dnewWhack-a-mole with a broken hammer: does a model internally track its automaton state?
TL;DR: We check whether a small model (Qwen2.5-1.5B) internally tracks its state in a simple DFA (modelled after a login protocol), given a series of events. This state is never written in the transcript the model sees,…
why score 0.519
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.71 ×0.25 0.178 contributability 0.02 ×0.15 0.003 venue 0.39 ×0.10 0.039 direct 0.00 ×0.20 0.000 tier-2: probe; 2 matching tag(s)
- 0.52LessWrong5dnewFrontier agents don't comply with standards, even when instructed to
TLDR: Our open testbed LARA examines the behavior of frontier LLMs in realistic agentic deployment contexts. Previous results showed all models routinely take actions that would violate EU law. This post follows up by…
why score 0.519
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.18 ×0.25 0.044 contributability 0.69 ×0.15 0.104 venue 0.71 ×0.10 0.071 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.47LessWrong5hnewA circuit prior in NN-bayes
Here are the slides of a talk Kaarel gave, presenting work with Dmitry establishing that (even arbitrarily overparametrized) neural net bayesian learning has a circuit prior — and thus, when learning a function which is…
why score 0.472
signal value weight points topic 0.25 ×0.30 0.075 liveness 0.93 ×0.25 0.233 contributability 0.57 ×0.15 0.085 venue 0.78 ×0.10 0.078 direct 0.00 ×0.20 0.000 tier-2: circuit
- 0.43LessWrong2dnewV&V takes on “Pacing the frontier”
[Cross-posted from The Foretellix CTO Blog. These short takes try to put a verification-and-validation slant on AI-safety / alignment topics – they are not full treatments. I co-originated coverage-driven verification…
why score 0.431
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.39 ×0.25 0.099 contributability 0.02 ×0.15 0.003 venue 0.30 ×0.10 0.030 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.42LessWrong4dnewRed vs Blue, but for Evals
🔵 The blue team proposes an evaluation protocol for some capability/propensity of interest. This consists of a suite of measurement tasks, together with a preregistered decision-making process they will follow based on…
why score 0.421
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.19 ×0.25 0.048 contributability 0.12 ×0.15 0.017 venue 0.56 ×0.10 0.056 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.42LessWrong13dModel Organisms of Sandbagging in the Wild
TL;DR All current model organisms (MOs) of sandbagging in LLMs are either fine-tuned to sandbag or prompted in a way that makes it clear that sandbagging is strategically useful. We found a case of non-egregious…
why score 0.419
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.01 ×0.25 0.003 contributability 0.02 ×0.15 0.003 venue 0.63 ×0.10 0.063 direct 1.00 ×0.20 0.200 tier-1: sandbagging
- 0.42LessWrong5dnewWhat If We Enforced AI Model Safety At the Level Of GPUs?
Tldr: AI Agents (e.g. based on models like Claude Opus and Fable) are now powerful enough to be used as autonomous tools for large-scale cyberattacks. This most powerful class of agents generally tends to be based on…
why score 0.419
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.17 ×0.25 0.042 contributability 0.42 ×0.15 0.064 venue 0.13 ×0.10 0.013 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.38LessWrong22hnewNatural Language Transcoders
Describing the computation performed in a stack of transformer layers Anwen Hao, mentored by Adrians Skapars Anthropic’s natural language autoencoders (NLAs) is a promising method to automatically generate explanations…
why score 0.383
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.73 ×0.25 0.184 contributability 0.02 ×0.15 0.003 venue 0.47 ×0.10 0.047 direct 0.00 ×0.20 0.000 1 matching tag(s)
- 0.37Hacker News2wThe Download: reward hacking explained, and suspected Iranian cyberattacks
why score 0.374
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.00 ×0.25 0.001 contributability 0.02 ×0.15 0.003 venue 0.21 ×0.10 0.021 direct 1.00 ×0.20 0.200 tier-1: reward hacking
- 0.37LessWrong2wSingle Forward Pass Evals on Fable, Opus 5, and GPT-5.6-Sol
This is a research update for an on-going replication of single-forward-pass evals done as part of the Second Look Fellowship. In following posts, we will run more comprehensive replications of previous work and release…
why score 0.373
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.00 ×0.25 0.001 contributability 0.02 ×0.15 0.003 venue 0.69 ×0.10 0.069 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.37LessWrong1dnewGPT-2's IOI behavior is defined where the paper's algorithm isn't
TL;DR: The IOI algorithm doesn't specify what to do when the indirect object token is duplicated. I observe that the model succeeds anyway. I'd like to know if I'm mistaken in the IOI paper's predictions, and I'd like…
why score 0.369
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.71 ×0.25 0.178 contributability 0.02 ×0.15 0.003 venue 0.39 ×0.10 0.039 direct 0.00 ×0.20 0.000 1 matching tag(s)
- 0.37LessWrong13dMatryoshka NLAs: training activation verbalizers to frontload reconstruction-relevant information
TLDR: We train a “matryoshka” NLA that, unlike standard NLAs, is trained to put the most important details at the start; it is trained by randomly truncating the verbalizer’s explanations before showing them to the…
why score 0.369
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.01 ×0.25 0.003 contributability 0.02 ×0.15 0.003 venue 0.63 ×0.10 0.063 direct 0.00 ×0.20 0.000 tier-2: feature, activation; 2 matching tag(s)
- 0.36LessWrong13dFunction vectors as a model diffing tool: 17 heads repair a bad fine-tune
I take two fine-tuned models trained to give bad medical advice, one on Qwen2.5-7B and one on Llama-3.1-8B, from the Model Organisms for Emergent Misalignment collection, and I found out that I could make one safe by…
why score 0.363
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.01 ×0.25 0.003 contributability 0.02 ×0.15 0.003 venue 0.57 ×0.10 0.057 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.36LessWrong12dAgenda: Infrastructure for Trading with Partially Misaligned AIs
This post can be read on its own, without checking the rest of this sequence. Unlike the other parts of the sequence, this post is mostly not about AI evaluation or oversight. Rather, it is meant as an explanation of…
why score 0.361
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.02 ×0.25 0.004 contributability 0.02 ×0.15 0.003 venue 0.55 ×0.10 0.055 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.36LessWrong2wWhen should we trust a latent representation?
Working on AI Safety, I spend a significant part of my time thinking about how frontier AI Safety research eventually gets translated into deployable safety systems. One thing I have constantly noticed is the transition…
why score 0.360
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.01 ×0.25 0.001 contributability 0.12 ×0.15 0.017 venue 0.41 ×0.10 0.041 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.35LessWrong2wRewrite All the Code, All the Time
This post is crossposted from my Substack, Structure and Guarantees, where I explore how formal verification and related ideas might scale to more complex intelligent systems. It has become a mainstream prediction that…
why score 0.354
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.01 ×0.25 0.002 contributability 1.00 ×0.15 0.149 venue 0.53 ×0.10 0.053 direct 0.00 ×0.20 0.000 1 matching tag(s)
- 0.35LessWrong2wDeliberate Alignment Faking as a Defense Against Model Poisoning
I want to discuss and brainstorm a counterintuitive approach to AI alignment: Inducing alignment faking on purpose, to prevent the model from developing emergent misalignment. To prevent this from going horribly wrong,…
why score 0.353
signal value weight points topic 1.00 ×0.30 0.300 liveness 0.00 ×0.25 0.001 contributability 0.02 ×0.15 0.003 venue 0.50 ×0.10 0.050 direct 0.00 ×0.20 0.000 2 matching tag(s)
- 0.35LessWrong7dBefore We Defer Research to AI: Measuring Apparent-Success-Seeking
Recently, I was improving a small LLM-powered classifier and noticed a few continuously failing test cases. As many would, I asked my AI code assistant to add a few more out-of-distribution examples to the classifier’s…
why score 0.348
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.07 ×0.25 0.018 contributability 0.86 ×0.15 0.130 venue 0.50 ×0.10 0.050 direct 0.00 ×0.20 0.000 1 matching tag(s)
- 0.34LessWrong3dnewLearning new facts can change LLM behaviour
TL:DR: I use synthetic document fine-tuning to train an LLM to believe that in 2027 ‘long-horizon’ frontier LLMs count as moral persons. I find the model scores highly on measures of belief depth, and that prompting…
why score 0.341
signal value weight points topic 0.50 ×0.30 0.150 liveness 0.34 ×0.25 0.085 contributability 0.27 ×0.15 0.040 venue 0.66 ×0.10 0.066 direct 0.00 ×0.20 0.000 1 matching tag(s)