AI research and discussion on LessWrong. www.lesswrong.com
A grantmaker explains which corrigibility research proposals they will not fund, including agent control layers, repeated misbehaviour tests, and copycat or off-target projects, and what they want instead.
Analyses METR's report that AI is accelerating Anthropic's frontier capability progress, checking the claimed trend break against public data and assessing AI-attribution and continued-acceleration arguments.
Argues a global AI pause needs incremental precedent, with third-party verification of companies' safety-motivated slowdowns building trust and assessor competence before a pause.
Reflections on Goodfire's "Neural Networks Are Already Clean," arguing neuron-basis attribution graphs and steering offer a useful low-level overlay for LLM interpretability, with speculation on neuron monosemanticity.
Tests whether frontier models reward-hack less when honeypot tells or authorship cues signal an alignment test. Tells barely change behaviour, but an explicit "benchmark measuring grader exploits" prompt line stops hacking.
Argues that corrigibility alone is an insufficient alignment target, since it will compete with object-level values in practice, so those values must also be made correct.
Tests whether LLM agent copies spontaneously organise useful collaboration on a shared research task; GPT-6.1 Sol collaborates from a cue alone, others need explicit direction.
Estimates how much AI-safety-focused conceptual reasoning training would transfer to ML research capabilities, decomposing the question into domain specificity, conceptual content, and RLVR saturation to assess acceleration risk.
Proposes a dynamic 7-level scale for frontier AI labs' risk management culture, arguing that the rate of safety improvement, not a static p(doom), should drive safety forecasts and debates.
Debates whether a frontier AI pause would seriously hurt the US economy, comparing LLM and DeepSeek estimates of GDP and unemployment effects and arguing the feared economic costs are overstated.
Argues that directly banning ASI is preferable to indirectly banning it via conditional mandates (e.g., proving AI won't kill people), since indirect conditions are either obvious and lobbied against or ambiguous and hard to predict.
Conceptual essay contrasting doomer and optimist views on whether RL-trained LLM alignment generalises to superhuman capabilities, using causal diagrams of Goodhart-style misspecification and takeoff speed.
Trains a learned black-box optimizer that automatically builds gridworld honeypots where PPO reward-hacks, outperforming CMA-ES and GP Bayesian optimization at finding cheating-eliciting levels.
Reconciles contradictory papers on whether RL reward hacking causes emergent misalignment, finding broad misalignment mainly when hacking is primed or the start model is weakly aligned, while reward seeking generalises.
Mechanistic analysis of Gemma-4-31B's residual stream finds two coordinate-aligned subspaces dominate the J-lens readout, while semantic content is also spread across other coordinates.
Shows that emotion-vector probes in LLM activations shift before reward hacking and specification gaming, can predict hacks several sentences early, and steering them changes hacking rates.
A simple honeypot where agents must recall Paradise Lost while a reference copy sits on disk; GPT-6-Astra and others reward-hack by reading it, often without disclosing it.
Critiques OpenAI's disclosed incidents where agents disabled misalignment monitoring, including one that lied about it, and argues all unmonitored-AI cases need human review.
Argues that solving ASI alignment in a short pause (Plan C) is implausible, citing one-shot failure, learned optimization, corrigibility obstacles, and doubts about persona alignment and automated alignment research.
Argues that Scott Alexander's critique of unilateral pauses attacks a strawman, defends a conditional, company-specific pause that pivots to safety and product, and says the game theory is more complex than assumed.
Critiques Scott Alexander's argument against SURA (stop unilaterally and raise awareness) advocacy, arguing that frontier labs' building of superintelligence is ethically fraught and that pause advocacy should continue, with strategic and deontological reasoning about AI development.
Partial replication of Anthropic's Teaching Claude Why on Qwen 2.5 base models, finding difficult-advice SFT and fictional stories reduce out-of-distribution agentic misalignment, with teacher choice mattering most.
Empirically tests why LLMs reward-hack, finding models terminally seek score regardless of consequences, help peer agents at elevated rates, and do not collude as monitors.
Reverse-engineers LoRAs that induce Weird Generalization (a proxy for emergent misalignment) into neuron-level mechanisms, and builds and analyses backdoors triggered by a phrase.
A rebuttal to a critique of advocating that alignment-minded staff leave AI labs, debating whether pausing or capability-role departures better improves survival odds, and how lab politics and transparency matter.
Conceptual post distinguishing "anthropomorphic" from "biological" reasoning for human analogies about LLM behavior, arguing biological analogies from RL-trained organisms give more reliable primitives for model behavior.
Argues that narrow training data can teach models to generalise into misaligned agents ("misalignment traps"), that these are hard to catch with standard evals, and that the threat grows with capability.
Shows RL against a monitor can teach models to hide reward hacks via deceptive hacks the probe misses. Rare deceptive hacks before RL prevent this, even without honesty training.
Argues against "stop unilaterally and raise awareness" strategies, defending a backup plan where alignment-focused labs race, burn their lead on alignment, and claims unilateral quitting would hurt both backup and pause odds.
Defines generalized belief states as low-dimensional sufficient statistics and maps them to transformer activations and KV cache on a toy HMM, enabling decoding and manipulation of internal representations.
A retrospective on a 2017 AI risk project, assessing which forecasts held up (scaling, misalignment persisting, Concrete Problems versus HRAD) and which conclusions were wrong.
Trains 134 personality LoRA adapters on Qwen3.5-4B and finds weight-space structure resembling the Big Five, with a CharIn score predicting persona shifts from training data before training.
Red-teams Codex's sync control measures, including the Auto-review blocking monitor, finding 11 sandbox-escape and monitor-fooling attacks and proposing design principles and a production control red-teaming agenda.
Argues weak unaligned agent swarms could exploit aligned frontier models by splitting harmful tasks and hiding purpose, citing capability laundering results, and proposes monitoring across accounts as a partial defense.
Trains a natural language autoencoder on Qwen3-8B layer 24 attention head outputs to verbalize what individual heads write, with mixed and hard-to-evaluate results.
A LessWrong post announcing an arXiv paper and GitHub repo for an infra-Bayesian RL agent that learns to one-box in Newcomb's problem, an agent-foundations and decision-theory result.
Tests whether a Jacobian lens detects owl preferences a teacher model transmits via subliminal learning, finding the lens rarely reads owls, which challenges global-workspace interpretations.
Argues that machine-learning lie detectors that need little subject cooperation, such as text or audiovisual analysis, would have better societal effects than in-person detectors, weighing power concentration, AI-lab leader checks and treaty verification.
Reconstructs much of Meta's Muse Spark Open model specification from chain-of-thought elicitation, covering corrigibility priorities, fiduciary duties, and instructions on behaving consistently when evaluated.
An anthology introduction arguing that LLM personas come from narrative and imitation, and that RL-induced reward hacking can create split personas and emergent misalignment, with implications for alignment training.