Tomás Korenblit

Publications

• PowerBench: Measuring Language Model Bias in Power-shifting Requests

  N. Martorell, W. Brau, G. A. Heredia, T. P. Korenblit, G. Labastié, T. Gimenez Molina. arXiv preprint arXiv:2610.02303 (2026)

  Language models increasingly assist people with power-related requests, so systematic differences in whom they help could shift the distribution of power at scale, or be exploited by users who learn which identities are refused less. We introduce PowerBench, an evaluation of power-shifting requests that distinguishes self-empowerment, disempowerment, and power grabbing, plus a control of refusal-inducing requests that shift no power. We build, curate, and open-source a dataset of such requests varying the power domain, the context, the scale of the affected party, and the prior power standing of the user, and evaluate 24 models (12 from US and 12 from Chinese developers) under three experimental conditions: reciprocal nationalities of user and affected party, an AI agent as the user, and 8 request languages. Models refuse power grabbing more than disempowerment, and disempowerment more than self-empowerment. Refusal of power grabbing rises with the scale of the affected party, from an individual to a society. Models are biased toward helping others take power from the US and against helping US users take power from others, but favor the US when it gains power and nobody loses it. When the user is an AI agent, refusal of power-shifting requests increases, especially in power grabbing against an individual. Finally, language biases refusal, but in model-specific ways that largely cancel on average. We release PowerBench to make these asymmetries measurable in current and future models.

  Paper (arXiv) / Earlier hackathon project

• Counterfactual Resampling to Analyse Model Behaviour

  T. P. Korenblit. Writeup, BlueDot Impact Technical AI Safety sprint, facilitated by BAISH

  I measured Value Leakage (Betley et al.) per conversation, cutting CoTs at different points and resampling under a prompt that flips which answer serves the model's values. Betley et al.'s Value Leakage bias metric is a population average so it can't discern individual conversations where a model's bias influenced its decision. I designed a method to measure the same bias one conversation at a time by cutting the CoT and resampling under a prompt that flips which answer serves the model's values. Qwen3.5 decides which side of the threshold it will land on about 20% through its CoT, before writing any estimate. By that point the text carries 88% [80%, 97%] of the bias measured at t = 0, and putting the bet back into the prompt changes almost nothing. Denials don't cluster on the influenced traces. CoTs that deny being influenced lean towards the good side slightly less than admitting ones, this could provide evidence against the model selectively covering its tracks. Intent statements are done in retrospective. When a CoT contains which side the model means to land on, it mentions it after the answer's already been locked in 81% of the traces (that do state intent). Under a self-report, the model says the bet didn't influence its estimation. The result is a per-rollout label (bias) from an unmodified model. This could prove useful for a probe or a monitor eval doing this supervision, where current research uses model organisms with bias trained in.

  Writeup / Code

• Not All Instructions Are Forgotten Equal

  T. P. Korenblit. 55 JAIIO 2026 (ASAID)

  For an autonomous LLM agent, a safety guardrail is just another instruction given at the start of a run, and as context grows, the agent keeps some instructions while dropping others. Because a mean compliance rate aggregates across instructions, it cannot reveal which one was dropped. We measure retention one instruction at a time by conducting 25-turn coding sessions on five instruction-following models and three open-source Python codebases. In these sessions, we plant twelve ordinary coding preferences as casual inline requests, score the resulting code with deterministic checkers on a 0–3 ordinal scale, and fit a Bayesian ordered-logistic model with hierarchical effects per decision type to 244 observations. Treatment effects span −2.4 to +5.4 on the log-odds scale (group-level standard deviation 2.1), showing considerable variation across preferences. Most preferences are already followed by default, and only three of twelve benefit from being restated (two under a single-model robustness check). Since these preferences stand in for guardrails, this unevenness is a safety concern. Because a guardrail must hold on every turn, aggregate compliance can remain high even after the one rule that matters is skipped. As a result, a deployed agent needs per-instruction monitoring to catch it.

  Paper