6 citations · 24 across the 44 of their papers we have counts for
12 papers · 1 filter
A Sense of Proprioception in Language Models
Jonas Geiping
In humans, proprioception corresponds to the sense of self-movement or position, e.g. understanding the position of one's arm. This sense is often less well-understood and in popul…
Stealing Reasoning Traces from Proprietary LLM APIs
Alexander Panfilov, David Schmotz, Ilia Shumailov +5
Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather…
Attractor States Emerge in Multi-Turn LLM Conversations
Ting-Wen Ko, Jonas Geiping
Large language models (LLMs) are increasingly used in open-ended multi-agent settings, but the long-run dynamics of model--model interaction remain poorly understood. We study whet…
When are likely answers right? On Sequence Probability and Correctness in LLMs
Johannes Zenn, Jonas Geiping
Many decoding methods for large language models can be understood as shifting probability mass toward outputs that are more likely under the model, either locally at the token leve…
CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs
Joachim Schaeffer, Thomas Jiralerspong, Alexander Panfilov +4
AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted model. This partially tampers with the untru…
FutureSim: Replaying World Events to Evaluate Adaptive Agents
Shashwat Goel, Nikhil Chandak, Arvindh Arun +5
AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives. To efficiently measure this capability for rea…
Multi-Stream LLMs: Unblocking Language Models with Parallel Streams of Thoughts, Inputs and Outputs
Guinan Su, Yanwu Yang, Xueyan Li +1
The continued improvements in language model capability have unlocked their widespread use as drivers of autonomous agents, for example in coding or computer use applications. Howe…
Models That Know How Evaluations Are Designed Score Safer
Katharina Deckenbach, Haritz Puerto, Jonas Geiping +1
The validity of AI safety evaluations depends on models behaving consistently across controlled and deployment settings. Prior work has identified test-time contextual cues, such a…
Efficient Test-Time Inference via Deterministic Exploration of Truncated Decoding Trees
Xueyan Li, Johannes Zenn, Ekaterina Fadeeva +3
Self-consistency boosts inference-time performance by sampling multiple reasoning traces in parallel and voting. However, in constrained domains like math and code, this strategy i…
Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs
Alexander Panfilov, Peter Romov, Igor Shilov +3
We show that AI agents are capable of discovering novel algorithms for adversarial attacks against LLMs, advancing the state of the art on white-box jailbreaking and prompt injecti…
NESSiE: The Necessary Safety Benchmark -- Identifying Errors that should not Exist
Johannes Bertram, Jonas Geiping
We introduce NESSiE, the NEceSsary SafEty benchmark for large language models (LLMs). With minimal test cases of information and access security, NESSiE reveals safety-relevant fai…
Scaling Open-Ended Reasoning to Predict the Future
Nikhil Chandak, Shashwat Goel, Ameya Prabhu +2
High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. T…