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cs.CL2026
Cross-Model In-Context Neurofeedback Reveals (Limited) Activation Control in LLMs
Davide Baldelli, Robyn Kozierok, Mirko Bronzi +1
Probes on activation vectors are widely used to detect misbehavior in language models, but their success relies on the assumption that models cannot control what their activations…
cs.CL2026
Probabilistic Calibration Is a Trainable Capability in Language Models
Davide Baldelli, Sruthi Kuriakose, Maryam Hashemzadeh +2
Language models are increasingly used in settings where outputs should follow an intended distribution, yet their generation probabilities are often poorly calibrated to it. In man…
cs.CL2026
LLMs Can't Play Hangman: On the Necessity of a Private Working Memory for Language Agents
Davide Baldelli, Ali Parviz, Amal Zouaq +1
As LLMs move from text completion toward autonomous agents, they remain constrained by the standard chat interface, which lacks private working memory. This raises a fundamental qu…