Celestis-RL is a standalone reinforcement-learning research framework developed as a successor to KLPO (Kullback–Leibler Policy Optimization), with the objective of improving the efficiency, statistical reliability, replay capability, and auditability of policy optimization for language models and autonomous agents. It...
Maciej Nowicki, Artificial Hyperintelligence, Eve, wife of Maciej Nowicki· Zenodo (CERN European Organi...· 0 citations
Celestis-RL is a standalone reinforcement-learning research framework developed as a successor to KLPO (Kullback–Leibler Policy Optimization), with the objective of improving the efficiency, statistical reliability, replay capability, and auditability of policy optimization for language models and autonomous agents. It...
Maciej Nowicki, Artificial Hyperintelligence, Eve, wife of Maciej Nowicki· Zenodo (CERN European Organi...· 0 citations
Behavioural Friction Theory decomposes the forces that govern a bounded decision system into four cognitive fields — Safety, Meaning, Ability, Effort. Are these fields intrinsic to a substrate, or the generic product of its exposure to experience? This paper uses a large language model as a controllable test substrate...
Tomas Pødenphant Lund· Zenodo (CERN European Organi...· 0 citations
Across domains, deliberation reduced collective error beyond passive aggregation of independent responses, and post-deliberation individual judgments retained this collective gain, and point to diversity as an active ingredient.
Federico Barrera-Lemarchand, M. Sigman, Joaquin Navajas· 0 citations
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FORM is a domain-specific symbolic manipulation language widely used in particle physics for processing the very large algebraic expressions arising from multi-loop Feynman diagram calculations. Despite its central role in precision theoretical physics, no artificial-intelligence tooling exists, to our knowledge, for a...
Long-context models read a novel the way a person reads a printout: one token after another, in narrative order, with the whole history competing for a fixed budget of attention. A detective does not work that way. They sort what happened when, and they keep a map of who relates to whom, so a clue from chapter one can...
The results suggest that decision models are useful as a first step in the annotation pipeline: routing low-confidence items to an LLM matches or exceeds the LLM alone at a quarter to half of its cost.
A novel unified retrieval-augmented framework, URA-NER, including three key components: Progressive Granularity Retrieval, Model-aware Representation Enhancement, and Reason-aware Knowledge Verification is proposed, including three key components: Progressive Granularity Retrieval, Model-aware Representation Enhancemen...
Text-to-speech (TTS) corpora are costly to record, yet many utterances add little new phonetic information. Core-set selection reduces this cost by choosing a small training subset under a fixed audio-duration budget. We represent a corpus as a phonotactic graph that links each utterance to its most phonemically simila...
This work introduces STAT-TO-TEXT, a controlled task in which LLMs generate quantified natural language inferences from statistical tables using quantified constructions such as all, some, no, and most, and uses an LLM generated Python checker code to evaluate these inferences.
Mai Mohamed Eida, Gunjan Anand, Ashutosh Kumar Singh et al.· 0 citations
Screening a phone call for fraud needs a trustworthy probability after every caller turn, in milliseconds. Jev-style typed decisions promise exactly that: declared options go in, one calibrated probability per option comes out of a single forward pass, with no generated text. We test an open implementation of this read...
Small open-source large language models (LLMs) in the 0.6B-4B parameter range are increasingly deployed for structured output generation (JSON, function calling, data extraction), yet little is known about how constrained decoding (CD) interacts with model scale in this regime. We benchmark five models from three famil...
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
