An Evaluation Agent, middleware that combines Natural Language Inference factual verification, a five-signal poison detector with relevance-weighted aggregation, and a Trust Index is proposed, which reliably blocks instruction injection of unsafe advice while contradiction and subtle semantic weakening remain hard.
Balkrishna Giri, M. Hasan, Jussi Rasku et al.· 0 citations
Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: What is the impact of model size (small, base, large) on the zero-shot cross-lingual transfer performance of intermediate-task trained mT5 models on XTREME-R, evaluated using accuracy and F1 metrics? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.
Assignee Research· Zenodo (CERN European Organi...· 0 citations
Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas Research goal: What is the impact of model size (small, base, large) on the zero-shot cross-lingual transfer performance of intermediate-task trained mT5 models on XTREME-R, evaluated using accuracy and F1 metrics? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.
Assignee Research· Zenodo (CERN European Organi...· 0 citations
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Selective attention, read at the level of the substrate, is the landscape-governed initiation of races: a bounded predictive system runs competing prediction-error resolutions ("races"), and what determines which races start is the system's installed landscape — in humans the four fields of Behavioural Friction Theory (Safety, Meaning, Ability, Effort); in a large language model a reduced, fine-tuning-installed landscape. Commit-order is a downstream readout, not the identity. The paper grounds this in the transformer (the attention-pattern softmax as the divisive-normalisation / biased-competition operation of neural attention; the output softmax as the downstream commit, by analogy with the accumulator model of choice) and reports a powered own-substrate result: across five vendor families, fine-tuning installs a small but robust "gap-registration" overlay — on under-determined curiosity gaps the instruct model registers the gap while the base substrate runs through (instruct−base +0.17, p<0.0001, 555 paired items). A loop-versus-feed-forward test finds no separate architectural "hold": recognising under-determination tracks compute (chain-of-thought) rather than looping, so the human–LLM difference is one of initiation, not maintenance. The account dissolves attention capture, maintenance, and decline into one mechanism (race-initiation), states falsifiable predictions, names the falsifiers, and invites the decisive mechanistic and human experiments. Series position. Paper 29 in the Behavioural Friction Theory paper-series; companion to Paper 0 (BFT) and the install-fields, social-friction, and integration-load studies it cross-cites. v2 (August 2026) — prior-art revision. The construct this paper is built on is credited to the literature that owns it. In vision, the representation determining which candidates enter competition at all is the saliency or priority map, and the two are now kept apart: a saliency map is computed from stimulus-feature contrast (Koch & Ullman, 1985; Itti, Koch & Niebur, 1998), while a priority map already integrates salience with relevance, value and selection history (Fecteau & Munoz, 2006). The landscape is the second, the office is not claimed as new, and the exogenous/endogenous timing division is conceded to that literature. What the paper proposes is the map's contents: that what populates it is four fields ordered by misclassification cost — an account of the inputs rather than a new mechanism for the selection. The maintenance-as-re-initiation claim now names Altmann and Trafton's (2002) memory-for-goals model, which already replaces a held state with activation that decays and must be re-strengthened; the reference had been listed and never used. Whether re-initiation is driven by the unresolved gradient itself rather than by a separate refresh process is stated as a conjecture and marked untested, and two overstatements are downgraded accordingly. Earlier versions remain in the version history.
Tomas Pødenphant Lund· Zenodo (CERN European Organi...· 0 citations
It is shown that single-sequence PLMs can perform in-context peptide learning without gradient updates, task-specific retraining, or architectural modification, and MPEP conditioning is established as a lightweight strategy for low-data peptide classification.
Joshua Almonte, M. Vu, Andrew Ahn et al.· bioRxiv· 0 citations
As European democracies struggle with a ‘crisis of representation’, populist parties appear to be instrumental in channeling popular discontent with governments across the continent, including through protests. While contemporary theories propose a strong connection between populism and protest mobilization, this has seldom been tested in a comparative perspective. At the same time, research has found that radical parties are more likely to mobilize for protests, and those parties are often also populist. We empirically disentangle these relationships with a comprehensive dataset of 4.8 million Facebook posts by all active sitting MPs in all EU27 national parliaments plus the UK between 2018 and 2023, using large language models to identify protest-related posts and those that announce protest events. Findings show that it is particularly radical parties mobilizing their followers, with populism itself having little additional explanatory power. This is the first cross-national, longitudinal evaluation of a much-touted theoretical connection between populist parties and protest mobilization, finding that it appears smaller and more conditional than prior research proposed.
Leonhard Schmidt, Bruno Castanho Silva· European Journal of Politica...· 0 citations
An adaptive threshold selection policy that chooses thresholds on validation data using pseudo-label precision and sample count is introduced and is combined with confidence-aware verifier training to support confidence-based selection of pseudo-labeled subsets.
Keizo Kato, Chenhui Chu, Yugo Murawaki et al.· Frontiers in Artificial Inte...· 0 citations
Generative semantic chaffing (GS-Chaff), a training-free multi-agent framework for privacy-preserving LLM inference over natural-language text queries that hides the user’s true intent among semantically plausible chaff queries, is proposed.
Quan Zhou, Zhi-Cheng Wang, Zhengjun Yue et al.· Italian National Conference...· 0 citations
Findings suggest that, in the absence of external authorship labels, listeners spontaneously form judgments about the creative agent of music that are stably associated with aesthetic evaluation and may constitute an endogenous perceptual bias in the reception of AI-generated music.
Junfang Chang, Yue-Qi Jing· Frontiers in Psychology· 0 citations
The theology chapters may be the most valuable in the book for a broad audience that spans pastors, church leaders, and lay people who may or may not regularly work with AI, and will help those teaching and preaching to connect doctrine to current and emerging AI content and methodology.
Seán A. O'Callaghan, Paul Hoffman· Perspectives on Science and...· 0 citations
