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Intelligence Artificielle

When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models

arXiv:2608.19208v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are frequently exposed to auxiliary textual context, the impact of which on visually grounded tasks remains underexplored. In this paper, we investigate the influence of task-irrelevant context by formulating it as a controlled intervention within a binary visual judgment framework. By…

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Intelligence Artificielle

Compliance, Capability, and Conflict: Benchmarking Multimodal LLMs under System Messages

arXiv:2608.19207v1 Announce Type: new Abstract: Production deployments of Multimodal Large Language Models (MLLMs) increasingly rely on system messages to govern model behavior. Yet existing benchmarks either evaluate constraints in text only or embed them into the user turn, leaving system-message adherence in multimodal contexts largely unmeasured; they also leave open…

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Intelligence Artificielle

Hallucination as a Feature, not a Defect: Evaluating a multi-agent architecture to transform speculative language-model outputs into testable scientific hypotheses

arXiv:2608.19206v1 Announce Type: new Abstract: Contemporary Large Language Models (LLMs) are increasingly aligned to suppress hallucinations, prioritizing factual retrieval over combinatorial creativity. While crucial for mitigating misinformation, this alignment may also restrict speculative Research and Development (R&D) by encouraging what this work operationally treats…

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Intelligence Artificielle

Asymmetric Attention Heads: Structured Head-Wise Context Allocation for Transformer Attention

arXiv:2608.19203v1 Announce Type: new Abstract: Standard multi-head attention (MHA) gives every head the same full causal context span, although heads can serve different contextual roles. Some heads may rely mainly on nearby lexical or syntactic context, while others may depend on longer-range relations such as entity interactions, discourse links, or state changes. We…

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Intelligence Artificielle

Automatic bioinformatic software named entity recognition from literature

arXiv:2608.19201v1 Announce Type: new Abstract: Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated…

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Intelligence Artificielle

Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa

arXiv:2608.19200v1 Announce Type: new Abstract: Text summarization refers to the task of condensing a document into a shorter version while preserving its key information. Automatic text summarization (ATS), driven by advancements in natural language processing (NLP), has developed rapidly in recent years. ATS methods are commonly categorized by input type (such as…

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