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Intelligence Artificielle

Measuring Fairness in Large Audio Language Models via Semantic-Aware Bias Estimation

arXiv:2608.13624v1 Announce Type: new Abstract: Large Audio Language Models (LALMs) have seen increasing use for audio understanding tasks such as speech recognition and audio question answering, raising concerns about fairness across demographic subgroups. Fairness evaluation in spoken-input settings is challenging due to confounding factors, including semantic variation in…

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Intelligence Artificielle

IterCOMP: Reasoning-aware Adaptive Prompt Compression for Multi-hop Question Answering

arXiv:2608.13588v1 Announce Type: new Abstract: Multi-hop question answering requires complex reasoning across multiple evidence segments, which often overwhelms retrieval-augmented generation systems with lengthy and noisy contexts, thereby undermining both efficiency and accuracy. While existing prompt compression methods attempt to address this issue, they are typically…

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Intelligence Artificielle

Jais 2: A Family of Arabic-Centric Open Large Language Models

arXiv:2608.13580v1 Announce Type: new Abstract: Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong performance across the Arabic and culturally grounded benchmarks evaluated in this report. The family includes, to our knowledge, the largest open…

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Intelligence Artificielle

BCMT: Blockwise Causal Memory Transformer

arXiv:2608.13578v1 Announce Type: new Abstract: Transformer architectures rely on dense self-attention to model long-range dependencies, but this mechanism exhibits quadratic complexity with respect to sequence length. We introduce BCMT (Blockwise Causal Memory Transformer), an architecture for long-context language modeling that decouples local token interactions from global…

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Intelligence Artificielle

Not All Tokens Are Equal: Inflation-Aware Routing for Agentic LLM Systems

arXiv:2608.13571v1 Announce Type: new Abstract: When a language model fails to answer a query on the first attempt, an agentic system retries, consuming additional tokens each time. This retry overhead creates a gap between what a model's per-token price implies and what a full workflow actually costs. We call this gap \emph{token inflation} and define it as the ratio of true…

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Intelligence Artificielle

Think in Latent, Explain in Language: Self-Explainable Latent Reasoning

arXiv:2608.13570v1 Announce Type: new Abstract: Latent reasoning has emerged as a powerful alternative to text-based Chain-of-Thought (CoT), offering significant gains in computational efficiency by compressing verbose reasoning into compact embeddings. However, compressing reasoning into the latent space renders the thinking opaque, hindering its interpretability. Current…

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