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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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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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Does a Language Server Save Tokens for Coding Agents? A Measurement Methodology and Preliminary Study

arXiv:2608.13568v1 Announce Type: new Abstract: Coding agents spend most of their context budget on retrieval. Lexical retrieval (grep) is universal, instant, and zero-setup, but noisy: it cannot tell a definition from a call from a comment. Semantic retrieval via the Language Server Protocol (LSP) is precise and typed, but needs a running, indexed server and pays a…

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Measuring Cross-Task Behavioral Consistency in Language Model Agents

arXiv:2608.13598v1 Announce Type: new Abstract: Agent evaluation relies almost entirely on outcome metrics such as success rate, which capture whether an agent succeeds but not how consistently it behaves. We argue that behavioral consistency across tasks is a distinct and measurable property, and we introduce the Behavioral Consistency Metric (BCM) to quantify it. BCM trains…

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Stable Miscalibration in Large Language Models: A Practical View of High-Confidence Errors

arXiv:2608.13591v1 Announce Type: new Abstract: High-confidence errors in large language models are often treated as evidence of fragile internal inference. We study a different possibility: stable miscalibration, where a confident wrong answer remains locally stable under small perturbations. We combine two diagnostics: a label-aware output-level audit score that ranks…

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BCMT: Blockwise Causal Memory Transformer

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

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

Does a Language Server Save Tokens for Coding Agents? A Measurement Methodology and Preliminary Study

Measuring Cross-Task Behavioral Consistency in Language Model Agents

Stable Miscalibration in Large Language Models: A Practical View of High-Confidence Errors