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Computation & Language / NLP
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Newest papers, page 138
Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection
A useful language model should know when to answer and when to say "I don't know.OpenPA: Forced-alignment-free Multi-granular Pronunciation Assessment with Mixed-transcript Adaptation
Recent advances in automatic pronunciation assessment (APA) have driven a shift from scripted to free-form assessment.Studying Comics at Scale with Artificial Intelligence: The PAGE Framework and Three Studies of American Comics and Japanese Manga
Comics are one of the great visual records of the twentieth century: hundreds of thousands of pages in which a mass readership saw its leaders, its wars and its heroes.Task Context Shapes Component Roles in Language Models
Large language models reuse the same attention heads across tasks, but identifying these shared heads does not explain what they do differently in each task.Structural Automata for Neural Semantic Parsing
Neural semantic parsers generate meaning representations token by token, yet well-formedness depends on references, scope, and graph relations that unfold during generation.When VLMs Destroy What They Know: Spatial Representations Fall Below Lexical Priors
Vision-Language Models (VLMs) increasingly analyze and reason about images, yet it remains unclear whether their internal representations faithfully encode the structured relations required for such…RSI-Anything: Unlocking Recursive Self-Improvement with World Models
Recursive self-improvement (RSI) aims to transform interaction experience into persistent improvements, yet external feedback is costly and world-model predictions may become unreliable as policies…Does System One Transfer? Calibrated Typed-Decision Models, Label Budgets, and Translation for Indonesian Regional Languages
Calibrated typed-decision ("System One") models return a label and a probability in a single forward pass at a small fraction of the cost of large language models (LLMs), yet their evaluation has…ClawLoop: Self-Improving Agents via Harness-Native Training Loop
The rapid advancement of Large Language Models (LLMs) has driven their deployment toward general-purpose harness environments, where LLM-based agents operate in sandbox workspaces and interact…The AI's Poker Face: Contrasting Persuasion and Sycophancy in Voice-Based Multi-Turn Conversations
As interactions with large language models (LLMs) become increasingly routine, concerns about their capacity to influence users' beliefs grow.OpenMobile-2: Building Versatile Mobile Agents with Scalable Environments and App-Native Tools
We introduce OpenMobile-2, a near-frontier mobile agent with fully open training environments and recipes.Next-Concept Modeling: Fast, Parallel Coarse-to-Fine Text Generation
Diffusion language models (DLMs) offer parallel sampling for faster generation than autoregressive models, an increasingly important advantage as inference becomes a dominant computational workload.AutoEnvScaling: Automating the Data Flywheel with Terminal Agents
Terminal agents solve tasks in software engineering and scientific research by executing commands in a terminal.Unconstrained CTI-REALM with Open-Weights
Organisations that want language-model agents for security work face a choice between open-weight models, which they can run and audit themselves, and far more expensive closed frontier models.Simple Linear Corrections for Compression in Late Interaction
Late-interaction (multi-vector) models represent documents using a set of token-level vectors, providing a middle ground between cross-encoders and dense encoders in terms of quality and cost of…Modeling the Arrival Time of Information-Bearing Events in Natural Language
Understanding how information is distributed over time is fundamental to modeling natural language.Learnable Low-Rank Polynomial Sketch for Effective Linear Attention
Softmax attention in Transformers suffers from quadratic complexity in sequence length, making it impractical for long-context applications.Harness^2: Recursive Agent Harnessing for an Open World
LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses.Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study
LLM data engineering requires iterative experimentation to determine how data selection, rewards, and curricula affect model learning, making it a natural setting for AI-for-AI (AI4AI) research.The Thinking Leakage in Reinforcement Learning for LLMs
Hybrid reasoning models such as the Qwen3 series switch between a THINK mode and a NO-THINK mode through a few chat-template tokens.