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Newest papers, page 151
Plan-and-Patch: Diffusion Language Models for Agentic Planning
Planning is increasingly important for long-horizon agents, where successful execution requires coordinating subgoals, tool use, and intermediate outcomes over many steps.NavGPT-3: Harnessing Context in a Hierarchical Navigation Runtime
Language models trained with long-horizon agentic reinforcement learning can generalize knowledge through reasoning, express precise actions, and pursue goals over many steps, raising the ceiling on…Grammar Concept Annotation at Scale: Deployed Fine-Tuned Small Language Models Outperform Prompted Frontier Models
Corrective feedback is among the best-evidenced drivers of second-language acquisition, yet corrections delivered during lessons rarely accumulate into an actionable view of grammar mastery.Real Long-Term Memory for AI: A 50-Million-Token Window That Is Faster and Cheaper Than Recompute
A large language model can only use the text that fits in its context window, and it recomputes its internal key-value (KV) state for a prompt every time the prompt is sent.Disentangling Linguistic and Paralinguistic Information with Routed Sparse Autoencoders
Self-supervised speech encoders contain linguistic and paralinguistic information in a shared, entangled representation space.Conversational Voice Aesthetic Model with Reinforcement Learning from Human Listeners
We introduce Conversational Voice Aesthetic Model, a speech large language model for describing the voice aesthetics of real or synthetic speech responses in natural conversational contexts.Sparse Attention Is Matrix Approximation, Not Choosing from a Bag of Values
Large Language Models (LLMs) achieve strong performance across many domains, but their efficiency is limited by the quadratic cost of attention with respect to prompt length.Stochastic Teacher Intervention for Agentic On-Policy Distillation
On-policy distillation (OPD) efficiently transfers capabilities from a stronger teacher to a student language model through dense token-level supervision on student-generated rollouts and has shown…Large Language Models for Machine Translation Quality Annotation: Humans and Models Are Both Challenged
Large Language Models (LLMs) are considered to be a more efficient and cost-effective alternative to human judgment for Machine Translation (MT) evaluation.Language Models for Page-Level Layout Decisions in E-commerce Search
E-commerce search pages are critical touchpoints for millions of online shoppers.StoreBench: A Live-Commerce Environment for Evaluating and Training Autonomous Operator Agents
Reinforcement learning environments are now a primary lever for improving large language model (LLM) capabilities in post-training, yet most agentic benchmarks remain static: the world moves only…AI4Fire: Evaluating Large Language Models on Wildfire Tasks
Large language models (LLMs) are entering wildfire management, where overstated evaluations can cost property and lives.When Citations Mislead? A Claim-Level Benchmark for Legal Hallucination Detection
Large language models are increasingly used in legal research and drafting, but they can still produce claims that sound convincing without being supported by the cited source.Back in Style: A Sociolinguistic Approach to Authoring and Measuring Persona Fidelity in User Simulation
As agentic systems gain commercial popularity, user simulators increasingly serve as measurement instrument for their evaluation.Prompts versus Rules: Auditing and Controlling Speech Naturalness Behaviors in Voice User Simulators
As voice agents gain more popularity commercially, the user simulators used to evaluate the deployed agents are also being developed to include more realistic, variable, and diverse speech…Loose Lips: Understanding Privacy Leakage in Agentic Dialogue
As LLM agents are increasingly deployed in high-stakes enterprise settings, understanding how they handle sensitive information in agentic dialogue has become critical.Disentangling Models from Personas in Heterogeneous LLM Simulations
Multi-agent simulations with large language models (LLMs) often operate networks of agents with a single base model.Quality-Aware Self-Correcting Speech Translation on an Edge Device
We present a fully offline speech-to-speech translation pipeline that runs on a Jetson Nano (4 GB) and corrects its own weak translations without retraining.HouseholdBench: Evaluating Large Language Models as Predictors of Household Economic Behavior
Large language models (LLMs) have the potential to meet a key goal in economics: a quantitative model of household decision making, across a variety of settings.Two Vectors Replace In-Context Demos: Structured Task Adaptation via Embeddings
In-context learning (ICL) adapts frozen large multimodal models (LMMs) to new tasks from a few demonstrations (demos), but re-encodes them at every query, where each demo image adds up to hundreds…