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Computation & Language / NLP
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Newest papers, page 120
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models
Code-switching (CS), the alternation between languages within an utterance, is common in multilingual speech but underrepresented in pretraining corpora.On the Monitorability of Natural Language Autoencoders: Identification vs Discrimination
Natural Language Autoencoders (NLAs) are proposed as oversight tools for language models: an Activation Verbaliser (AV) writes an English explanation of an activation vector, and an Activation…PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards
Reinforcement learning (RL) from unit tests is the common recipe for post-training code models, but a unit test only asks whether the final program passes.Looking Complete Is Not Being Grounded: Evaluating LLM Sitreps Against Human-Authored Situation Reports
Evaluation for automatic crisis reporting should measure what practitioners need, not what is convenient to compare.Rubrics from the Task, Verdicts from Evidence: Pointwise Rubric Rewards with Tool-Augmented Judges
Rubric-based reward models (RMs) evaluate responses against explicit criteria.BLADE: Auditing Information Use for Failure Attribution in LLM Multi-Agent Systems
Large language model multi-agent systems extend individual agents' capabilities through collaboration across specialized roles, tools, and workflows.Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents
Long-horizon agents accumulate progress in persistent task state, yet repeatedly process interaction histories describing how that progress was achieved.Where Do LLM Ensemble Gains Come From? Decomposing Candidate Value, Diversity, and Aggregation
LLM ensemble gains are often measured against a single baseline, but the value attributed to additional candidates can change with the comparison used.SEAR: Self-Evolving Agentic Robotics
Frontier language-model agents can now improve robots on their own: they write control code, test it, and keep what works as skills and memories for the next task.SkillScaffold: Scaffolding Agentic Reinforcement Learning with Policy-Adaptive Skill Credit Assignment and Selective Injection
Agentic reinforcement learning (RL) enables large language models (LLMs) to learn from multi-turn interactions with verifiable outcomes.Self-Evolving Agent Harness
Harness self-evolution revises an agent's software scaffolding, including prompts, memory, workflows, and executable components, while keeping model weights fixed.Back-Translation Still Matters: Leveraging Monolingual Data for Low-Resource Machine Translation with LLMs
Pre-trained Large Language Model (LLMs) are effective on many tasks in zero-shot and few-shot settings.Read the Room Before Your Model Generates
Large language models (LLMs) generally rely on sampling temperature to regulate the amount of randomness in their outputs.OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding
With the rapid development of multimodal large language models (MLLMs), evaluating their video understanding capabilities has become increasingly important.The Last Remote Sensing Model Built by Humans? Casting Any Earth Observation Task as Recursive Self-Improvement
Earth observation (EO) models are still built by hand.Hybrid Reasoning Agents
How can we build an agent that thinks beyond human language, yet still acts through it in the external environment?DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning
Reasoning models achieve strong performance through long chains of thought (CoT), but often spend more tokens than needed to solve a problem.Tev: Scaling Long-Horizon Agentic Environments with Dense Progress Rewards
Long-horizon agents have drawn growing attention for their ability to work autonomously -- from hours to days -- on economically valuable tasks, a capability essential to automated discovery and…ChunkAct: Action Chunking for Agentic Robotics
Agentic robotics places a large language model (LLM) agent in the robot control loop, where it composes perception, motion, and vision-language-action (VLA) tools under the guidance of a memory and…The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery
Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, analyze their results, and autonomously operate…