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Newest papers, page 146
Recurrent Harness for Agentic Continual Learning
Language model agents increasingly operate over long interaction streams, requiring online adaptation to distribution shifts through continual learning.GeoFitBench-LLM: A Benchmark for Assessing Large Language Models' Capabilities in Geospatial Data Requirements Elicitation for Geo-Analytic Tasks
Large language models are becoming central to GeoAI systems, yet their evaluation rarely considers the elicitation of geospatial data requirements.Activating the Annotator: Operationalising Lived Experience in Data Annotation
Perspectivist NLP has established that annotator positionality shapes labels for subjective tasks, but the field still lacks methods for operationalising it.Unsupervised Multimodal Reward Modeling
Vision-language reward models are trained on annotated preference pairs, which are expensive to collect, can disagree, and are ranked by benchmark accuracy.U4RASD at KnowledgeGraphEval-2026: Ontology-Constrained Encoder and LLM Systems for Arabic Relation Extraction
This paper details our submission to KnowledgeGraphEval 2026 Subtask 2 on directed Arabic relation extraction.MADS: Multi-Agent Dialog Simulation for Robust Conversational Agents
Task-oriented conversational agents remain fragile under realistic conditions, especially when users exhibit non-cooperative behavior.DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models
Language models have been adopted faster than evaluation practices that reliably assess their risks, and a growing share of use is affective rather than informational.REDaCT: Collapse-Free Unlearning in Large Reasoning Models
Unlearning in large reasoning models (LRMs) requires removing forget-set information from both the reasoning trace and the final answer.Large Language Models, Machine-Assisted Cultural Analytics, and the Agentic Turn
Technologies like large language models make it easier to convert cultural material into analyzable variables, while LLM-driven agentic systems extend machine assistance to other parts of the…Meta-Reflective Evolution Effectively Improves Production AI Systems
Large Language Model (LLM)-guided evolutionary optimization has successfully improved prompts, programs, and even entire AI systems.People Making Conversation: Evaluating How Naturally Speech Models Converse
A conversational speech model has to decide, at every pause, whether to speak.VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails
LLM agents increasingly operate over sensitive resources, such as private documents and mutable system states, which can create serious safety and security risks.Is the Future Predictable? Modeling Future Predictability for Reliable Temporal Reasoning
Large language models (LLMs) remain unreliable when reasoning about the future.Fin_Agent at FinEval 2026: Task-Aware Methods for Multilingual Financial NLP
We present Fin_Agent, our submission to the FinEval 2026 shared task on multilingual fi nancial language understanding.Adversarial Images Hijack Web Agents from Visual Grounding to Browser Execution
Modern web agents built on large vision-language models process webpages, select relevant UI elements, and translate model outputs into browser actions.Dialect-Robust Speech Language Models with Synthetic Pseudo-Dialect Augmentation
Speech Language Model (SLM) performance often degrades on dialects due to data scarcity.OnlineQAT: On-Policy Distillation for Ultra-Low-Bit Large Language Models
Quantization-aware training (QAT) can recover much of the accuracy lost when large language models are compressed below four bits.TopoGraphRAG-Bench: Evaluating Multimodal GraphRAG on Layout-Grounded Evidence Reasoning
Real-world documents distribute evidence across text, tables, figures, and captions within complex page layouts.The Confidence Game: Strategic Miscalibration in Human-AI Delegation
Calibrated uncertainty quantification is essential to ensuring AI agents are trustworthy and reliable.Expert Coupling in MoE Pretraining: Reducing All-to-All Overhead with Correlated Placement and Token Shuffling
Mixture-of-Experts (MoE) layers replace the feed-forward block of a Transformer with E expert networks, and each token is routed to k of these experts.