AI & Machine Learning · Computer Science & AI
Computation & Language / NLP
The newest papers in Computation & Language / NLP that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 17 September 2026 to 5 November 2026: 5,000 arXiv preprints.
Get the few papers worth your time in Computation & Language / NLP every morning.
Follow Computation & Language / NLP and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 40
Your Policy Is Secretly a Harness: The Decision Geometry of Agent Interfaces
Language model agents are post-trained and deployed through a harness, the prompts, tools, and control logic that present a task to the model and execute its outputs.REGLAT at ArGuard Shared Tasks: An Ensemble-Based Model Integrating NER for Harmful Content Detection in Arabic
Detecting harmful content in Arabic is challenging because of dialectal variations and the semantic overlap between different harm categories.Watermark Basins: Black-Box Detection of Distilled LLMs
Proprietary language models and their reasoning traces are vulnerable to API-based distillation, where an adversary trains a student model on the teacher model's outputs.Where Should the Evaluation Budget Go in LLM-Driven Agent Scaffold Evolution?
Large language model (LLM)-driven agent scaffold evolution improves agent workflows through iterative candidate generation and evaluation.Modeling Preference Dependencies with Directed Acyclic Graphs for Narrative-Driven Recommendation
Narrative-driven recommendation allows users to express multifaceted needs through free-form text where stated preferences naturally qualify and condition one another.Multi-Label Agreement: A Flexible Framework for Inter-Annotator Reliability
Reliable evaluation of inter-annotator agreement (IAA) is important for assessing the consistency of annotation processes.UCVE: Graph Structured Retrieval for Compliance Validation
Structured compliance validation requires determining whether every obligation in a predefined requirement space is satisfied, violated, or unaddressed by a given document.EmbeddingGemma 2: Unified and Efficient Multimodal Representations
We present EmbeddingGemma 2, an open multimodal embedding model which natively represents text, images, audio, video, and combinations thereof in a unified space.Same Verdict, Different Readers: Personalizing Fact-Checking Explanations from Relative Preferences
Fact-checking explanations help Web users understand why a claim receives its verdict, yet the same explanation may not satisfy readers with different backgrounds and preferences.LinMix: Data Mixing as Linear Domain Attribution
Language model performance depends strongly on the mixture of domains in the pretraining data corpus.Game-Theoretic Solvers with Bayesian Opponent Modeling and Moral Rhetoric Framing for GLEE
We describe the agent we submitted to the GLEE Competition, which plays bargaining, negotiation, and persuasion games against a shared pool of human and AI opponents.Representing the Unseen: Rethinking Visual Imagination for Spatial Reasoning
Visual world models increasingly support spatial reasoning by generating RGB views of unobserved states.ETAMem: Decoupling and Aligning Heterogeneous Memory for Long-Term Conversational Agents
Long-term memory enables Large Language Models (LLMs) to maintain coherent and personalized interactions across extended conversations.Test-Time Communication in Trading Agent Swarms
Communication between agents has mostly been studied on tasks with a shared objective and fast, clear feedback.What Carries Forward? Pretraining Disclosure Across Language Model Releases
Understanding how language models were pretrained requires information about their data, processing procedures, and computational resources.Follow the Gradient: Tracing Safety Degradation Back to Training Data
Fine-tuning Large Language Models (LLMs) on instruction data can quietly erode their safety, even when no training example looks harmful.Teach reality, not ethics: a drop-in continued-pretraining corpus installs coupled human-biosphere-AI welfare dispositions across model families
Large language models (LLMs) are increasingly used to make and execute decisions that affect living systems.semanticfa: An R Package for Response-Free Semantic Factor Analysis of Psychometric Scales
The items of a reflective psychometric scale can be read as a proposal about a construct's structure.EnvSched: Environment-Aware Scheduling for Rollout-Efficient Agent Reinforcement Learning
As executable environments for language agents scale, generating and verifying their rollouts becomes increasingly expensive.Rare, Relevant, and Still Wrong: How Memory Fails LLM Group Decisions
When an LLM group's discussion is compressed into a short memory for a final decision maker, what should the memory keep?