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 89
Medical Fine-Tuning Reshapes, Not Reduces, Patient-Facing AI Risk: A Rubric-Based, LLM-Judge Evaluation Framework
Patient-facing medical question answering with large language models (LLMs) is moving from research demonstrations to production deployments, yet evaluation remains anchored on factual correctness.Ask, Don't Guess: Targeted Clarification for Speech Recognition Errors
Speech recognition in real time in far-field conversational settings still incurs significant errors.Efficiency Measured in English: Tokeniser Coverage Bounds Small Language Models on Indic Scripts
Small Language Models with quantisation and parameter-efficient adaptation are widely offered as the route to multilingual AI under compute constraints, and their efficiency is almost always…MisleadQA: Diagnosing LLM Failures under Misleading Context
Large language models (LLMs) are widely used to answer questions from retrieved or user-provided context.Learning to Program Retrievals: Composing Search Primitives via Reinforcement Learning
Retrieval-augmented language models increasingly use multi-step search to solve knowledge-intensive tasks, yet existing systems typically expose retrieval through fixed pipelines or collections of…Dual Task Vector Merging: Leveraging Emotion and ASR Knowledge for Low-Resource Alzheimer's Disease Detection
Alzheimer's Disease (AD) detection from speech benefits from both paralinguistic and linguistic cues, yet model training is frequently constrained by limited data.Bellman Language Models: Solving the Recurring Decisions of LLM Agents
A general observation in agentic workflows, especially bounded ones, is that the decision space of the LLM, when we separate a decision from generation, starts approaching saturation: the agent…The Missing Compliance Class: LLM Graders That Collapse Jailbreak Success, and a Free Check
Jailbreak papers report an attack success rate, the share of responses that deliver the harmful request, and read it as a property of the target model.The One-Pixel of Reasoning: One Truthful Relation Is Enough to Mislead LLM Agents
Can one sentence manipulate an agent's belief by targeting a single implicit warrant linking evidence to a conclusion?Measuring Data Diversity with Embeddings: A Taxonomy and Benchmark for Diversity Measures
Diversity is a critical property of text corpora, with direct implications for training data curation, test set design, and model generalization.LastWords: Boring Problems Revisited
In 2022, the second author published a Last Words piece in Computational Linguistics in which he pointed out that, despite the advances in AI up to that point, there were still some problems in…Peer Pressure: Execution-Verified Co-Evolution of Attacker and Defender Populations for Code Security
LLM code agents increasingly ship code with little oversight, yet defenders trained on static vulnerability datasets fall behind adapting adversaries.HybridResearcher: Bridging the Structured-Unstructured Boundary for Deep Research Agents
While LLM agents are increasingly capable of gathering and synthesizing evidence from a single homogeneous source, such as structured tables or unstructured data, real-world analytical questions…Compositional Activation Steering Across Languages and Behaviors with Contextual Gates
Activation steering has emerged as an effective and precise way to influence the behaviours of language models.A Cross-Domain Evaluation of Prompting Techniques for Large Language Models
Prompt engineering has become a central mechanism for guiding the behavior of Large Language Models (LLMs) across a wide range of reasoning and language understanding tasks [1], [2].Arctic-SWE: Revisiting the Long Horizon Agent Training by Modeling-System Co-Design
Long-horizon agent training, including software engineering, is advancing rapidly, but the recipe and infrastructure needed for a strong reinforcement-learning baseline remain difficult to reproduce.How Well Can LLMs Verbalize Mathematical Formulas? A Multi-Domain Evaluation
Large Language Models (LLMs) have shown strong capabilities across natural language tasks.CARTA: Self-Supervised Reusable Transaction Embeddings via Heterogeneous Tokenization and Cross-Attention
Acquiring fraud labels for payment transactions is expensive and inconsistent across tasks, and high-cardinality identifiers such as merchant names make one-hot feature pipelines infeasible at scale.AtomSD: Procedural Atom-Guided Self-Distillation for LLM Agents
Large language models (LLMs) possess broad factual knowledge and strong reasoning capabilities, but solving complex tasks also requires procedural knowledge about how to act.Do We Need the Largest LLMs for Every Clinical Decision?
Large clinical language models often require multi-GPU infrastructure, limiting local deployment.