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 18 September 2026 to 6 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 176
The Geometry of Knowledge Accessibility in Large Language Models
Large language models (LLMs) contain broad knowledge, but they cannot access all of it reliably.HARPO: Hallucination-Aware Reinforcement Learning for Faithful and Creative Language Generation
Large Language Models (LLMs) are prone to generating hallucinated content, which compromises their reliability in knowledge-intensive tasks.SecJev: Bringing Security Expertise to System One Decision Models
Security workflows need models that turn complex observations and explicit policies into decisions.Unmasking Propaganda: A Comparative Analysis of Masked and Causal Language Models
Propaganda detection is an essential task in natural language processing (NLP), particularly in the context of manipulative political communications.An automated pipeline for standardised speech-unit annotation in spontaneous dialogue
Quantifying conversational dynamics requires reliable identification of interactional units and their temporal boundaries, but speech activity alone does not distinguish conversational turns from…MintEval: Do LLMs Implement the Trading Strategy You Asked For? A Behavioural-Equivalence Benchmark for Natural-Language-to-Strategy Code
Large language models are moving from producing trading signals to writing the code that executes them.Peer Influence across Heterogeneous AI Models
When two AI agents disagree, who persuades whom?Ask, Relax, or Act? Evaluating Actionable Indeterminacy in LLM Preference Reasoning
An LLM agent can recognize uncertainty yet still choose the wrong next step: asking when action is already justified, or seeking clarification when the constraints must change.Emergent Structure in the Marginal Attention Space of Language Models
While representation similarity across independently trained language models is well-documented, how internal mechanics such as attention behave across models remains far less characterized.Ontological Instability and Statistical Amplification: The Paradox of "Humanizing" LLM-Generated Text
Supervised AI-text detectors report high benchmark accuracy, but it is not clear what their decisions are based on.Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals
Matching candidates to vacancies is central to recruitment, and a recruiter needs to see why a candidate fits, not only a single opaque relevance score.The Fragility of Trigger-Tag Mechanisms for Misuse Detection in Open-Weight LLMs
Open-weight language models can be downloaded, modified, and deployed beyond their developers' control, limiting the effectiveness of centrally enforced safeguards.Benchmarking Literature Retrieval for a Model Organism: A Dictyostelium Case Study
Biological literature retrieval systems are often developed and evaluated using broad biomedical corpora and general-purpose search tasks.Investigating the Role of Reasoning-Language Alignment in Monolingual Retrieval-Augmented Generation
Reasoning traces improve large language models (LLMs), but current models are trained to reason mostly in English.Predicting Steering Vectors and Adapter Weights for Few-Shot Author-Style Transfer
Adapting large language models to an individual author's style from a few examples is challenging, and scientific writing sharpens the difficulty: formal conventions leave little surface variation…Hindsight-Guided Rationale Distillation for Rare Disease Diagnosis
We study hindsight-guided distillation for rare disease diagnosis on ZebraMap: a 1.5B student is fine-tuned on chain-of-thought traces from a 8B teacher that observes the ground-truth diagnosis…Gains and Collapse in On-Policy Distillation:A Reinforcement Learning Perspective
On-policy distillation (OPD) has become an important approach to language model post-training.Not Until the Evidence Says So: Teaching LLM Investigators When to Close a Case
Accident, defect and outage investigations end with a decision that ordinary question answering never faces: whether the evidence gathered so far is enough to close the case.Source Preference in the Wild: How LLM Agents Favor Items by Source, and How to Reduce It
As LLM agents decide on users' behalf which product to buy, which hotel to book, or which paper to cite, a preference for items from certain sources (the sites or services they come from) shapes…KV$^2$: A Self-Refining KV Cache
The memory footprint of the key-value (KV) cache constrains the practical use of long-context models, and it dominates cost when one prefilled context must later serve many different queries.