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 180
Copying Before Suppression: What Drives a Below-Chance Dip During Language Model Training?
Mechanistic interpretability usually studies fully trained models, yet the computations that drive a behaviour can change while the model is still learning the task.Representational Control over Self-Report & Behavior Coherence in LLM Risk-Taking
Self-report is an appealing low-cost probe of an LLM's dispositions, but recent work finds only selective agreement between what models report and how they behave.What Gradients Add to Text Leakage in Split Language Models, Counted per Token and per Document
Split learning lets a client train a language model on a server without sending its text.InvestigationWorlds: An Agentic Environment for Legal Investigation
We introduce InvestigationWorlds, an agentic environment for legal investigation.PB-GRPO: Learning Socially Adaptive LLM Agents from Persona-Driven Simulation with Preference-Batched GRPO
Building LLMs that behave well socially, not merely correctly, requires Building LLMs that behave well socially, not merely correctly, requires more than producing locally helpful responses.ExpertMuon-Compass: Alignment-Guided Step Sizes for Mixture-of-Experts Training
Mixture-of-experts (MoE) language models send each token to a few experts, so each expert is trained on a different part of the data, and this part changes during training.Trajectory-Derived Confidence for Reliable, Resource-Aware Clinical Text-to-SQL Agents
LLM agents for clinical text-to-SQL applications reason autonomously over multiple steps but cannot assess whether their own reasoning or outputs can be trusted.AuraForge: Scaling Security Supervision for Training Coding Agents
Coding agents are now proficient enough to generate complex software applications from a single prompt.Child-Adapted Structured Phonological Representations for Interpretable Speech Sound Analysis
Structured phonological representations provide an interpretable alternative to generic speech embeddings, but existing models are largely trained on adult speech.Kinematic MeanFlow: One-Step Action Generation Policy for Robotic Foundation Models
In this paper, we study how to achieve one-step action generation in Robotic Foundation Models (RFMs), aiming to overcome the high inference latency of multi-step flow matching.DeBERTa-ConPara: Attack-Aware and Deployment-Realistic Detection of AI-Generated Text
Robust detection of AI-generated text under deployment conditions is challenging: distribution shifts across domains and generators, adversarial perturbations of the input surface, and the absence…ActiveSaddler: Automated Curriculum Learning for Agent Harness Optimization
Automated harness optimization can substantially improve LLM agents by iteratively updating their prompts, tool interfaces, and control logic from execution feedback.The Geometry of Contextual Relations: Language Models Address Facts by Order of Mention
Human reasoning depends on how objects are related within propositions. \textit{How do relations organize the language representations of contextual contents?} We give an LLM a list of facts in its…Efficient Task Adaptation in Large Language Models: A Survey of Weight-Based, Prompt-Based, and Embedding-Based Adaptations
As large language models are increasingly deployed across diverse downstream tasks, efficient task adaptation has emerged as a central challenge.ReHoPER: Receding-Horizon Planning for Enhanced Reasoning
We propose ReHoPER, an inference-only, zero-shot method that improves large language models' reasoning by generating and answering intermediate questions along multiple paths before the final answer.Beyond Leaderboards: Tokenomics of Agentic Small Language Model Ensembles
As large language models (LLMs) move from standalone assistants into agentic workflows, evaluation must extend beyond scalar leaderboard accuracy to account for operational reliability, cost…Role-aware Heuristic Episodic Attention for Conversational LLMs
Large language models often lose track of persistent instructions and relevant information as multi-turn conversations grow.RPTune: Learned Context Curation for LLM Catalog Search
For small merchant businesses (SMBs) whose catalogs fit within a long-context LLM, full-catalog prompting offers a compelling alternative to multi-stage retrieval designed primarily for large…A Citation-Grounded Benchmark for Trustworthy Earnings Call Transcript Analysis with Large Language Models
Large language models (LLMs) have been increasingly used for financial document analysis, including earnings call transcripts (ECTs).The Devil Is in the Reconstruction Loss Scale: Rethinking Optimization in LLM Quantization
Post-training quantization (PTQ) methods typically use sequential quantization that partitions a pre-trained LLM into a series of units (e.g., transformer blocks), with one unit quantized at each…