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 217
Grounding Memory Summarization in Utility Intent
Existing summarizers for memory systems are typically optimized for human-facing criteria such as faithfulness, which misaligns with their true objective: preserving the evidence needed to support…TeacherGRPO: Closing the Capacity Gap in Reasoning Distillation via Teacher Alignment
Reasoning distillation from powerful teacher models to smaller students faces the Gap Curse: as teachers grow more sophisticated, their complex distributions increasingly diverge from what students…CORA: A Protocol for Diagnosing Boundary Robustness in Text-to-Audio Retrieval under Query Reformulations
Text-to-Audio (T2A) retrievers are typically evaluated with caption style queries, but the same user intent can be expressed in many forms.SMAT: Simple and Efficient Merge-Aware Training
Model merging integrates the capabilities of multiple experts without joint retraining, but standard expert training optimizes task loss alone and does not guarantee good performance after merging.Raven: The Harness of Harnesses for Composable Agentic Intelligence
As large language models advance, AI agents are moving beyond isolated, domain-specific tasks toward long-horizon, cross-domain workflows.MIC: Explaining Image-Claim Inconsistencies in AI-Generated Multimodal Misinformation
Claims paired with AI-generated images are a rapidly growing form of misinformation.Context Spanning: A Communication Framework for Full-Duplex Speech Models and External LLM Backends
Full-duplex spoken dialogue models can listen and speak simultaneously like the real-time dynamics of human conversation.Rethinking Token Reweighting for SFT: Suppress, Reverse, and Extrapolate Learned Features
Supervised fine-tuning (SFT) learns most aggressively from tokens that the model deems least likely.GSM: Efficient Language Modeling with Shared Global State
Efficient language models must reduce not only the cost of individual accesses to past context but also the overhead of repeatedly selecting and processing historical information across layers.A Cheap Verifier is Good Enough: LLM Post-training is Robust to Erroneous Rewards
When post-training large language models on tasks with semi-verifiable rewards, there are many factors (training steps, base model size, training order, data quality, verifier accuracy, etc.) that…DISCO: Distributed Long Context Scaling with Grounding-Reasoning Disaggregation
While Large Language Models (LLMs) advertise million-token context windows, reasoning quality often collapses as inputs grow -- a phenomenon termed context rot.LLMs Trust Their Own: Identity-Dependent Conformity in Multi-Agent Systems
Large language models (LLMs) are increasingly deployed in multi-agent settings, where agents observe and influence one another, making social influence a key dimension of AI behavior and safety.E-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding
Natural Language Understanding (NLU) plays a crucial role in various applications, yet its performance suffers from weaknesses in handling the complexities of human languages, ranging from lexical…ManiEdit: Sequential Unstructured Knowledge Editing for Language Models from a Manifold Perspective
Large language models (LLMs) inevitably generate some incorrect or outdated content, necessitating efficient and precise mechanisms for continual knowledge updates.Jev Matches 7B Language Models for Speech-Neuroprosthesis Rescoring
A speech neuroprosthesis decodes attempted speech from brain activity and ends by rescoring the decoder's candidate sentences with a language model of several billion parameters, the only component…TGRL: Temperature-Grouped Reinforcement Learning for Efficient Exploration in LLMs
Efficient exploration often remains a central bottleneck in reinforcement learning with verifiable rewards (RLVR).Quizzing the Translation: A Prover-Grounded Evaluation Metric for NL$\rightarrow$FOL
A standard pipeline for symbolic reasoning over natural-language problems translates them into first-order logic and invokes a theorem prover.ParaAgent: Reinforcing Parallel Acting in Open-World Tool Environments
Language model agents are increasingly deployed in open-world tool environments, which require balancing exploring unknown capabilities and exploiting known ones.Safety Reconstructed: Generative Modeling via Masked Diffusion Builds Strong Safety Guardrails
Guard models are the last line of defense between a language model and a harmful output, yet their training objective is surprisingly narrow.Quantifying Behavioral Tails in Black-Box Language Models
We introduce RareTrap, a framework for estimating the probability of severe behaviors in black box large language models (LLMs).