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 173
Understanding Errors in LLM-Based Question Answering over Imperfect Tables
Answering questions over imperfect tables requires handling errors that can affect the answer.SepRQ : Self-Supervised Speech Mixture Representation Learning via Mask-Free, Multi-Scale Source Separation
Self-supervised learning (SSL) is standard for speech representation learning, but mainstream models are designed around single-speaker audio, limiting their usefulness in multi-speakers scenarios.Penumbra: Sample-Efficient Adversarial Search for Regulatory Obligations
Agents are entering finance, healthcare and law, sectors where a violation leaves no lexical signature and carries real penalties.Not Self-Decidable: LLMs Cannot Draw the Boundary of What an Agent Verifier Can Check
A verifier for an agent faces rules of two kinds: the ones a fixed check can settle and the ones that require a judge.WNet: Discrete Wavelets Transform for Efficient Token Mixing
In a Transformer, token mixing is the step that lets each token draw information from other tokens, and it dominates the cost of encoding long sequences.Verb-ICL: Rethinking In-Context Learning for Structured Prediction
Structured prediction tasks pose unique challenges for in-context learning (ICL): their compositional outputs require modeling fine-grained, token-level patterns that sentence-level approaches fail…More Value per Key: Asymmetric Sparse Attention for Faster LLM Decoding
Autoregressive generation in Large Language Models (LLMs) is constrained by the memory and computational demands of attention mechanisms.How Do People Challenge Racial Stereotypes Online? Counter-Story Detection Across Reddit Communities
Counter-storytelling is a powerful mechanism people use to challenge dominant narratives.Stabilizing language models under continual learning via condition-anchored distillation
Continual adaptation of language models can change their output distribution on prompts learned earlier, while retaining every old prompt-answer pair may be undesirable or impossible.EMODE: Dynamic Para-Semantic Experts for Emotion-Aware Speech Language Modeling
Large speech language models have demonstrated strong capabilities in unified cross-modal understanding and generation, yet paralinguistic cues, especially emotion, remain difficult to preserve.Verdicts Without Annotated Evidence: Rejection Sampling or Label-Only Post-Training for Evidence Recovery?
In many review workflows the verdict is the only thing retained.WavePrune: One period is often enough for RoPE
Rotary Position Embedding (RoPE) encodes token positions by rotating each two-dimensional channel of the query and key vectors at a channel-specific frequency, making the attention logits invariant…AegisFlow: A Multi-Agent Agentic AI Framework for Autonomous Remediation and Self-Healing in Fragile Data Ecosystems
Traditional data pipelines are notoriously brittle, often failing due to upstream schema drift, API contract changes, or website DOM modifications.QuanLing: Cross-Branch Validation of Language Distance Quantification on Western Romance
Quantifying language distance among closely related languages remains a core challenge in quantitative linguistics.Learning When to Commit from Partial Speech for End-to-End Simultaneous Speech Translation
Simultaneous speech translation must emit useful target text before the source is complete while preserving every committed token.VERSE: Verified Self-Evolving Optimizer for Agent Harnesses
Harness evolution improves an LLM agent's prompts, tools, and workflow, while the optimizer's own tools and procedures often remain fixed.Large Language Continuous Diffusion Models
Despite the success of discrete diffusion language models (dLMs) for fast parallel decoding, their non-smooth, high-dimensional space hinders trajectory steering for reasoning and inference…LEAP: Learning Efficient Action Proposals For LLM Agents
LLM agents are known to be slow in rollouts.Asterism: Exploring and Synthesizing Scattered Observations into Literature-Grounded Hypotheses and Theories
A theory draws many independent observations into one framework with novel hypotheses.Large language models exhibit unreliable updating of clinical judgment as patient evidence evolves
Large language models (LLMs) are increasingly explored for clinical reasoning, but whether they appropriately revise judgments as patient evidence evolves remains unclear.