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Computation & Language / NLP
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The newest 5,000 papers, from 18 September 2026 to 6 November 2026: 5,000 arXiv preprints.
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Newest papers, page 197
Group-Marginalized Self-Rewarding RL Drives Zero-Label Self-Evolving
Self-rewarding reinforcement learning (RL) enables large language models (LLMs) to self-evolve without human labels.Does a prosody-trained representation help beyond trainable fusion? A parameter-matched study with frozen HuBERT
Explicit prosodic cues may help automatic speech recognition (ASR) of spontaneous speech, but auxiliary representations typically require additional trainable components, making it unclear whether…QuantMLA: Function-Aligned Dual-Path Quantization for Low-Bit MLA KV Caching
Multi-Head Latent Attention (MLA) enables expressive multi-head attention with compact caches for its content and decoupled RoPE paths, yet cache memory still scales linearly with context length and…Seeing What Should Be Heard: Diagnosing and Repairing Cross-Modal Shortcuts in Omni-Modal LLMs
Omni-modal large language models (LLMs) are expected to answer a question using the modality it explicitly refers to.VAA-CSEC: Vote-guided Advantage Allocation for Chinese Semantic Error Correction
Chinese Semantic Error Correction (CSEC) targets semantic errors in Chinese text, which are typically more subtle and complex than spelling and grammatical errors but remain relatively underexplored.CorrGRPO: Correlation-Normalized GRPO for Multi-Reward Learning
Group Relative Policy Optimization (GRPO) is widely used to train reasoning language models, where it computes advantages by centering and normalizing rewards across rollouts of the same prompt.On-Policy Visual Evidence Distillation
Visual agents solve problems by interleaving reasoning with image operations, and on-policy distillation (OPD) provides guidance from a strong teacher on student-generated interaction trajectories.Rethinking Multimodal Fake News Detection in the Generative AI Era
Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipulated material into complex forms in which…Harness Evolution as Learning: Approximation, Generalization, and Optimization Limits of Self-Improving Personal Agents
As the capabilities of large language models (LLMs) continue to advance, increasing attention is turning to how to translate their abilities into useful behavior.Momentum-Coupled Rubric Adaptation for Detailed Image Captioning
Detailed image captioning requires accurate and comprehensive descriptions of fine-grained visual content, yet caption quality spans factual accuracy, information coverage, and clarity.RAEGNet: Relation-Aware Evidence Graph Network for Harm-Aware Multimodal Fake News Detection
Existing multimodal fake news detection methods often introduce external information to assist detection.MultiTalk: Scaling Full-Duplex Speech Models to Long, Multi-Party, Bilingual Conversation
End-to-end full-duplex speech models have brought open-source machine conversation closer to human-like interaction, yet existing systems remain limited in two intertwined dimensions: long-context…BaLEEN: Biasing with Latent Encoded Entities for Context-Aware ASR
Transcribing domain-specific entities and rare proper nouns remains a major challenge in automatic speech recognition (ASR).Can Language Models Learn to Forecast Stock Prices
Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning, software engineering, and computer use.Benchmarking Automatic Speech Recognition Tools for Iberian Languages
Comprehensive evaluations of automatic speech recognition (ASR) for Iberian languages remain limited, and low-resource languages, biases, and efficiency trade-offs are underexplored.Dating the Model: Hidden Dates in System Prompts Affect LLM Evaluation
Reproducibility is essential for scientific research, yet prior work shows that LLM outputs vary with hardware and batching.CoEM: Empowering Long-Context Reasoning with Commit-on-Evidence Memory
Long-context reasoning is essential for complex and long-horizon tasks, yet the performance of large language models (LLMs) degrades as context length increases.ER-JEPA: Experience Replay Improves Joint-Embedding Predictive Learning in Language Models
Large language models (LLMs) excel at token-level generation but may learn undesirable abstract semantics and lack comprehensive perception.Cool the Sampler, Not the Learner: Sampling Temperature Moves the Staleness Cliff of Importance-Corrected GRPO
Production RL for language models lets the sampler fall behind the learner and repairs the resulting mismatch with a truncated importance weight.VStress: Correlation-Aware Auditing and Adaptive Budget Allocation for Repeated Verifiers
Repeated verifier calls are useful only when they contribute conditional information.