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Newest papers, page 39
Self-Keyed In-Parameter Memorization for External Memory-Free Dialogue Recall in Frozen Language Models
A large language model maintains conversational content in an external storage that is outside the model and grows as the dialogue continues.Mining the Nineteenth Century for the Thirteenth: Register-Scale Forced Alignment and HTR Bootstrapping via Legacy Regesta and Antiquarian Editions
Handwritten text recognition (HTR) for the registers of Pope Honorius III depends on modern expert transcriptions, which exist only for regnal years 1-2 of Reg.AutoVend: Benchmarking LLM Agents on Long-Horizon Autonomous Vending Operations
Large language model (LLM) agents are extending agent-mediated commerce on the Web from shopping for consumers to operating businesses for sellers.The State and Fate of Multilingual, Contextual Evaluation in the NLP World
Multilingual evaluation benchmarks are the primary instrument for assessing whether large language models generalize beyond English, yet the adequacy of these benchmarks has received little…SpectRAG: Weighted Chunk Graphs for Token-Budgeted Context Selection in Multi-Hop Retrieval-Augmented Generation
Retrieval-augmented generation (RAG) systems ground large language models (LLMs) by retrieving external evidence and conditioning generation on the retrieved context.V-LUMEN: Visual Lookup Memory for Embedding Scaling in Vision-Language Models
Embedding-level scaling expands the capacity of language models through lookup-based memory rather than by increasing model depth, width, or dense computation.Zooming without Zooming v2: From Seeing Details to Connecting Evidence for Generalizable Multimodal Perception
Multimodal perception forms the foundation of multimodal intelligence.Guided-RLVR: Guided Exploration in Reinforcement Learning with Verifiable Rewards
Reinforcement learning with verifiable rewards (RLVR) has shown strong empirical gains in improving the reasoning ability of large language models (LLMs) on tasks such as mathematics and programming.Exploring the Limits of the Delta Learning Hypothesis: Single-Pair Generator Biases in Direct Preference Optimization
Direct Preference Optimization (DPO) typically relies on preference pairs generated from diverse model pools and ranked by an external LLM judge.EnergyCal: Taming Loss Spikes in Muon-Family Optimizers for Sparse MoE Training
Sparse mixture-of-experts (MoE) language-model training can exhibit large loss spikes with Muon-family optimizers.Hierarchical Low-Rank Reparameterization for Parameter-Efficient Fine-Tuning
Parameter-efficient fine-tuning (PEFT) methods adapt large models by introducing a small number of trainable parameters.Text Generation in Low-Resource Languages Using Human-Curated Vocabularies
Synthetic text generation offers a promising approach to alleviating data scarcity in low-resource languages.Self-Regularized Flow Language Models
We introduce SERF (Self-Regularized Flow Language Models), a simple self-supervised framework for improving continuous diffusion language models by exploiting the denoising trajectory itself.Prospector: learning a compact graph traversal policy for scientific reasoning
Scientific reasoning in specialized engineering fields depends on knowledge scattered across the literature, often proprietary and absent from frontier LLM training data.Mechanistic Failure Modes of Weight Pruning in Transformer Language Models
Pruned language models are judged almost entirely by accuracy and perplexity, which assumes that preserved output behaviour implies preserved internal computation.What Survives Distillation? A Mechanistic Interpretability Study of GPT-2 and DistilGPT-2
Knowledge distillation compresses large language models into compact students that preserve aggregate metrics, yet the mechanistic consequences of depth-reduction distillation remain largely…Coverage Under Contradiction: A Benchmark for Negation-Aware Retrieval
Coverage-evaluation benchmarks quantify whether retrieved evidence covers the different pieces of information needed to satisfy an information need.Uncovering Shortcut Amplification in Video RLVR through the Lens of Perceptual Uncertainty Collapse
Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective post-training paradigm for enhancing the video reasoning capability of Large Multimodal Models (LMMs).Decision Models as Judges for Hallucination Detection
Large language models are widely used as judges of model outputs (LLM-as-a-Judge), but they are costly and can hallucinate or be overconfident.Aligning Large Language Models for Safety in African Languages: A Case Study of Hausa
Safety alignment research for large language models (LLMs) remains concentrated on high-resource languages, leaving open how existing alignment methods perform in low-resource, culturally distinct…