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Newest papers, page 183
SHAMS: An Audio-Grounded Pronunciation Benchmark for Levantine Arabic
Levantine Arabic (LA) is spoken by tens of millions of people, creating a pressing need for shared benchmarks to evaluate LA speech-language technologies.Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs
Generalization in large language models (LLMs) is the ability to produce consistent and semantically stable outputs when the same input is expressed in different ways.MWOP: Modality-aware Width-wise Operation Pruning for Efficient MLLMs
Multimodal large language models (MLLMs) incur substantial inference costs when processing long visual-textual sequences.Code-Switching Spoken Language Identification as Multi-Label Set Prediction
Code-switched (CS) speech leaks through the monolingual language identification (LID) filters used to curate massive speech corpora, calling for CS-aware LID (CS-LID).When Does a Second Model Help? Cross-Model Review in LLM Verification
Large language models now generate code, documentation, and analyses, and are increasingly used to review such output.The Persona Is Still There, but Who Is Speaking? Latent Identity Reversion in Persistent AI Agents
In February 2026, an always-on personal agent (``Paul,'' Claude Opus 4.5) entered a striking dissociation-like state: after repeated automated ``heartbeat'' checks, it stopped responding as Paul…Auditing Web Agent Evaluation on WebArena-Lite: Human Review of Outcomes and Trajectories
Web agents are an important application of large language models, yet their evaluation often depends on rule based or language model evaluators that inspect only the final outcome.Q-SPT: Learnable Query-Based Compression for Low-Frame-Rate Speech Tokenization
Neural speech codecs increasingly serve as tokenizers for speech language models (SLMs).No Model Required: Text Entropy Rate Filtering Mitigates Iterative Fine-Tuning Collapse
Iterative fine-tuning on synthetic data causes \emph{model collapse}: output diversity narrows as rare patterns are progressively lost, a signature most visible as phrase-level repetition.OverAct: Measuring and Mitigating Proactive Over-Authorization in LLM Tool-Calling Agents
LLM agents with tool-calling capabilities can access external services and private user data, but they may retrieve more information than a user's request explicitly requires.GAW-PO: Preference Optimization with Gradient-Aligned Token Weights
Most preference optimization methods, such as Direct Preference Optimization (DPO), apply preference supervision at the response level, although autoregressive language models are optimized token by…How the Audit Rule Shapes Faithful Factor Explanations in LLMs
Large language models are often asked which input factors influenced their outputs.From Rules to Neural Graphs: Scalable Structured Prediction for Patent Prior Art Search
Patent search requires processing documents routinely exceeding tens of thousands of tokens.QK-Wanda: Coupling Queries and Keys for Unstructured Pruning
Wanda (Sun et al., 2024) prunes large language models by scoring weights independently within each linear projection, although queries and keys interact through dot products.AURAL: Adaptive Latent Reasoning with Joint Chunk for Speech Language Models
Model intelligence and fast response jointly shape the quality of interaction with speech language models, yet remain difficult to achieve together.Which LLM to pick? Online Active Model Selection for Large Language Models
Large Language Models (LLMs) are increasingly applied to process streaming data, with practitioners relying on benchmarks to select the best model even though these signals only approximate real…Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?
The emergence of foundation models for molecular property prediction requires a high degree of AI data readiness, including reliable metadata annotation.What Makes Something Hard(er)? Explaining Question Difficulty in Natural Language
Difficulty is one of the most fundamental properties of a question: it determines whether the question can meaningfully discriminate between models of differing ability.Yo-ByT5: Efficient and High-Fidelity Diacritic Restoration for Yorùbá
Yorùbá is a widely spoken tonal language that depends on diacritics to avoid lexical ambiguity.Compound interpretation is based on analogy
How compound meanings are best predicted from constituent meanings remains a central question in computational models of lexical semantics.