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Newest papers, page 219
Controlling Speaking Rate in Autoregressive TTS via Activation Steering
Autoregressive text-to-speech (TTS) systems synthesize natural speech but, once trained, offer little control over speaking rate.ChemOPD: Multi-Teacher On-Policy Distillation for Multi-Task Chemical Reasoning
Large language models are increasingly expected to support diverse chemical reasoning capabilities within a unified model.Laya as a Typed Probabilistic Assessor: An Independent Reproduction and a Preregistered Study of Calibration and Selective Escalation
The shipped Laya Typed-Decisions checkpoint, a 421M-parameter ModernBERT-large assessor that answers typed choice/noul/score questions over workflow state, is uniformly under-confident.Rethinking Contextualization by Reinterpreting Attention Head Channels
Contextualization, the core operation of language modeling, transmits information across words to build sentence-specific word representations.Program-Verified Self-Evolution for Vision-Language Models
Self-evolving vision-language models train on questions they generate from unlabeled images.In-Context Adaptation of Encoder-Decoder Models in Speech Recognition
In-context learning offers an appealing approach to adapt automatic speech recognition (ASR) models to new speakers, accents, and domains by providing speech-text pairs as demonstrations at…Population Physics, Population Problems: Safety and Emergence in LLM Societies
The collective behaviour of large language model (LLM) societies is not the sum of their individual outputs.Lost with a Map: Conversational State and Behavioral Reliability in Language Models
Task-oriented dialogue requires maintaining and updating information across turns, yet language models expose no explicit belief-state object.LLMs learn different forms of metacognition when trained to predict their own accuracy
Large language models are trained to always produce an answer, regardless of whether they possess the relevant knowledge, which leads them to fabricate facts.Faster Block-Diffusion Serving with Distribution-Free Risk Guarantees
Block-diffusion language models are served at hand-picked operating points, such as acceptance thresholds, buffer depth, schedule, checkpoint and precision, and each point is chosen by its mean…Where Activation Sparsity and KV-Cache Sparsity Cross in LLM Decoding
At each step, decoding one sequence with a large language model rereads the projection weights, whose traffic is fixed, and the key-value (KV) cache, whose traffic grows with context.NSV-Shift: A Contrastive Benchmark for Non-Speech Vocalization Understanding and Response Adaptation in Speech-to-Speech Models
We introduce NSV-Shift, a contrastive benchmark for evaluating whether speech-to-speech models can understand non-speech vocalizations (NSVs) and adapt their responses accordingly.SlopBench: How Well Can We Rank Language Models by Slop? A Multi-Domain Benchmark of Repetitive AI Writing
SlopBench asks which models produce the stiff, repetitive prose readers call AI slop, a question detectors leave open once they have classified a text as machine-written.Quantization Error Is Spectrally Flat: A Single Random Probe Is a Calibrated, Data-Free Sensitivity Estimator, with Application to Budget-Targeted Mixed-Precision Quantization
A single random Gaussian probe gives an unbiased estimate of the squared Frobenius norm of a layer's quantization error.Simple Diffusion Language Models Are More Effective Few-Step Generators Than Reported
Diffusion language models (DLMs) promise fast parallel generation, yet high-quality samples often require large number of refinement steps, which diminishes their advantage in practice.On the Token Value Inequality in Efficient Reasoning
Chain-of-Thought reasoning has enabled large language models to achieve substantial performance gains on complex tasks.Do System One Decisions Add Up? A Study of Probabilistic Coherence
A decision model can give probabilities that sum to one for every question yet disagree with itself when the same decision is broken into smaller steps.Beyond Solo and Consistency: Vindicating Multi-Agent Debate via Conditional Progressive Pruning
Large Language Model (LLM) based Multi-Agent Debate (MAD) is one of the most effective test time scaling techniques.High-Level Text Preprocessing for Semantic Similarity Analysis of Discursive Texts: A Framework and Empirical Demonstration
Semantic Textual Similarity (STS) methods assume that a document's lexical content faithfully represents what it asserts.Opera: A Verbal Critic Framework for Long-horizon Coding Agents
Long-horizon coding agents need timely corrections, yet feedback can be ineffective or even harmful when it misjudges ongoing work or fails to address the underlying problem.