Shashi Kumar, Yacouba Kaloga, Petr Motlicek, Ina Kodrasi, Andrea Cavallaro
原文摘要
Large language models solve complex problems by generating lengthy chains of explicit reasoning tokens. While effective, this makes reasoning expensive, length-sensitive, and constrained to (discrete) natural language. While latent reasoning offers a continuous alternative, determining useful structures for intermediate latent states is an open challenge. In this paper, we formulate latent reasoning as a geometric path-approximation problem within the model's pretrained token-embedding space. We introduce Geometric Latent Reasoning (GLR), which uses a lightweight transition head to predict iterative direction updates in embedding space. Using textual chain-of-thought traces as anchors, GLR learns to approximate discrete reasoning trajectories while permitting continuous deviations from exact token embeddings. Evaluations on mathematical reasoning benchmarks using Qwen3 models reveal an emergent phenomenon: geometric latent reasoning induces substantially shorter generations without an explicit length objective. By replacing early explicit reasoning with continuous latent steps, models often reach correct answers using substantially fewer total generation steps. These findings suggest that continuous trajectories act as compact intermediate reasoning states, exposing a new tradeoff between latent computation budget, output length, and accuracy. Our code is publicly available at: https://github.com/idiap/GLR. The project page is available at: https://glr-llm.github.io/.
Audio-text models such as Contrastive Language-Audio Pretraining (CLAP) typically pool frame and token sequences uniformly into global embeddings. This discards temporal structure and impairs both retrieval and localization, especially for short events in long recordings. We introduce SCLAP, a sequence-level contrastive framework that aligns audio frames and text tokens using monotone one-dimensional optimal transport. Its learned marginals and transport plan provide fine-grained temporal supervision from clip-level captions alone. On short-form audio (i.e., Clotho and TACOS datasets with 15-30s recordings), SCLAP remains competitive for retrieval while improving zero-shot localization over CLAP and Text-aware Attention Pooling. On long-form audio (i.e., CASTELLA dataset with up to 300s recordings), SCLAP improves retrieval performance and its localization advantage grows as the event-to-context ratio decreases. At inference, SCLAP requires no optimal transport computation since retrieval can use pooled embeddings and localization can use inexpensive frame-text cosine scores.
The performance of speech classification systems such as speech emotion recognition or pathological speech detection can degrade substantially when there is a domain shift between training and test data. This shift can arise due to e.g., differences in recording setup, acoustic environment, speaker, language, or speaking style. Adversarial domain adaptation addresses this problem by encouraging representations that are less sensitive to domain-specific differences. However, many adversarial approaches rely on training a separate domain discriminator, which can be unstable and difficult to train. In this paper, we propose to use a discriminator-free adversarial learning network (DALN) for unsupervised domain adaptation in speech classification tasks. DALN reuses the task classifier as an implicit discriminator and aligns domains through the nuclear-norm Wasserstein discrepancy on classifier outputs. We extensively evaluate the performance of DALN on intra-lingual, cross-lingual, and noisy adaptations for speech emotion recognition and pathological speech detection. Extensive experimental results show that DALN generally achieves the best performance across the considered tasks and domain shifts, outperforming discriminator-based domain adaptation approaches.