Ping Lin

Kuang Wang, Lai Wei, Qibing Bai et al.

Speech Language Models (SLMs) exhibit strong semantic understanding, yet their generated speech often sounds flat and fails to convey expressive intent, undermining user engagement. We term this mismatch the semantic understanding-acoustic realization gap. We attribute this gap to two key deficiencies: (1) intent transmission failure, where SLMs fail to provide the stable utterance-level intent needed for expressive delivery; and (2) realization-unaware training, where no feedback signal verifies whether acoustic outputs faithfully reflect intended expression. To address these issues, we propose SA-SLM (Self-Aware Speech Language Model), built on the principle that the model should be aware of what it thinks during generation and how it speaks during training. SA-SLM addresses this gap through two core contributions: (1) Intent-Aware Bridging, which uses a Variational Information Bottleneck (VIB) objective to translate the model's internal semantics into temporally smooth expressive intent, making speech generation aware of what the model intends to express; and (2) Realization-Aware Alignment, which repurposes the model as its own critic to verify and align acoustic realization with intended expressive intent via rubric-based feedback. Trained on only 800 hours of expressive speech data, our 3B parameter SA-SLM surpasses all open-source baselines and comes within 0.08 points of GPT-4o-Audio in overall expressiveness on the EchoMind benchmark.

Yang Liu, Li Zhang, Fang Liu et al.

In recent years, large language models (LLMs) have advanced rapidly, substantially enhancing their code understanding and generation capabilities and giving rise to powerful code assistants. However, in practical repository development, excessively long-horizon conversational context may overwhelm models, causing the loss of critical information and degraded performance, thereby limiting the utility of code assistants. Existing context management methods proposed to mitigate this context dilemma primarily target general-purpose conversations, while repository-oriented solutions remain largely unexplored, which is largely due to the lack of reliable evaluation benchmarks. To bridge this gap, we present LoCoEval, the first long-horizon conversational context management benchmark tailored to repository-oriented development scenarios. Adhering to three key principles, LoCoEval is constructed via an LLM-driven pipeline that generates realistic and diverse repository-oriented conversations, capturing key interaction patterns such as iterative requirements, noisy input, and retrospective questions. We evaluate 7 baselines, including 4 representative context management methods, using 3 advanced backbone LLMs on LoCoEval. The results reveal substantial challenges faced by standalone LLMs and existing approaches, especially memory systems, in repository-oriented conversational scenarios. To address these limitations, we further propose an improved method integrating conversational and repository information into a unified memory, which outperforms all baselines (*Oracle* excluded) and demonstrates robustness. Additionally, we investigated the impact of various factors on method performance, providing actionable insights for future research.