News
Distilling the Essence: Efficient Reasoning Distillation via Sequence Truncation
arXiv:2512.21002v2 Announce Type: replace Abstract: Distilling the capabilities from a large reasoning model (LRM) to...
Distilling Multilingual Vision-Language Models: When Smaller Models Stay Multilingual
arXiv:2510.26271v1 Announce Type: new Abstract: Vision-language models (VLMs) exhibit uneven performance across languages, a problem...
Disco-RAG: Discourse-Aware Retrieval-Augmented Generation
arXiv:2601.04377v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) has emerged as an important means of...
Digging for clues about the North Pole’s past
In the past, even with an icebreaker and during peak melt season, getting to the...
DiFlow-TTS: Discrete Flow Matching with Factorized Speech Tokens for Low-Latency Zero-Shot Text-To-Speech
arXiv:2509.09631v1 Announce Type: cross Abstract: Zero-shot Text-to-Speech (TTS) aims to synthesize high-quality speech that mimics...
Diffusion Language Models Know the Answer Before Decoding
arXiv:2508.19982v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as an alternative...
Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for Reasoning
arXiv:2510.27469v1 Announce Type: new Abstract: In recent years, large language models (LLMs) have witnessed remarkable...
Did You Forget What I Asked? Prospective Memory Failures in Large Language Models
arXiv:2603.23530v1 Announce Type: new Abstract: Large language models often fail to satisfy formatting instructions when...
Did I Faithfully Say What I Thought? Bridging the Gap Between Neural Activity and Self-Explanations in Large Language Models
arXiv:2506.09277v2 Announce Type: replace Abstract: Large Language Models (LLM) have demonstrated the capability of generating...
DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction
arXiv:2510.09211v2 Announce Type: replace Abstract: When performing reasoning tasks with user-specific requirements, such as strict...
DIALEVAL: Automated Type-Theoretic Evaluation of LLM Instruction Following
arXiv:2603.03321v1 Announce Type: new Abstract: Evaluating instruction following in Large Language Models requires decomposing instructions...
DiaBlo: Diagonal Blocks Are Sufficient For Finetuning
arXiv:2506.03230v1 Announce Type: cross Abstract: Finetuning is a critical step for adapting large language models...
