The rising demand for long-video understanding presents significant challenges due to massive information and complex temporal dependencies. Direct processing with Large Visual Language Models (VLMs) incurs high computational costs and risks information loss due to frame sampling. Meanwhile, existing agent-based methods often struggle with fragmented contexts and the high overhead of iterative processing. Therefore, we propose VideoNav, which introduces a new paradigm of index-centric navigation, reformulating video understanding as cost-aware structured query processing. VideoNav decouples heavy perception from online reasoning, including offline index construction and online adaptive navigation. In the offline phase, we transform unstructured video data into a structured, multi-granular index tree. During the online phase, the framework executes a top-down, coarse-to-fine navigating strategy. Leveraging the LLM agents, it efficiently locates relevant information and answers the query with navigating guidance, multi-level navigation, dual-path routing and visual inspection. Crucially, this adaptive mechanism enables early termination when high-level information suffices, minimizing redundant computation. Extensive experiments across different benchmarks demonstrate the effectiveness of our framework. VideoNav achieves decent accuracy while delivering 2.5x-5.7x cost savings and a 2.9x-5.8x speedup.

On-policy distillation reduces train-inference mismatch by supervising student-generated trajectories, but full-vocabulary token-level distribution matching remains expensive for large language models. A common approximation truncates reverse-KL distillation to the student’s top-k tokens, which preserves efficiency but drops tail-token supervision and ignores support-level mass mismatch between the student and teacher. We analyze these approximation gaps and propose Stochastic Support Sampling with Teacher Mass Reweighting, which samples fixed-size supports from the student distribution and uses teacher support mass as a reliability weight for the conditional distillation loss. Under the same support budget, our method outperforms the strongest baseline by +1.4 hard-math average points on Qwen3-8B→Qwen3-1.7B-Base, and by +1.1 math average points and +2.8 code average points on DeepSeek-R1-Distill-Llama-8B→Meta-Llama-3.1-8B-Base.

Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have become a dominant framework for building intelligent assistants. In real-world applications such as ChatGPT with web search, the retrieved document often comes from diverse, potentially unreliable sources and may contain inconsistent claims. Unlike traditional search engines that rely on users to manually compare information, LLM-based systems typically feed all retrieved content into the model’s context, requiring LLMs to autonomously identify, differentiate, and reason over conflicting viewpoints. Unlike mainstream LLM evaluation tasks like math and code generation that are primarily focused on reasoning with factual context, question-answering with multi-source references requires fundamentally different capabilities to identify and reason over knowledge contradictions. In this paper, we introduce ConfRAG, a benchmark for evaluating LLMs’ reasoning capability over real-world conflicting documents retrieved from the web. It consists of 1,814 real-world questions, each paired with an average of 9.58 retrieved paragraphs from heterogeneous online sources. A total of 57.2% of the questions exhibit explicit contradictions. We further propose three structured evaluation tasks, answer clustering, answer coverage, and reason coverage, to quantify a model’s ability to organize and explain contradictory content. Experiments with state-of-the-art models such as GPT-4.1 and Claude-3-7-Sonnet reveal substantial performance gaps, highlighting the need for more targeted research in contradiction-aware question answering. To the best of our knowledge, ConfRAG is the first benchmark specifically designed to evaluate contradiction-aware reasoning on real-world long web documents.

Mixture of experts (MoE) is a popular technique to improve capacity of Large Language Models (LLMs) with conditionally-activated parallel experts. However, serving MoE models on memory-constrained devices is challenging due to the large parameter size. Typical solutions such as memory swapping or expert pruning may lead to significantly higher latency or severe accuracy loss.In this paper, we introduce SwapMoE, a framework for efficient serving of MoE-based large language models with tunable memory budgets. The main idea of SwapMoE is to keep a small dynamic set of important experts, namely Virtual Experts, in the main memory for inference, while seamlessly maintaining how the Virtual Experts map to the actual experts. Experiments have shown that SwapMoE can reduce the memory footprint while maintaining reasonable accuracy. For example, on text summarization tasks with Switch Transformer, SwapMoE can reduce the memory consumption from 14.2 GiB to 4.7 GiB, together with 50% latency reduction and a slight Rouge-2 score drop of 0.041.