Long-form video question answering hinges on locating brief, sparse evidence buried in minutes of dense action. Flattening the entire video into a single caption stream forces the LLM to sift through thousands of tokens, where decisive cues are easily drowned out by surrounding context. We present MSTree, a training-free framework that organizes captions into a Multi-Scale Evidence Tree. The tree is built by a coordinated dual-modal segmentation method that combines visual scene transitions and textual coherence under temporal contiguity, producing segments that are both visually and semantically consistent. Inference follows a mine-then-reason workflow: the LLM first scans coarse segment summaries to locate the most relevant event, then reasons over its fine-grained captions. When the first-round prediction falls below a confidence threshold, Contrastive Targeted Reflection (CTR) activates. The model articulates a discriminative gap that specifies what evidence is still missing to separate the top candidate answers, and an answer-anchoring constraint requires any newly retrieved evidence to both support the revised prediction and contradict the original. On EgoSchema, NExT-QA, and Video-MME, MSTree reaches 68.4%, 77.5%, and 55.7% accuracy, outperforming all training-free baselines.

Existing indirect prompt injection (IPI) attacks commonly face a stealth-effectiveness tradeoff: features that make payloads effective can also make them detectable because attack intent is localized in a single payload. LogicInception moves IPI to discourse-level inference: attack logic is decomposed into register-matched declarative fragments distributed across tool outputs and linked by fabricated causal dependencies that the agent reconstructs through reasoning. To our knowledge, it is the first IPI framework to demonstrate successful unauthorized tool execution with no directive instruction anywhere in the injected content. No fragment contains a command or exploits instruction-following; the attack emerges from the agent’s own inference. We observe an empirical capability-safety amplification pattern: discourse-level amplification correlates with task capability across the evaluated models. On 1,139 scenarios with 5 defenses, LogicInception achieves 42.9-48.8% ASR at 88% per-fragment stealth, while all baselines remain below 8% composite success. The three prompt-level defenses reduce baseline ASR by up to 72% but reduce LogicInception by less than 7%; we trace this asymmetry to three architectural factors that motivate discourse-level rather than content-level countermeasures. This defense asymmetry is larger for higher-utility models in our evaluation, suggesting a tension between reasoning capability and safety.

Recent advancements in language models (LMs) have demonstrated strong capabilities in semantic understanding and contextual modeling, which have flourished in generative speech enhancement (SE). However, many LM-based SE approaches primarily focus on semantic information, often neglecting the critical role of acoustic information, which leads to acoustic inconsistency after enhancement and limited generalization across diverse SE tasks. In this paper, we introduce LLaSE-G1, a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement. LLaSE-G1 offers the following key contributions: First, to mitigate acoustic inconsistency, LLaSE-G1 employs continuous representations from WavLM as input and predicts speech tokens from X-Codec2, maximizing acoustic preservation. Second, to promote generalization capability, LLaSE-G1 introduces dual-channel inputs and outputs, unifying multiple SE tasks without requiring task-specific IDs. Third, LLaSE-G1 outperforms prior task-specific discriminative and generative SE models, demonstrating scaling effects at test time and emerging capabilities for unseen SE tasks. Additionally, we release our code and models to support further research in this area.