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Agents combine reasoning with tools to interact with external systems and complete real-world tasks. Early agents typically interleave reasoning and actions along a single execution chain. On complex tasks, this chain becomes unreliable because growing histories obscure intermediate dependencies and allow early plannin...
Multimodal continual instruction tuning (MCIT) aims to enable multimodal large language models to acquire new capabilities from sequential tasks while preserving previously learned knowledge. Existing methods primarily mitigate catastrophic forgetting by constraining parameter updates or separating task-specific adapta...
Class-Incremental Learning (CIL) requires models to recognize new classes over time without forgetting previously learned ones. With the rise of vision-language pre-training, CLIP has become a strong foundation for CIL. A common design in CLIP-based CIL is to construct textual classifier weights by encoding class-name...
This paper systematically identifies one of the fundamental challenges behind CIL, named feature collision, where the features learned by the current task-specific model may collide with those of the previous models, leading to forgetting of previously learned tasks and hindering the learning of new tasks.
PeakBench is a benchmark of executable multi-tool workflows with execution-grounded dependency annotations and measured resource profiles that shows that strong logical planning does not reliably translate into safe or efficient execution under resource constraints, and exposes resource information to reduce avoidable...
ResiSpec, a framework that strategically reforms the proposal distribution during verification to anchor the residual target mass within the draft model's high-confidence regions, prevents candidate obsolescence and achieves up to 1.92$\times$ speedup over state-of-the-art multi-candidate methods.
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