最近 7 天
2
次命中
Keyword Tracking
关键词追踪：instruction tuning
这个页面会长期追踪你配置里关心的关键词，并把命中的论文按日期沉淀下来。
最近 30 天
8
次命中
全部历史
11
次命中
近期走势
最近一次命中来自 LM：SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment
2026-06-15
0
2026-06-16
0
2026-06-17
2
2026-06-18
1
2026-06-19
0
2026-06-20
0
2026-06-21
0
2026-06-22
0
2026-06-23
1
2026-06-24
0
2026-06-25
1
2026-06-26
0
2026-06-27
0
2026-06-28
0
命中明细
按日期回看匹配到这个关键词的论文标题，并保留来源 feed 信息。
2026-06-25
SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment
Sparse Mixture-of-Experts (MoE) architectures have emerged as an increasingly influential paradigm as they offer a strategic balance between parameter scalability and computationa…
2026-06-23
Evaluation Awareness Is Not One Capability: Evidence from Open Language Models
Safety benchmarks assume that test-condition behavior predicts deployment behavior, an assumption that fails if models detect evaluation cues and adapt. This opens a gap between b…
2026-06-18
Trade-offs in Medical LLM Adaptation: An Empirical Study in French QA
The development of large language models (LLMs) has led to an increased focus on their adaptation to specialized domains and languages, yet the effectiveness of domain adaptation…
2026-06-17
LoopCoder-v2: Only Loop Once for Efficient Test-Time Computation Scaling
Looped Transformers scale latent computation by repeatedly applying shared blocks, but sequential looping increases latency and KV-cache memory with the loop count. Parallel loop…
VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Termination
MDLMs generate text by denoising a preallocated masked response canvas, making response-length modeling central to instruction tuning. Existing MDLMs often inherit the autoregress…
2026-06-03
Exploring Adversarial Robustness and Safety Alignment in Multilingual Multi-Modal Large Language Models
Multimodal Large Language Models integrate visual perception into language reasoning, introducing a continuous attack surface susceptible to adversarial attacks. Prior work on MLL…
Large Language Models Are Overconfident in Their Own Responses
Prior work has shown that instruction-tuned large language models (LLMs) are less well calibrated than their base pre-trained counterparts. However, little is known about the freq…
2026-06-02
ProtoAda: Prototype-Guided Adaptive Adapter Expansion and Geometric Consolidation for Multimodal Continual Instruction Tuning
Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, but real-world deployment requires them to continually acquire new vision-language…
2026-05-26
MAGIC: Multimodal Alignment & Grounding-aware Instruction Coreset for Vision-Language Models
Instruction tuning of large vision-language models (LVLMs) increasingly depends on massive multimodal corpora, yet these datasets contain samples with substantial redundancy, low…
2026-05-12
Dynamic Cross-Modal Prompt Generation for Multimodal Continual Instruction Tuning
Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world deployment often requires continual capability expansion across sequ…
2026-04-16
MAny: Merge Anything for Multimodal Continual Instruction Tuning
Multimodal Continual Instruction Tuning (MCIT) is essential for sequential task adaptation of Multimodal Large Language Models (MLLMs) but is severely restricted by catastrophic f…