LLMs recover dominant binomial orders from corpora but align less closely with exact preference distributions, with preference strength partially encoded in middle-to-late layers and manipulable via steering.
Llava-mole: Sparse mixture of lora experts for mitigating data con- flicts in instruction finetuning mllms
9 Pith papers cite this work. Polarity classification is still indexing.
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Flatness Preference Optimization (FlatPO) improves multimodal PEFT generalization by flattening a small set of sharp dimensions that dominate performance.
AsyMoE adds hyperbolic geometry for cross-modal hierarchies and evidence-priority experts to address vision-language asymmetry in LVLMs, reporting 1.5% average gains and 25.45% fewer active parameters.
ViSA proposes expert-driven token generation and dual-branch local fusion modules for view-aware semantic alignment in AGPReID, reporting up to 10.06% mAP gains on the CARGO benchmark.
SMoES improves MoE-VLM performance and efficiency via soft modality-guided expert routing and inter-bin mutual information regularization, yielding 0.9-4.2% task gains and 56% communication reduction.
MoRAM learns continually by adding small rank-1 adapters that act as associative memory items, using input-key similarity to retrieve and mix only the relevant adapters at test time.
LoRA-Mixer routes modular LoRA experts into attention projection matrices with an adaptive Routing Specialization Loss to improve multi-task performance while using fewer trainable parameters than prior LoRA-MoE methods.
CogniRoute adds a cognitive schema and route-aware RL to an omni-modal MoE, reaching 59.38% accuracy on a new 118K-example social video QA benchmark and beating prior baselines by 15-27 points.
MorphoQuant proposes DABC and MDQFO for 4-bit quantization of omni-modal LLMs, claiming superior performance over SOTA W4A4 methods and even W4A16 baselines on benchmarks like ScienceQA.
citing papers explorer
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Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models
LLMs recover dominant binomial orders from corpora but align less closely with exact preference distributions, with preference strength partially encoded in middle-to-late layers and manipulable via steering.
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5% > 100%: Flatness Preference is All You Need for Multimodal Parameter-Efficient Fine-Tuning
Flatness Preference Optimization (FlatPO) improves multimodal PEFT generalization by flattening a small set of sharp dimensions that dominate performance.
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Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Models
AsyMoE adds hyperbolic geometry for cross-modal hierarchies and evidence-priority experts to address vision-language asymmetry in LVLMs, reporting 1.5% average gains and 25.45% fewer active parameters.
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View-Aware Semantic Alignment for Aerial-Ground Person Re-Identification
ViSA proposes expert-driven token generation and dual-branch local fusion modules for view-aware semantic alignment in AGPReID, reporting up to 10.06% mAP gains on the CARGO benchmark.
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SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs
SMoES improves MoE-VLM performance and efficiency via soft modality-guided expert routing and inter-bin mutual information regularization, yielding 0.9-4.2% task gains and 56% communication reduction.
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Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts
MoRAM learns continually by adding small rank-1 adapters that act as associative memory items, using input-key similarity to retrieve and mix only the relevant adapters at test time.
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LoRA-Mixer: Coordinate Modular LoRA Experts Through Serial Attention Routing
LoRA-Mixer routes modular LoRA experts into attention projection matrices with an adaptive Routing Specialization Loss to improve multi-task performance while using fewer trainable parameters than prior LoRA-MoE methods.
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CogniRoute: Learning to Route Social Evidence in Omni-Modal Models
CogniRoute adds a cognitive schema and route-aware RL to an omni-modal MoE, reaching 59.38% accuracy on a new 118K-example social video QA benchmark and beating prior baselines by 15-27 points.
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MorphoQuant: Modality-Aware Quantization for Omni-modal Large Language Models
MorphoQuant proposes DABC and MDQFO for 4-bit quantization of omni-modal LLMs, claiming superior performance over SOTA W4A4 methods and even W4A16 baselines on benchmarks like ScienceQA.