ReasonAudio benchmark reveals that state-of-the-art text-audio retrieval models struggle with reasoning tasks like negation and duration, and multimodal LLMs lose reasoning ability after contrastive fine-tuning.
Title resolution pending
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
background 2
baseline 1
citation-polarity summary
representative citing papers
SET detects input-level backdoors in T2I diffusion models by learning a benign cross-attention response space from clean samples and flagging deviations under multi-scale perturbations.
IdeaBlocks modularizes divergent intents into Exploration Blocks with multi-level reuse options, enabling 2.13 times more images explored and 12.5% greater visual diversity than baseline in a comparative user study.
RankElastor mitigates embedding collapse via spectrum-robust token mixing and GLU-based P-FFNs, yielding better performance and scaling on industrial recommendation datasets.
VISOR is a VLM-based automated test oracle that evaluates robot task correctness and quality from videos while reporting its own uncertainty, tested on GPT and Gemini across four tasks and over 1000 videos with Gemini showing higher recall and GPT higher precision but low uncertainty-correctness tie
GTPBD-MM is the first multimodal benchmark for global terraced parcel extraction, integrating image, text, and DEM data with experiments showing that textual and terrain cues improve delineation accuracy over image-only approaches.
CIR is a cross-platform container image format for Python/R-style apps that defers dependency assembly to deployment, cutting image size by 95% and deployment time by 40-60% versus traditional bundled images.
citing papers explorer
-
ReasonAudio: A Benchmark for Evaluating Reasoning Beyond Matching in Text-Audio Retrieval
ReasonAudio benchmark reveals that state-of-the-art text-audio retrieval models struggle with reasoning tasks like negation and duration, and multimodal LLMs lose reasoning ability after contrastive fine-tuning.
-
Scaling Exposes the Trigger: Input-Level Backdoor Detection in Text-to-Image Diffusion Models via Cross-Attention Scaling
SET detects input-level backdoors in T2I diffusion models by learning a benign cross-attention response space from clean samples and flagging deviations under multi-scale perturbations.
-
IdeaBlocks: Expressing and Reusing Divergent Intents for Graphic Design Exploration using Generative AI
IdeaBlocks modularizes divergent intents into Exploration Blocks with multi-level reuse options, enabling 2.13 times more images explored and 12.5% greater visual diversity than baseline in a comparative user study.
-
Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation
RankElastor mitigates embedding collapse via spectrum-robust token mixing and GLU-based P-FFNs, yielding better performance and scaling on industrial recommendation datasets.
-
VISOR: A Vision-Language Model-based Test Oracle for Testing Robots
VISOR is a VLM-based automated test oracle that evaluates robot task correctness and quality from videos while reporting its own uncertainty, tested on GPT and Gemini across four tasks and over 1000 videos with Gemini showing higher recall and GPT higher precision but low uncertainty-correctness tie
-
GTPBD-MM: A Global Terraced Parcel and Boundary Dataset with Multi-Modality
GTPBD-MM is the first multimodal benchmark for global terraced parcel extraction, integrating image, text, and DEM data with experiments showing that textual and terrain cues improve delineation accuracy over image-only approaches.
-
CIR: Lightweight Container Image for Cross-Platform Deployment
CIR is a cross-platform container image format for Python/R-style apps that defers dependency assembly to deployment, cutting image size by 95% and deployment time by 40-60% versus traditional bundled images.
- FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoning