A blank-image ablation test reveals that high probe accuracy on VLM spatial reasoning frequently reflects priors or inverted signs rather than image grounding, with horizontal grounded, vertical prior, and depth inverted.
super hub Mixed citations
Gemma 3 Technical Report
Mixed citation behavior. Most common role is background (70%).
504
Pith papers citing it
Background
70% of classified citations
abstract
We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision understanding abilities, a wider coverage of languages and longer context - at least 128K tokens. We also change the architecture of the model to reduce the KV-cache memory that tends to explode with long context. This is achieved by increasing the ratio of local to global attention layers, and keeping the span on local attention short. The Gemma 3 models are trained with distillation and achieve superior performance to Gemma 2 for both pre-trained and instruction finetuned versions. In particular, our novel post-training recipe significantly improves the math, chat, instruction-following and multilingual abilities, making Gemma3-4B-IT competitive with Gemma2-27B-IT and Gemma3-27B-IT comparable to Gemini-1.5-Pro across benchmarks. We release all our models to the community.
hub tools
citation-role summary
background 50
baseline 10
other 4
dataset 3
method 2
citation-polarity summary
claims ledger
- abstract We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision understanding abilities, a wider coverage of languages and longer context - at least 128K tokens. We also change the architecture of the model to reduce the KV-cache memory that tends to explode with long context. This is achieved by increasing the ratio of local to global attention layers, and keeping the span on local attention short. The Gemma 3 models are trained with distillation and achieve superior performance to Gem
authors
co-cited works
representative citing papers
Noisy expert imitation learning requires exponential samples for offline methods but polynomial for a variant of on-policy distillation under a noise condition.
MedCUA-Bench provides 18 clinical scenarios in 10 domains as a testbed for computer-use agents on medical UIs, with evaluations of 23 agents showing low success rates especially on real systems like OpenEMR.
Looped linear transformers with LN provably converge via GD to implement the power method on principal component prediction.
MM-JudgeBench shows substantial cross-lingual performance variance in 22 LVLM judges, with model size and architecture as poor predictors of multilingual robustness.
SAHM is the first Arabic financial benchmark with seven tasks including AAOIFI standards QA, fatwa reasoning, accounting exams, sentiment analysis, summarization, and event-cause reasoning, showing that Arabic fluency does not imply strong financial reasoning in 20 tested LLMs.
ArgBench unifies 33 existing datasets into a standardized benchmark for testing LLMs across 46 argumentation tasks and analyzes the impact of prompting techniques and model factors on performance.
VAREX benchmark shows structured output compliance limits models under 4B parameters more than extraction ability, with layout-preserving text giving the largest accuracy gains over images.
CELM is the first EEG-to-language foundation model that generates clinical reports from variable-length EEG recordings using a new dataset of 9,922 reports paired with 11,000 hours of data from 9,048 patients.
Molmo2 delivers state-of-the-art open-weight video VLMs with new grounding datasets and training methods that outperform prior open models and match or exceed some proprietary ones on pointing and tracking tasks.
EgoSafetyBench shows VLMs reliably spot hazard-containing videos but miss specific contextual hazards and are degraded by misleading in-scene text.
Self-generated QA supervision for language models is fragile due to non-uniform question selection and instruction compliance during answering, with mitigations that reduce compliance from 88% to 13%.
RCT dataset with sequence-preserving splits demonstrates that tactile-to-text models achieve only 25.1% Recall@1 on held-out materials, exposing generalization as the core challenge.
VSE perturbs images only to probe visual ambiguity in VLMs, clusters outputs into semantic prototypes, and computes mass-weighted dispersion, outperforming prior entropy methods on five VQA benchmarks across five models.
LaViD distills LLM conceptual knowledge to vision models via LLM-generated MCQ soft labels, outperforming vision-language distillation baselines on fine-grained benchmarks while improving robustness on spurious correlation datasets.
PACE is a clipped per-coordinate controller added to AdamW that improves the limiting error of the returned iterate average in both quadratic analysis and LM experiments.
UrduMMLU is a new native-source MCQ benchmark for Urdu that reveals top LLMs reach only ~90% accuracy with large gaps on region-specific humanities content.
NAVI-Orbital performed the first claimed in-orbit demonstration of a zero-shot vision-language model for autonomous Earth observation using Gemma 3 on April 16, 2026.
Introduces MMBU benchmark for VLMs in biomedicine and demonstrates that established benchmarks mask perception deficiencies in evaluated models.
Muon outperforms Adam by reducing curvature penalty via lower Normalized Directional Sharpness, as shown via Taylor approximation on LLM training and proven on stylized quadratic problems with heterogeneous curvature.
A bag-of-position-tagged-words embedding guides text-to-image diffusion models as effectively as full contextual text embeddings from standard encoders.
SEA-NLI benchmark shows low performance across 17 LLMs on Southeast Asian cultural NLI, mainly due to missing cultural knowledge, with gains from SEA-adapted models and culture-aware prompting.
GeoDrive-Bench is a new multimodal benchmark and distillation method for testing and improving VLMs on region-specific traffic-rule reasoning in autonomous driving across six countries.
Presents FRANZ framework and SQUARE corpus for multi-dimensional audit of LLM response framing on subjective cultural queries, applied to three models to reveal differences and couplings.
citing papers explorer
-
ST-BiBench: Benchmarking Multi-Stream Multimodal Coordination in Bimanual Embodied Tasks for MLLMs
ST-BiBench reveals a coordination paradox in which MLLMs show strong high-level strategic reasoning yet fail at fine-grained 16-dimensional bimanual action synthesis and multi-stream fusion.
-
VLA-GSE: Boosting Parameter-Efficient Fine-Tuning in VLA with Generalized and Specialized Experts
VLA-GSE uses spectral decomposition of the VLA backbone to create generalized and specialized experts, enabling effective robot task adaptation while updating only 2.51% of parameters and achieving 81.2% zero-shot success on LIBERO-Plus.
-
A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
The survey frames VLA models as pipelines that generate progressively grounded action tokens and classifies those tokens into eight types to guide future development.
-
RLDX-1 Technical Report
RLDX-1 outperforms frontier VLAs such as π0.5 and GR00T N1.6 on dexterous manipulation benchmarks, reaching 86.8% success on ALLEX humanoid tasks versus around 40% for the baselines.