SVHalluc benchmark shows open-source audio-visual LLMs achieve near-random accuracy on semantic and temporal speech-vision alignment tasks while Gemini 2.5 Pro performs substantially better.
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LLaMA: Open and Efficient Foundation Language Models
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We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.
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- abstract We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.
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Zero-Run auditing supplies valid lower bounds on differential privacy parameters from fixed member and non-member datasets by modeling and correcting distribution-shift confounding via causal-inference techniques.
Fragmentation strictly raises optimal finite-context log-loss on Markov sources while tokenization can make a short token window equivalent to a longer source window under reliability and compression conditions.
Allowing each quantization group to select among multiple 4-bit grids improves accuracy over single-grid FP4 for both post-training and pre-training of LLMs.
Adaptive scheduling of interventions in discrete diffusion language models, timed to attribute-specific commitment schedules discovered with sparse autoencoders, delivers precise multi-attribute steering up to 93% strength while preserving generation quality.
SignSGD provably beats SGD by a factor of d under sparse noise via matched ℓ1-norm upper and lower bounds, with an equivalent result for Muon on matrices, and this predicts faster GPT-2 pretraining.
An adversary controlling an intermediate pipeline stage in decentralized LLM post-training can inject a backdoor that reduces alignment from 80% to 6%, with the backdoor persisting in 60% of cases even after subsequent safety training.
First study of 1,899 MCP servers finds eight distinct vulnerabilities (only three traditional), 7.2% with general issues, 5.5% with tool poisoning, and 66% with code smells, urging MCP-specific security practices.
BEAVER is the first text-to-SQL benchmark from private enterprise data warehouses, revealing SOTA agentic frameworks achieve only 10.8% accuracy on complex real-world queries.
MME-RealWorld is the largest manually annotated high-resolution benchmark for MLLMs, where even the best models achieve less than 60% accuracy on challenging real-world tasks.
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ORPO performs preference alignment during supervised fine-tuning via a monolithic odds ratio penalty, allowing 7B models to outperform larger state-of-the-art models on alignment benchmarks.
BLaIR is a new benchmark and 570M-review dataset showing that LLM performance rankings on recommendation tasks have little correlation with rankings on general embedding benchmarks like MTEB.
Mamba is a linear-time sequence model using input-dependent selective SSMs that achieves SOTA results across modalities and matches twice-larger Transformers on language modeling with 5x higher inference throughput.
MMMU provides 11.5K heterogeneous college-level multimodal questions that current models solve at 56-59% accuracy, establishing a new standard for expert multimodal evaluation.
Tree of Thoughts enables language models to solve complex planning tasks by generating, evaluating, and searching over coherent intermediate thoughts in a tree, raising Game of 24 success from 4% to 74% with GPT-4.
API-Bank is a new benchmark and training dataset for tool-augmented LLMs that shows fine-tuned models can approach GPT-3.5 tool-use effectiveness.
GPT-4-generated instruction data produces superior zero-shot performance in finetuned LLaMA models versus prior state-of-the-art data.
LA-SR redefines unpaired super-resolution in language space by projecting images into a semantically rich representation and applying vision-language model guided losses to handle real-world degradations extracted from depth variations.
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Scalable Object Detection in the Car Interior With Vision Foundation Models
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Enhancing Speech Large Language Models through Reinforced Behavior Alignment
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HFX jointly designs scheduling and scaling for multi-SLO LLM serving, achieving up to 4.44x higher SLO attainment, 65.82% lower latency, and 49.81% lower cost than prior systems on multi-task workloads.
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Shared representations in brains and models reveal a two-route cortical organization during scene perception
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Temperature and Persona Shape LLM Agent Consensus With Minimal Accuracy Gains in Qualitative Coding
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A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
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MemOS: A Memory OS for AI System
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A Survey on Vision-Language-Action Models: An Action Tokenization Perspective
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Revisit What You See: Revealing Visual Semantics in Vision Tokens to Guide LVLM Decoding
ReVisiT refines LVLM output distributions during decoding by projecting selected vision tokens into text space via context-aware constrained divergence minimization.
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FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model
FedShield-LLM integrates pruning and FHE on LoRA parameters to support secure, scalable federated fine-tuning of LLMs such as Llama-2.
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How Far Are We from Generating Missing Modalities with Foundation Models?
Evaluates 42 variants of foundation models across three formalized paradigms for missing modality reconstruction, identifies shortfalls in semantic extraction and validation, and introduces an agentic framework that reduces FID by at least 14% for images and MER by at least 10% for text.
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GradPower: Powering Gradients for Faster Language Model Pre-Training
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Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration
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Efficient compression of neural networks and datasets
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RAP: Runtime Adaptive Pruning for LLM Inference
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Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings
Log-likelihood vectors establish a consistent KL divergence scale across pretraining, model sizes, seeds, quantization, fine-tuning, and layers, revealing subdiffusive trajectories and early stabilization in Pythia models.
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Kalman Filter Enhanced GRPO for Reinforcement Learning-Based Language Model Reasoning
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EntroLLM: Entropy Encoded Weight Compression for Efficient Large Language Model Inference on Edge Devices
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Advancing AI Research Assistants with Expert-Involved Learning
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Finite-Precision Conjugate Gradient Method for Massive MIMO Detection
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Q-Agent: Quality-Driven Chain-of-Thought Image Restoration Agent through Robust Multimodal Large Language Model
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Qwen2.5-Omni Technical Report
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Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models
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Mimicking How Humans Interpret Out-of-Context Sentences Through Controlled Toxicity Decoding
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LLM-based User Profile Management for Recommender System
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SEDD: Scalable and Efficient Dataset Deduplication with GPUs
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LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance
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Exploring Cross-lingual Latent Transplantation: Mutual Opportunities and Open Challenges
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SVGFusion: A VAE-Diffusion Transformer for Vector Graphic Generation
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TemporalVLM: Video LLMs for Temporal Reasoning in Long Videos
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HunyuanVideo: A Systematic Framework For Large Video Generative Models
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SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generation
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Training and Evaluating Language Models with Template-based Data Generation
TDG uses GPT-4 to generate meta-templates that synthesize over 7 million verifiable grade school math problems for training and aligning LLMs on reasoning tasks.
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Mitigating Extrinsic Gender Bias for Bangla Classification Tasks
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VeriGraph: Scene Graphs for Execution Verifiable Robot Planning
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The Phantom of PCIe: Constraining Generative Artificial Intelligences for Practical Peripherals Trace Synthesizing
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On the Diagram of Thought
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AdaComp: Extractive Context Compression with Adaptive Predictor for Retrieval-Augmented Large Language Models
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HexiScale: Facilitating Large Language Model Training over Heterogeneous Hardware
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Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
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MiniCPM-V: A GPT-4V Level MLLM on Your Phone
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ProTrain: Efficient LLM Training via Memory-Aware Techniques
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ChatSR: Multimodal Large Language Models for Scientific Formula Discovery
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PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
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Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Mini-Gemini enhances VLMs via high-resolution visual refinement, curated reasoning data, and self-guided generation to reach leading zero-shot benchmark results across 2B-34B LLMs.
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InternLM2 Technical Report
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Retrieval-Augmented Generation for AI-Generated Content: A Survey
A survey classifying RAG foundations for AIGC, summarizing enhancements, cross-modal applications, benchmarks, limitations, and future directions.
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Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
POVID generates AI-created preference data to fine-tune vision-language models with DPO, reducing hallucinations and improving benchmark scores.
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World Model on Million-Length Video And Language With Blockwise RingAttention
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