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LLMs Can Now Talk in Real-Time with Minimal Latency: Chinese Researchers...
Researchers at the Institute of Computing Technology, Chinese Academy of Sciences, have introduced LLaMA-Omni2, a family of speech-capable large language models (SpeechLMs) now available...
How AI Agents Store, Forget, and Retrieve? A Fresh Look at...
Memory plays a crucial role in LLM-based AI systems, supporting sustained, coherent interactions over time. While earlier surveys have explored memory about LLMs, they...
RWKV-X Combines Sparse Attention and Recurrent Memory to Enable Efficient 1M-Token...
LLMs built on Transformer architectures face significant scaling challenges due to their quadratic complexity in sequence length when processing long-context inputs. Methods like Linear...
Scaling Reinforcement Learning Beyond Math: Researchers from NVIDIA AI and CMU...
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities across diverse tasks, with Reinforcement Learning (RL) serving as a crucial mechanism for refining their...
Multimodal Queries Require Multimodal RAG: Researchers from KAIST and DeepAuto.ai Propose...
RAG has proven effective in enhancing the factual accuracy of LLMs by grounding their outputs in external, relevant information. However, most existing RAG implementations...
Google Researchers Advance Diagnostic AI: AMIE Now Matches or Outperforms Primary...
LLMs have shown impressive promise in conducting diagnostic conversations, particularly through text-based interactions. However, their evaluation and application have largely ignored the multimodal nature...
Oversight at Scale Isn’t Guaranteed: MIT Researchers Quantify the Fragility of...
Frontier AI companies show advancement toward artificial general intelligence (AGI), creating a need for techniques to ensure these powerful systems remain controllable and beneficial....
LLMs Can Now Reason in Parallel: UC Berkeley and UCSF Researchers...
Large language models (LLMs) have made significant strides in reasoning capabilities, exemplified by breakthrough systems like OpenAI o1 and DeepSeekR1, which utilize test-time compute...
LLMs Can Learn Complex Math from Just One Example: Researchers from...
Recent advancements in LLMs such as OpenAI-o1, DeepSeek-R1, and Kimi-1.5 have significantly improved their performance on complex mathematical reasoning tasks. Reinforcement Learning with Verifiable...
Subject-Driven Image Evaluation Gets Simpler: Google Researchers Introduce REFVNLI to Jointly...
Text-to-image (T2I) generation has evolved to include subject-driven approaches, which enhance standard T2I models by incorporating reference images alongside text prompts. This advancement allows...
Training LLM Agents Just Got More Stable: Researchers Introduce StarPO-S and...
Large language models (LLMs) face significant challenges when trained as autonomous agents in interactive environments. Unlike static tasks, agent settings require sequential decision-making, cross-turn...
Xiaomi introduced MiMo-7B: A Compact Language Model that Outperforms Larger Models...
With rising demand for AI systems that can handle tasks involving multi-step logic, mathematical proofs, and software development, researchers have turned their attention toward...
Building the Internet of Agents: A Technical Dive into AI Agent...
As large language model (LLM) agents gain traction across enterprise and research ecosystems, a foundational gap has emerged: communication. While agents today can autonomously...
DeepSeek-AI Released DeepSeek-Prover-V2: An Open-Source Large Language Model Designed for Formal...
Formal mathematical reasoning has evolved into a specialized subfield of artificial intelligence that requires strict logical consistency. Unlike informal problem solving, which allows for...
Meta AI Introduces ReasonIR-8B: A Reasoning-Focused Retriever Optimized for Efficiency and...
Addressing the Challenges in Reasoning-Intensive Retrieval
Despite notable progress in retrieval-augmented generation (RAG) systems, retrieving relevant information for complex, multi-step reasoning tasks remains a significant...
Mem0: A Scalable Memory Architecture Enabling Persistent, Structured Recall for Long-Term...
Large language models can generate fluent responses, emulate tone, and even follow complex instructions; however, they struggle to retain information across multiple sessions. This...
Exploring the Sparse Frontier: How Researchers from Edinburgh, Cohere, and Meta...
Sparse attention is emerging as a compelling approach to improve the ability of Transformer-based LLMs to handle long sequences. This is particularly important because...
Diagnosing and Self- Correcting LLM Agent Failures: A Technical Deep Dive...
Deploying large language model (LLM)-based agents in production settings often reveals critical reliability issues. Accurately identifying the causes of agent failures and implementing proactive...
UniME: A Two-Stage Framework for Enhancing Multimodal Representation Learning with MLLMs
The CLIP framework has become foundational in multimodal representation learning, particularly for tasks such as image-text retrieval. However, it faces several limitations: a strict...
ThinkPRM: A Generative Process Reward Models for Scalable Reasoning Verification
Reasoning with LLMs can benefit from utilizing more test compute, which depends on high-quality process reward models (PRMs) to select promising paths for search...
The WAVLab Team Releases of VERSA: A Comprehensive and Versatile Evaluation Toolkit...
AI models have made remarkable strides in generating speech, music, and other forms of audio content, expanding possibilities across communication, entertainment, and human-computer interaction....
ViSMaP: Unsupervised Summarization of Hour-Long Videos Using Meta-Prompting and Short-Form Datasets
Video captioning models are typically trained on datasets consisting of short videos, usually under three minutes in length, paired with corresponding captions. While this...
Tiny Models, Big Reasoning Gains: USC Researchers Introduce Tina for Cost-Effective...
Achieving strong, multi-step reasoning in LMs remains a major challenge, despite notable progress in general task performance. Such reasoning is crucial for complex problem-solving...
Researchers from Sea AI Lab, UCAS, NUS, and SJTU Introduce FlowReasoner:...
LLM-based multi-agent systems characterized by planning, reasoning, tool use, and memory capabilities form the foundation of applications like chatbots, code generation, mathematics, and robotics....
ByteDance Introduces QuaDMix: A Unified AI Framework for Data Quality and...
The pretraining efficiency and generalization of large language models (LLMs) are significantly influenced by the quality and diversity of the underlying training corpus. Traditional...
Optimizing Reasoning Performance: A Comprehensive Analysis of Inference-Time Scaling Methods in...
Language models have shown great capabilities across various tasks. However, complex reasoning remains challenging as it often requires additional computational resources and specialized techniques....
This AI Paper from China Proposes a Novel Training-Free Approach DEER...
Recent progress in large reasoning language models (LRLMs), such as DeepSeek-R1 and GPT-O1, has greatly improved complex problem-solving abilities by extending the length of...
LLMs Can Now Simulate Massive Societies: Researchers from Fudan University Introduce...
Human behavior research strives to comprehend how individuals and groups act in social contexts, forming a foundational social science element. Traditional methodologies like surveys,...
Meta AI Introduces Token-Shuffle: A Simple AI Approach to Reducing Image...
Autoregressive (AR) models have made significant advances in language generation and are increasingly explored for image synthesis. However, scaling AR models to high-resolution images...
AgentA/B: A Scalable AI System Using LLM Agents that Simulate Real...
Designing and evaluating web interfaces is one of the most critical tasks in today’s digital-first world. Every change in layout, element positioning, or navigation...
Google DeepMind Research Introduces QuestBench: Evaluating LLMs’ Ability to Identify Missing...
Large language models (LLMs) have gained significant traction in reasoning tasks, including mathematics, logic, planning, and coding. However, a critical challenge emerges when applying...
Skywork AI Advances Multimodal Reasoning: Introducing Skywork R1V2 with Hybrid Reinforcement...
Recent advancements in multimodal AI have highlighted a persistent challenge: achieving strong specialized reasoning capabilities while preserving generalization across diverse tasks. "Slow-thinking" models such...
Mila & Universite de Montreal Researchers Introduce the Forgetting Transformer (FoX)...
Transformers have revolutionized sequence modeling by introducing an architecture that handles long-range dependencies efficiently without relying on recurrence. Their ability to process input tokens...
Microsoft Research Introduces MMInference to Accelerate Pre-filling for Long-Context Vision-Language Models
Integrating long-context capabilities with visual understanding significantly enhances the potential of VLMs, particularly in domains such as robotics, autonomous driving, and healthcare. Expanding the...
Meta AI Releases Web-SSL: A Scalable and Language-Free Approach to Visual...
In recent years, contrastive language-image models such as CLIP have established themselves as a default choice for learning vision representations, particularly in multimodal applications...
Sequential-NIAH: A Benchmark for Evaluating LLMs in Extracting Sequential Information from...
Evaluating how well LLMs handle long contexts is essential, especially for retrieving specific, relevant information embedded in lengthy inputs. Many recent LLMs—such as Gemini-1.5,...
AWS Introduces SWE-PolyBench: A New Open-Source Multilingual Benchmark for Evaluating AI...
Recent advancements in large language models (LLMs) have enabled the development of AI-based coding agents that can generate, modify, and understand software code. However,...
NVIDIA AI Releases Describe Anything 3B: A Multimodal LLM for Fine-Grained...
Challenges in Localized Captioning for Vision-Language Models
Describing specific regions within images or videos remains a persistent challenge in vision-language modeling. While general-purpose vision-language...
Muon Optimizer Significantly Accelerates Grokking in Transformers: Microsoft Researchers Explore Optimizer...
Revisiting the Grokking Challenge
In recent years, the phenomenon of grokking—where deep learning models exhibit a delayed yet sudden transition from memorization to generalization—has prompted...
LLMs Can Now Learn without Labels: Researchers from Tsinghua University and...
Despite significant advances in reasoning capabilities through reinforcement learning (RL), most large language models (LLMs) remain fundamentally dependent on supervised data pipelines. RL frameworks...
Decoupled Diffusion Transformers: Accelerating High-Fidelity Image Generation via Semantic-Detail Separation and...
Diffusion Transformers have demonstrated outstanding performance in image generation tasks, surpassing traditional models, including GANs and autoregressive architectures. They operate by gradually adding noise...
LLMs Can Now Retain High Accuracy at 2-Bit Precision: Researchers from...
LLMs show impressive capabilities across numerous applications, yet they face challenges due to computational demands and memory requirements. This challenge is acute in scenarios...
Long-Context Multimodal Understanding No Longer Requires Massive Models: NVIDIA AI Introduces...
In recent years, vision-language models (VLMs) have advanced significantly in bridging image, video, and textual modalities. Yet, a persistent limitation remains: the inability to...
LLMs Still Struggle to Cite Medical Sources Reliably: Stanford Researchers Introduce...
As LLMs become more prominent in healthcare settings, ensuring that credible sources back their outputs is increasingly important. Although no LLMs are yet FDA-approved...
Stanford Researchers Propose FramePack: A Compression-based AI Framework to Tackle Drifting...
Video generation, a branch of computer vision and machine learning, focuses on creating sequences of images that simulate motion and visual realism over time....
ReTool: A Tool-Augmented Reinforcement Learning Framework for Optimizing LLM Reasoning with...
Reinforcement learning (RL) is a powerful technique for enhancing the reasoning capabilities of LLMs, enabling them to develop and refine long Chain-of-Thought (CoT). Models...
LLMs Can Think While Idle: Researchers from Letta and UC Berkeley...
Large language models (LLMs) have gained prominence for their ability to handle complex reasoning tasks, transforming applications from chatbots to code-generation tools. These models...
LLMs Can Be Misled by Surprising Data: Google DeepMind Introduces New...
Large language models (LLMs) are continually evolving by ingesting vast quantities of text data, enabling them to become more accurate predictors, reasoners, and conversationalists....
Fourier Neural Operators Just Got a Turbo Boost: Researchers from UC...
Fourier Neural Operators (FNO) are powerful tools for learning partial differential equation solution operators, but lack architecture-aware optimizations, with their Fourier layer executing FFT,...
Meta AI Introduces Collaborative Reasoner (Coral): An AI Framework Specifically Designed...
Rethinking the Problem of Collaboration in Language Models
Large language models (LLMs) have demonstrated remarkable capabilities in single-agent tasks such as question answering and structured...
NVIDIA Introduces CLIMB: A Framework for Iterative Data Mixture Optimization in...
Challenges in Constructing Effective Pretraining Data Mixtures
As large language models (LLMs) scale in size and capability, the choice of pretraining data remains a critical...
LLMs Can Now Solve Challenging Math Problems with Minimal Data: Researchers...
Language models have made significant strides in tackling reasoning tasks, with even small-scale supervised fine-tuning (SFT) approaches such as LIMO and s1 demonstrating remarkable...
LLMs Can Now Learn to Try Again: Researchers from Menlo Introduce...
The domain of LLMs has rapidly evolved to include tools that empower these models to integrate external knowledge into their reasoning processes. A significant...
Meta AI Released the Perception Language Model (PLM): An Open and...
Despite rapid advances in vision-language modeling, much of the progress in this field has been shaped by models trained on proprietary datasets, often relying...
Meta AI Introduces Perception Encoder: A Large-Scale Vision Encoder that Excels...
The Challenge of Designing General-Purpose Vision Encoders
As AI systems grow increasingly multimodal, the role of visual perception models becomes more complex. Vision encoders are...
Do Reasoning Models Really Need Transformers?: Researchers from TogetherAI, Cornell, Geneva,...
Effective reasoning is crucial for solving complex problems in fields such as mathematics and programming, and LLMs have demonstrated significant improvements through long-chain-of-thought reasoning....
Do We Still Need Complex Vision-Language Pipelines? Researchers from ByteDance and...
MLLMs have recently advanced in handling fine-grained, pixel-level visual understanding, thereby expanding their applications to tasks such as precise region-based editing and segmentation. Despite...
Biophysical Brain Models Get a 2000× Speed Boost: Researchers from NUS,...
Biophysical modeling serves as a valuable tool for understanding brain function by linking neural dynamics at the cellular level with large-scale brain activity. These...
SyncSDE: A Probabilistic Framework for Task-Adaptive Diffusion Synchronization in Collaborative Generation
Diffusion models have demonstrated significant success across various generative tasks, including image synthesis, 3D scene creation, video generation, and human motion modeling. However, their...
MIT Researchers Introduce DISCIPL: A Self-Steering Framework Using Planner and Follower...
Language models predict sequences of words based on vast datasets and are increasingly expected to reason and perform complex linguistic manipulations. Yet, despite their...
Model Compression Without Compromise: Loop-Residual Neural Networks Show Comparable Results to...
The transformer architecture has revolutionized natural language processing, enabling models like GPT to predict the next token in a sequence efficiently. However, these models...
Transformers Can Now Predict Spreadsheet Cells without Fine-Tuning: Researchers Introduce TabPFN...
Tabular data is widely utilized in various fields, including scientific research, finance, and healthcare. Traditionally, machine learning models such as gradient-boosted decision trees have...
From Logic to Confusion: MIT Researchers Show How Simple Prompt Tweaks...
Large language models are increasingly used to solve math problems that mimic real-world reasoning tasks. These models are tested for their ability to answer...
LLM Reasoning Benchmarks are Statistically Fragile: New Study Shows Reinforcement Learning...
Reasoning capabilities have become central to advancements in large language models, crucial in leading AI systems developed by major research labs. Despite a surge...
Reflection Begins in Pre-Training: Essential AI Researchers Demonstrate Early Emergence of...
What sets large language models (LLMs) apart from traditional methods is their emerging capacity to reflect—recognizing when something in their response doesn’t align with...
Transformers Gain Robust Multidimensional Positional Understanding: University of Manchester Researchers Introduce...
Transformers have emerged as foundational tools in machine learning, underpinning models that operate on sequential and structured data. One critical challenge in this setup...
Multimodal Models Don’t Need Late Fusion: Apple Researchers Show Early-Fusion Architectures...
Multimodal artificial intelligence faces fundamental challenges in effectively integrating and processing diverse data types simultaneously. Current methodologies predominantly rely on late-fusion strategies, where separately...
Underdamped Diffusion Samplers Outperform Traditional Methods: Researchers from Karlsruhe Institute of...
Diffusion processes have emerged as promising approaches for sampling from complex distributions but face significant challenges when dealing with multimodal targets. Traditional methods based...
Foundation Models No Longer Need Prompts or Labels: EPFL Researchers Introduce...
Foundation models, often massive neural networks trained on extensive text and image data, have significantly shifted how artificial intelligence systems handle language and vision...
Reasoning Models Know When They’re Right: NYU Researchers Introduce a Hidden-State...
Artificial intelligence systems have made significant strides in simulating human-style reasoning, particularly mathematics and logic. These models don't just generate answers—they walk through a...
NVIDIA AI Releases UltraLong-8B: A Series of Ultra-Long Context Language Models...
Large language mdoels LLMs have shown remarkable performance across diverse text and multimodal tasks. However, many applications, such as document and video understanding, in-context...
LightPROF: A Lightweight AI Framework that Enables Small-Scale Language Models to...
Large Language Models (LLMs) have revolutionized natural language processing, with abilities on complex zero-shot tasks through extensive training data and vast parameters. However, LLMs...
Google AI Introduce the Articulate Medical Intelligence Explorer (AMIE): A Large...
Developing an accurate differential diagnosis (DDx) is a fundamental part of medical care, typically achieved through a step-by-step process that integrates patient history, physical...
Allen Institute for AI (Ai2) Launches OLMoTrace: Real-Time Tracing of LLM...
Understanding the Limits of Language Model Transparency
As large language models (LLMs) become central to a growing number of applications—ranging from enterprise decision support to...
Can LLMs Debug Like Humans? Microsoft Introduces Debug-Gym for AI Coding...
The Debugging Problem in AI Coding Tools
Despite significant progress in code generation and completion, AI coding tools continue to face challenges in debugging—an integral...
This AI Paper from Salesforce Introduces VLM2VEC and MMEB: A Contrastive...
Multimodal embeddings combine visual and textual data into a single representational space, enabling systems to understand and relate images and language meaningfully. These embeddings...
LLMs No Longer Require Powerful Servers: Researchers from MIT, KAUST, ISTA,...
HIGGS — the innovative method for compressing large language models was developed in collaboration with teams at Yandex Research, MIT, KAUST and ISTA.
HIGGS makes...
Balancing Accuracy and Efficiency in Language Models: A Two-Phase RL Post-Training...
Recent advancements in LLMs have significantly enhanced their reasoning capabilities, particularly through RL-based fine-tuning. Initially trained with supervised learning for token prediction, these models...
RoR-Bench: Revealing Recitation Over Reasoning in Large Language Models Through Subtle...
In recent years, the rapid progress of LLMs has given the impression that we are nearing the achievement of Artificial General Intelligence (AGI), with...
OpenAI Open Sources BrowseComp: A New Benchmark for Measuring the Ability...
Despite advances in large language models (LLMs), AI agents still face notable limitations when navigating the open web to retrieve complex information. While many...
ByteDance Introduces VAPO: A Novel Reinforcement Learning Framework for Advanced Reasoning...
In the Large Language Models (LLM) RL training, value-free methods like GRPO and DAPO have shown great effectiveness. The true potential lies in value-based...
T* and LV-Haystack: A Spatially-Guided Temporal Search Framework for Efficient Long-Form...
Understanding long-form videos—ranging from minutes to hours—presents a major challenge in computer vision, especially as video understanding tasks expand beyond short clips. One of...
This AI Paper Introduces a Machine Learning Framework to Estimate the...
Large Language Models (LLMs) have demonstrated significant advancements in reasoning capabilities across diverse domains, including mathematics and science. However, improving these reasoning abilities at...
Unveiling Attention Sinks: The Functional Role of First-Token Focus in Stabilizing...
LLMs often show a peculiar behavior where the first token in a sequence draws unusually high attention—known as an "attention sink." Despite seemingly unimportant,...
Salesforce AI Released APIGen-MT and xLAM-2-fc-r Model Series: Advancing Multi-Turn Agent...
AI agents quickly become core components in handling complex human interactions, particularly in business environments where conversations span multiple turns and involve task execution,...
This AI Paper from ByteDance Introduces MegaScale-Infer: A Disaggregated Expert Parallelism...
Large language models are built on transformer architectures and power applications like chat, code generation, and search, but their growing scale with billions of...
Sensor-Invariant Tactile Representation for Zero-Shot Transfer Across Vision-Based Tactile Sensors
Tactile sensing is a crucial modality for intelligent systems to perceive and interact with the physical world. The GelSight sensor and its variants have...
This AI Paper Introduces an LLM+FOON Framework: A Graph-Validated Approach for...
Robots are increasingly being developed for home environments, specifically to enable them to perform daily activities like cooking. These tasks involve a combination of...
This AI Paper Introduces Inference-Time Scaling Techniques: Microsoft’s Deep Evaluation of...
Large language models are often praised for their linguistic fluency, but a growing area of focus is enhancing their reasoning ability—especially in contexts where...
RARE (Retrieval-Augmented Reasoning Modeling): A Scalable AI Framework for Domain-Specific Reasoning...
LLMs have demonstrated strong general-purpose performance across various tasks, including mathematical reasoning and automation. However, they struggle in domain-specific applications where specialized knowledge and...
University of Michigan Researchers Introduce OceanSim: A High-Performance GPU-Accelerated Underwater Simulator...
Marine robotic platforms support various applications, including marine exploration, underwater infrastructure inspection, and ocean environment monitoring. While reliable perception systems enable robots to sense...
Scalable and Principled Reward Modeling for LLMs: Enhancing Generalist Reward Models...
Reinforcement Learning RL has become a widely used post-training method for LLMs, enhancing capabilities like human alignment, long-term reasoning, and adaptability. A major challenge,...
This AI Paper from Anthropic Introduces Attribution Graphs: A New Interpretability...
While the outputs of large language models (LLMs) appear coherent and useful, the underlying mechanisms guiding these behaviors remain largely unknown. As these models...
Anthropic’s Evaluation of Chain-of-Thought Faithfulness: Investigating Hidden Reasoning, Reward Hacks, and...
A key advancement in AI capabilities is the development and use of chain-of-thought (CoT) reasoning, where models explain their steps before reaching an answer....
Scalable Reinforcement Learning with Verifiable Rewards: Generative Reward Modeling for Unstructured,...
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing LLMs' reasoning and coding abilities, particularly in domains where structured reference answers allow...
This AI Paper Introduces a Short KL+MSE Fine-Tuning Strategy: A Low-Cost...
Sparse autoencoders are central tools in analyzing how large language models function internally. Translating complex internal states into interpretable components allows researchers to break...
NVIDIA AI Releases HOVER: A Breakthrough AI for Versatile Humanoid Control...
The future of robotics has advanced significantly. For many years, there have been expectations of human-like robots that can navigate our environments, perform complex...
Meet Open-Qwen2VL: A Fully Open and Compute-Efficient Multimodal Large Language Model
Multimodal Large Language Models (MLLMs) have advanced the integration of visual and textual modalities, enabling progress in tasks such as image captioning, visual question...
Researchers from Dataocean AI and Tsinghua University Introduces Dolphin: A Multilingual...
Automatic speech recognition (ASR) technologies have advanced significantly, yet notable disparities remain in their ability to accurately recognize diverse languages. Prominent ASR systems, such...
This AI Paper Introduces FASTCURL: A Curriculum Reinforcement Learning Framework with...
Large language models have transformed how machines comprehend and generate text, especially in complex problem-solving areas like mathematical reasoning. These systems, known as R1-like...