Multimodal foundation models (MFMs) like GPT-4o, Gemini, and Claude have shown rapid progress recently, especially in public demos. While their language skills are well studied, their true ability to understand visual information remains unclear. Most benchmarks used today focus heavily on text-based tasks, such as VQA or classification, which often reflect language strengths more than…
Visual reasoning tasks challenge artificial intelligence models to interpret and process visual information using both perception and logical reasoning. These tasks span a wide range of applications, including medical diagnostics, visual math, symbolic puzzles, and image-based question answering. Success in this field requires more than object recognition—it demands dynamic adaptation, abstraction, and contextual inference. Models…
Introduction: The Challenge of Synthesizable Molecule Generation In modern drug discovery, generative molecular design models have greatly expanded the chemical space available to researchers, enabling rapid exploration of new compounds. Yet, a major challenge remains: many AI-generated molecules are difficult or impossible to synthesize in the laboratory, limiting their practical value in pharmaceutical and chemical development. While template-based…
Autoregressive video generation is a rapidly evolving research domain. It focuses on the synthesis of videos frame-by-frame using learned patterns of both spatial arrangements and temporal dynamics. Unlike traditional video creation methods, which may rely on pre-built frames or handcrafted transitions, autoregressive models aim to generate content dynamically based on prior tokens. This approach is…
Introduction As large language models (LLMs) advance in software engineering tasks—ranging from code generation to bug fixing—performance optimization remains an elusive frontier, especially at the repository level. To bridge this gap, researchers from TikTok and collaborating institutions have introduced SWE-Perf—the first benchmark specifically designed to evaluate the ability of LLMs to optimize code performance in…
The Allen Institute for Artificial Intelligence (AI2) has introduced AutoDS (Autonomous Discovery via Surprisal), a groundbreaking prototype engine for open-ended autonomous scientific discovery. Distinct from conventional AI research assistants that depend on human-defined objectives or queries, AutoDS autonomously generates, tests, and iterates on hypotheses by quantifying and seeking out “Bayesian surprise”—a principled measure of genuine…
Generative reward models, where large language models (LLMs) serve as evaluators, are gaining prominence in reinforcement learning with verifiable rewards (RLVR). These models are preferred over rule-based systems for tasks involving open-ended or complex responses. Instead of relying on strict rules, LLMs compare a candidate response to a reference answer and generate binary feedback. However,…
Handling extremely long documents remains a persistent challenge for large language models (LLMs). Even with techniques such as length extrapolation and sparse attention, models often suffer from performance degradation and high computational costs. To address this, researchers from ByteDance Seed and Tsinghua University introduce MemAgent, a reinforcement learning-based memory agent designed to enable long-context processing…
Transforming Human-Computer Interaction with Generative Interfaces Recent advances in generative models are transforming the way we interact with computers, making experiences more natural, adaptive, and personalized. Early interfaces, command-line tools, and static menus were fixed and required users to adapt to the machine. Now, with the rise of LLMs and multimodal AI, users can engage…
Diffusion LLMs as a Paradigm Shift in Code Generation LLMs have revolutionized natural language processing with impressive results across tasks from dialogue to code generation. Masked diffusion models have emerged as an alternative and are scaled up into diffusion-based LLMs such as LLaDA and Dream. This model iteratively refines the entire sequence in parallel, allowing…