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Francois Chollet argues that symbolic learning represents a fundamental alternative to gradient descent and neural networks rather than a replacement for coding agents, offering a low-level, general-p

arXiv:2602.21307v2 Announce Type: replace Abstract: What mathematical functions do neural network components learn? Symbolic distillation addresses this question by expressing neural network component

arXiv:2605.08412v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have made rapid progress in single-video understanding, yet their ability to reason across multiple independent

arXiv:2605.08835v1 Announce Type: new Abstract: The expansion of Artificial Intelligence-generated content service requires diffusion model serving to simultaneously achieve high throughput and low ta

arXiv:2605.08190v1 Announce Type: new Abstract: Autonomous systems increasingly rely on machine-learning (ML) components for safety-critical tasks such as perception and control in autonomous vehicles

arXiv:2605.08724v1 Announce Type: new Abstract: Unifying multimodal understanding and generation is a compelling frontier that is beginning to emerge in the medical field. However, the limited existin

arXiv:2605.10129v1 Announce Type: new Abstract: Large language models (LLMs) rely on web-scale corpora for pre-training. The noise inherent in these datasets tends to obscure meaningful patterns and u

TabPFN-3 is a pre-trained tabular foundation model that supports datasets up to 1,000,000 rows × 200 features , representing a significant scaling improvement for the TabPFN family. The model delivers

arXiv:2605.09424v1 Announce Type: new Abstract: Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modality, r

arXiv:2605.09539v1 Announce Type: new Abstract: Multi-agent systems (MAS) have emerged as a promising paradigm for solving complex tasks. Recent work has explored self-evolving MAS that automatically

arXiv:2605.09536v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer a promising paradigm for parallel text generation, but in practice they face an accuracy-parallelism tra

arXiv:2506.01352v2 Announce Type: replace Abstract: Decentralized training of large language models offers the opportunity to pool computational resources across geographically distributed participant

arXiv:2505.11604v5 Announce Type: replace Abstract: Editing presentation slides is a frequent yet tedious task, ranging from creative layout design to repetitive text maintenance. While recent GUI-bas

Talked to a friend at a top AI lab. Their whole team is former journalists, training models on poems, summaries, and creative writing. I use AI every day and can see that it tends to flatten my writin

arXiv:2602.04611v2 Announce Type: replace-cross Abstract: The synthetic control method (SCM) estimates causal effects in panel data with a single-treated unit by constructing a counterfactual outcome

arXiv:2605.08440v1 Announce Type: cross Abstract: Adversarial purification with diffusion models seeks to project adversarial examples back toward the data manifold, but balancing semantic preservatio

arXiv:2605.10165v1 Announce Type: cross Abstract: Noisy labels are common in large-scale medical imaging datasets due to inter-observer variability and ambiguous cases. We propose a statistically grou

arXiv:2605.10202v1 Announce Type: cross Abstract: LLM decoding often relies on the model's predictive distribution to generate an output. Consequently, misalignment with respect to the true generating

arXiv:2508.05463v2 Announce Type: replace-cross Abstract: Neural networks excel across a wide range of tasks, yet remain black boxes. In particular, how their internal representations are shaped by th

arXiv:2605.09810v1 Announce Type: cross Abstract: Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizin

arXiv:2605.10634v1 Announce Type: new Abstract: LLM-based automatic heuristic design has shown promise for generating executable heuristics for combinatorial optimization, but existing methods mainly

arXiv:2605.10247v1 Announce Type: new Abstract: Using Large Language Models (LLMs) to process graph-structured data is an active research area, yet current state-of-the-art approaches typically rely o

arXiv:2605.09311v1 Announce Type: cross Abstract: Unlike most static material properties widely studied in the machine learning literature, ionic transport properties are inherently dynamic, making th

arXiv:2605.09922v1 Announce Type: cross Abstract: While recent self-training approaches have reduced reliance on human-labeled data for aligning LLMs, they still face critical limitations: (i) sensiti

arXiv:2605.09929v1 Announce Type: new Abstract: Deploying large language models in telecommunications requires more than task accuracy. In realistic workflows, a model may inherit partially completed

Yohei Nakajima shared recent funding and news updates about OpenAI and Anthropic on X, including information about a trial involving the wokeloai MCP, a tomorrow acquisition, and additional details (c

arXiv:2510.22767v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) typically come with a fixed architecture, despite growing evidence that not all layers contribute equally to ever

arXiv:2507.11198v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) enable new possibilities for qualitative research at scale, including annotation and qualitative coding of educat

arXiv:2605.08153v1 Announce Type: new Abstract: With the rapid development of machine learning applications on time-series data, accurately assessing the value of training samples has become essential

arXiv:2605.10388v1 Announce Type: new Abstract: End to end (E2E) autonomous driving trajectory prediction is often trained with camera frames sampled at the highest available temporal frequency, assum

arXiv:2512.13618v3 Announce Type: replace Abstract: Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Vari

arXiv:2605.09967v1 Announce Type: new Abstract: While researchers are finding concepts represented as linear directions in language models, a bag of linear directions fails to capture relational struc

arXiv:2605.09046v1 Announce Type: new Abstract: In many real-world robotic tasks, robots must generate dynamically feasible motions that reliably reach desired goals even under uncertainty. Yet existi

arXiv:2605.09329v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by using a fast draft model to generate tokens and a more accurate target model to verify them. Its perfo

arXiv:2605.08215v1 Announce Type: new Abstract: Visual Foresight VLA (VF-VLA) has become a prominent architectural choice in the recent VLA due to its impressive performance. Nevertheless, the inheren

arXiv:2605.08181v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning methods introduce a small number of training parameters, enabling pre-trained models to adapt rapidly to new data dist

arXiv:2601.02366v2 Announce Type: replace-cross Abstract: Graph-based recommendation has achieved great success in recent years. The classical graph recommendation model utilizes ID embedding to store

arXiv:2605.06047v2 Announce Type: replace-cross Abstract: Tabular foundation models (TFMs), such as TabPFN-2.6, TabICLv2, ConTextTab, Mitra, LimiX, and TabDPT, achieve strong zero-shot performance thr

Nous Research held a 'Hermes Agent Jam' event where developers showcased projects built using or related to their Hermes agent framework. The event appears to have been a community-driven showcase hig

Thanks to the @huggingface team for adding Hermes Agent to local apps and shipping a native Hermes traces viewer! 🆕 Hugging Face 🤝 Hermes Agent 🔥 > we added Hermes Agent to local apps: run it locally

Would it shock you to hear that Android 17 is filled with new AI-enabled features, like improved dictation and vibe-coded widgets? Fortunately, that's not all. The platform is getting non-AI updates t

arXiv:2505.11577v4 Announce Type: replace-cross Abstract: Recent application programming interface (API) restrictions on major social media platforms challenge compliance with the EU Digital Services

The agent also reimplemented the “Blues Improvisation” experiment by @douglas_eck and @SchmidhuberAI in 2002 which show that LSTMs can learn temporal structure in music. Finding temporal structure in

arXiv:2605.10754v1 Announce Type: new Abstract: LLM-based foundation agents that perceive, reason, and act across thousands of reasoning steps are rapidly becoming the dominant paradigm for deploying

arXiv:2605.10334v1 Announce Type: new Abstract: Recent deepfake detection methods demonstrate improved cross-dataset generalization, yet the underlying mechanisms remain underexplored. We introduce th

arXiv:2605.09225v1 Announce Type: cross Abstract: Jailbreak attacks -- adversarial prompts that bypass LLM alignment through purely linguistic manipulation -- pose a growing operational security threa