Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv:2610.08716v1 Announce Type: cross Abstract: Generative retrieval trains a language model to generate the identifier of a relevant document. Recent work replaces the autoregressive decoder with...
arXiv:2610.07522v1 Announce Type: new Abstract: Post-training quantization is a powerful tool for compressing large language models. The most scalable methods quantize every layer in parallel, but qu...
arXiv:2610.07208v1 Announce Type: new Abstract: Predicting migration flows remains a significant challenge for traditional gravity-based forecasting models, which primarily rely on structured socio-e...
arXiv:2610.07559v1 Announce Type: new Abstract: Recent progress in tabular foundation models suggests that training on synthetic tasks can substantially improve in-context learning capabilities, with...
arXiv:2610.07767v1 Announce Type: new Abstract: Reinforcement learning (RL) for post-training large language models (LLMs) incurs substantial computation and memory overhead during rollout generation...
arXiv:2610.07904v1 Announce Type: new Abstract: We introduce ApexQuant, a calibration-free quantization method that recursively re-quantizes the residual error, serving as a refinement layer on top o...
arXiv:2610.07967v1 Announce Type: new Abstract: As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their r...
arXiv:2610.08075v1 Announce Type: new Abstract: Conditional neural fields represent signals continuously, but their effectiveness depends on how the conditional latent representations are inferred fr...
arXiv:2610.08402v1 Announce Type: new Abstract: Multi-turn LLM agents often receive sparse task feedback across several interactions, while generating each response token by token. This creates two r...
arXiv:2610.08694v1 Announce Type: new Abstract: Gene expression is widely measured in biomedicine, yet clinical outcome prediction remains challenging due to high dimensionality, strong feature corre...
arXiv:2610.07269v1 Announce Type: cross Abstract: Cross-view geo-localization is commonly solved as an image retrieval problem, matching a ground-level image against a database of satellite tiles thr...
arXiv:2610.07572v1 Announce Type: cross Abstract: In-context learning (ICL) adapts frozen large multimodal models (LMMs) to new tasks from a few demonstrations (demos), but re-encodes them at every q...
arXiv:2610.07585v1 Announce Type: cross Abstract: We propose a scalable roto-reflection-group-equivariant vision transformer based on windowed group-convolutional self-attention and a hierarchical fe...
arXiv:2610.07720v1 Announce Type: cross Abstract: Multi-reference image generation requires preserving the appearance of multiple subjects while composing them into a coherent scene. However, existin...
arXiv:2610.07723v1 Announce Type: cross Abstract: Safety alignment in Large Language Models (LLMs) remains vulnerable to backdoor attacks. Existing LLM backdoors are almost all input-centric: activat...
arXiv:2610.07825v1 Announce Type: cross Abstract: Time series forecasting models are typically compared on pointwise error, which scores a prediction in isolation from the decision it is produced for...
arXiv:2610.07894v1 Announce Type: cross Abstract: Large language models (LLMs) are expected to answer questions faithfully based on the provided context, abstaining when the context information is in...
arXiv:2610.08463v1 Announce Type: cross Abstract: Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an...
arXiv:2509.18169v4 Announce Type: replace Abstract: Tasks on complex systems require high-precision numerical computation to support decisions. However, current large language models (LLMs), even wit...
arXiv:2602.02427v4 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved significant breakthroughs across various domains, but they can still produce unreliable or misleading ou...