Knowledge catalogue
arXiv:2610.21147v1 Announce Type: new Abstract: Large language models increasingly rely on external memory to answer queries grounded in long-horizon histories such as multi-session dialogues and leng
America’s next top model, Reflection’s Beam, is now live on Together AI. Trained on Together GPU Clusters, Beam is Reflection’s first open-weight model. • ~501B-parameter MoE, 23B active • Built for c
arXiv:2610.21063v1 Announce Type: cross Abstract: AI agents now pause for approval before they send money, delete files or email outsiders, and that approval is usually treated as a second factor. We
Beautiful work from @perplexity_ai @AravSrinivas with weights on @huggingface of course! https://huggingface.co/perplexity-ai/pplx-decider-flash-v1.2 https://huggingface.co/perplexity-ai/pplx-decider-
arXiv:2512.20159v2 Announce Type: replace-cross Abstract: Code from LLM-assisted development may contain defects like API hallucinations, motivating the development of code evaluation metrics to asses
arXiv:2610.21006v1 Announce Type: new Abstract: To use language models to help us make decisions we would like to understand the rationale behind the language model's outputs. In principle, the langua
arXiv:2610.21453v1 Announce Type: cross Abstract: While generalist vision-language-action (VLA) models have enabled diverse open-world manipulation, real-world robotic assistance requires two critical
arXiv:2610.22292v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming prevalent in scenarios where honesty is critical. However, despite alignment training, these models still ex
arXiv:2610.22016v1 Announce Type: new Abstract: Multi-agent LLM systems communicate in text, which is lossy and wasteful: the sender throws away the reading it already did. A growing literature instea
arXiv:2602.02583v2 Announce Type: replace Abstract: The rapid growth of renewable energy penetration has intensified the need for reliable probabilistic forecasts to support grid operations at aggrega
congrats to the StepFun team on releasing Step 5 Preview open weights today we’re excited to be a launch partner and to support another StepFun release as they bring Step 5 Preview to developers Step
arXiv:2610.21123v1 Announce Type: cross Abstract: Series decomposition has emerged as a widely acknowledged paradigm in deep time series forecasting models. However, existing approaches predominantly
arXiv:2610.21467v1 Announce Type: cross Abstract: Transcribing expressive piano performances into scores requires interpreting individual note timings within an underlying metrical structure. However,
arXiv:2610.22333v1 Announce Type: new Abstract: Large language models (LLMs) can often tell when they are being tested. This ability, known as evaluation awareness, can lead models to act more cautiou
arXiv:2610.20637v1 Announce Type: new Abstract: We expand the exponential kernel of attention into a truncated Taylor series of n terms and factorize each term into a tensor-power inner product, which
arXiv:2610.21018v1 Announce Type: new Abstract: When every rollout for a problem fails, GRPO receives neither a successful exemplar nor a nonzero group-relative advantage, so it learns nothing from ex
arXiv:2610.21937v1 Announce Type: new Abstract: Vision--language pre-training has enabled substantial progress in image--text retrieval and zero-shot recognition by learning transferable cross-modal r
arXiv:2603.11611v2 Announce Type: replace-cross Abstract: Rotary Positional Embedding (RoPE) is widely used in transformers to encode relative positional information, yet the effect of applying RoPE t
arXiv:2610.22343v1 Announce Type: cross Abstract: Recent 3D world models increasingly use features from geometry foundation models (GFMs) as predictive world states, benefiting from their rich geometr
arXiv:2610.21167v1 Announce Type: new Abstract: Deep research agents are increasingly deployed to retrieve precise information from large corpora, yet multi-hop, long-horizon tasks remain challenging.
arXiv:2610.22031v1 Announce Type: new Abstract: Behavioral consistency across repeated executions provides an informative signal for assessing the reliability of large language model (LLM) agents. How
arXiv:2610.22014v1 Announce Type: cross Abstract: Longitudinal electronic health records (EHRs) contain complementary diagnostic and laboratory information about patient health. However, irregularity,
arXiv:2609.33465v3 Announce Type: replace Abstract: Improving language model efficiency requires not only cheaper operations but also eliminating redundant computation. We argue that effective modelin
arXiv:2610.21407v1 Announce Type: new Abstract: Early stress detection and risk prediction are crucial for timely mental health assessment and support. While large language models have recently shown
arXiv:2509.16998v2 Announce Type: replace Abstract: Design for Robotic Assembly (DfRA) remains largely dependent on manual planning and heuristic simulation, limiting scalability and robustness in com
I know I am probably missing the point of this benchmark, but I wanted to see if the model is capable of generating something better or is that the best it can do. So I gave it some encouragement. I f
arXiv:2610.21411v1 Announce Type: new Abstract: Multi-agent LLM systems rely on generative models for expert execution, workflow planning, and artifact review. Yet many coordination operations require
arXiv:2610.21109v1 Announce Type: new Abstract: When humans collaborate with AI agents on long-horizon tasks, what happens to the agents' contributions? For solving long-horizon tasks, humans place gr
arXiv:2610.21356v1 Announce Type: new Abstract: Healthcare institutions routinely accumulate high-dimensional computed tomography (CT) volumes, but only a fraction can be annotated by experts. We pres
arXiv:2601.21132v2 Announce Type: replace Abstract: I demonstrate that large language models can infer ethnicity from names with accuracy exceeding that of Bayesian Improved Surname Geocoding (BISG) w
arXiv:2610.22179v1 Announce Type: new Abstract: Lossless tensor compression could reduce memory usage during training, but training requires tensors to be repeatedly re-encoded. We introduce Losslessl
arXiv:2610.21977v1 Announce Type: new Abstract: Physics-informed Gaussian processes (GPs) provide a principled framework for scientific inference by expressing uncertainty over empirical measurements,
arXiv:2610.22316v1 Announce Type: cross Abstract: Large language model (LLM)-based agents and multi-agent systems increasingly tackle complex tasks through long-horizon tool use and inter-agent collab
arXiv:2610.22169v1 Announce Type: new Abstract: The vocabulary of a language model is its native semantics: the embedding and the unembedding fix the meaning of a state at the two ends of the residual
arXiv:2610.21318v1 Announce Type: cross Abstract: Multimodal document retrieval-augmented generation (RAG) has focused largely on retrieval, yet its answers depend on how well a vision-language model
arXiv:2610.21091v1 Announce Type: new Abstract: Agent memory enables large language model (LLM) agents to reuse past experiences without updating their parameters. However, semantic similarity alone d
arXiv:2610.21007v1 Announce Type: new Abstract: Data selection reallocates training support across visual concepts, including objects, attributes, color, texture, and backgrounds. Different allocation
Mellum2.1 Highlights Mellum2.1 is the next version of Mellum2 Thinking. The architecture is unchanged (a 12B mixture-of-experts model with 2.5B active parameters), and almost all of the work for this
arXiv:2610.22043v1 Announce Type: cross Abstract: Which inputs drive a language model's financial forecast? We construct synthetic earnings announcements that independently reverse 20-day momentum, no
arXiv:2610.22163v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being adopted as teaching tools in computing education, particularly for developing AI tutors capable of a
arXiv:2610.22294v1 Announce Type: new Abstract: Few-shot prompting is useful when instructions and rewards are hard to specify. One such application is editing by example, where a demonstrated formatt
arXiv:2610.21062v1 Announce Type: new Abstract: As large language models become increasingly capable of generating, and assisting with, scientific writing, there is a need for greater transparency and
Muse Spark Open is now live on Wally, day 0 Best US open model thanks to @finkd 370 tok/s, ~2x faster than any provider on OpenRouter. $5 Free on signup. Media 1/ muse spark open is the most capable U
arXiv:2610.21429v1 Announce Type: new Abstract: Sound-Triggered Mobile Manipulation (STMM) requires robots to respond to sound events by navigating to the associated objects and performing appropriate
arXiv:2609.08183v2 Announce Type: replace Abstract: Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into
arXiv:2610.22092v1 Announce Type: new Abstract: Aerial robots operating near people must treat a pedestrian, a parked car, and a wall differently, yet conventional perception--planning stacks reduce a
arXiv:2603.26107v1 Announce Type: cross Abstract: People increasingly use multiple Multimodal Large Language Models (MLLMs) concurrently, selecting each based on its perceived strengths. This cross-pl
arXiv:2610.21345v1 Announce Type: new Abstract: Agentic knowledge graph question answering(KGQA) relies on sequences of executable operations, but trajectory-level rewards obscure their individual con
arXiv:2610.20782v1 Announce Type: new Abstract: The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examp
arXiv:2610.21496v1 Announce Type: new Abstract: Reliable repository-level coding requires agents not only to generate correct code but also to preserve constraints and respond to tool feedback across
arXiv:2610.22363v1 Announce Type: new Abstract: We introduce P^{3}-WAM, a unified world action model for egocentric dexterous manipulation that learns 3D perception, action-conditioned prediction, and
arXiv:2610.20881v1 Announce Type: new Abstract: While foundation models and vision transformers (ViTs) have redefined visual representations, current active learning (AL) strategies for these backbone
arXiv:2510.08667v3 Announce Type: replace-cross Abstract: Modern software teams frequently encounter delays in resolving recurring or related issues due to fragmented knowledge scattered across JIRA t
arXiv:2610.21382v1 Announce Type: new Abstract: Relational databases (RDBs) remain the primary infrastructure for storing and managing structured data across enterprise and scientific domains. Natural
arXiv:2607.18915v2 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), modern systems not only possess strong foundational capabilities and extensive knowledge
Hey r/LocalLLaMA! We're excited to share ORL (On-Policy Residual Learning), our new research on making diffusion language models (dLLMs) significantly faster without sacrificing generation quality. Wi
arXiv:2610.20807v1 Announce Type: cross Abstract: Smartphones have become integral to modern life due to their diverse applications. However, the development of applications for these devices faces si
arXiv:2610.22336v1 Announce Type: new Abstract: We develop a robust reinforcement learning (RL) framework for learning adaptive treatment policies that generalize across patients and remain reliable u
arXiv:2610.20686v1 Announce Type: new Abstract: Multi-head latent attention (MLA) compresses the content of a key-value cache into a shared latent per token, making low-bit storage attractive for long
arXiv:2610.21642v1 Announce Type: new Abstract: Vision Transformer architectures have showcased impressive performance on semantic segmentation, yet their quadratic attention complexity hinders practi