Knowledge catalogue
arXiv:2609.33246v1 Announce Type: new Abstract: Posterior sampling for reinforcement learning (PSRL) is one of the simplest and most effective exploration methods, but a basic question has remained op
arXiv:2609.33893v1 Announce Type: new Abstract: Large audio-language models (LALMs) produce fluent responses about audio but often hallucinate by making plausible yet ungrounded claims. Existing audio
arXiv:2609.32656v1 Announce Type: cross Abstract: Multivariate Time Series Forecasting (MTSF) models that mix information across channels assume that the past of one channel carries information about
arXiv:2609.31718v1 Announce Type: cross Abstract: Images often carry recommendation constraints that text metadata only hints at. A movie may need to look dark, a product may need a minimal style, and
arXiv:2609.31975v1 Announce Type: new Abstract: This paper introduces model casting, a mid-training recipe that drastically sparsifies the activations within the Feed-Forward Network (FFN) layer. With
arXiv:2603.15033v4 Announce Type: replace Abstract: Machine unlearning for vision models is rapidly becoming a practical requirement, driven by privacy regulations, data errors, and the need to remove
arXiv:2609.32745v1 Announce Type: new Abstract: Multi-robot task allocation (MRTA) in dynamic environments faces a fundamental tension: effective coordination requires learned structure, but that stru
arXiv:2609.33207v1 Announce Type: cross Abstract: Spiking Vision Transformers (SViTs) are developed as an energy-efficient alternative to conventional ViTs for computer vision tasks at the edge. Howev
arXiv:2606.12876v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed across heterogeneous hardware with varying resource constraints, the ability to adaptively
arXiv:2609.35429v1 Announce Type: cross Abstract: Estimating conditional statistics and learning representations of a population of conditional distributions are central problems in many data-driven a
arXiv:2609.32876v1 Announce Type: new Abstract: State-of-the-art pathology foundation models, trained on millions of histology tiles, can fail to preserve tissue similarity when comparisons cross slid
arXiv:2609.34915v1 Announce Type: new Abstract: Muon is increasingly used for language-model pretraining, yet its large-step dynamics are not captured by the classical edge-of-stability (EoS) picture
arXiv:2609.33120v1 Announce Type: new Abstract: Electrical distribution grid operations require inference across heterogeneous networks from sparse, noisy, and incomplete time series measurements. In
arXiv:2609.31669v1 Announce Type: new Abstract: We present NanoForecast v0.5, a 6.5M-parameter forecaster that competes with models 31x its size (TimesFM, 200M parameters) after training pipeline fixe
arXiv:2609.33450v1 Announce Type: new Abstract: Extracting who is where, on which team, wearing which number from a broadcast frame is typically done by stitching a detector, an OCR engine, and classi
arXiv:2607.10490v2 Announce Type: replace-cross Abstract: Tool-using large language model (LLM) agents are attractive for network operations, but tickets, alerts, logs, runbooks, and ChatOps messages
arXiv:2609.35752v1 Announce Type: new Abstract: We introduce Neural Harmonic Measure Operator (NHMO), a neural solver for elliptic PDE problems on variable-shape domains. The harmonic measure of a dom
arXiv:2609.32289v1 Announce Type: cross Abstract: Neural ODEs learn dynamics from trajectory losses, but their adjoint gradients lack the regressor-times-parameter-error structure on which Lyapunov an
arXiv:2602.16954v3 Announce Type: replace Abstract: While deep generative models excel at capturing graph data distributions, they struggle to satisfy complex, hard constraints. In unconstrained setti
arXiv:2511.19573v2 Announce Type: replace Abstract: Neural solvers provide fast solutions to graph combinatorial optimization problems, but training alone does not guarantee solution quality. Exact me
arXiv:2609.35445v1 Announce Type: new Abstract: Prioritizing central nervous system (CNS) interventions requires predicting how a dose, route, and schedule act on a partially observed microenvironment
arXiv:2609.34745v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (MOPD) combines independently developed domain teachers into a single student by distilling their predictions on st
arXiv:2609.32597v1 Announce Type: new Abstract: Symbolic regression (SR) aims to discover compact and meaningful mathematical equations from data, but searching the vast combinatorial space of symboli
arXiv:2609.34572v1 Announce Type: cross Abstract: Reasoning language models that can call tools must decide during inference whether to answer unaided or delegate. Any self-reflection mechanism for th
arXiv:2609.33186v1 Announce Type: cross Abstract: Evaluating a target policy using data generated by a different behavior policy remains a fundamental challenge in reinforcement learning. While most e
Hi, I'm a long time subscriber of their pro plan. In the past, they were adding the open weight models to their catalog as soon as they dropped. Since this new change in their payment plans, I haven't
arXiv:2609.31714v1 Announce Type: new Abstract: Building omni-modal models with physical intelligence requires fine-grained supervision that captures physical evidence such as contact, support, deform
arXiv:2609.32569v1 Announce Type: new Abstract: Smart-home assistants are expected to handle diverse, realistic requests that arise in daily life. In such interactions, users often rely on the surroun
arXiv:2603.24458v3 Announce Type: replace Abstract: While proprietary systems such as Seedance-2.0 have achieved remarkable success in omni-capable video generation, the academic research community la
arXiv:2609.31631v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models enable efficient scaling of large language models but face critical deployment challenges due to massive memory requirem
arXiv:2609.34549v1 Announce Type: new Abstract: Parameter space symmetries and conservation laws play an important role in understanding the loss landscapes and implicit biases of neural networks. Ins
arXiv:2609.35259v1 Announce Type: new Abstract: On-policy learning has been argued to reduce catastrophic forgetting, produce sparser parameter updates, and improve generalisation. However, existing c
arXiv:2609.35611v1 Announce Type: new Abstract: Instruction-based image editing has achieved strong performance in single-turn settings, yet practical editing is often iterative, with each instruction
arXiv:2609.34666v1 Announce Type: cross Abstract: Differentiable physics is increasingly used in robotic material manipulation for system identification, trajectory or skill optimization, demonstratio
arXiv:2609.33833v1 Announce Type: new Abstract: Adversarial attacks have long posed a fundamental threat to machine learning systems. As multimodal large language models (MLLMs) rapidly evolve and bec
arXiv:2609.32316v1 Announce Type: new Abstract: Traffic lights are a key regulatory signal for autonomous driving at urban intersections, yet existing traffic signal perception is still predominantly
arXiv:2609.34978v1 Announce Type: new Abstract: Consumer earbuds already stream inertial motion data from the head, one of the most widely worn sensor locations on the body. We ask how much of the 3D
arXiv:2609.31670v1 Announce Type: new Abstract: Iterative and generative denoisers are increasingly used under the assumption that multi-step refinement outperforms a single regression pass. We show t
arXiv:2606.19253v2 Announce Type: replace Abstract: Existing approaches to 3D scene understanding in Vision-Language Models (VLMs) either rely on complex, model-specific geometry encoders or large tra
Sarah Perez / TechCrunch: OpenAI expands ChatGPT plugins with interactive panels and file viewers, launches a Plugin Creator tool, and adds MCP Events support for plugin automations — OpenAI isn't onl
OpenAI just announced the 200 Pro plan is back but with half the total API spend included, offset by some of their smaller (NOT Astra!) getting a 50% price drop. Does this impact the average user? No.
OpenAI Group PBC today used its DevDay developer conference to launch Dots, a set of always-on artificial intelligence agents in ChatGPT. The release is aimed at moving ChatGPT users beyond working on
OpenAI is responding to Meta's buzzy Muse AI with agentic helpers of its own: Dots. During its DevDay keynote on Tuesday, OpenAI announced that Dots will serve as always-on AI assistants that can 'do
Maximilian Schreiner / The Decoder: OpenAI prices GPT-6.1 Sol at 2/1M input and 10/1M output tokens, the same as GPT-6 Sol and Claude Sonnet 5.5, and says it performs well on safety tests — Key Points
Just a day after OpenAI Group PBC said it’s unwilling to release its latest model GPT-6.1 Astra on concerns over its safety, it has released a newer, almost as powerful model called GPT-6.1 Sol, which
arXiv:2609.32810v1 Announce Type: cross Abstract: Multidisciplinary tumor boards integrate multimodal clinical observations and longitudinal patient histories through specialist discussions, yet bench
arXiv:2609.35052v1 Announce Type: new Abstract: Video world models must preserve the visual state of the world over time, but existing evaluation protocols often rely on generated histories, video ref
arXiv:2609.32949v1 Announce Type: new Abstract: We study online dynamic pricing with censored demand, where an arbitrary inventory level is revealed before pricing and may adapt to past observations,
arXiv:2609.32897v1 Announce Type: new Abstract: Sampling-based motion planners guided by diffusion models produce high-quality trajectories in a single run, yet the stochastic diversity available at i
arXiv:2609.33927v1 Announce Type: new Abstract: This study explores the optimization of the Phi-2 Small Language Models (SLMs) for real-time chatbot applications through Parameter-Efficient Fine-Tunin
arXiv:2609.32996v1 Announce Type: new Abstract: Policies trained from the same base model can appear to solve different problems. An oracle that chooses the best policy for each problem may therefore
arXiv:2609.34843v1 Announce Type: new Abstract: Audio-video generation using heterogeneous multimodal references has emerged as a new challenge, requiring both compositional control over generation an
arXiv:2603.21877v4 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) enhances Large Language Model (LLM) reasoning but is suffer from advantage collapse: when all
arXiv:2609.31651v1 Announce Type: new Abstract: Historical manuscripts remain largely absent from modern vision-language benchmarks, leaving open how well multimodal large language models (MLLMs) hand
arXiv:2609.31717v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies have shown promising performance in language-conditioned robotic manipulation. However, most existing VLA systems
arXiv:2609.33077v1 Announce Type: new Abstract: Three-dimensional dense convolutional networks are the strongest performers on volumetric medical image segmentation, but their parameter counts scale p
arXiv:2609.32833v1 Announce Type: new Abstract: A trained neural network can be represented by a parameter vector in high dimensions. Learning distributions over these vectors enables the generation o
arXiv:2601.04692v2 Announce Type: replace-cross Abstract: In this work, we examine hateful memes from three complementary angles - how to detect them, how to explain their content and how to intervene
arXiv:2609.33558v1 Announce Type: new Abstract: 3D Visual Query Localization (3DVQL) retrieves the latest contiguous occurrence of a queried object in an RGB--point-cloud sequence and predicts a 9-DoF
arXiv:2609.34804v1 Announce Type: cross Abstract: Federated learning enables industrial operators to train shared intrusion detection models without disclosing proprietary operational telemetry. Howev