Naga: Vedic Encoding for Deep State Space Models
arXiv:2511.13510v2 Announce Type: replace Abstract: This paper presents Naga, a deep State Space Model (SSM) encoding approach inspired by structural concepts from Vedic mathematics. The proposed meth
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arXiv:2511.13510v2 Announce Type: replace Abstract: This paper presents Naga, a deep State Space Model (SSM) encoding approach inspired by structural concepts from Vedic mathematics. The proposed meth
arXiv:2610.16902v1 Announce Type: cross Abstract: Bayesian Networks (BNs) are established tools for letting agents reason under uncertainty in sequential decision-making. They split a joint distributi
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arXiv:2603.19233v2 Announce Type: replace Abstract: A fine-tuned Vision-Language-Action (VLA) policy can easily place the alphabet soup in the basket on command, just as it saw during fine-tuning. How
arXiv:2610.16452v1 Announce Type: new Abstract: Explainable face forgery detection requires not only correct judgments but also forensic evidence supported by the input image. However, training cues a
arXiv:2610.16895v1 Announce Type: cross Abstract: LLMs are used as high-level controllers for embodied agents, but it remains unclear whether they support collaborative reasoning, such as anticipating
arXiv:2508.06163v2 Announce Type: replace-cross Abstract: Merging multiple task-specific fine-tuned checkpoints into a single multi-task model provides an attractive alternative to joint multi-task tr
arXiv:2610.17656v1 Announce Type: new Abstract: Reliable video reasoning requires models to produce correct answers and expose supporting visual evidence. Open-o3-Video enables verifiable reasoning by
arXiv:2610.14157v1 Announce Type: cross Abstract: We introduce OpenMobile-2, a near-frontier mobile agent with fully open training environments and recipes. We make three key advances: (1) Diverse env
arXiv:2610.16308v1 Announce Type: new Abstract: Contact-rich insertion requires robots to combine visual alignment with tactile adjustment. However, existing visuotactile chunk-based policies collapse
arXiv:2610.17900v1 Announce Type: cross Abstract: Extended chain-of-thought reasoning improves problem-solving performance but increases inference cost and latency, motivating more accurate answers wi
arXiv:2505.03539v4 Announce Type: replace Abstract: Panoramic imaging enables capturing 360{eg} images with an ultra-wide Field-of-View (FoV) for dense omnidirectional perception, which is critical to
arXiv:2610.17066v1 Announce Type: new Abstract: Virtual cell models should capture phenotypes induced by specific perturbations. However, image-based models are typically trained and evaluated to matc
arXiv:2610.15428v1 Announce Type: new Abstract: Context-based offline meta-reinforcement learning (COMRL) has advanced almost entirely through better task representations, which separate task identity
arXiv:2610.14558v1 Announce Type: new Abstract: Large pretrained models have achieved remarkable success across diverse domains, yet adapting them to multiple downstream tasks remains challenging: ful
All seems a bit pointless now. Either pump out AI papers and neglect your own development, or take it slow and fall behind in the publishing race. I already have two papers made largely without LLMs (
arXiv:2610.18485v1 Announce Type: new Abstract: Recent progress in generative models has made talking-head deepfakes highly realistic and easy to create, jeopardizing public trust. However, most audio
arXiv:2610.16129v1 Announce Type: new Abstract: Deploying LLM agents in non-stationary environments requires continually revising their instructions as the task drifts, yet rewriting after every repor
arXiv:2601.19060v2 Announce Type: replace Abstract: Multimodal Retrieval-Augmented Generation (MM-RAG) augments visual question answering (VQA) with external knowledge, but many systems rely on modula
arXiv:2607.15655v2 Announce Type: replace-cross Abstract: Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternativ
arXiv:2610.17991v1 Announce Type: new Abstract: Recovering high-quality images from degraded underwater observations is crucial for underwater exploration. Polarization observations show promise as an
arXiv:2610.14001v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) uses a self-teacher conditioned on privileged information to turn the sparse outcome signal of reinforcement learning
arXiv:2610.13982v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses sev
arXiv:2610.18054v1 Announce Type: cross Abstract: Anti-money laundering (AML) in financial networks requires modeling transaction patterns that span multiple institutions. In practice, however, banks
arXiv:2610.14894v1 Announce Type: new Abstract: We present a robust deep generative framework for unsupervised image prototype discovery, built on a heavy-tailed Student's t mixture prior, that bridge
arXiv:2610.15614v1 Announce Type: new Abstract: Multi-label learning typically pursues model construction through the joint optimization of label fitting, correlation modeling, and structural regulari
arXiv:2610.18481v1 Announce Type: new Abstract: Protein sequence, structure, and functional annotations each capture complementary aspects of protein biology, yet existing tokenizers encode each modal
arXiv:2610.13989v1 Announce Type: cross Abstract: Evaluating recommender systems before deployment requires simulators that remain realistic over long, closed-loop interaction cycles. Current LLM-base
arXiv:2610.14737v1 Announce Type: new Abstract: Training language agents on long-horizon interactive tasks is challenging because sparse terminal rewards provide weak credit signals for intermediate a
arXiv:2610.17936v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting (3DGS) provides an efficient framework for dynamic scene reconstruction. Most existing approaches model scene dynamics wit
arXiv:2610.14698v1 Announce Type: new Abstract: Feed-forward 3D reconstruction models have achieved remarkable progress through large-scale training and strong learned priors. However, they remain fra
arXiv:2506.19549v2 Announce Type: replace-cross Abstract: Identifying which tokens and activations truly influence model predictions is critical for both the efficiency and interpretability of large a
arXiv:2610.14326v1 Announce Type: new Abstract: Traffic density estimation from camera feeds requires connecting visual perception with short-horizon forecasting. We present a two-stage pipeline that
arXiv:2610.14397v1 Announce Type: new Abstract: Augmenting worker capabilities in the physical world through guidance has been a decades-long goal for AI agents. One key supporting capability is to un
arXiv:2610.15806v1 Announce Type: new Abstract: Recovering visually faithful content from damaged video bitstreams is important for reliable multimedia communication and storage. Existing methods expl
arXiv:2610.17549v1 Announce Type: cross Abstract: Identifying a robot task from a short instruction is sensitive to which descriptions are available for retrieval. We investigate this effect in the na
arXiv:2610.14043v1 Announce Type: cross Abstract: Frontier language models reliably refuse to explain how to cause physical harm. We ask whether this refusal survives when the same models control a ro
arXiv:2610.16082v1 Announce Type: new Abstract: Heterogeneous medical image data motivate domain-specialized diffusion experts, but expert specialization alone does not determine how their predictions
arXiv:2610.15079v1 Announce Type: new Abstract: Squeeze-and-Excitation (SE) modules improve convolutional networks by modeling channel-wise dependencies, but global average pooling collapses spatial s
arXiv:2411.18954v5 Announce Type: replace Abstract: Maximum a posteriori inference in a finite Markov random field is generally NP-hard. We study an instance-wise optimizer that minimizes the exact mu
arXiv:2610.16022v1 Announce Type: new Abstract: Neuromorphic sensing and computing promise low latency and high energy efficiency for robotics, yet most event-based visual place recognition (VPR) pipe
arXiv:2610.14474v1 Announce Type: new Abstract: Medication recommendations seek to deliver personalized drug combinations based on patients' clinical information, where ensuring transparent decision-m
arXiv:2610.18598v1 Announce Type: new Abstract: Multimodal LLMs are typically trained by first aligning a vision encoder with a language model via projector training on large image-caption datasets, f
arXiv:2610.15445v1 Announce Type: new Abstract: In recent years, the proliferation of smartphones has led to an upsurge in the digitization of document files via these portable devices. However, image
arXiv:2610.16391v1 Announce Type: cross Abstract: Multi-step retrosynthesis planning is often considered solved once a route to purchasable starting materials is found, but such a route should also be
arXiv:2610.16666v1 Announce Type: new Abstract: Vision-language models (VLMs) like CLIP have shown great success in image tasks, however, applying CLIP to open-vocabulary video recognition poses chall
arXiv:2610.17590v1 Announce Type: new Abstract: Diffusion language models (DLMs) have attracted wide attention for their fast, parallel decoding, but they are believed to be far less efficient to trai
arXiv:2511.00655v3 Announce Type: replace Abstract: Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its scalability is limited by
arXiv:2610.14572v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves the reasoning capabilities of large language models, but remains computationally expensive and s
arXiv:2610.18699v1 Announce Type: new Abstract: Evaluating robot policies before deployment typically requires producing new trajectories through real-world execution, simulators, or learned world mod
arXiv:2610.16820v1 Announce Type: cross Abstract: In simulation-based inference (SBI), neural posterior estimators trained on a bounded range of sample sizes can become inaccurate on larger datasets b
arXiv:2610.16396v1 Announce Type: new Abstract: Guided sampling with sparse observations and PDE residuals has recently been combined with neural operator priors to solve partial differential equation
arXiv:2610.17874v1 Announce Type: new Abstract: Discovering what drives an outcome from data is central to science and often requires deciding what is worth investigating before a specific hypothesis
arXiv:2610.16104v1 Announce Type: new Abstract: Test-Time Reinforcement Learning (TTRL) enables large language models to adapt directly on unlabeled test problems by constructing pseudo-labels from mu
arXiv:2601.07674v3 Announce Type: replace-cross Abstract: Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing appl
arXiv:2610.14082v1 Announce Type: cross Abstract: Robotic perception must remain reliable when extreme environmental conditions severely degrade the primary RGB observation. Although robotic platforms
arXiv:2610.15540v1 Announce Type: new Abstract: Deep-learning models increasingly rival numerical simulation in Earth-system forecasting, yet forecast error alone cannot determine whether their learne
arXiv:2610.14835v1 Announce Type: new Abstract: Branch-and-bound (BnB) solves mixed-integer linear programs by constructing and exploring a search tree. Node selection and branching are two recurrent
arXiv:2610.15258v1 Announce Type: cross Abstract: We introduce Sequent-Prover, a family of verifier-using language-model agents, and the post-training framework that produces them. Agents reason in an
arXiv:2606.12956v2 Announce Type: replace Abstract: Long-horizon robot mobile manipulation requires continual reasoning about localization, environment changes, and task progress, all of which are cha