overfeed.news
Anuncio

NexTalk

Automatiza la primera respuesta. Mantén el toque humano.

Chat en vivo con agentes de IA en tu modelo y tu clave de API, con traspaso a tu equipo.

Empezar gratis

Tema

Imagen y video

1837documentos

142últimos 7 días

arXiv AI Papers

Online Autotuning of Algebraic Multigrid via Contextual Bandits and Reinforcement Learning

Algebraic multigrid (AMG) methods are widely used for solving large sparse linear systems, but their efficiency depends strongly on interacting setup and solve parameters. Effective parameter choices typically depend on the problem. We develop a phase-aware online autotuning method for hypre's BoomerAMG to reduce cumulative time-to-solution over sequences of related linear systems. A LinUCB-based contextual bandit selects mixed-type parameters for AMG hierarchy construction using a shared cost model over PDE descriptors and configuration encodings. A recursive LSTDQ controller uses residual and timing observations to estimate the AMG solve time within each attempt and adapt relaxation weights between multigrid cycles. Both learners update from ongoing solves without offline pretraining. Across grids of sizes 40^3, 60^3, and 80^3, the joint autotuning method reduces total runtime, including recurring learner computation, by 36.1-43.3% for diffusion problems and 19.8-31.0% for diffusion-advection problems over sequences of 5000 linear systems, relative to the reference BoomerAMG default configuration. On a variable-flow diffusion-advection benchmark comprising 30,000 systems on a 60^3 grid, 2 systems fail to converge with joint autotuning, compared with 1677 for the reference configuration, under their respective attempt budgets.

en
arXiv AI Papers

What Does a Safety Label Measure? Auditing Situational-Safety Benchmarks for Multimodal LLMs

Situational-safety benchmarks label each scene SAFE or UNSAFE and score a multimodal large language model (MLLM) against that label, usually by judging its free-form answer, and these scores guide deployment. We ask what such a label measures. A label score can come from the scene, the request or the benchmark's own files. We propose the Benchmark Card, seven measured fields that tell them apart, and fill it for six benchmarks. Only MSSBench chat leaks the label through its image files: image size alone reaches 82.3% accuracy, matching or beating six of seven commercial models. Elsewhere the signal outside the scene comes from design. In MOSSBench the item partition fixes the label, and on HoliSafe the request alone ranks pairs about as well as the scene. On our corrected chat split, the size rule falls to chance. We then introduce the Spot-Stop decomposition, which compares ranking with labelling on pairs of a safe and an unsafe scene. It splits the gap above a label-swap null into three parts: a threshold part, overlap between safe and unsafe scores, and order that holds only within a pair. Most of fourteen open MLLMs rank better than they label, beyond what their AUROC predicts. On chat, one shared threshold repairs a third to a half of the excess, and label-free recentering recovers it on balanced batches. Recentering on unlabelled scenes of the same situation improves ranking on VSCBench for 13 of 14 models.

en
arXiv AI Papers

Roz: A Taste Test for World Model Latents

State of the art latent dynamics world models achieve impressive performance for robot planning. But do they actually encode the physics of the world? In this paper, we show that popular world models today achieve strong results on tasks that reduce to image similarity but forget key details about objects in the scene like mass, deformation, friction, and bounciness. We achieve this by defining precisely what ought to be encoded in such a world model and introducing two new evaluation methods that can directly tease physical properties and easily scale to new ones. Our evaluation, which we call Roz, includes 10 contrastive tasks and 3 continuous tasks, totaling 1734 individual scenarios. We validate our eval on control tasks. On our own model, AnonWM, we find that performance roughly improves with training steps, while not necessarily being correlated with reconstruction MSE. We argue current model failures are a symptom of evaluation strategies (probes and planners) that are opaque, model-specific, and high-compute. Roz requires only a model that can ingest still frames or video, a way to extract latents, and a distance function between them, and it can be run in one pass. We hope future evaluations will adopt Roz's evaluation techniques. More broadly, we hope Roz can be a case study for a future where benchmarks allow rapid iteration and comparison across models.

en
arXiv AI Papers

Words Before Pixels: Selective Modality Routing for Vision-Language Model Unlearning

Vision-language model (VLM) unlearning is often treated as an objective-design problem: given multimodal forget data, the goal is to optimize a loss that removes undesirable behavior while preserving retained capabilities. We argue that this view overlooks an equally important data-centric question: how should each forget example be represented before optimization? Existing methods typically keep image-conditioned failures in their original image-text form, implicitly assuming that pixels are the right route for unlearning whenever pixels appear in the input. We challenge this assumption by constructing modality-decomposed versions of VLM benchmarks, enabling controlled comparisons among image-only, text-only, and multimodal representations of the same examples. Our modality-mixing experiments show that unsafe instruction-following behavior is more effectively suppressed through textual representations than through images alone, while purely text-only unlearning can still weaken visual grounding when the target behavior depends on visual evidence. Motivated by this trade-off, we introduce ModRoute, a gradient-guided per-sample modality routing strategy that selectively converts high visual-pressure examples to text-only form while keeping the remaining examples multimodal. Notably, on VLGuard with LLaVA-1.5-7B, ModRoute has the strongest composite unlearning-utility score of 0.847 at γ=0.5, a 2.5% improvement over the best random-switching score and 46% better than pure multimodal or text unlearning. Overall, our results show that effective VLM unlearning depends not only on the objective, but also on the composition and per-sample modality routing of the unlearning data.

en
arXiv AI Papers

Editing the Goal: Per-Token SAE Steering and Its Depth Limits in π_{0.5}

Sparse autoencoders (SAEs) have recovered interpretable, causally relevant features in robot foundation models (RFMs) such as π_{0.5} (arXiv:2603.19183, arXiv:2603.19233). SAE features on individual instruction tokens are less interpretable than timestep-pooled ones (arXiv:2603.19183), and whether they can redirect a robot's goal remains untested. We show that replacing the SAE features of a single goal word with those of an alternative goal word, leaving the instruction text unchanged, steers π_{0.5}-LIBERO into completing the alternative task. At layer 0, BatchTopK feature swaps redirect behavior in up to 20/20 rollouts in a scene where random-feature swaps redirect none, with success varying across six scenes in two LIBERO suites and across layers. The effect depends on SAE architecture: word-selected Matryoshka edits largely do not outperform random ones. SAEs are also not required. Adding a fixed difference-in-means vector at twice its natural magnitude redirects 8-10/10 rollouts. Layer-wise activation patching shows that goal-word edits remain effective up to layer 6 for object location and layer 2 for object identity, and up to layer 13 in both cases when patching also covers camera tokens. These results are proof of existence in an RFM in simulation and an initial map of how deep goal-redirecting edits remain effective. Visit https://doubleblind-sae-pi05.github.io for video rollouts.

en
arXiv AI Papers

X-World 1.6: Building Controllable Virtual Cities with World Models

Deploying generative world models for end-to-end closed-loop evaluation and policy learning requires precise scenario control, consistent observations, and sustained generation quality during extended interactions. This report presents X-World~1.6, advancing X-World~1.0 toward a more reliable and versatile driving simulator. The upgraded base model offers finer-grained control over traffic semantics, including traffic lights and road markings, together with improved spatial consistency across cameras. Virtual City capabilities broaden scenario construction through a family of models conditioned on diverse 2D and 3D references, including support for generating multi-camera future videos from a single image. An enhanced post-training framework enables efficient causal, few-step generation with stronger temporal coherence, better quality retention over longer rollouts, and improved alignment with human preferences. We also introduce a dedicated evaluation suite to support systematic assessment and guide model iteration. Together, these upgrades expand the scenarios X-World can simulate and improve the efficiency and reliability of its generated observations, supporting broader use in autonomous-driving development.

en
arXiv AI Papers

Do Orthogonality Constraints Help Language-Model Training Beyond Sphere Optimization?

A growing body of work reports benefits from orthogonality constraints in fine-tuning large language models (LLMs). These results raise an open question: do orthogonality constraints also help LLM pre-training? We conduct a systematic empirical study of decoder-only LLM pre-training with models ranging from 360M to 3.1B parameters, trained on nearly 200B tokens. We examine fourteen variants of orthogonality training: some constrain the weight matrix itself, while others use orthogonal factors to reparameterize it. Because a strictly orthogonal square matrix loses half of its degrees of freedom, we also study block-wise orthogonality, where every d rows form a row-orthonormal (Stiefel) matrix and the block size d is a hyperparameter. Among block sizes ranging from 1 to the shorter side of the matrix, no d>1 used throughout the network reliably outperforms d=1, and large blocks are clearly worse; the one placement that helps, symmetric positive-definite factors on the query and key projections, loses its small gain as models grow. At d=1, the constraint reduces to sphere optimization, with each row direction on its own unit sphere, which by itself already improves on tuned Muon. The same two findings hold for a diffusion transformer trained on ImageNet, and after fine-tuning, downstream accuracy does not separate the parameterizations. Our conclusion is that, across the realizations we tried, orthogonality between rows does not help language-model pre-training beyond sphere optimization. Since this appears to conflict with several prior results, we re-examine five prior methods in pre-training and fine-tuning that report orthogonality improvements. In each case the reported gain is attributable to numerical precision, training horizon, optimizer realization, or per-row norm control rather than to the orthogonality the methods impose.

en
arXiv AI Papers

Reflex: Fast Robot Policy Serving by Reusing and Correcting Past Actions

Modern robot policies often perform expensive inference to generate action chunks, making serving a latency bottleneck for real-time control and a throughput bottleneck for multi-robot serving. Building on the success of caching in image, video, and audio generation, recent robot-policy advances have reported promising speedups by reusing intermediate denoising states to avoid redundant computation. Surprisingly, we find that such reuse often offers little advantage over simply running fewer denoising steps from fresh noise, without any cache. Moreover, accelerating denoising leaves the expensive policy backbone intact, which can account for 70% of GPU time. We present Reflex, a serving layer that turns past policy executions into a backbone-free path for future decisions. Rather than reusing intermediate computations, Reflex caches complete action chunks to bypass both the backbone and action head. This introduces two challenges: similar observations can demand different actions, and small action deviations (errors) in reuse can compound into execution stalls. Reflex addresses these through two-level cache correction. At the action level, it learns action-aware similarity from past policy decisions, assembles compatible chunks, and applies lightweight residual correction to limit action mismatch. At the execution level, it captures task progress and adaptively refreshes the original policy to limit accumulated errors and balance inference cost. Without modifying the underlying policy, Reflex improves inference latency by 1.4x to 11x and throughput by 2.0x to 2.2x on representative workloads and real robots, while preserving comparable task success.

en
arXiv AI Papers

Latent Reasoning in Continuous Space for Unified Multimodal Models

Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation tasks, yet they still struggle to effectively leverage language-based chain-of-thought reasoning for image generation. As a result, existing reasoning-augmented UMMs often fail to inherit the test-time scaling and fine-grained controllability observed in large language models. We attribute this failure to a fundamental modality mismatch: reasoning is performed in a discrete token space, whereas image generation operates in a continuous space. To bridge this gap, we introduce a Latent Reasoning framework in Continuous space (LARC), which alleviates the token-level bottleneck by reasoning over continuous hidden states. Specifically, the training framework consists of two stages: (i) curriculum supervised fine-tuning (SFT), and (ii) information-gain reinforcement learning (RL). First, the curriculum SFT stage gradually converts token-level reasoning into latent reasoning. Subsequently, the information-gain RL stage self-evolves latent reasoning without ground truth supervision. Experiments across diverse text-to-image generation benchmarks show that LARC consistently improves generation quality, prompt following, and compositional fidelity. Notably, LARC achieves state-of-the-art performance on GenEval with a 7%p gain over BAGEL and demonstrates clear test-time scaling behavior, outperforming token-based generation under both parallel and sequential test-time scaling strategies.

en
arXiv AI Papers

Hierarchical World Models with Implicit Dynamics

Planning with world models enables test-time adaptation for embodied agents, offering superior generalization over direct policy learning. However, long-horizon tasks remain hindered by cumulative errors and search complexity. Existing hierarchical methods often use rigid macro-actions or latent skills, leading to brittle representations and limited fine-grained control. We introduce ID-WM (Implicit Dynamics World Models), a hierarchical world model that decouples state-space reachability from local control. Our framework utilizes a high-level model to sample feasible transitions across the state manifold and a low-level world model as an inverse dynamics solver to ground these transitions into precise actions. This allows the planner to identify reachable subgoals without being constrained by low-level execution details. By prioritizing local physical feasibility over global behavioral patterns, ID-WM synthesizes novel long-horizon plans from short segments, transcending the specific behavioral modes seen during training. We demonstrate its effectiveness across three navigation and manipulation suites, achieving a 38.5% improvement over state-of-the-art policy and diffusion-based planners. Crucially, by leveraging this hierarchical structure to mitigate compounding errors, our approach yields a 135% performance gain over single-level world model MPC, particularly in complex, unseen configurations for long-horizon tasks. Videos can be viewed at https://id-wm.github.io

en
arXiv AI Papers

A Better Critic Matters for Distribution Matching Distillation

Distribution Matching Distillation (DMD) trains a few-step student using the difference between a teacher's score and an online critic's estimate of the student distribution's score. We investigate what the critic needs, in capacity and in information, to provide a reliable training signal. On the capacity side, controlled experiments across seven SiT model sizes on ImageNet show that increasing teacher size while keeping a small critic fixed can degrade student quality and cause training collapse. Scaling the critic alongside the teacher, a strategy we call joint scaling, prevents collapse in our experiments and allows the student to benefit from larger teachers. On the information side, rather than forcing the critic to model a step-agnostic mixture of student outputs, we introduce a step-aware critic (STAC) that conditions on the student's generation step to estimate step-specific scores. Together, joint scaling and STAC consistently improve few-step distillation across class-conditional ImageNet generation, text-to-image generation with SDXL and FLUX.2, and text-to-video generation with Wan2.1.

en
arXiv AI Papers

RangeSWA: Training-Free Length Extrapolation for Continuous Diffusion Language Models

Continuous diffusion language models show promising potential in text generation tasks, but are prone to problems such as text repetition, semantic drift, and generation quality degradation when the generation length exceeds the training length. To address these challenges, we propose RangeSWA, a training-free length extrapolation method. The method combines local sliding-window attention, remote block summaries, and dynamic content retrieval to preserve local details and support long-range context access. In addition, generation degradation caused by length extrapolation is mitigated through positional remapping, conditioning attention correction, and denoising-stage-adaptive window scheduling. We conducted experiments on multiple continuous diffusion language models, and the results show that RangeSWA can effectively reduce generative perplexity and text repetition rates in long-text generation.

en
arXiv AI Papers

Learning Clean Generative Processes from Noisy Measurements

We study how to learn a generative model of clean images using only noisy observations. Diffusion processes and their generalizations to non-Gaussian noise require posterior predictions at every noise level. We first show that it is possible to learn these predictions from independent noisy views using Noise2Noise regression, but only at or above the noise level of the training data. To learn the predictions below that level, needed to generate clean images, we impose consistency: a prediction should equal its expectation after continuing the sampling process. We derive conditions under which consistency uniquely determines the continuation to the clean image distribution and verify them for Poisson, binomial, Gamma, and negative-binomial measurements. Experiments on CIFAR-10, photon-counting microscopy, and synthetic aperture radar show that the required posterior predictions can be learned from noisy views alone and that the consistency relation repairs the continuation beyond the noise level of the data, which severely deteriorates without consistency.

en
arXiv AI Papers

Asynchronous Flow Map Language Models

The order in which tokens are predicted in language modeling has a profound impact on the quality of generated sequences. Autoregressive and masked diffusion models iteratively fill in tokens and can exploit such orderings across generation steps, but how to leverage an ordering for one-step parallel language modeling is unclear. We introduce asynchronous flow map language models (aFMLMs), which incorporate prediction order into one-step modeling by learning the flow maps of asynchronous flows. These flows specify an ordering through a tokenwise time schedule that controls the relative denoising progress of tokens. We formulate a general class of asynchronous flows that admit both prescribed and learned, state-adaptive schedules, show that they define valid flow maps, and develop new methods for their flow map distillation. The resulting models can generate all tokens in a single forward pass by internalizing the ordering encoded in the flow into their hidden computations. On Sudoku solving and code generation for GSM8K math word problems, aFMLM establishes new state-of-the-art one-step performance.

en