让下一篇重要论文，不再错过。

We provide a rigorous random matrix theory analysis of spiked cross-covariance models where the signals across two high-dimensional data channels are partially aligned. These models are motivated by multi-modal learning and form the standard generative setting underlying Partial Least Squares (PLS), a widely used yet theoretically underdeveloped method. We show that the leading singular values of the sample cross-covariance matrix undergo a Baik-Ben Arous-Peche (BBP)-type phase transition, and we characterize the precise thresholds for the emergence of informative components. Our results yield the first sharp asymptotic description of the signal recovery capabilities of PLS in this setting, revealing a fundamental performance gap between PLS and the Bayes-optimal estimator. In particular, we identify the SNR and correlation regimes where PLS fails to recover any signal, despite detectability being possible in principle. These findings clarify the theoretical limits of PLS and provide guidance for the design of reliable multi-modal inference methods in high dimensions.

Minimal filling architectures describe the smallest hidden-layer widths for which the neurovariety of a polynomial neural network fills its ambient function space. It was conjectured that every such architecture has unimodal hidden widths. We construct counterexamples to this conjecture using a frontier search and certify the main example through finite-field Jacobian computations and recursive dimension bounds. In the opposite direction, we prove that every minimal filling architecture of depth at most three is unimodal. A nonunimodal minimal filling architecture of depth four shows that the depth bound is sharp.

World-action models adapt pretrained video generators, and vision-language-action models build on pretrained vision-language backbones; both tie policy architecture and inference cost to large transformers designed for generation rather than control. It remains unclear how much these generation-oriented choices affect control. In this work, we introduce Latent Action JEPA (La-JEPA), a compact world-action model whose policy transformer is trained from scratch in the feature space of a pretrained V-JEPA 2.1 encoder. Following Fast-WAM, La-JEPA learns to predict future features jointly with actions during training and generates only actions at inference. Among policies without embodied pretraining, La-JEPA achieves state-of-the-art performance in clean and randomized RoboTwin 2.0 scenes and on LIBERO-Plus with correct instructions, while using far fewer parameters than the strongest competing policies. It runs substantially faster than Fast-WAM at comparable performance and outperforms the closest JEPA-based methods, LeapBot-WA and JEPA-WAM, on LIBERO-Plus and RoboTwin 2.0 without their pretrained policy backbones. On four real-robot tasks, its average success is comparable to pi0.5 and above non-embodied-pretrained baselines. Through a design study of the choices behind La-JEPA, we show that JEPA features outperform video VAE latents, most clearly out of distribution, and that training-time future prediction choices significantly impact the policy even when future prediction is skipped at inference. We correct the LIBERO-Plus instructions, as the officially released evaluator corrupts them for most variants, and re-evaluate several strong open baselines. Without a generative pretrained policy backbone, La-JEPA is better and faster than far larger policies, suggesting that robot foundation models may benefit from architectures designed for control rather than inherited from generation.

We have applied a novel combination of classic relational database modeling and modern reinforcement learning to automated intrusion response to enable inductive decision agents. Unlike previous work in the area, our approach allows agents to handle variable quantities of network assets without retraining. We test the operational performance of our approach in a virtualized computer network with variable topologies, red-team automation and simulated user agents. The task of the agents is to prevent hosts in the network from being accessed by the red-team agent, while minimizing the availability costs induced from defensive measures. We test a combination of heuristic agents and policies trained using relational reinforcement learning. The reinforcement learning agents were overall more efficient at defending the system than the heuristic policy, and the closest to the performance of human operators at the task.

Semantic communication (SemCom) using a relay node is considered, where the relay assists in conveying task-relevant information from a source to a destination, with meaning defined by classification labels. A joint end-to-end design of the source encoder, relay transformation, and destination decoders is developed to optimize both semantic inference accuracy and perceptual reconstruction quality for image data. Relay-assisted schemes are shown to outperform direct transmission, with gains that become more pronounced in low signal-to-noise ratio (SNR) regimes across different relaying strategies. Alongside these performance benefits, however, a key security vulnerability introduced by the relay is identified, and a Byzantine attack is formulated in which the relay injects structured, learned perturbations into its transmitted signal. The attack operates on intermediate communication representations subject to channel effects, with perturbations generated through a learned mapping applied at test time. It is shown to significantly degrade semantic inference accuracy at the destination. These results reveal a clear trade-off between SemCom gains and security risks in relay-assisted systems, motivating the need for secure relay designs that account for insider threats.

This study evaluates the added prognostic value of day-1 multimodal MRI for predicting three-month functional outcome after acute ischemic stroke (AIS). Sixty-six AIS patients with ADC, DWI, FLAIR, and clinical data were analyzed. Three-dimensional pretrained ResNet embeddings were extracted from each MRI modality, reduced using fold-isolated principal component analysis, and combined with structured clinical variables and ADC-derived lesion biomarkers. Linear support vector machines were evaluated using eight-fold stratified group cross-validation across MRI modality combinations. Clinical-only modeling achieved strong performance (AUC = $0.899 \pm 0.151$), while MRI-only models showed limited discrimination (AUC $\approx$ $0.67$-$0.73$). Among multimodal configurations, FLAIR-clinical fusion achieved the highest mean cross-validated performance (AUC = $0.948 \pm 0.061$), followed by ADC-clinical fusion (AUC = $0.941 \pm 0.094$). Full ADC-DWI-FLAIR fusion did not consistently outperform single-modality clinical fusion. Feature-importance analysis identified age, NIHSS subscores, ADC lesion burden, and learned FLAIR representations as key contributors, while qualitative FLAIR activation maps showed responses over lesion-like regions. These findings suggest that compact day-1 FLAIR and ADC representations provide complementary prognostic information beyond clinical variables and support interpretable, data-efficient outcome prediction in AIS.

We introduce Hyper Input Convex Neural Networks (HyCNNs), a novel neural network architecture designed for learning convex functions. HyCNNs combine the principles of Maxout networks with input convex neural networks (ICNNs) to create a neural network that is always convex in the input, theoretically capable of leveraging depth, and performs reliably when trained at scale compared to ICNNs. Concretely, we prove that HyCNNs require exponentially fewer parameters than ICNNs to approximate quadratic functions up to a given precision. Throughout a series of synthetic experiments, we demonstrate that HyCNNs outperform existing ICNNs and MLPs in terms of predictive performance for convex regression and interpolation tasks. We further apply HyCNNs to learn high-dimensional optimal transport maps for synthetic examples and for single-cell data, where they oftentimes outperform ICNN-based neural optimal transport methods and other baselines across a wide range of setting.

Tokamak experiments record diverse measurements, including waveforms, profiles, fields, and scalars, which are used to reconstruct the plasma, forecast its evolution, recover associated control signals, and estimate disruption risk. Most models are built separately for one task and a fixed set of inputs and outputs, and each relies on a prescribed subset of diagnostics rather than structuring the full multimodal observation set to extract global, cross-signal information about the discharge. These limitations motivate a reusable foundation model that adapts to each task's input-output schema and models heterogeneous target signals jointly, rather than retraining a separate model for every configuration. In this work, we extend the TokaMark benchmark with two groups of tasks for actuator action inversion and disruption prediction, yielding 20 MAST tasks in six groups with explicit schemas and evaluation protocols. Then, we introduce GenToka, a generative tokamak foundation model that jointly generates schema-selected targets through a shared conditional diffusion Transformer, with signal-specific codecs preserving native time series, profiles, fields, and scalars; foundation pretraining and task-specific adaptation transfer cross-signal structure across all six groups. Evaluation on MAST measurements shows that GenToka reduces macro-average normalized root mean squared error relative to TokaMind by 4.7% on Groups 1-4 and by 17.7% on Group 5, and improves macro-average window-level F1 on Group 6 by 8.4% at a common validation-selected threshold. GenToka provides a reusable, generative, and multimodal pathway for experimental tokamaks, linking plasma-state reconstruction, actuator inference, and disruption warning from shared measurements to support more integrated, data-driven fusion plasma prediction and control research.

Recent Hypergraph-based retrieval-augmented generation (HyperRAG) methods use hyper edges to connect multiple entities simultaneously, enabling more efficient multi-entity evidence organization than pairwise graph structures. However, existing HyperRAG methods often rely on LLM-generated summaries to construct hyperedges, which can introduce hallucinations while also incurring high indexing costs. In addition, during retrieval, existing methods typically rely on either one-hop neighbor expansion or PageRank diffusion. The former may miss useful multi-hop evidence, while the latter can suffer from uncontrolled propagation over excessive hub nodes, leading to semantic drift and noisy reasoning chains. To address these challenges, we propose HyperSU, a novel hypergraph-based RAG framework featuring semantic-unit hyperedges and clue guided bidirectional retrieval. During construction, HyperSU formulates hyperedge construction as an entity-aware minimum-description length (MDL) optimization problem, inducing source-grounded semantic-unit hyperedges that balance sentence-level semantic coherence and entity compactness. It then constructs a hypergraph by modeling each semantic unit as a hyperedge over its co-mentioned entities. During retrieval, HyperSU performs clue-guided bidirectional expansion over the semantic-unit hypergraph, enabling both multi-hop evidence discovery and evidence-anchored noise reduction. Experiments show that HyperSU consistently improves answer accuracy over standard, graph-based, and hypergraph-based RAG baselines, raising average accuracy on GraphRAG Bench by 5.5 points over the strongest baseline (68.7 vs. 63.2), with larger gains on reasoning intensive tasks, where the relative improvement reaches 14.1%.

We study two variants of the mirror descent-ascent (MDA) algorithm for solving min-max problems on the space of measures: simultaneous and alternating. We work under assumptions of convexity-concavity and relative smoothness of the payoff function with respect to a suitable Bregman divergence, defined on the space of measures via flat derivatives. We establish non-asymptotic convergence rates to mixed Nash equilibria, measured in the Nikaidô-Isoda error, proving an $\mathcal{O}(N^{-1/2})$ rate for simultaneous MDA and an improved $\mathcal{O}(N^{-2/3})$ rate for alternating MDA. The main technical contribution is an infinite-dimensional dual space analysis that relates Bregman divergences on measures to dual Bregman divergences on spaces of bounded continuous functions, allowing us to control asymmetric commutator terms created by alternating updates. The results substantially generalize prior analyses restricted to bilinear objectives and also apply to nonlinear convex-concave problems on measure spaces, thereby providing a unified theoretical foundation for MDA in mean-field min-max optimization.

Linear attention has recently gained significant attention for long-context inference due to its constant decoding cost with respect to context length. However, existing serving systems typically serve linear attention by recurrently computing and updating a large linear attention state in every decoding step. Since the state is much larger than the per-token key and value, recurrent decoding incurs substantial memory access and becomes inefficient for serving linear attention. In this paper, we propose KVBuffer, an IO-aware serving mechanism for linear attention. By buffering recent keys and values, KVBuffer enables serving systems to compute linear attention outputs in more flexible and memory-efficient ways. For decoding, KVBuffer enables chunkwise computation, which reduces average memory access and decoding latency by deferring state updates and applying them in batch. For speculative decoding, KVBuffer verifies draft tokens in parallel and avoids storing temporary states. For short contexts, KVBuffer computes attention outputs directly from buffered keys and values, without creating or updating the linear attention state. We implement KVBuffer in SGLang for Qwen3.5 and Nemotron-3 series models. Our evaluations show that KVBuffer can speedup batch decoding by up to 1.47x and increase the maximum number of serving requests by 5x for speculative decoding when verifying four draft tokens.

Dense optical flow allows small drones to avoid obstacles. However, few dense optical flow networks are designed for microcontrollers (MCUs) with neural accelerators, whose on-chip SRAM limits the input resolution. We present PACE-NAS, a macro-to-micro neural architecture search framework that decouples memory from latency: a macro design fixes the backbone and its peak SRAM, and a micro design searches operators and depths to trade accuracy against compiler-estimated latency. We apply it to EdgeFlowNet on a Grove Vision AI V2 board (Ethos-U55), where profiling locates the peak SRAM at the full-resolution output head. Replacing transposed convolutions with bilinear upsampling and convolutions lowers the peak SRAM by 18%, and two lightweight context modules lower the endpoint error (EPE) without raising the peak. All searched subnets share this peak, so the search needs no memory constraint. We name the Pareto-optimal models MCUFlowNet; the fastest variant, MCUFlowNet-S, runs 1.76x faster than a retrained EdgeFlowNet at a 1.6% higher EPE and, in simulated forest flights at 2 m/s, reaches the goal without collision in 51.5% of trials, against 41.8%. PACE-NAS also applies to semantic segmentation, where a searched PIDNet-S variant runs 1.17x faster at similar accuracy. Code is available at https://gitlab.kuleuven.be/emedia-research-lab/esat-stadius/mcuflownet/mcuflownet.

This paper presents a novel task of describing human facial expressions of a facial image in natural language, which captures the nuances of facial actions and emotional states beyond traditional emotion categories or facial action units (AUs). To achieve the facial expression captioning model, we propose a three-stage training framework that trains a vision-to-language model using synthesized image-text pairs and the BLIP-2 pre-training techniques. To overcome the challenge of missing training image-text pairs for facial expression captioning, we propose a strategy that involves synthesizing and combining captions using GPT-3.5 and existing annotations on either emotion categories or AUs. Experiments demonstrate the effectiveness of our method in generating captions that describe details of facial actions and emotions, as well as the inferential relationship between them, even when those emotions are not present in the training data. It is also demonstrated that the vision-to-language task enhances the performance of the intermediate visual features on both AU detection and emotion classification tasks.

Production deployments often use different models to balance cost and quality through cascading, routing, and switching models during long conversations. Context accumulates across turns. Each switch requires the target to repeat prefill over this context. Existing methods transfer full-attention (FA) K/V caches across models. Hybrid linear-attention models also retain recurrent states (REC) and convolution histories (Conv). The interaction between Conv and REC complicates cross-model state transfer. Conv perturbations can change keys and values in recurrent layers and alter REC updates. Conv histories are overwritten, but changes in REC can still affect later outputs. More accurate REC reconstruction need not improve continuation. Building on this, we propose Hybrid State Transfer (HST) for all three state types. It uses structured REC maps and preserves history order during Conv alignment. We calibrate the adapters using the frozen target's outputs during continuation. FA cache map updates are merged offline. Across five benchmarks, Qwen3.5 4B$\rightarrow$27B and 9B$\rightarrow$27B achieve mean task retentions of 89.88% and 94.11%, respectively. With source caches already available, separate timing on synthetic 16K inputs yields handoff body speedups of $21.66\times$ and $21.91\times$.

Predictive interference management supports ultra-reliable low-latency communication (URLLC) by allocating finite-blocklength resources against a high conditional quantile of next-slot interference. Building on an extreme-value-theory/kernel-density-estimation (EVT/KDE) predictor with quantile-defined interference states, this paper makes the predictor resilient to unannounced shifts in the interference distribution. Its cumulative refitting on all past observations does not specify how the state partition and retained samples should evolve after a regime change. We show that drift first makes the quantile partition inefficient: cumulative refitting preserves tail support and coverage but cannot recover conditional resolution, and pays for it in channel use, whereas discarding history starves the tail estimator. We therefore propose event-triggered quantile-space adaptation. A nonparametric cumulative sum (CUSUM) on the predictor's level stream detects change. At each confirmed event, the quantile partition is re-derived from post-change observations, retained history is re-binned under it, and each state is refit from a bounded per-state memory, capped to balance stale-regime bias against tail-estimation variance, with a closed-form L-moment tail estimator. At a nominal coverage target $η\!=\!0.99$, relative to matched cumulative refitting with fixed states, the proposed method reduces channel use by $5.7$--$13.2\%$ and quantile pinball loss by $25$--$50\%$ across abrupt, incremental, compound, downward, and reversal drift, with coverage changing by $-0.5$ to $+1.0$ percentage points. At matched achieved coverage, it saves $4$--$18\%$ of channel uses, while resource use in load-only and stationary cases changes by less than $0.5\%$. With $K$ samples per state and $L$ states, predictor memory is bounded at $O(KL)$ rather than growing as $O(t)$.

Music source separation extracts vocals and instruments from a mixed recording, enabling remixing, content production, and music analysis. Existing systems commonly operate on waveforms or full-band spectrograms. Waveform inputs contain many samples even in short segments, whereas a single spectrogram resolution imposes a shared time-frequency resolution trade-off across frequency ranges. We propose SubRoFormer, a Transformer-based system comprising a subband module and a residual RoFormer module. The subband module decomposes the mixture into frequency-localized, lower-rate signals for separation. It then reconstructs the target waveforms from the estimated subbands. The residual RoFormer module models temporal and cross-band relationships on temporally compressed features. It combines this context with fine-rate features through gated residual fusion to estimate source-specific complex masks. On the MUSDB18-HQ test set, training only on MUSDB18-HQ yields an average signal-to-distortion ratio (SDR) of 10.63 dB across four stems using target-specific models, achieving state-of-the-art performance among the compared systems without extra training data. With 500 additional training songs, a single model jointly separating all four stems achieves an average SDR of 11.59 dB, establishing state-of-the-art performance among the compared single-model multi-stem systems trained with extra data. Code and checkpoints are released at https://github.com/ArchiMickey/SubRoformer.

Current 4D occupancy world models use heavy cross-attention or deep recurrence to disentangle ego-motion from scene dynamics. This requires significant computational capacity. However, this effort is largely redundant since the ego-vehicle's intended future motion is provided by the downstream motion planner. We present FreeOcc, a lightweight continuous-time world model that introduces explicit $SE(2)$ geometric decoupling into a conditional flow-matching framework. By mathematically factoring out self-motion, we create a purely dynamic feature space. This space supports attention-free temporal fusion and allows physics-constrained ODEs to operate strictly on residual agent kinematics. To maximize deployment efficiency, FreeOcc introduces an uncertainty-aware blending mechanism. This routes well-observed voxels to a single-pass decoder, reserving the ODE solver exclusively for ambiguous, high-uncertainty regions. Requiring only 28.3M parameters-roughly 40% of comparable models-our optimal ensemble mode (FreeOcc-E) runs at 27 FPS. On nuScenes, FreeOcc achieves state-of-the-art forecasting performance with a 0.47 m Near Field Chamfer Distance (NFCD) and 32.1% mIoU. Furthermore, the model transfers zero-shot to SemanticKITTI. It also proves highly robust, retaining 92.8% of its ideal mIoU under significant sensor packet loss and timestamp jitter.

Rare genetic disorders are difficult to diagnose due to their diversity and overlapping clinical features. Facial morphology can provide diagnostic clues, yet most diagnostic models offer limited interpretability and are not designed to support broad screening for undiagnosed disease. We present ProtoGestalt, an interpretable prototype-based framework for facial phenotyping, rare-disease screening, and syndrome identification. The method extends prototype learning by constraining learned visual features to select facial regions, allowing predictions to be explained through localized and additive evidence. We assess ProtoGestalt's ability to distinguish syndromic from unaffected faces on multiple control datasets and class prevalence values. We further evaluate it's diagnostic performance on syndromes observed during training. The learned prototype-based representations provide significant separation between syndromic and case-matched control images and retain useful performance as the proportion of controls increases. The framework also demonstrates the ability to differentiate among syndromes. Although reasonably accurate specialized models for facial classification of rare disorders already exist, ProtoGestalt offers an interpretable alternative that links predictions to anatomically meaningful facial regions, potentially supporting clinical decision-making and the discovery of phenotype-gene associations.

Self-supervised training is monitored without labels, through the loss and statistics of the learned features. We report a case where we know the encoders lost part of what they had learned, so we can check whether these signals noticed. We trained LeJEPA encoders on synthetic images with known generating factors. In nine of ten seeds, the readability of those factors fell abruptly and stayed down, nearly always along the same direction in factor space. Meanwhile the loss kept falling, and most of the eleven label-free signals we tracked kept improving and rated the declining runs better than runs that did not decline. Standard change-point detection rarely flagged the decline near its onset, and selecting checkpoints by these signals mostly chose ones after it.

Repeated directed reachability queries arise in online path planning, task-agent allocation, and swarm control on static grid maps, where reachability is asymmetric and full transitive closure is storage-prohibitive. We introduce H-BRICK, a Hierarchical Boundary Reachability Index with Compressed Kleene closure for exact cell-to-cell queries on directed grid graphs. H-BRICK partitions the graph into tiles, computes a local Boolean Kleene closure within each tile, and projects it onto the tile's boundary ports. The resulting directed port-to-port summaries are then composed recursively across shared interfaces up to a single root covering the graph. We prove algebraically that the construction is exact: in the Boolean Kleene algebra of binary relations, every composed summary equals the boundary projection of the regional reflexive transitive closure, and every cell-to-cell query agrees with reachability in the original graph. Experiments on directed grid graphs across diverse map topologies compare H-BRICK against directed search and component-based baselines, and compare its storage requirements against full transitive closure. A batched reachability evaluation further demonstrates that sharing endpoint lifts across many-source/many-target queries amortizes hierarchical traversal and, at k=64, yields a 5.0x-22.9x throughput improvement over the scalar query loop. The results show that hierarchical boundary compression is most advantageous in query-intensive spatial graphs where online search traverses large reachable regions and the hierarchy maintains compact boundary interfaces.

Large language models are classically trained in stages: pretraining on raw text followed by post-training for instruction following and reasoning. However, this separation creates a fundamental limitation: many desirable behaviors such as safety, factuality, overall generation quality, and reasoning ability are only added at a late stage, even though the patterns learned earlier strongly shape a model's capabilities. To tackle this issue, we introduce a new way to pretrain and mid-train models that incorporates these behaviors earlier. We utilize an existing strong, post-trained model to both rewrite data and to judge policy model rollouts, thus introducing thinking and reinforcement earlier in training. We further improve reasoning at all stages by introducing the concept of self-improving post-thinking: improving the model's reasoning and self-judging ability by reasoning after generation. In our experiments, we show these approaches can give strong gains across multiple axes.

Low Earth Orbit (LEO) satellite-IoT systems increasingly provide connectivity to mobile assets in remote areas, which makes their resilience to radio-frequency jamming a growing concern. Timely jamming detection, i.e., before complete link failure, has been studied for terrestrial networks and Global Navigation Satellite Systems (GNSS), but it remains unclear whether these approaches are effective on LEO links, where propagation varies rapidly, the received signal is weak, and jamming should be detected from the messages that are still correctly received. This paper investigates the extent to which jamming of Iridium LEO satellite-IoT links can be detected from the physical layer of correctly decoded messages. To this aim, we collect and publicly release a real-world dataset of Iridium messages under 47 jamming conditions, spanning two propagation environments, static and mobile receivers, and different jammer distances, powers, and signal types. We use this dataset to analyze three one-class autoencoder-based anomaly detectors operating on physical-layer information and compare them against two signal-quality estimators commonly used as jamming indicators in GNSS-based applications. Our results show that detectability is governed by the rise of the receiver noise floor, through which jammer power, distance, and receiver mobility act. With 1000-sample chunks, i.e., 40 ms of received signal at 25,000 samples per second (about four correctly decoded messages), the autoencoders reach an AUC of at least 0.84 whenever a noise jammer raises the noise floor by at least 9 dB, whereas the $C/N_0$ and SNR estimators fail, since received messages, although correctly decoded, retain a high SNR under jamming. Therefore, timely jamming detection for LEO satellite-IoT links like Iridium should rely on physical-layer information beyond the signal-quality metrics adopted in the GNSS domain.

Self-hosting AI is often presented as a way to reduce inference costs and increase control, but deployment introduces its own costs. We report our experience deploying a hybrid AI stack at Grayhat Developers, a small software company with an AI-intensive development workflow. Our initial motivation was to counter the growing cost of commercial AI usage: Claude/Codex incurred approximately \$325 per month, with more than 25.93 billion recorded tokens. We experimented with local open-source models as replacements for commercial coding agents, but after learning that their performance and inference speed were insufficient, we adopted a hybrid approach in which commercial agents remained responsible for complex tasks while local models handled lower-complexity tasks, organizational information retrieval, and synchronization with self-hosted tools including Outline, Plane, and ERPNext. We estimated that approximately 30--40\% of our commercial AI workload was associated with context transfer, documentation synchronization, task management, and related operational coordination. By moving this work to local models, we estimate that approximately 90\% of this coordination workload could be handled without commercial inference, corresponding to an estimated 27--36\% reduction in overall commercial-AI workload or cost-equivalent usage. Our first internal deployment nevertheless failed to achieve adoption because it introduced UX friction. Replacing it with a centrally configured deployment substantially improved feedback and adoption. We conclude that local inference is most useful not as a wholesale replacement for commercial AI, but as part of a hybrid architecture that assigns tasks according to their capability requirements, cost, context demands, and operational sensitivity.

We give a $n^{O(\ell)}$-time algorithm for solving semirandom planted instances of a $k$-ary Boolean constraint satisfaction problem (CSP) with $m \gtrsim \left(\frac{n}{\ell}\right)^{k/2} \ell \, \mathrm{polylog}(n)$ constraints, for any choice of the runtime parameter $\ell$. Up to $\mathrm{polylog}(n)$ factors, this matches the known runtime vs.\ number of constraints trade-off for refuting semirandom CSPs, as well as the trade-off for solving fully random planted CSPs. Prior work on designing algorithms for semirandom CSPs follows a high-level blueprint of decomposing the constraint hypergraph $\mathcal{H}$ of the instance into pieces, each with an explicit pseudorandom property that allows the standard algorithm for random CSPs to succeed on every piece. We depart considerably from this framework and instead implicitly \emph{learn} such a decomposition, without appealing directly to explicit properties such as expansion. More generally, we construct such a learning algorithm from the assumption that semirandom $k$-XOR has Sum-of-Squares certificates of refutation. This yields the broader implication that any Sum-of-Squares refutation algorithm for semirandom $k$-XOR gives a corresponding algorithm for solving semirandom planted CSPs, and instantiating this reduction with known Sum-of-Squares algorithms for refuting semirandom $k$-XOR gives our main algorithm. To the best of our knowledge, this is the first reduction from search to refutation for (semi)random CSPs.

State-space models (SSMs) recover low-dimensional latent dynamics from high-dimensional observations, but latents are generally not identifiable from observational data alone. Interventional state-space models (iSSMs) use perturbations to break observational symmetries and identify parameters up to permutation, scaling, and shift. However, prior work restricts identifiability to linear latent dynamics and piecewise-linear emissions, excluding many behaviors characteristic of neural circuits. We extend iSSMs to a substantially broader model class: we prove that (i) affine identifiability holds under general injective emission functions and Gaussian latent transitions, and (ii) with interventions, identifiability up to permutation, scaling, and shift is preserved under general nonlinear latent dynamics. We validate our theory on simulated FitzHugh-Nagumo dynamics, and then demonstrate two applications: adjudicating between competing models of decision-making; and predicting responses to unseen micro-stimulation in macaque and human neural recordings, with proof-of-concept generalization to a held-out stimulation site in the latter. Finally, simulations yield practical guidance for designing stimulation protocols that improve identifiability.

World-action models (WAMs) jointly model future traffic states and driving actions for end-to-end autonomous driving in intelligent transportation systems. However, their action learning remains dominated by expert-trajectory imitation, leaving unsafe alternatives close to expert trajectories insufficiently constrained. To improve WAM robustness in safety-critical scenarios, we propose NegWAM, a safety post-training framework for unified WAMs that learns explicit safety boundaries from near-expert failure trajectories. NegWAM constructs negative trajectories that are geometrically close to the expert trajectory yet carry safety risks, uses them as repulsive anchors to steer predictions away from dangerous behavior, and encodes them as scene-conditioned rider tokens to learn scene--trajectory compatibility. During training, NegWAM jointly optimizes the flow-matching and near-expert negative objectives, while the auxiliary rider branch is discarded at inference, incurring no additional overhead. On NAVSIM v2, NegWAM achieves 40.4 EPDMS on navhard, a 4.1% relative improvement over the strongest compared scorer-free WAM baseline, and achieves 93.3 EPDMS on navtest. These results demonstrate that NegWAM achieves strong performance in safety-critical scenarios while preserving routine-driving performance. Zero-shot closed-loop evaluation on HUGSIM further demonstrates cross-benchmark generalization. The code is available at https://github.com/Zizizi-hao/NegWAM.

High scores on public data-cleaning benchmarks can reflect knowledge of familiar entities, making it difficult to assess how well an LLM repairs unfamiliar data. We present CoRE (Cleaning or Recalling), a protocol comparing public benchmarks with fresh real records and paired name surrogates built from English words or syllables. The surrogates retain error positions and non-identity values while transferring the benchmark's error mechanisms. Across 21 controlled tables and eight LLMs, repair performance varies substantially with entity names on Hospital and, for commercial models, Movies. Renaming removes some information needed for repair, such as a film's identifier mapping. It also affects repairs whose candidates remain available: replacing entity names with word-based surrogates lowers DeepSeek-V4-Pro's year accuracy from 91% to 18%. On Hospital and Movies, commercial models score higher on fresh records than on word-based surrogates. A screened continuation probe finds no positive evidence of benchmark-row memory in the tested models. Familiar facts can conflict with the task. Even with explicit table-evidence instructions, commercial models rewrite assigned directors at rates of 0.000-0.404 on known films. Reference scoring can also disagree with external records. On Rayyan, an ISSN correction matching a title-matched PubMed record receives no credit, whereas 94% of TabClean's credited repairs reproduce the reference date transformation. We provide controlled tables, scoring code and materials for offline replay.

Vision-based rover autonomy requires integrating perception, localization, planning and platform-specific control under limited sensing and computing resources. We present an open-source ROS 2 stack for following directional arrows, reaching a terminal cone and returning autonomously to the start. It connects YOLOv8n detection and geometric direction, range and bearing estimation with RGB-D SLAM, wheel-inertial state estimation, kinematics-aware planning, behavior-tree mission management and STM32 actuation. Standard interfaces separate reusable autonomy from the hardware layer; a proportional path-following plugin supports low-speed rover motion. Public resources include software, configuration, a labeled dataset, trained weights and evaluation logs. On 478 held-out images, the detector achieves mAP@0.5 of 0.720 under the Ultralytics evaluator and outperforms a classical OpenCV baseline under a shared evaluation protocol. The reported perception latency is 25 ms on a Jetson Orin NX. Three missions, one indoor and two outdoor, demonstrate marker-guided navigation and autonomous return without operator motion commands or reported collisions. Field results document integration failures and corrective choices; repeated instrumented trials remain necessary to quantify physical navigation errors and reliability.

Video compression at ultra-low bitrates remains challenging due to the loss of perceptually important details under limited bit budgets. Recent generative video compression methods leverage pretrained video priors to improve perceptual quality and temporal coherence, but many follow a compress-then-generate paradigm, where generative priors are primarily exploited during reconstruction. Moreover, representations learned for generation and reconstruction are not inherently optimized for entropy coding. In this work, we propose \method, which adapts pretrained video representations into compression-oriented representations while preserving their reconstruction capability. Specifically, we adapt the pretrained causal video VAE of MiniMax-H3 into a discrete video tokenizer and develop a small autoregressive entropy model that captures local and global token dependencies, with grouped prediction for parallel decoding. To further improve coding efficiency, we jointly optimize the tokenizer and entropy model using a differentiable rate surrogate, allowing rate gradients to adapt token assignments toward lower coding costs while preserving reconstruction quality. Experiments on UVG, MCL-JCV, and HEVC Class B demonstrate superior perceptual rate--distortion performance, achieving BD-LPIPS savings of 82.2\%--87.5\% and BD-DISTS savings of 78.3\%--85.0\% relative to GLC-Video.