Computer Science & Mathematics

Spatiotemporal forecasting plays an important role in real-world applications, where probabilistic approaches provide uncertainty estimates for decision-making. Real-world systems are inherently non-stationary, leading to distribution shifts between training and test data. However, existing probabilistic methods mainly focus on modeling data distributions under standard settings, while distribution shift methods often handle such shifts in a coarse manner, failing to characterize shift degree and adapt prediction strategy accordingly. As a result, they often produce miscalibrated and unreliable forecasts. To address this issue, we propose REMIND, a retrieval-guided probabilistic framework for spatiotemporal forecasting under distribution shift. REMIND leverages retrieval to estimate how well current observation are supported by historical patterns, enabling adaptive prediction under different shift regimes. It employs spatiotemporal retrieval to construct a memory bank and introduces a dual branch architecture, combining a memory-calibrated probabilistic branch for mild shifts with a deviation-aware probabilistic branch for severe shifts. A shift-adaptive gate, guided by retrieval similarity and memory dispersion, balances the two branches according to shift degree. We further design a hierarchical Gaussian mixture to capture heterogeneous uncertainty over the dual branch predictions, and introduce a memory perturbation training scheme to improve generalization. Experiments on real-world datasets with distribution shifts demonstrate that REMIND consistently outperforms state-of-the-art methods in both forecasting accuracy and probabilistic prediction quality.

Robot manipulation involves localized changes: picking up a cup moves the cup while leaving the surrounding scene largely unchanged. Yet existing 3D visual pretraining methods, including recent dynamics-aware variants, model state transitions at the scene level, compressing the motion of everything in a scene into a single global latent action. This conflates semantically distinct transitions in scenes with multiple moving parts, obscures dependencies between changing regions, and leaves the learned representation vulnerable to task-irrelevant scene content. We introduce DynaSlots, an action-free, label-free pretraining framework that decomposes each point cloud frame into a small set of temporally consistent slots and models dynamics at this per-slot level rather than the scene level. A temporal slot propagation module promotes consistent slot assignments across frames; an inverse dynamics model then extracts one latent action per slot from feature differences, and a diffusion-based forward dynamics model, paired with a self-supervised interaction head, predicts each slot's future state while learning pairwise proximity-and-motion relations. Experiments on Meta-World, Adroit, and a RealMan RM65-B robot demonstrate that DynaSlots outperforms prior scene-level baselines. It achieves superior success rates in simulation and exhibits strong robustness to scene clutter and unseen objects in real-world manipulation. Code is available at \href{https://zhucezhanghao12345-source.github.io/DynaSlots-Anonymous/}{https://zhucezhanghao12345-source.github.io/DynaSlots-Anonymous/}.

Humans efficiently process multimodal streams spanning hours to years, building persistent, fine-grained spatiotemporal memories that support reasoning about and predicting long-horizon events. In contrast, existing benchmarks focus on short- and medium-context tasks, and most foundation models struggle to preserve fine-grained information over long horizons. To evaluate and improve long-horizon spatiotemporal reasoning, we introduce the LongVideo dataset and the 4DMemoryBench. LongVideo comprises about 900 hours of video and over 5,000 manually reviewed questions covering visual, textual, and spatiotemporal reasoning across diverse context lengths. We further develop the 4DMemory Harness, which integrates four complementary skills, 22+ specialized tool models, and a model-specific retrieval-augmented generation system. Evaluating 13 leading models, we find that standalone foundation models struggle with long-horizon tasks, while the 4DMemory Harness improves all of them by over 50\%. Compared to previous benchmark, the same models exhibit a performance drop of more than 10\% on 4DMemoryBench. We hope 4DMemory will facilitate the development of spatiotemporal agents capable of addressing complex, long-horizon tasks.

When LLM-powered agents interact, information is repeatedly shared, expanded, and forgotten, yet multi-agent evaluation typically focuses on final task outcomes. We study such interaction as claim-level information dynamics and propose RIVER, which decomposes agent outputs into atomic claims and evaluates three complementary dimensions: convergence, exploration, and retention. Convergence captures cross-agent claim correspondence and stance alignment; exploration captures semantic breadth and extension beyond the source; and retention tracks source preservation and the persistence of new claims across stages and lineages. We evaluate five interaction strategies on 40 controlled news-generation cases. Repeatedly pooled interaction promotes convergence but narrows the information space, whereas distributed relay yields broader, less shared information. Further comparisons show that method-specific constraints increase exploration while weakening convergence and retention. Our findings suggest that collective information dynamics are better explained by underlying information-flow mechanisms than by simple downstream performance.

Pre-trained code models are exposed to backdoor poisoning, in which crafted training samples make the model misbehave on a trigger. A capable attacker minimizes the number of such samples, since each adds cost and exposure and a lower poisoning rate leaves a smaller footprint for defenses. Which samples to poison therefore matters, but existing data-efficient selection methods target image classification, offer no scoring mechanism for autoregressive generation, and have not been studied on code. We propose Trigger-Neutralized Counterfactual Scoring (TNCS), which isolates a trigger's contribution to the target behavior by comparing the scoring model's output on a triggered input with its output on a trigger-neutralized counterfactual, covering classification and generation alike. TNCS attains higher attack success rates than the baselines at very low poisoning ratios, across three CodeXGLUE tasks and five trigger families, without degrading clean-task performance, and its margin grows as the ratio decreases. It is the only method effective on every trigger family, and its selected poison set transfers to mismatched victims.

Fuzzy logics with truth values in the unit interval vary in their choice of propositional connectives and their semantics, ranging from just minimum and maximum as in Zadeh logics to truncated addition and subtraction as in Łukasiewicz logics or their extensions with truth constants, known as Pavelka logics. In the more specific world of fuzzy modal logics, non-expansive fuzzy modal logic has recently been proposed as a sweet spot between Łukasiewicz (more precisely, Pavelka) and Zadeh fuzzy modal logic; it restricts Łukasiewicz propositional connectives to have one argument constant, equivalently extends the Zadeh propositional base with constant shift operators. It retains reasonable expressiveness both in practical terms and abstractly; in particular, it characterizes the standard notion of behavioural distance on fuzzy Kripke frames. At the same time, it allows for satisfiability checking in the same complexity as for its two-valued correspondent (the modal logic K, or the description logic ALC), in contrast to the notorious computational hardness of Łukasiewicz modal logic. In the present work, we show that non-expansive fuzzy modal logic admits uniform interpolation, a key property in modularization, information hiding, summarization, and other applications that fails for both Łukasiewicz (or Pavelka) and Zadeh modal logic.

Retrieving the right skill from a large library is essential for LLM agents, yet requests and skills are written for different purposes and in different words. Existing retrievers narrow this request-skill gap with one or two representation choices each, so it is unknown whether their gains hold in combination. No prior work measures the gap itself, so it is also unclear which part of it each choice narrows. We define a representation design space by five choices -- encoder fine-tuning, query-side and document-side expansion, background projection and a lexical channel -- and evaluate them together for the first time, as modules of one retriever, SkillRep. With a 0.6B encoder and LLM expansion, SkillRep improves MRR@10 on SRA-Bench over the strongest same-size retriever by 7.02 points with the official skill pool and 8.79 with UniPool, a 111,247-skill pool we build from public registries, and is best on SkillsBench. The value of each choice depends on the others. Fine-tuning, for instance, reverses the ranking of the four training-free modules and turns them from complementary into overlapping. To understand why, we measure the gap with three quantities: background, the embedding component that does not separate skills; unsaid vocabulary, the gold skill's content a request leaves unsaid; and dilution, the information lost when a skill is encoded into a single vector. They show which part each module narrows and by what mechanism. This reveals that modules narrowing the same part compete for it, while those narrowing different parts complement each other.

In unsupervised multivariate time series anomaly detection (MTSAD), local variation can inflate reconstruction and forecasting errors, making observation-space discrepancy an unreliable indicator of abnormal system behavior. Since latent-space scoring can mitigate this limitation, we reframe one-class MTSAD as current-point system-state compatibility estimation and propose TENET, the Time-point Encoding Network for Multivariate Time Series Anomaly Detection. We define the current-point system state as a latent representation of the target time point, constructed from variable-level temporal context in the input window. TENET constructs this state through a temporal layer that retrieves window context relative to the current point, encodes time with current-point-centered embeddings, and adaptively mixes the retrieved context with the current representation. With this design, TENET treats the input window not as the object to reconstruct, forecast, summarize, or score, but as context for constructing a latent representation evaluated with a fixed standard-normal negative log-likelihood score. Across benchmark datasets, TENET achieves state-of-the-art performance. Additional experiments further show that TENET remains effective under alternative one-class objectives and outperforms substitutions based on generic sequence encoders, suggesting that its advantage comes from decision-aligned representation construction rather than from a specialized scoring rule. These results indicate that one-class MTSAD can be strong when the latent representation is designed around the current time point.

Large language model (LLM)-based coding agents increasingly resolve real-world software engineering tasks, yet still fail on many of them. Prior trajectory studies characterize these failures through task-agnostic patterns of agent behavior, such as repeated actions. However, an agent may follow a sound workflow yet act on a faulty understanding of the existing code, the task requirements, or a candidate fix. Reasoning models, which power most state-of-the-art coding agents, reveal this understanding in their native chain-of-thought (CoT), whose claims about the task can be checked against the facts. Yet it remains unclear how many such checkable claims the CoT contains, how many are faulty, and whether faulty claims actually cause incorrect patches. We present the first empirical study of such claims, which we term task-grounded reasoning, analyzing 400 trajectories of four open-weight reasoning models on SWE-bench Pro. We extract atomic claims from the CoT with an LLM, assess them against evidence gathered by an LLM agent, and manually trace the defects of incorrect patches back to faulty claims, confirming each link with a claim-corrected replay. Task-grounded reasoning covers only 18.5% of the CoT tokens, yet yields 191 atomic claims per trajectory, at least 9.6% of which are faulty. Faulty claims also occur in 96.7% of the resolved trajectories, so their mere presence is a weak signal of failure. Yet two thirds of the 188 incorrect patches trace back to faulty claims. Correcting such a claim makes the agent fix the linked defect in 89.0% of the cases, and correcting all such claims resolves 11.7% of the failed tasks. Our findings show that the CoT carries task-specific signals for diagnosing and repairing agent failures, but exploiting them requires pinpointing the few faulty claims that actually cause patch defects.

We determine the optimal regret in agnostic multiclass online learning with arbitrary, possibly infinite, label spaces. For every hypothesis class of multiclass Littlestone dimension $d

Many functionally important materials, such as silicon, possess high-symmetry crystal structures that forbid even-order nonlinear optical responses, thereby limiting their application in e.g., photonics and optoelectronics. Strain gradients can effectively break symmetries, activating otherwise forbidden optical responses. Although such flexo-photoresponses have attracted considerable interest recently, a comprehensive microscopic theoretical framework is still lacking. Here, we develop a first-principles approach that can systematically evaluate a wide range of flexo-photoresponses in realistic materials. By mapping strain gradients to long-wavelength phonons within the established theory of electron-phonon coupling, our approach captures the nonlocal quantum-mechanical effects of lattice deformations on nonlinear optical responses. Applying the framework to prototypical centrosymmetric materials, including Si, cubic $\rm SrTiO_3$, and monolayer black phosphorus, we demonstrate that a strain gradients of $10^5\sim10^6~\rm m^{-1}$ can induce flexo-second-harmonic generation and flexo-bulk photovoltaic effects comparable to the intrinsic responses of non-centrosymmetric crystals. Our work establishes a rigorous foundation for strain gradient engineering of light-matter interactions, opening new avenues for nanoscale optomechanics, optoelectronics, and beyond.

Over-the-air computation (OAC) exploits the superposition property of wireless channels to compute functions of distributed data, reducing the communication overhead associated with its multiple access. However, existing OAC frameworks are often designed around specific classes of functions, limiting their applicability to more general nonlinear computations. This paper investigates type-based multiple access (TBMA) as a general framework for OAC, enabling the receiver to recover a histogram of the distributed observations and compute a broad class of functions without modifying the underlying communication scheme. We formulate TBMA as a histogram estimation problem and propose two digital detectors. We prove that a low-complexity successive cancellation algorithm achieves the optimal maximum-likelihood (ML) performance. We derive analytical expressions for the mean squared error (MSE) and compare TBMA with conventional schemes in terms of computation accuracy, transmission duration, bandwidth and energy consumption. Numerical experiments validate the analytical results and demonstrate that, unlike existing OAC techniques, TBMA exhibits an exponential MSE decay with increasing signal-to-noise ratio (SNR) and constant transmit energy, independently of the function being computed. These findings position TBMA as a step toward more versatile OAC architectures, compatible with existing transmission schemes, and broadening the range of functions that can be computed directly over the wireless channel and enabling new possibilities for distributed computation and decision-making.

Automated Vulnerability Repair (AVR) has emerged as a critical defense against software security flaws, aiming to triage and fix bugs autonomously. Early LLM-based approaches focused on patch generation under the assumption of perfect fault localization, while recent end-to-end methods attempt to repair vulnerabilities directly from sanitizer logs. However, these methods often fail when the root cause is deeply hidden--either physically distant from the crash site or logically masked by interdependent errors--making the initial report insufficient. We propose DeepAVR, a multi-agent framework for end-to-end AVR that performs deep analysis by transforming intermediate repair outcomes into structured diagnostic feedback. DeepAVR decomposes repair into specialized roles--Fault Localization, Patch Generation, Compile Validation, Security Validation, Functionality Validation, and Patch Review--and routes feedback across these roles to iteratively refine both localization and patch hypotheses. In particular, DeepAVR introduces patch-conditioned re-localization, which treats execution results from intermediate patches as diagnostic signals to uncover hidden root causes, and a root-cause-oriented patch review stage that detects symptom-level fixes and incomplete repairs beyond execution-based validation. Evaluation on the real-world C/C++ vulnerability repair dataset VulnLoc shows that DeepAVR significantly outperforms prior methods, generating correct patches for 92.3% of vulnerabilities. Furthermore, results on SAN2VULN demonstrate strong generalization, with DeepAVR effectively repairing vulnerabilities unseen during training.

Determining the communication complexity of graph connectivity has been one of the longest standing open problems in communication complexity since the seminal work of Babai, Frankl, and Simon [FOCS'86]. A simple protocol transmits a spanning forest using $O(N\log N)$ communication on $N$-vertex graphs. While this was shown optimal for deterministic protocols by Hajnal, Maass, and Turán [STOC'88], the optimal nondeterministic and randomized complexities have remained unresolved for decades. We settle the nondeterministic communication complexity of graph connectivity by proving an optimal $Ω(N\log N)$ lower bound. The best prior bound was $Ω(N\log\log N)$ due to Raz and Spieker [Combinatorica'95]. Our bound is shown by upper bounding the maximum size of $1$-monochromatic rectangles. Generalizing our techniques to weighted, almost $1$-monochromatic rectangles yields an $Ω(N \cdot \log(\min(1/\varepsilon,N)))$ lower bound for randomized protocols with error probability $\varepsilon$. In particular, this bound is optimal for any polynomially small $\varepsilon$. For constant error probability, we also prove that any $r$-round protocol requires $Ω(N/r \cdot \log N)$ communication, which is optimal for every constant $r$. Previously, optimal $Ω(N\log N)$ lower bounds for graph connectivity were known only for $1$-round [Sun, Woodruff; APPROX/RANDOM'15] and $2$-round protocols [Radhakrishnan, Reddy, Venkat; ITCS'26]. Our proof introduces a new communication problem which can be viewed as a composition of inner product and pointer chasing problems. Our lower bounds can be extended to other fundamental graph properties, and also imply optimality of known query and streaming algorithms for related problems.

Many manipulation tasks require a robot to remember what it has done and what it has seen. Frontier vision-language models can plan such tasks without task-specific training, but how such a planner should remember an episode is an open question. We argue that its memory must keep track of what the planner has done and learned, and must let the planner choose what to look at, since every frame costs tokens; a note rewritten at every step meets both poorly. We propose LogLook, whose execution-grounded memory lets the system keep the record, an execution log and the model's earlier notes, while the model decides where to look: frames are returned only where it expects a change, and demonstrations are skimmed before it inspects the parts it chooses. Measured with the benchmark's motion planner, LogLook matches the success of a note-only memory with 43% fewer tokens and less than half the images, and fails far fewer episodes through what it remembered or saw. With direct control, LogLook solves 93.5% of the RoboMME test set.