Science & Mathematics

World Action Models (WAMs) have shown strong potential for robot manipulation by jointly learning visual dynamics and executable actions. However, their video and action objectives do not explicitly constrain the 3D geometry that is crucial for precise manipulation. Recently, geometry-aware WAMs introduce depth or 4D prediction branches, whereas representation alignment methods supervise intermediate WAM features with pretrained 3D foundation models. Yet we find that such pointwise alignment benefits the video features but degrades action learning, since dense geometric feature targets overly restrict compact action features. To address this limitation, we propose Relational Geometry Forcing (RGF), which transfers geometric priors from a frozen 3D foundation model by aligning pairwise feature relations instead of pointwise features. Specifically, RGF applies three complementary relational objectives, where intra-frame and cross-view relations transfer scene geometry and multi-view correspondence to the video features, while action-temporal relations couple action dynamics to geometric scene evolution. Applied only during training, RGF requires no explicit geometry branch or extra inference module. Extensive experiments in simulation and the real world demonstrate the effectiveness of Relational Geometry Forcing, showing consistent gains across diverse manipulation scenarios and WAM paradigms.

Managed and wild bees sustain roughly a third of the global food products, yet colonies face mounting stress from climate-change including extreme weather patterns, altered parasite and disease patterns. Timely assessment of colony condition such as understanding colony strength, brood area, honey and pollen storage, comb usage, is essential to adaptive management. However, manual hive frame inspection is slow, costly, and quality depends on scarce expertise. We present an artificial targeted intelligent system framework focusing on edge deployable pipeline that turns hive frame photographs into interpretable and action-oriented inspection report. The pipeline includes an object detector to detect biologically-meaningful comb-cell classes; detections are distilled into structured fact schema; facts are converted into constrained factual text; the text triggers a grounded knowledge-base for interpretations and recommendations. Knowledge-base entries are required to be triggerable by evidence expressed in the fact schema. We built our system based on open source FAIRHiveFrames-1K dataset and favour reproducible, edge-deployable models over large general purpose frontier models. We additionally release FAIRHiveText-1K dataset that turns FAIRHiveFrames-1K dataset into interchangeable facts and text interpretation of the facts, making it easy for further LLM-based research into this area. We also propose a pathway to develop the grounded knowledge-base that will be helpful in providing interpretable and actionable reports from the facts.

Let \(A=\GR(p^e,r)\) have residue field \(\F_q\), and let \(λ\in A^\times\). We relate coefficient compression of monic divisors of \(X^N-λ\) to support-primitive decomposition of constacyclic codes. At a simple-root length, the support period of every nonconstant monic divisor is preserved at each coefficient precision and equals the order of the multiplicative stabilizer of its root set. For a cyclic Frobenius-orbit polynomial, the multiple equal-difference structure determines its support period, core degree, and least relative degree of an unramified extension over which all its basic-irreducible factors are binomials. Coordinate projections give every constacyclic code over a finite commutative ring a unique maximal interleaving into copies of one support-primitive code. At simple-root lengths over \(A\), the intrinsic period of a code is the greatest common divisor of the periods of its torsion codes. The decomposition yields distance and duality formulas and the arithmetic Singleton bound for free codes. At every length, a nonzero code over \(A\) has the same minimum Hamming distance as its highest torsion code, which gives a refined bound. At repeated-root lengths, identical torsion filtrations can occur with different intrinsic periods, while a class of nonzero codes obtained by substitution from simple-root generators retains the same core and minimum distance. Trace matrices recover the coefficients of basic-irreducible cores using unit pivots, and local Hensel corrections lift compressed factors directly. For \(X^n-1\) with \(p\nmid n\), lifting and output have quasi-linear bit cost in the compressed representation when the arithmetic data and residue factorizations are given.

Drying battery electrodes faster saves energy but drives dissolved binder toward the film surface, depleting the current-collector interface and weakening adhesion. Models of this migration stop convecting the binder when film consolidation ends, and models of the subsequent pore emptying omit the binder or start from an assumed binder field, so no simulation has followed binder convection through both stages that a drying schedule acts on. This study presents a continuous simulation of the dissolved binder field across both stages, driven by the published drying rates and completed by effective descriptions of the two questions the base transport physics leaves open: where the liquid of the consolidating film carries the binder, and when the binder stops moving as the pores empty. With a single parameter set, the simulation reproduces three types of published measurements: the change of the surface-to-interface binder ratio with drying rate over a factor of 5.6 and the surface and interface binder levels of films six times thicker; concentration profiles through the film interior; and the response of the interface binder to the time at which the drying rate is switched mid-process. Applied without further adjustment to a published three-stage schedule, the model reproduces the shortened drying and the full interface recovery when the rate is lowered early, and shows that its final high-rate stage begins after the binder has stopped. Combined with a coater-scale process model, the model can be used to identify operating points for electrode production that improve energy efficiency and product quality together.

Rule-based link prediction combines symbolic and statistical artificial intelligence methods by inductively learning a set of rules from a knowledge graph (KG), with the aim of completing the graph when a query cannot be answered from the KG alone. Several systems have been proposed, and while they perform well on classical benchmarks, even compared with embedding-based systems, many of them produce a large number of rules. This compromises scalability in the query-answering phase, especially under strong hardware constraints. In this work, we investigate the behavior of AnyBurl, a state-of-the-art rule learner for link prediction, to determine whether running it for a given time and then pruning the learned rules yields better query-answering performance at test time than a simpler approach that stops rule generation earlier.

Why large language models trained via log-loss minimization over massive and diverse datasets exhibit broad capabilities and generalizable skills is not yet fully understood. Grau-Moya et al. (ICML 2024) offer a conceptual explanation through the lens of meta-learning: if sequence-generating environments $ν$ are drawn from a prior $w_ν$, minimizing population log-loss recovers the corresponding Bayesian mixture predictor $ξ=\sum_νw_νν$. In the idealized limit in which $w$ is a universal prior, the optimal predictor corresponds to Solomonoff universal induction. Practical machine learning algorithms, however, generally produce only \emph{approximately} optimal predictors. This raises a natural question: \emph{Does the prediction performance remain robust under approximation}? In this paper, we first provide a tight bound on the worst-case effect of approximation on prediction error. We show any predictor $ρ$ satisfying $D_{KL}(ξ\|ρ)\leq η$ retains bounded cumulative-expected-squared prediction error. However, while the Bayesian mixture predictor $ξ$ has error $O(\log(1/w_μ))$ in an environment $μ$, we establish a tight $Θ(η/w_μ)$ bound on the excess error of $ρ$ relative to $ξ$. Thus, approximation can substantially degrade performance in environments assigned small prior probability. In sharp contrast, when environments are averaged according to prior $w$, we prove that the excess prediction error is only $O(η)$. Furthermore, we establish a distribution-shift theorem for dominated shifts from a training prior $w$ to a test prior $w'$, interpolating our average-case and worst-case guarantees. Finally, we extend our robustness analysis to a finite-sampling setting relevant to learning universal predictors with neural networks and Transformers, where $D_{KL}(ξ\|ρ)$ could be unbounded. [For the full abstract, see the paper.]

An LLM multi-agent system (MAS) is only as good as the agents it chooses to trust: its accuracy and cost hinge on its topology, meaning which agents are included and how they communicate. A single task score, however, does not reveal which agents help or hinder performance. We propose Contrastive Attribution Topology Search (CATS), which searches for compact teams within a fixed supergraph of specialized agents. An LLM attributer compares execution traces across candidate topologies and assigns credit to individual agents. CATS uses these credits to update supergraph edges while progressively pruning weak edges and agents, yielding a small team for deployment. With small open-weight models and matched candidate evaluations, CATS improves mean accuracy on BigCodeBench by 5.3 percentage points over score-only heuristic search while using 54$\%$ fewer deployment tokens. On MBPP, CATS matches the strongest baselines with fewer tokens than the other searches. In a HotpotQA prompt-poisoning stress test, CATS excludes all adversarially prompted agents in all three runs, whereas random search retains some at comparable accuracy. Per-agent credit thus supports both team optimization and the pruning of potentially harmful agents.

Large language models (LLMs) are increasingly used to support consequential decisions, yet their reliability under equivalent representations of uncertainty remains unclear. Existing work has documented prompt sensitivity, cognitive biases, and confidence miscalibration, but it is still unknown whether high-stakes advice changes when the same probability is expressed through complementary gain and loss frames. We introduce FrameBench-HPS, a controlled benchmark of 2,880 prompts derived from 240 high-stakes scenarios across healthcare, finance, education/career, and legal/consumer-rights domains. Across seven LLMs, gain-loss framing changes the recommendation category in 32.4% of complete groups, with most changes involving uncertainty rather than direct reversals. Human evaluation confirms corresponding shifts in perceived risk and directiveness. Robustness experiments show that framing effects persist under conversational paraphrases and exceed ordinary repeated-generation variability. Probability canonicalization substantially reduces framing sensitivity, identifying probability representation as a practical target for improving consistency in high-stakes LLM decision support.

Finding K most similar elements for a query in a large gallery of neural network (NN) embeddings is essential in various areas of computer science and industrial applications of artificial intelligence. This paper proposes search algorithms for structured latent spaces (LS) of NNs which allow to speed up top K item retrieval owing to O(1) complexity small candidate gallery subset search. ArcFace-pretrained IResNet50 models are finetuned using Latent Space Configuration (LSC) methodology to obtain new distribution of embeddings with desired geometric properties. Applying the proposed algorithms to embeddings obtained from finetuned models leads to several times speed-up over baseline exhaustive search. Specifically, we report up to 3 times speed-up on GPU and 40% speed-up on CPU compared to the baseline method GPU speed. These results are obtained for the original and reduced numbers of LS dimensions. The results show that LS dimensionality reduction allows to further accelerate the search and even improve rank-N metrics.

The Fréchet distance (also known as the dog-leash distance) is a well-studied and prevalent distance measure for polygonal curves. In this paper we present parallel algorithms for computing the discrete \frechet\ distance between two curves, in both the PRAM and MPC models. For two polygonal curves of length $n$, we show that the distance between them can be computed in the PRAM model in $O(\log n/ σ)$ rounds and $\tilde{O}(n^{2+σ})$ total work for any $0

Generative recommenders replace the retrieve-then-rank cascade with a single model: beam search over semantic item identifiers performs retrieval, and the same likelihood performs ranking. We show that this shared score makes them forget long-tail items twice. In retrieval, beam search keeps identifier prefixes by the probability mass of their subtrees, so tail items are pruned early, and a wider beam admits few more of them. In ranking, the likelihood places tail items below head items even when they are admitted, so expanding retrieval alone brings little or no gain. Both failures reflect missing item-level memorization (i.e., direct recall of which items co-occur), which collaborative filtering provides and shared-prefix generation lacks. To this end, we propose CAPE (Co-occurrence-Aware Pool Expansion), a training-free plug-in that restores memorization at both stages: (i) a memorization channel retrieves co-occurrence-supported tail items outside the beam and scores them with the frozen generator, and (ii) memorization-calibrated ranking fuses the generator likelihood with co-occurrence evidence while keeping the generator's first item. Experiments with two generative backbones on product and music domains show that CAPE significantly improves long-tail accuracy over the backbones and over re-ranking baselines that share its candidates, preserves or improves overall accuracy, and adds a small fraction of the latency.

Semantic communication in multi-agent systems often assumes that task-relevant information benefits the team once delivered. Yet semantic sharing can alter the receiver's decision and increase inter-agent dependence, potentially reducing team utility. Motivated by this phenomenon, we develop an interaction-aware framework for wireless collaboration between heterogeneous agents. We analytically relate communication-induced dependence to expected best-of-team utility and define a team-level semantic value for candidate messages. We consider semantic units at different abstraction levels and formulate a deadline- and power-constrained problem that maximizes predicted team-level net surplus. We derive an interaction-aware policy that selects at most one unit and its transmit power, or chooses not to transmit. Experiments on two verifier-scored optimization tasks show that full-solution exchange induces stronger structural convergence and more adverse team-level effects, whereas method hints exhibit pronounced directionality. Across the evaluated power budgets, our policy achieves the highest net team-level surplus while consuming less energy than semantic-aware and full-solution baselines.

Heuristic solvers for general-sum extensive-form games produce a stream of iterates, and practice reports the NashGap of the returned profile. We ask what a solver can know about its own iterates from the regret accumulators and the terminal-distribution accumulators its updates already maintain. In two-player games the NashGap of a weighted average strategy equals, at the maximizing player, the weighted external regret plus a signed value correction. Bounding the regret by the running counterfactual-regret accumulator gives an online certificate whose cost per checkpoint is linear in the number of terminal histories. The certificate stays sound under alternating updates and in games with three or more players. The mismatch between what alternation evaluates and what the averages record enters as one signed comparator term, maximized in a single backward pass per player whose comparator weight is nonzero. The bound is read off the regret state, with no best response against the average. Run a portfolio of training-free regret-matching solvers, output the iterate with the smallest certificate, and verify that iterate once. On the benchmark's biased Leduc family a pool of sixteen training-free solvers, seven original and nine recent variants, solves $89\%$ of the $64$ instances at threshold $10^{-5}$ this way, matching the trajectory-minimum union of the solvers. The same protocol solves $92\%$ of the $64$ pre-registered held-out instances and $89\%$ again on a third set of $64$ instances that no earlier stage used. The zero-best-response certificate certifies all $57$ in-sample solves and all $57$ fresh ones, and $58$ of the $59$ held-out ones. Its exact-comparator refinement certifies every solve in the three sets. The certificate states both and attributes its bound. Code is available at https://github.com/lbn187/CertNE.

Language models are being explored as the interface through which space robots interpret operator commands and select among verified skills. Running them onboard calls for low-bit compression, and flight use adds requirements that generic post-training quantization leaves implicit: a fixed memory budget and storage format, a static artifact under configuration control, and behavior close to the full-precision model validated on the ground. We treat compression as a ground-segment step and study QSelect, which composes one static low-bit model from compatible AWQ, GPTQ, and AutoRound checkpoints by assigning each linear module to one quantizer family and scoring every substitution at the final next-token distribution. The default fidelity mode needs no labels or tuned thresholds and accepts a substitution only when the forward KL divergence from the BF16 reference decreases; an optional target-adapted mode adds answer likelihood on a small labeled set. On DeepSeek-R1-Distill-Qwen-7B, Llama-3.1-8B, and Llama-3.2-3B at 3 and 4 bits, composed models reach lower mean held-out perplexity than every single-family model in all six settings, and the fidelity mode lowers perplexity and KL further. In a matched scaffold, final-output scoring gives lower held-out KL than representation-similarity scoring in all 12 paired runs. With labels from training or development splits of the evaluated task families, the target-adapted mode raises 3-bit pooled accuracy on the two Llama models by 4.3 and 4.5 points over the best single-family model. At 3 bits, unquantized embedding and output layers make up 43-46% of the packed artifact. All evaluations use general-domain benchmarks on a ground GPU; space-domain tasks and flight hardware are left to future work.

Multimodal knowledge editing aims to update visually grounded facts in Multimodal Large Language Models (MLLMs) while preserving unrelated text and visual behaviors. However, standard answer-level metrics can hide evidence misgrounding: an edit may produce the target answer on the original image yet remain sensitive to incidental context rather than the intended visual evidence. To examine this issue in a controlled setting, we construct WaterBird-Edit, an analysis set that decouples bird habitat labels from background context and measures whether edited predictions remain stable when the target evidence is preserved but the background changes. Motivated by this diagnosis, we propose VICE (Visual In-Context Editing), a retrieval-based framework that stores edits in external memory and applies them through visual in-context demonstrations designed to expose task-relevant evidence. Experiments on three standard benchmarks show that VICE achieves strong lifelong editing performance, while results on WaterBird-Edit show that it substantially reduces sensitivity to the controlled background shifts. Code and dataset are available in https://github.com/Wang-Z-W/VICE-main.