Science & Mathematics

Vision-Language Models (VLMs) excel at 2D recognition but often fail to maintain consistent 3D relations across sparse viewpoints, leading to spatial hallucinations under occlusion and perspective changes. Existing methods commonly fuse all frames with dense geometry, incurring substantial computation and context overhead while encouraging blind reliance on imperfect estimators. We present Think-Capture, a query-conditioned framework that first identifies task-critical objects and then acquires their geometry on demand. A 3D toolkit lifts zero-shot masks into a common coordinate frame, and a Symbolic 3D State Abstraction compresses object and camera states into structured text. Hierarchical Group Relative Policy Optimization (GRPO) trains the VLM on cases in which geometry either helps or misleads, teaching it when to use tool evidence and when to fall back on visual cues. Think-Capture-4B reaches 54.4% on MindCube-Tiny (+21.5 points over its backbone), 39.1% on the MMSI-Bench positional subset (+7.9), and 42.8% on ViewSpatial (+5.2). It also remains robust under coordinate and mask perturbations. The full stack fits one 24 GB GPU and serves as an asynchronous high-level reasoner on an Agibot G1. Our code is available at https://github.com/819346675/Think-Capture.

Retrieval-augmented generation over multi-viewpoint queries must preserve distinct answers and positions from the retrieved evidence while operating under a fixed context budget. We introduce LMVU, a lightweight ranker that learns to prioritize candidates by the marginal viewpoint coverage they add to the current selection. Training uses viewpoint annotations from each benchmark's development split to compare candidates within the same selection state. At inference, LMVU repeatedly scores budget-feasible candidates as the context grows, without accessing these annotations. We evaluate two forms of recovered viewpoint coverage: RVC$_1$ assigns each viewpoint full credit for at least one supporting source unit, while RVC$_2$ assigns half credit for one and full credit for two or more distinct source units. Exact oracles measure achievable coverage under the same evidence pool and budget. Across four viewpoint-annotated benchmarks at a 1,024-token budget, LMVU achieves gains of up to 0.047 and 0.052 points in RVC$_1$ and RVC$_2$, respectively, over the strongest non-oracle baseline for each benchmark and metric, and reaches the exact viewpoint-breadth ceiling on two benchmarks.

Magnetism in transition-metal dichalcogenide monolayers has attracted considerable attention, yet its nature remains elusive, particularly in monolayer 1T-VSe$_2$. Our first-principles calculations show that Stoner instability can drive ferromagnetism in XS$_2$ and XSe$_2$ monolayers (X = Ti, V, Co, Mo, and Ru), associated with a reduced density of states at the Fermi level. We demonstrate that mixing nonmagnetic Ti, Co, Mo, and Ru disulfides or diselenides can induce magnetism. Conversely, mixing magnetic VS$_2$ or VSe$_2$ with other compounds can completely suppress magnetism. Both scenarios can lower the total energy. Moreover, substituting a small number of V atoms with Co defects can also disrupt ferromagnetism in 1T-V(S,Se)$_2$. Even in pristine 1T-VSe$_2$, we find ferrimagnetic configurations with reversed local V moments that are nearly degenerate with the ferromagnetic state. Their competition may contribute to the suppression of long-range ferromagnetic order. This reduced energy penalty for magnetic and structural disorder arises from a suppressed density of states at the Fermi level, resembling the mechanism underlying the Peierls transition. Electronically driven energy lowering near the Fermi level may therefore be widespread among transition-metal dichalcogenide monolayers in various forms.

Collecting relevant data from Internet of Things devices under freshness and energy constraints is a key challenge in 6G edge networks. A lightweight neural network on the device filters irrelevant observations, but storing it in dynamic random access memory consumes refresh energy during standby. This letter proposes a pull-based framework where an edge server remotely activates and approximates memory and jointly selects the activation timing, refresh period, model size, and transmission threshold. We derive the expected freshness and energy over erasure channels in closed form and show that the scheme consumes less energy than an always-on baseline under the same freshness constraint.

Multi-agent inverse reinforcement learning offers a powerful framework not only for imitation learning but also for understanding the neural computations supporting social interactions and decision-making. Inferring rewards from interacting agents is challenging because observed choices reflect both personal preferences and expectations about others. Existing methods often require additional environment interaction, repeated policy optimization as an inner loop, or restrictive assumptions about environment dynamics and action spaces, limiting their applicability to fixed behavioral datasets. We introduce Multi-Agent Unilateral Inverse Temporal Difference Learning (MA-UITD), an offline method for inferring explicit agent-specific rewards from joint demonstrations in continuous-state environments with either discrete or bounded continuous actions. Its core idea is to represent the long-term consequences of each agent's own actions through belief-conditioned successor features that average over other agents' behavior. Shared reward features and agent-specific preference vectors connect these unilateral values to the target reward function, while inverse temporal-difference consistency and noise-contrastive behavioral learning enable inference without known dynamics or an inner policy-optimization loop. Across simulated cooperative, competitive, and general-sum tasks, MA-UITD outperforms baselines in discrete-action settings and infers behaviorally compatible reward profiles in continuous-action settings. When applied to experimentally collected social-foraging trajectories, MA-UITD inferred distinct spatial and interaction-dependent reward structures for leaders and followers, providing a quantitative characterization of latent behavioral objectives that may be relevant for understanding the neural computations underlying social behavior.

In this paper, we investigate why vision-language models (VLMs) produce errors by decomposing them into two sources: visual-representation failures, in which the visual evidence required for the correct answer is not encoded, and visual-utilization failures, in which this evidence is encoded but not successfully used. We propose to estimate the visual-utilization failure rate using lightweight probes and derive theoretical bounds on the true rate that account for probe imperfections. We introduce VELens (Visual Evidence Lens), a rank-based probe that traces visual evidence for both a target object and its ground-truth attribute, such as color, material, or shape, across VLM layers. We evaluate VELens on over 6,000 images using LLaVA-1.6-7B and Qwen2.5-VL-7B. Our method achieves ROC-AUC scores of 0.95 and 0.91, respectively, and outperforms alternative probes in both low-resource and unseen-attribute settings. Using it to diagnose errors, we estimate that visual-utilization failures account for 84.4% of LLaVA errors and 70.0% of Qwen errors, with conservative lower bounds of 77.5% and 61.0%, respectively. Overall, our study identifies visual utilization as the dominant bottleneck in VLMs and suggests that a central opportunity for improvement lies in developing better mechanisms to locate, route, and use the visual information they already encode.

Thermal cameras enable robots to perceive human activities in darkness while revealing less personally identifiable appearance information and fewer details of the surrounding environment than RGB cameras. As vision-language models (VLMs), originally developed for RGB imagery, are increasingly integrated into embodied systems for scene understanding and high-level planning, thermal sensing offers a promising way to extend activity perception to low-light and privacy-sensitive settings. However, the failure modes of VLMs in thermal human activity understanding for human-robot interaction (HRI) remain underexplored. We fill this gap by evaluating seven locally deployable VLMs, including models developed for physical AI and robotics, on two person-centric thermal datasets. Our diagnostic tasks examine fine-grained action recognition, activity timeline reconstruction, and the ability to withhold unsupported answers. Paired RGB recordings serve as a performance reference, while controlled variations in frame sampling, thermal input presentation, and reasoning are used to assess whether the observed errors can be mitigated. Our results show that models can recognize broad activity categories but struggle with fine-grained actions such as coughing, sneezing, and yawning. They also produce inaccurate event timelines and hallucinate actions involving objects. Simple thermal input preprocessing and additional reasoning offer only limited, model-dependent gains. Abstention behavior also varies across models even under explicit instructions, limiting their reliability for practical robotic use.

Language-model agents are increasingly asked to run experiments, yet benchmarks rarely measure whether those experiments improve the decisions they are meant to inform. We introduce CrashGym, a deterministic, replayable limit-order-book simulator in which an agent must choose between a deleveraging intervention and a liquidity backstop for a market about to suffer a shock-triggered crash. Before committing, the agent may query intervention experiments in a laboratory realization of the market. Its choice is then scored on an independently seeded target realization in which the simulator executes every feasible intervention, so regret relative to the best intervention is computed by directly simulating every alternative on the same target repeats, with no human or model-based judging. The simulator implements each amplification channel---persistent price impact, liquidity withdrawal, margin-driven liquidation, and an evolving strategy mix---as an explicit mechanism that can be switched on in stages. In a prospectively specified study on 24 newly sampled worlds, laboratory access reduces the mean regret of the one deployment with a complete record (Luna) from 8.878 to 3.785, a paired reduction of 5.092 utility units (nominal 95% world-bootstrap interval: 1.028--9.507), or 57.4% of the OBSERVE-ONLY mean. Prospective comparisons for two further deployments are incomplete; in a separate exploratory study, mean regret is lower with laboratory access for all four deployments evaluated. A laboratory whose strategy mix evolves endogenously yields no established advantage over one with a fixed mix. CrashGym thus measures the value of experimentation as avoided decision loss and lets simulator detail be judged by the decisions it supports.

Predictive representations for robot manipulation should connect scene geometry with the state changes induced by actions. Our intuition is that understanding what an action will change starts with understanding where things are: complementary RGB and depth observations ground a common scene representation from which to infer those changes. Guided by this intuition, we introduce DepState-JEPA, a dual-stream RGB--Depth joint-embedding predictive architecture that connects spatial understanding with action-conditioned prediction through a shared latent state, DepState. Built on V-JEPA~2.1, the framework uses depth both as a complementary observation stream and to ground both modalities in metric 3D space, aggregating shared information into DepState while retaining modality-private features for decoding. Ray-conditioned readouts predict local features without target depth; after an action-conditioned transition, the same decoders predict future RGB and depth features using the predicted state and fixed initial private features. Robot-state readouts provide recursive proprioceptive inputs; object-trajectory supervision and calibrated forward kinematics shape state learning through motion targets and joint--TCP consistency. Comparisons show stronger RGB-encoder transfer after RGB-D adaptation, feature completion, conditioned visual forecasting, and manipulation success. Within-model ablations identify contributions from depth content, metric coordinates, and future-state matching, with additional control gains from FK regularization. The project page is available at https://shawnleung-sii.github.io/depstate-jepa/.

One-step generative models enable fast text-guided image editing, yet they often introduce unintended changes to regions that should remain unchanged. Temporal aggregation stabilizes editing fields across noise levels, but leaves their spatial allocation insensitive to local variations in reliability. Consequently, temporal stability alone does not ensure spatially consistent editing. To address this lim- itation, we propose ER-Chord, a training-free and inversion-free framework for reliability-guided spatial redistribution. Our key insight is that agreement between editing fields at neighboring noise levels provides a local reliability signal for allocating editing strength. We formulate this allocation as an equal-energy op- timization that redistributes control energy according to local reliability while preserving the original editing directions and total control energy. This formu- lation admits a unique global solution that can be efficiently computed using existing field evaluations, requiring no additional denoiser calls. Experiments on two benchmarks, PIE-Bench and TEdBench, demonstrate improvements over state-of-the-art one-step editing baselines, achieving superior source preservation alongside consistent gains across multiple generative backbones.The project page is at https://github.com/Peizeli1/ER-Chord.

Omni-modal assistants must remember not only what users say, but also what they show and convey. Existing long-term memory benchmarks are mostly text-and-image or report a single end-to-end score, so they cannot locate whether evidence is lost when it is represented, retrieved, delivered, or read. We introduce MOSAIC, a benchmark of 923 questions over persona-grounded histories of 11-24 sessions spanning text, image, video, and audio. MOSAIC constructs targeted spatial, temporal, and prosodic signals rather than decorative media, with the strongest causal validation for spatial information and selector-level validation for annotated prosody, and pairs them with a matched diagnostic protocol that freezes the question, reader, prompt, and budget while intervening on representation, retrieval, and delivery. Matched decomposition shows that representation loss is modality-dependent, residual reading dominates even under gold evidence, and abstention remains weak on these controls: strong answerable scores coexist with failures to refuse. MOSAIC thus turns omni-modal memory evaluation from a leaderboard into a diagnosis of write, retrieve, deliver, and read failures.

Agentic search depends on the interface through which a language-model agent accesses and explores a corpus. Existing interface designs are fixed by hand and applied unchanged to every corpus, although effective designs may depend on the corpus. We introduce SearchWeaver, a framework for automatic search interface optimization guided by downstream search performance. A proposer uses execution trajectories and task rewards to jointly revise corpus artifacts, executable tools, and usage instructions. This pro cess optimizes what information is prepared in advance, which operations tools perform, and which decisions remain with the search agent. With GPT-5.4-nano, the optimized interfaces improve answer accuracy from 71.6% to 78.4% on BrowseComp-Plus and average nDCG@10 from 45.7 to 55.0 across four BRIGHT subsets over Direct Corpus Interaction. These findings highlight the value of jointly optimizing corpus artifacts, executable tools, and usage instructions to develop effective search interfaces.

The bulk-boundary correspondence establishes a fundamental connection between bulk band topology and the emergence of boundary states, underpinning topological phenomena across electronic, acoustic, photonic, and many other wave systems. A broad class of singular flatband systems, however, exhibits an unconventional form of bulk-boundary correspondence, in which robust boundary modes (RBMs) originate from flatband-touching points (FBTPs) and the real-space topology associated with incomplete sets of compact localized states (CLSs), rather than conventional momentum-space bulk topological invariants. To date, experimental studies of such boundary modes have been limited to systems hosting lower-order FBTPs. Here, we experimentally demonstrate unconventional bulk-boundary correspondence in a photonic cross-bedded Kagome-like lattice featuring a higher-order FBTP formed by the touching of two degenerate flatbands and a dispersive band. Using continuous-wave laser writing, we observe shape-preserving propagation of both bulk CLSs and boundary RBMs. Remarkably, unlike previously reported flatband systems, the RBMs retain their boundary localization under input-field perturbations involving both singular and nonsingular CLSs, revealing an unconventional boundary response associated with the higher-order FBTP in this lattice. Our results establish the Kagome-like lattice as a new platform for exploring flatband physics and unconventional bulk-boundary correspondence, with potential applications in wave localization, flatband lasers, and other emerging topological photonic devices.

Federated learning (FL) keeps data at its source, but locality alone does not make a federation trustworthy. Without sufficient observability, an aggregator cannot determine whether the client service was healthy, the local data pipeline was trustworthy, or contractual constraints were properly monitored during training. We take the position that trustworthy FL needs an evidence plane in addition to its model-update process. Each participant continuously summarizes local infrastructure, data into minimal, signed trust evidence associated with each FL round, while raw data, logs remain local. A local TinyLLM may assist in interpreting observability signals, deriving contextual evidence, and providing natural-language explanations to operators. We instantiate this vision through FAIR-O, an observability extension of FAIR principle. Rather than benchmarking FL accuracy, a three-level trust model has been proposed and a two-participant Python prototype demonstrates evidence generation and trust assessment. We further discuss future integration of FAIR-O Trust Evidence into participant selection, evidence weighting, and federated aggregation.

People share and understand others' experiences differently based on their social identities; often, people empathize more when the other person belongs to their ingroup compared to an outgroup. While this intergroup empathy gap is well-studied in social psychology, it remains unclear how it manifests in naturally occurring online social interactions. To this end, we measure how commenters express social judgments, as an indirect outcome of their understanding towards the posters, and empathy in text, based on whether commenters share the poster's gender or are of similar ages. We focus on posts where the poster self-reports these demographics, and analyze 126,716 comments associated with 36,163 posts from 18,111 commenters in r/AmItheAsshole, while controlling for commenter and poster group identities, post features (topic, length, and score), and both commenter- and post-level variance. Our results show that when commenters and posters are ingroup, in either gender or age, commenters are more likely to provide a Not the Asshole verdict and show greater text-based understanding as measured by LLMs. We also observe that older commenters behave differently from younger ones. Furthermore, in a subset of posts where the gender and age of the other party in the conflict are explicitly mentioned, we find that commenter-poster ingroup effects are moderated by the group relationships between the poster and the other party when commenters make social judgments. Our work highlights the importance of intergroup context and social group nuances in understanding online discourse.