Science & Mathematics

Subgraph enumeration in partitioned graphs across distributed-memory systems is beset with challenges in balancing data movement with the local computation. Especially, counting the number of triangles induced by the closed wedges (i.e., length-3 cycles) is a measure of connectedness in a graph, but often limited by the significant search space caused by open wedges or length-2 paths (usually greater than the number of edges in a graph), which must be inspected to assess whether a particular wedge is closed. To mitigate triangle counting on massive graphs, distributed-memory partitioned-graph approaches are viable (i.e., assessing closed wedges independently within a partition), but load imbalance due to arbitrary communication can overshadow any gains from enhanced concurrency, owing to the inherent lack of structure in regular graphs. In this paper, we discuss a distributed-memory exact graph triangle counting implementation, and assess its performance over billion-edge real-world graphs on contemporary scale-out and scale-up based NVIDIA GPU platforms (considering NVIDIA ``Ampere'' and ``Hopper'' architectures), depicting a scalability spectrum of multiple orders of magnitude. Comparisons with existing state-of-the-art approaches demonstrates up to 10$\times$ improvements relative to distributed-memory CPU baseline, 2-100$\times$ over single-GPU vendor-optimized NVIDIA RAPIDS cuGraph and 10-60\% over multi-GPU $G^2$Miner, in terms of end-to-end execution times.

Autoregressive video diffusion models generate videos chunk by chunk, yet producing plausible continuations does not guarantee that they capture how scenes evolve. Under teacher forcing, strong temporal redundancy may allow local visual continuity to serve as a shortcut for current-chunk denoising, reducing the pressure to model visual evolution. We introduce Future Chunk Prediction (FCP), an auxiliary objective jointly optimized with teacher-forced denoising that predicts temporally separated chunks without access to the intervening visual context. Drawing supervision directly from training videos, FCP requires no additional pretrained visual encoder and leaves autoregressive inference unchanged. Our method achieves superior physical plausibility over standard teacher-forced autoregressive training with a lower overall training compute budget, highlighting the effectiveness and efficiency of future-chunk supervision for modeling visual evolution. Intriguingly, our visualizations suggest that FCP fosters a more structured representation space, which may be valuable for building autoregressive world models.

Causal discovery from nonlinear multivariate time series is challenging in high-dimensional systems, where the number of candidate directed relations grows quadratically with the number of variables. Existing methods are often limited by target-wise model fitting, repeated conditional testing, or expensive graph-search procedures. We propose SCDM, a shared-parameter meta-learning framework for scalable temporal causal discovery. SCDM treats each target variable as a task while learning a shared temporal predictor and a shared causal-strength matrix across tasks. Each meta-episode updates only a subset of target-variable tasks, allowing the model to accumulate structural evidence across the full system without exhaustive target-wise optimization. After training, a post-training graph readout converts the learned predictor into continuous directed causal scores for threshold-independent evaluation. We also provide a finite-error recovery principle showing that, under fully observed delayed-system assumptions and a positive risk-gap condition, thresholding SCDM scores recovers the graph when statistical, meta-optimization, and readout errors are below the population margin. Experiments on controlled synthetic, neuroimaging-inspired, high-dimensional, and real-world temporal benchmarks demonstrate competitive causal recovery and improved scalability in large temporal systems.

We study lattice-based collective construction of a target polyomino on the integer grid, where robots place unit square blocks subject to two physical constraints: the partial structure must remain connected at all times, and no placement may create an unfillable one-cell gap. We present a wavefront sweep algorithm that computes a dependency structure satisfying the local conditions required for safe assembly. Given a list of n cells sorted lexicographically by coordinates, the orientation can be computed in O(n) time using O(n) auxiliary space.

Long-term memory for multimodal agents remains challenging, as it must preserve fine-grained visual evidence while tracking entity evolution across sessions. Existing abstraction-based memories compress images into text, often discarding visually discriminative details, whereas pixel-preserving memories retain raw observations but struggle to reliably associate diverse observations with persistent entity identities and track their evolving states. To address these limitations, we propose MHG-Mem, a multimodal hypergraph memory that jointly organizes entity identity, state evolution, and provenance to visual evidence. Specifically, MHG-Mem represents distinct entity mentions and their observed facts as individual nodes, grouping identity-consistent mentions and facts through occurrence hyperedges. It then synthesizes aspect-specific evolution states from these grouped observations and links them to supporting entities, facts, and source evidence through evolutionary and evidential hyperedges. To navigate this structured memory, we introduce hypergraph-based agentic search, which adaptively selects an entry layer, traverses typed hyperedges across memory levels, and inspects source observations when fine-grained verification is required. These designs support both detailed visual verification and reasoning over cross-session state changes. Extensive experiments across three benchmarks demonstrate the effectiveness of MHG-Mem, improving performance by 8.20-9.98% over the strongest baseline.

Natural-language software requirements often omit assumptions about performance, operating conditions, user behaviour, and domain constraints, affecting architecture, testing, and deployment. If undiscovered until implementation, resolving them is far more costly than addressing them early. Although ambiguity and vagueness are widely studied, less attention has been given to assumptions embedded in requirements that appear clear and reasonable. We propose HiddenLens, a novel six-layer BERT-LLM pipeline and, to our knowledge, the first framework engineered to detect, classify, question, and resolve hidden assumptions rather than merely flag ambiguity. Layer 1 uses BERT attention entropy to localise unresolved spans; Layer 2 uses CoT-prompted LLM reasoning to articulate each assumption and its failure risk; Layer 3 uses fine-tuned BERT to classify assumptions into six non-functional requirement categories: performance, security, scalability, usability, reliability, and domain; Layer 4 generates ranked clarification options using FS LLM prompting; Layer 5 enables human validation through confirmation, editing, or rephrasing; and Layer 6 rewrites the requirement using the confirmed resolution, producing an explicit, testable, and traceable requirement. We also introduce the HiddenLens Corpus (HLC), comprising 312 requirements annotated with 847 hidden assumptions across six NFR categories. Inspired by PROMISE and PURE, HLC addresses the lack of annotated data for this task and is, to our knowledge, the first dataset developed specifically for it. HiddenLens outperforms keyword-based and QuARS-Adapted baselines by 71% and 61%, respectively, achieving an end-to-end F1-score of 0.79; its BERT-based NFR classifier achieves an F1-score of 0.847. These results indicate that HiddenLens can help analysts resolve hidden assumptions before they propagate through software development.

Despite recent progress in dexterous reference tracking, existing models focus primarily on single-hand manipulation and a limited range of interactions, leaving the bimanual dexterous behaviors common in real life unaddressed. The scarcity of contact-rich bimanual manipulation data and the increased complexity of coordinating contacts make developing a generalist bimanual tracker particularly challenging. Therefore, we present 1) DONIC, the first generalist bimanual tracker jointly trained across operation categories and human-motion sources, together with 2) BiHOI, a multi-view dataset of 1,575 sequences spanning 229 objects and emphasizing sustained bimanual interaction, where reconstructed interactions are retargeted into robot references. DONIC combines proximal policy optimization with behavioral cloning in a feedback loop that recycles verified successful executions into training demonstrations. An object-centered per-hand reward maintains object-motion feedback while supervising each hand according to its reference contact role. Clip-Centered Advantages remove reference-dependent advantage offsets while preserving within-trajectory differences. Together, these designs enable one single policy to learn coordinated manipulation across heterogeneous references. Without finetuning, a single checkpoint achieves 86.79% success on unseen trajectories of training objects and 57.32% on held-out objects, surpassing the state-of-the-art bimanual tracking baselines by 77.36 and 45.73 percentage points.

Reinforcement learning (RL) based on PPO-style clipping has become an effective approach to improving the reasoning capabilities of large language models (LLMs). However, standard token-level clipping measures policy change only through the conditional probability of the current token, while the prefix conditioning context is itself generated by the policy. This mismatch becomes especially consequential in long-response and off-policy training. We reformulate autoregressive generation as a finite-horizon Markov decision process and derive its performance-difference identity under behavior-policy sampling. The resulting change of measure identifies the cumulative likelihood ratio of the generated prefix as the exact importance weight for each stepwise policy-improvement term. Starting from the monotonic-improvement principle of trust-region policy optimization, we then construct a prefix-level trust region using the exact reverse-KL coordinate $r-1-\log r$, whose behavior-policy expectation equals the corresponding prefix-distribution KL divergence. This yields EPIC, a PPO-style sequence-clipping method that directly regulates prefix-level policy drift without relying on a local quadratic approximation or expert-routing replay. Experiments on off-policy reinforcement learning with a sparse mixture-of-experts language model show that EPIC consistently outperforms standard and sequence-level clipping baselines across mathematical-reasoning benchmarks, while remaining competitive with routing-replay-based alternatives. Code is available at https://github.com/LiChengxi666/EPIC.

Flexible intelligent metasurfaces (FIMs) create geometry-dependent array responses that provide spatial diversity but complicate multiuser channel estimation. This paper studies uplink estimation using pilot observations collected under multiple prescribed and calibrated morphing configurations. Each user contributes a matrix--vector block that couples its diagonal reference-array response with its morphing-dependent phases, while its third-mode vector is the known pilot scaled by the complex channel gain. We develop a pilot-constrained structured block term decomposition (BTD) estimator whose alternating least-squares updates exploit both the known pilot directions and the diagonal spatial factors. The undeformed configuration sets the columnwise scaling convention, and a unit first-entry normalization assigns the remaining block scale to the channel gain. Matching the recovered factors to the calibrated spatial--morphing manifold then yields the physical angles and configuration-dependent channels. We characterize the rank conditions for unique conditional least-squares updates, distinguish these conditions from global factor and physical-angle identifiability, and summarize the training, coherence-time, and computational requirements. Simulations show accurate two-dimensional angle and channel recovery in the tested settings, equal total pilot-symbol overhead in rigid-panel comparisons, improved steering-vector estimates as morphing diversity increases, and degradation as user loading or pilot correlation increases.

We consider the problem of binary sequence reconstruction where a sequence is transmitted multiple times over a channel, and each undergoes substitution errors by an arbitrary error vector of Hamming weight exactly $t$. We determine $N_{\mathrm{wc}}$, the minimum number of distinct channel output observations guaranteeing unique reconstruction by an optimal decoder, and show that the bit-wise majority-vote decoder, which was proved by Levenshtein (2001) to be optimal for channels corrupting up to $t$ bits, is in fact suboptimal here. Despite this suboptimality, we prove a condition that admits a practical early-termination rule, necessary for every decoder at any number of outputs $N$, and sufficient for bit-wise majority-vote even below $N_{\mathrm{wc}}$. We evaluate the number of outputs at which this condition is triggered, numerically, and show that it frequently occurs below $N_{\mathrm{wc}}$ when the block-length $k>2t+1$. Finally, we propose an alternative algorithm aimed at guaranteeing unique reconstruction with fewer observations than bit-wise majority-vote, and demonstrate through exhaustive enumeration that it successfully decodes channel output-sets where bit-wise majority-vote fails.

Large organizations increasingly face what this book calls the cognitive complexity wall: at the scale of hundreds of interacting components, maintaining the dependency map and reacting at the pace of incidents exceeds what a human team can deliver. This conceptual volume proposes AAM/5C, in which five capabilities (Capture, Comprehend, Choose, Coordinate, Control) are instantiated across seven logical layers, L1 to L7. A single authorization rule, R-AUT, separates who proposes, who decides and who executes: a probabilistic planner (L4) proposes; a governance layer (L6) decides under signed policies and records its verdict in a signed evaluation proof; a security layer (L5) issues a short-lived act token bound to that proof; a policy enforcement point verifies both before the target layer materializes the act. Under an explicit guarantee contract, two analytical statements (H7a, H8a) hold by construction. The other statements of its ten hypotheses (H1 to H10), including log completeness (H7b) and non-bypass of the enforcement point (H8b), are refutable, and the protocol that could refute them is fixed before any measurement. The contribution of the book is a prescriptive reference model that articulates, under this single authorization rule, known mechanisms and adds three constructs, all to be validated: the Validation step of the Distributed Autonomy Loop, the ephemeral authority surface and the cognitive dependency plane. The book positions them against autonomic computing, multi-agent systems, enterprise architecture and agent protocols (MCP, A2A), gives an authorization policy in Rego and a partial TLA+ specification of peer consensus, and sets out a validation program with quantified milestones. It reports no empirical results. This English edition is a translation of the French original, produced with the assistance of AI tools and validated by the author.

Alarm root-cause analysis is studied on data that cannot be shared. Operator logs are confidential, so what the field publishes is a causal graph between alarm types rather than per-incident causes, a fixed scenario set, gated, or never released. Two recent benchmarks carry per-incident root causes at scale; neither states a download location or a licence. We give a generator rather than a dataset. It runs on real backbone topologies from the Internet Topology Zoo and records the exact root cause and the propagation path of every alarm -- a by-product of generation, not a labelling pass. Every emitted field binds to a term in NORIA-O, RFC 8632, RFC 8345 or TMF642, and an unknown term fails the run. We ship fixed configurations, splits, a scorer and nine baselines. A one-line rule -- rank the device that raised the earliest critical alarm -- takes top-1 at 0.650 against 0.575 for our own temporal method, and wins 13 of 15 runs over three backbones and five seeds. An LLM given the same view reaches 0.600, the same model allowed to query the graph 0.625, and the rule wins on real operator alarms too, at 0.773: a property of alarm streams, not of our generator. One fault class separates the table: when a site loses power its PoP reports nothing, so every method that ranks alarmed devices scores 0.000 or near it, while the two that walk the topology to a silent candidate take 1.000 and 0.833.

In this paper, we establish a tight upper bound for the size of a $(v,5,3,1)$ optical orthogonal code (OOC) and call such a code optimal when it achieves this upper bound. In addition, if an optimal OOC has the property that all non-identity elements of $\mathbb{Z}_v$ are covered by the list of differences of its codeword-sets then it is called perfect. In this paper, we present direct and recursive constructions for infinite classes of optimal and perfect $(v,5,3,1)$-OOCs.

AI-generated text detectors are increasingly deployed in consequential settings---peer review, education, admissions, journalism, and content moderation---where their outputs directly affect individuals. Yet the strongest detectors are fine-tuned neural classifiers whose decisions are opaque, while transparent alternatives (stylometric and linguistic-feature classifiers) underperform them by wide margins. We present a pipeline that converts a fine-tuned neural detector into a classifier that operates on human-readable concepts. We accomplish this by training sparse autoencoders (SAEs) directly on a detector's residual stream, assigning auto-interpretation labels to each SAE feature, and retaining only features whose generated descriptions pass a global fidelity threshold. We then train a lightweight classifier on just the interpretable features. We find that our SAEs recover latent concepts that correspond to highly specific and predictive linguistic "tells" of AI-generated text. Classifiers built on these extracted features are not only highly interpretable but exhibit stronger performance on out-of-distribution texts than either neural or stylometric alternatives.

Semantic segmentation of remote sensing imagery often places class boundaries a few pixels away from where they truly lie, and this position error is concentrated in the thin band measured by Boundary IoU (BIoU). We treat boundary refinement as the iterated application of a small learned local rule: the Boundary Refinement Neural Cellular Automaton (BRNCA), that starts from the class probabilities of a frozen, pre-trained segmenter and corrects them cell by cell. We generalise the binary rNCA to the multiclass case by keeping the visible channels on the probability simplex through an Aitchison (log-space) update and by adding free hidden channels. The best configuration improves the mean BIoU over the base segmenter by 2.36 points (from 46.63 to 48.99). The gain is concentrated on the irregular woodland class (+8.5 points BIoU); this suggests that position error is correlated with the fractal dimension of the class boundaries.