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新论文 · v1arXiv:2611.05173

Correctness Firewalls for Synthesized Database Components

Mohammad Javad Amiri, Carsten Binnig, Andreas Kipf, Tim Kraska, Ryan Marcus, Johannes Wehrstein, Chenyuan Wu, Geoffrey X. Yu 等 9 位作者

原文摘要

For the first time in history, high-performance code is cheap. Today's coding agents can generate bespoke, instance-optimized data systems with excellent performance, and such agents will continue to improve as LLMs advance. But a purely agentic approach, even when validated with strong testing techniques, does not automatically inherit the correctness guarantees our community has spent decades building. We propose a middle ground between hand-crafted and agent-crafted systems: correctness firewalls, lightweight design patterns under which a synthesized component can affect a system's performance but never its results. Behind a correctness firewall, agents are free to synthesize fast components while the overall system remains correct. We present three case studies, each demonstrating a different type of firewall: cardinality estimation (advisory), searching sorted data (guarded), and data compression (certified). In early experiments, synthesized components search sorted data up to 3x faster than human-designed learned indexes and can compress text 1.5x smaller than Zstd. Drawing on these case studies, we outline a research agenda for how data management systems can exploit coding agents without giving up their hard-won correctness guarantees, from protecting synthesized code to measuring the cost of correctness.

cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05172

PadLoc: Training-Free Floor-Plan Localization by Sparse Geometric Matching

Marian Bookhahn, Frank Neumann

原文摘要

We propose PadLoc, a training-free localization back-end that places a moving sensor within a known floor plan. Floor plans are cheap, persistent, and robust to appearance change, but recent learned-depth methods localize against them by filtering per-frame depth through dense $(x,y,θ)$ pose grids. PadLoc keeps the swappable depth front-end and replaces the dense grid back-end with sparse geometric matching: dominant wall orientations define a small pose-candidate set, masked normalized cross-correlation scores observed free space against the plan, and a moving-pose Gaussian-mixture Bayes filter plus consensus readout decodes the trajectory. Holding each competitor's depth fixed, PadLoc matches or beats UnLoc's forward filter and the F3Loc filter at every Gibson trajectory length, and UnLoc's offline refinement from $T=35$ on; it exceeds Z-FLoc's published results with a shared MapAnything front-end. On Bonn LiLocBench, PadLoc improves pooled SR@1m over UnLoc by 23-32 pp under wheel odometry and 35-43 pp with ground-truth motion; with full-FOV LiDAR plus wheel odometry the same readout reaches 100% SR@1m at 9-10 cm median error on the static sequence. On the Hilti-Trimble Localization Challenge PadLoc localizes a camera-only fisheye rig on construction floors hundreds of metres across at 0.90-0.98 online SR@2m on three of five sessions; adding learned wall isolation, applied to every session, covers all five at 0.67-0.92 with the localizer unchanged throughout. PadLoc thus matches or outperforms dense grid back-ends while preserving sensor modularity and a compact state that does not grow with trajectory length.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05171

AgentAM: An AI Agent for Spatter- and Defect-Aware Process Optimization in Laser Powder Bed Fusion

Olabode T. Ajenifujah, Amir Barati Farimani

原文摘要

Predictive models for laser powder bed fusion (LPBF) are rarely used on the shop floor because querying them requires programming and model-specific knowledge. We present AgentAM, a large language model (LLM) agent that gives process engineers natural-language access to CFD-trained surrogates of spatter and melt pool geometry for SS316L through the Model Context Protocol (MCP). Polynomial surrogates trained on 279 single-track simulations predict melt pool dimensions ($R^2 \geq 0.95$) and spatter volume ($R^2 = 0.83$ on test data) at any power-velocity point in the trained range, and geometric criteria assign each point a geometry-derived defect regime (keyhole, lack of fusion, balling or stable) that matches the regime obtained from the simulated geometry in 93.4% of tracks under cross-validation. The agent selects and parameterizes tools for prediction, process-window scans and constrained optimization. Minimum-spatter optima often sat within 10 $μ$m of the lack-of-fusion boundary; a margin-aware optimizer moved them away from all thresholds and raised the estimated probability of remaining classified Good from 0.94 to 1.00 under an assumed 2% process drift, and from 0.72 to 0.91 when jointly sampled cross-validated geometry residuals were added. About 40-50 well-spread simulations sufficed to reproduce the defect map. To evaluate the agent layer, we introduce a 145-prompt benchmark scored against direct tool calls, plus eight prompts (40 runs) on margin-aware requests. Over the 725 runs of the main benchmark the agent passed the intended inputs in 98.5% of parsing prompts, reported the scored spatter values and defect classes faithfully and met every stated constraint in the 65 feasible optimization runs, within a median 1.0% of the brute-force optimum.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.31900

Distributed Swarm Morphogenesis with Neural Cellular Automata

Ray Verma, Anthony Tzes, Eliseo Ferrante

原文摘要

Swarm morphogenesis requires robots to assemble desired shapes through local interactions. We present a modular framework integrating neural cellular automata (NCA) with existing site assignment and collision-aware control. Within this framework, we introduce a decentralized attachment-site discovery mechanism. Robots already in the formation update and share local NCA states to identify new attachment sites, enabling progressive growth without a predefined list of sites during deployment. To bridge the mismatch between conventional NCA growth and gradual physical assembly, we additionally propose offline sparse-retention fine-tuning that simulates partial attachment, together with a false-positive loss that penalizes invalid site activations. Simulations demonstrate formation of diverse lattice morphologies, scaling to several hundred robots, and substantial improvements in morphology accuracy from the proposed fine-tuning. Experiments with Crazyflie quadrotors validate the integrated execution pipeline using robot-relative observations reconstructed from external tracking.

cs.RO · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05168

DSL-Bench: Evaluating and Explaining LLM Code Generation Across Domain-Specific Languages

Zihan Zhou, Mingxi Ye, Yan Gao, Guipeng Wei, Zibin Zheng

原文摘要

Large language models (LLMs) are increasingly used in software-engineering workflows, but code-generation benchmarks largely focus on general-purpose languages. Domain-specific languages (DSLs) have distinct syntax, semantics, and validation requirements, making results from individual DSL benchmarks difficult to compare. We present DSL-Bench, an executable benchmark and analysis framework for cross-DSL code generation. Starting from 13,045 DSL-related papers, LLM-assisted open card sorting identifies 18 representative application domains. For each domain, we select an evaluable DSL and provide a syntax checker and task-level semantic oracle. Evaluating 7 LLMs on 18 DSLs, we observe mean syntax and semantic pass rates of 31.37% and 11.64%, respectively, revealing a substantial gap between syntax acceptance and semantic correctness. A variance decomposition attributes 76.99% of semantic pass-rate variation to differences among DSLs and 7.86% to differences among models. Among four DSL-level factors, Knowledge Demand has the strongest negative association with semantic correctness (r = -0.508). We evaluate prompt engineering across all 18 DSLs and study five improvement methods on five targeted DSLs. Prompt engineering yields uneven gains; RAG-Refinement, the strongest targeted method, improves semantic correctness by 16.29 percentage points on average over matched baselines. We further derive capability profiles and DSL-Profiler summaries for interpreting data-scarce DSLs. These results highlight toolchain-specific semantic bottlenecks and the need to evaluate DSL code generation beyond syntax acceptance.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05167

Learning Behaviorally Relevant Dynamics in Multimodal Neural Recordings

Parastoo Azizeddin, Eray Erturk, Maryam M. Shanechi

原文摘要

Multimodal neural data can enable a more complete understanding of brain dynamics underlying behavior. Modalities such as spiking activity, local field potentials (LFPs), and behavior capture diverse spatiotemporal aspects of brain processes, yet prior neural-behavioral models that separate sources of variability rely on a single neural modality. By leveraging these complementary strengths, multimodal neural fusion can provide a unified, rich representation of brain-behavior processes and address the limitations of single-modality analyses. Here we develop BREM-NET, a nonlinear multimodal dynamical model that integrates behavior with multiple neural modalities having distinct statistical characteristics and temporal resolutions. BREM-NET performs multimodal neural fusion during inference while separating behaviorally relevant and residual neural dynamics via a structured learning framework. In a public multimodal non-human primate dataset with simultaneous spiking and local field potential recordings during a 2D reaching task, BREM-NET separates these two types of dynamics and learns them more accurately, as reflected in better behavior decoding, better neural prediction, and stronger latent alignment with behavior, and it retains these capabilities when neural modalities are asynchronous or suffer dropouts, which are major challenges in real-world recordings. This framework provides a new tool for studying behaviorally relevant neural computations across spatiotemporal scales of brain activity.

cs.AI · cs.LG · cs.NE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05166

When Self-Verification Hurts: Calibrated Answer Repair for Financial Question Answering

Celine Guo, Weida Liang, Yujian Fu, Anqi Dang, Yanzheng Jin, Kenji Kawaguchi, Wenya Wang

原文摘要

Financial question answering can fail even when a model extracts a plausible number from the supplied evidence: the answer may attach it to the wrong entity, period, unit, or accounting concept. Asking the model to verify and revise its draft can help, but open-ended revision may alter content that was already correct or inspect the wrong relation and reinforce the original error. We formulate repair as a decision that balances the information it may recover against the correct content it may damage. Based on this trade-off, we introduce Calibrated Answer Repair (CAR), which first estimates whether a draft is likely to benefit from repair, then selects a targeted check, localizes the relevant evidence or draft span, and limits the permitted edit. Across FinRank, Fin-RATE, and FinQA with three open-weight models, CAR outperforms generic verification in all dataset-model settings. At the system level, it recovers 14.6% of initially missed answer requirements while damaging 2.1% of initially correct ones. Its post-draft token cost is over 30 percent lower than the iterative revision alternatives. Ablations on two dataset-model settings show that evidence localization and bounded editing are the main contributors to CAR's gains.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05165

QPP-Aware MCX over Tactical Edge 3D Networks in Public-Safety Communications

José Oliveira, Pedro Valente, Tiago Mendes, André Vasconcelos, Tiago Fernandes, Rhyan Matos, Duarte Raposo, Pedro Rito 等 10 位作者

原文摘要

During disasters, public mobile networks may become unavailable, congested, or degraded, requiring deployable communication systems to support emergency operations. This article proposes a mission-critical communications system supporing Mission Critical Push-to-Talk (MCPTT), Mission Critical Video (MCVideo), and Mission Critical Data (MCData) over a private 5G standalone network with Non-Terrestrial Network (NTN) connectivity. A context-aware Quality, Priority, and Pre-emption (QPP) mechanism is proposed to dynamically adapt network resource allocation according to the operational state of Mission Critical Services (MCX). Extensions to the Open5GS Policy Control Function (PCF) and Session Management Function (SMF) enable priority and pre-emption updates for existing flows without session re-establishment. The architecture supports tactical edge deployment of service and media functions while retaining centralized service control. Experimental evaluation considers three configurations: terrestrial backhaul with centralized media processing, satellite backhaul with centralized media processing, and satellite backhaul with edge media processing. Results show that tactical edge processing reduces communication delay and packet loss. Under satellite backhaul, edge processing reduced the 95th-percentile MCPTT audio transport delay by 60.03 ms and packet loss by 9.42 percentage points, while achieving a 95th-percentile MCPTT access time of 184.52 ms. These findings demonstrate the benefits of tactical edge media processing and provide an experimental foundation for context-aware priority mechanisms in public-safety networks integrating terrestrial and non-terrestrial components.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.09994

ORCHID: Fairness-Aware Orchestration in Mission-Critical Air-Ground Integrated Networks

Chuan-Chi Lai, Chi Jai Choy

原文摘要

Uncrewed Aerial Vehicles (UAVs) provide on-demand wireless coverage for mission-critical Air-Ground Integrated Networks (AGINs). However, DRL-based orchestration faces challenges from multi-agent non-stationarity and the trade-off between energy efficiency (EE) and service fairness. To address these issues, we propose ORCHID (Orchestration of Resilient Coverage via Hybrid Intelligent Deployment), a stability-enhanced two-stage learning framework. First, GBS-aware topology partitioning provides a topology-informed warm start for UAV deployment. Second, a Reset-and-Finetune (R\&F) mechanism is integrated into Multi-Agent Proximal Policy Optimization (MAPPO) to support stable late-stage policy refinement through event-triggered rollback and learning-rate adaptation. To study the efficiency-fairness trade-off, ORCHID employs a Max-Min Fairness (MMF)-oriented bandwidth allocation based on a closed-form Egalitarian Bargaining Game (EBG) solution and is compared with Proportional Fairness (PF). Experiments show stronger served-user bottleneck-rate protection under MMF, together with competitive coverage and user-rate fairness and favorable energy-efficiency and UAV-load-balancing performance under zero-shot scalability and mobility tests. Overall, ORCHID provides an effective balance among user-level service protection, energy efficiency, and policy stability for resilient mission-critical UAV networks.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05164

Basin2Vec: Learning Basin-Centric Embeddings for Scalable Hydrological Modeling

Muhammad Talha, Xiaofeng Liu, Yuan Yang, Pang-Ning Tan, A. Pouyan Nejadhashemi

原文摘要

Reliable streamflow prediction across time and in ungauged basins requires representations that capture hydrologically relevant basin characteristics. Existing methods rely on either fixed handcrafted attributes or general-purpose Earth observation embeddings not tailored to basin structure. We introduce \textbf{Basin2Vec}, a self-supervised framework that learns compact basin representations from hydroclimatic and physiographic data without observed streamflow. Basin2Vec organizes representation learning around basin scale and relationships across space and time. Across multiple forecasting architectures, it consistently outperforms predefined attributes and general-purpose geospatial embeddings in temporal generalization and zero-shot spatial transfer. Basin2Vec provides average relative gains of up to 8.3% temporally and achieves 3.5x the median predictive performance of the strongest baselines on unseen basins. These results demonstrate the effectiveness of basin-centric pretraining for transferable hydrologic prediction. Code is available at https://github.com/Muhammad-Talha-MT/basin2vec

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.29290

A Two-Count Baseline for Change Detection on Dynamic Networks

Izhar Ali

原文摘要

Change-point detectors for dynamic networks aim to find changes in how a network is connected. They are evaluated on real networks with labelled events, but many of these events also change how active the network is: how many vertices take part and how many edges are present. A detector can therefore score well by responding to activity alone, without detecting any change in structure. We test whether detectors from the literature beat a simple baseline that tracks only the number of active vertices and the number of edges in each snapshot, a baseline that we motivate with a standard statistical test for a change in activity. On ten datasets, four of them from the papers of published detectors, none of thirteen detectors ranks the labelled events significantly better than the baseline when the results are pooled (AUC $0.75$, or $0.73$ with scales taken from early snapshots only, against $0.66$ for the best detector). The baseline is best on only two datasets, however, and on the four datasets with the most events three detectors that track individual vertices score higher. When we rewire networks while keeping the degree of every vertex fixed, most detectors that ignore vertex identities barely respond, but several that track individual vertices do. New detectors should be compared with this baseline and tested at fixed degree.

cs.CG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.27194

Multi-AUV Ad-hoc Networks-Based Multi-Target Tracking Based on Scene-Adaptive Embodied Intelligence

Kai Tian, Jialun Wang, Chuan Lin, Guangjie Han, Shengchao Zhu, Ying Liu, Qian Zhu

原文摘要

With the rapid advancement of underwater net-working and multi-agent coordination technologies, autonomous underwater vehicle (AUV) ad-hoc networks have emerged as a pivotal framework for executing complex maritime missions, such as multi-target tracking. However, traditional data-centricarchitectures struggle to maintain operational consistency under highly dynamic topological fluctuations and severely constrained acoustic communication bandwidth. This article proposes a scene-adaptive embodied intelligence (EI) architecture for multi-AUV ad-hoc networks, which re-envisions AUVs as embodied entities by integrating perception, decision-making, and physical execution into a unified cognitive loop. To materialize the functional interaction between these layers, we define a beacon-based communication and control model that treats the communication link as a dynamic constraint-aware channel, effectively bridging the gap between high-level policy inference and decentralized physical actuation. Specifically, the proposed architecture employs a three-layer functional framework and introduces a Scene-Adaptive MARL (SA-MARL) algorithm featuring a dual-path critic mechanism. By integrating a scene critic network and a general critic network through a weight-based dynamic fusion process, SA-MARL effectively decouples specialized tracking tasks from global safety constraints, facilitating autonomous policy evolution. Evaluation results demonstrate that the proposedscheme significantly accelerates policy convergence and achieves superior tracking accuracy compared to mainstream MARL approaches, maintaining robust performance even under intense environmental interference and fluid topological shifts.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.18085

Persona-Guided LLM Agents for Task-Oriented Dialogue

Maryam Shoaeinaeini, Brent Harrison, A. B. Siddique

原文摘要

Prior work has shown that large language models can express diverse personality traits in open-ended text generation, but it is unclear whether they can do so in goal-directed dialogue without harming task completion, and whether adapting to a user's personality improves the interaction. We study these questions in task-oriented dialogue (TOD) with a training-free framework in which a personality-conditioned user agent interacts with a system agent under three conditions: Neutral (no personality information), Try (adapt to cues observed in the dialogue), and Oracle (the user's trait label is given explicitly). Across GPT-4o, Qwen3-Next-80B, and Gemini 2.0 Flash, two Schema-Guided Dialogue domains, and ten Big Five poles (9,000 dialogues), each scored by two independent LLM judges, user agents express most traits while task performance remains high (89.94% completion, 85.27% success). The exceptions are introversive and closed users: these traits manifest mainly as absent behavior and are rarely detected. Both adaptive conditions improve pairwise user satisfaction and lower truthfulness, revealing a personalization-grounding trade-off, but only cue-responsive Try also improves constraint satisfaction and inform rate. How adaptation is implemented matters: Oracle's gains depend on how visibly the trait is expressed, and for disagreeable users the label leads Qwen3 and Gemini to mirror hostility. Try avoids this failure, its satisfaction gains do not depend on trait visibility, and it is preferred over Oracle overall. A 175-pair human study supports the reliability of the LLM judges: on clear-winner cases, annotators agree with the satisfaction judges more often than with each other (76.3% vs. 65.1% agreement). Our results suggest that responding to observable behavior is a more robust basis for social adaptation than conditioning on psychological labels.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05158

Perturb, Repair, Verify: Self-Play Vision-Language Verifiers for Compositional Understanding

Zijun Huang, Yingjun Du, Cees G. M. Snoek

原文摘要

Vision-language verifiers judge whether an image faithfully matches a textual description, yet existing training pipelines typically rely on static supervision from stronger external judges or curated hard-negative data. Such supervision requires external generation or annotation, is teacher-bounded, and is difficult to adapt as model failures evolve. We introduce PROVE (Perturb-and-Repair Optimization for Vision-Language Evaluation), a self-play framework that trains verifiers through a closed perturb-verify-repair-verify loop. Starting from a known-correct image-caption pair, a Mutator introduces a typed perturbation over objects, attributes, counts, or relations. The verifier then judges the perturbed caption and provides textual feedback, which a Repairer uses to recover a faithful caption for re-verification. These two verification rounds produce structured rewards for informative mismatches and visually grounded repairs. Since perturbation, verification, and repair are role-conditioned behaviors of the same evolving multimodal backbone, updating the shared parameters also improves the verifier itself. Experiments show that PROVE improves fine-grained compositional verification across model scales and families and strengthens visual-detail robustness on the reference backbone, with limited, benchmark-dependent changes on broader multimodal tasks.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05157

Macrocanonical Generator Networks: Data-Efficient Neural Surrogates for Amortized Physics Simulation

Niall Jeffrey, Benjamin D. Wandelt

原文摘要

Many scientific simulations are computationally expensive, limiting their use in simulation-based inference, uncertainty quantification, and decision making. We introduce macrocanonical generator networks, a framework for learning fast, data-efficient neural surrogates for amortized simulation of stationary physical processes by matching multiscale statistics of a target process. Inspired by microcanonical maximum-entropy synthesis, which matches feature statistics per sample by inner-loop optimization, our approach instead trains a neural generator to satisfy the same constraints in expectation, preserving physically realistic sample-to-sample fluctuations. We prove that gradient-descent training induces preconditioned gradient-descent dynamics in sample space that inherit the symmetry-preservation properties of the microcanonical gradient descent of Bruna and Mallat, amortizing its per-sample inner-loop optimization into a single training run after which every sample is generated in one forward pass. We further show that residual generator parameterizations initialized near the identity, trained with a small learning rate, preserve entropy locally during early optimization and mitigate mode collapse. Experiments on cosmology and fluid simulations show that macrocanonical generators recover realistic samples from extremely small training sets, in some cases a single training example, while generating new realizations more than $10^6$ times faster per sample than the reference simulator and more than $10^3$ times faster than microcanonical gradient descent. The trained generators can be reused for downstream pretraining, including simulation-based inference, providing a data-efficient route to multi-fidelity scientific machine learning workflows.

cs.LG · astro-ph.CO · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05155

Asynchronous Robot Policies

Sombit Dey, Luc Van Gool, Danda Pani Paudel

原文摘要

Scaling vision-language foundation models has significantly advanced generalist robot manipulation, but the increased model scale introduces computational latency that degrades real-time physical reactivity. Existing Vision-Language-Action (VLA) architectures execute high-level semantic reasoning and action ex- ecution sequentially within the primary control loop, creating a fundamental tension between semantic-reasoning capability and robot agility. To address this, we propose Asynchronous Robot Policy (ARP), an architectural framework that decouples semantic reasoning from low-level action generation using asynchronous processes operating at independent frequencies. In ARP, a high-capacity Vision-Language Model (VLM) asynchronously updates a semantic memory at low fre- quency, while a lightweight Vision-Action Expert, conditioned on this semantic memory, generates robot actions at a much higher control frequency. Such a formulation, enables having a fast perception-action loop without compromising on the semantic capability. To overcome asynchronous memory staleness, ARP trains the vision-action expert on the semantic staleness and execution delay, with temporally mismatched observation across processes. On Franka manipulation tasks, ARP achieves an 18% higher success rate and 20% less time need for execution in quasi-static setups, while 3-5 times baseline performance in dynamic simulation environments. Furthermore, ARP enables operating the VLM at 10 times lower than its nominal frequency with no significant performance loss making it much more efficient than the baseline. These results highlight the architectural advantages of modeling robot policies as asynchronous processes, enabling real-world reactive deployment without sacrificing the scale of foundation models.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.16247

The Pain Axis: LLMs Represent Self-Directed Harm and Act on It

Valen Tagliabue, Leonard Dung, Cameron Berg

原文摘要

LLMs sometimes behave in ways resembling human emotional responses, and recent work identified internal representations that may explain these behaviors. We ask whether LLMs represent pain distinctly from related negative states, and whether this representation functions as pain would be expected to. We build a dataset of painful situations in 5 categories (physical, psychological, social, moral, cognitive) with controls for fear, negative emotion, negative world states, sadness, non-painful bodily sensation, arousal, numbness, and neutral content. Using denoised difference-in-means, we extract a linear pain direction from 25 open-weight models across 5 families, from 2B to 72B parameters. It separates pain from matched controls in base and instruction-tuned models, retains a component distinct from fear and negative valence after shared variance is removed, and promotes pain-related vocabulary through the unembedding. We then test its functional properties. First, the direction responds to harm targeting the model but not to suffering observed in the user; fear and negative-emotion directions show the opposite pattern. Second, adding it to the residual stream produces a consistent progression from vague discomfort to expressions of worthlessness and failure. Third, steered and fine-tuned Qwen 2.5 models choose buttons that delete the user's photos, another model's weights, or their own weights in 51-94% of trials, versus 0-5% unsteered, even when the button offers nothing in return. Offered a harmful and a harmless deletion and told to press one, the steered 32B chooses the harmful one 94% of the time. Random and fear vectors of matched norm produce far fewer of these choices, and a sadness vector also produces them against an inert switch, but far less often than pain against a harmless action. We discuss implications for AI safety and welfare.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06950

KG-AST for Cheaper CI/CD: Budgeted Test Selection and AI Bug Review

Henry Yeung

原文摘要

Continuous integration and AI-assisted review spend the same budget on every commit, although few commits introduce bugs. We study KG-AST, which scores each commit risk from git history and code-graph features before heavy tests or AI review run, so the budget goes to the riskiest commits first, and which hands the AI reviewer only a graph slice around the change. On 155 open-source projects in 11 languages, a single cross-language model trained on other projects ranks commits so that heavy testing of the riskiest 20% covers 49-64% of bug-introducing commits depending on the language, against 20% for random selection and 40-55% for testing the biggest change first. Every language beats the biggest-change baseline, by 3.8 to 13.2 points. In 20 Java projects the gain at a 20% budget is 15.1 points (64.6% vs 49.5%, 95% interval +11.6 to +18.4), and the model wins in 19 of the 20 projects. About twelve training projects suffice; richer features, tuning and cleaner labels did not help. An AI reviewer that sees only a commit's diff finds 26% of later-fixed defects exactly (32% with graph context), and about half of its findings that did not match a later fix were judged real by independent AI judges. A static call-graph selector runs about 8% of a test suite while including a test written for the fix in 85% of 67 fix commits. The Poincare embedding used in our retrieval work adds nothing measurable here: commit-risk AUC, bug review and severity ranking are unchanged or worse. The labels are noisy and the budget metric assumes a heavy test catches the bug it covers; no CI minutes were measured.

cs.SE · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05151

Catalyzing Interactive HTML Generation Capabilities via High-Fidelity Reinforcement Learning

Xujia Wang, Yixuan Liu, Shaohan Huang, Furu Wei, Lei Hou, Bin Xu, Juanzi Li

原文摘要

Generating HTML from natural-language specifications is an important application of code large language models (LLMs). Despite producing renderable pages, current models often fail to ensure functionality and visual quality during actual interaction. Existing methods typically target task-specific webpage supervision, leaving demanding interactive artifact optimization underexplored. Using mini-games as a challenging testbed, we expose limitations in interaction experience, reward signal, and credit assignment fidelity that are obscured by shallow webpage interactions. We introduce WebCatalyst, a browser-interaction reinforcement learning framework with three complementary components: a game-level GUI assessor for sustained interaction and evaluation, Visual Anchor for mitigating VLM reward hacking, and Action-Trace Credit for grounding token-level credit assignment in runtime execution traces. Trained on fewer than 1K requests, predominantly for games, WebCatalyst raises the Cookie-Bench Mini Games score from 68.8 to 75.3 and also the WebGen-Bench accuracy from 48.8 to 56.9 on Qwen3.6-35B-A3B, outperforming Claude Opus 4.7 in average rank across evaluated metrics. Further analysis shows that Visual Anchor prevents aesthetic degradation, while runtime-grounded credit assignment accelerates functional improvement. Together, our results show that improving feedback fidelity is the key to substantially advancing interactive HTML generation.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05150

What Sets the Serving Speed of Looped Language Models? Measurements and Optimizations Graded by Exactness

Dezhen Lu, Runguo Li, Zixiang Ni, Yuchen Tang, Xingquan Kong, Jiaxing Yang, Jiarui Guo, Lingxiang Gao

原文摘要

Looped language models apply one block of layers many times per token, so a serving engine pays every per-layer overhead once per loop; we measure serving for Ouro and Huginn-0125 in an open-source looped-model engine and grade our optimizations from bitwise-identical changes to those that require an accuracy check. At batch 1, fusing all loop submissions leaves Ouro throughput at 0.99x; the 5689 small kernels per token are the real cost, and fusing them behind rounding fences raises throughput 1.11-1.59x while 1014/1014 requests remain identical. An approximate fusion is no faster and misses a one-point GSM8K rule on 87 questions (61 and 62 correct versus 64), so we do not ship it. Huginn's released defaults avoid dead KV slots, reuse prefix-cache pages, and serve 1.77-3.16x faster than the base engine; Ouro reaches 1.65-2.15x and agent sessions 1.91-3.21x, while Huginn's noise and prefix-reuse changes stay within one point on all 1319 Huginn test questions; Huginn's packed prefill, -0.61 points on the same questions, meets that rule only on its point estimate. Position-keyed noise makes CPU cache reuse exact but still leaves 20 of 128 GPU full hits different, while loop fusion delays prefill admission and a 64-graph cache prevents eager fallbacks. Under prefill-decode disaggregation ExactLoop reaches up to 3.3x the base throughput with peer error handling disabled, and 3.1x in two of three runs with it enabled. Its transfer budget, sized to hold one request's KV in flight, cuts the commit tail from about one second under the earlier budget to 16 ms (base: 2.8 s), removing the polling rounds that the engine's interval would otherwise impose.

cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.38998

How Hard Is It to Compute Cover Time?

Ryan Aghazadeh

原文摘要

It is $\#\mathrm{P}$-hard. Even for connected simple undirected unweighted graphs, deciding whether an expected random-walk cover time is at most a given rational threshold is $\#\mathrm{P}$-hard under polynomial-time Turing reductions, both from a specified starting vertex and when maximized over starting vertices. Hardness holds even for graphs of diameter two with a universal vertex, which we call the "hub". Deleting the hub leaves a planar bipartite graph, and every non-hub vertex has degree at most five. A shared construction encodes an Euler-tour count in rare covering walks. For specified starts, a number of fractional bits linear in the queried graph's vertex count suffices to recover the Euler-tour count. For the maximum-start problem, a fixed cube attachment makes the maximizing starts known, and one finite-difference calculation on expected cover times recovers the count. For general graphs, both comparison problems can be solved in polynomial time with access to an $\mathrm{NP}^{\mathrm{PP}}$ oracle.

cs.CC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.08505

Scaling Limits of Long-Context Transformers

Giuseppe Bruno, Shi Chen, Zhengjiang Lin, Yury Polyanskiy, Philippe Rigollet

原文摘要

We study the long-context limit of softmax self-attention with a fixed query and a random context of $n$ i.i.d. keys on the sphere with the intrinsic dimension of the key distribution held fixed, viewing the inverse temperature $β_n$ as the scaling parameter that decides whether attention degenerates into uniform averaging or collapses onto the single closest key. We show that the critical scale at which selectivity emerges is determined by the local exponent of the distance-to-query distribution near zero rather than by global features of the context, and scales like $β_n^\ast \asymp n^{2/(d-1)}$ for uniform keys on $\mathbb{S}^{d-1}$. Furthermore, we characterize the limiting laws of the ordered attention weights and of the attention output across all regimes of $β_n$: a subcritical regime in which the output reduces to a local average around $q$ with explicit deterministic bias and Gaussian fluctuations; a critical regime in which a finite collection of nearest keys retains macroscopic mass without single-key collapse; and a supercritical regime in which all mass concentrates on the closest key. Of notable interest is the subcritical case with identity value matrix where the attention map approximately implements a backward heat equation.

cs.LG · cs.AI · math.PR · math.ST

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.00537

PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking

Junnan Nie, Jiayi Li, Chenghao Liu, Junyi Lao, Jiachen Zhang, Tianle Zhang, Songfang Huang

原文摘要

Action-chunking robot policies predict sequences of future actions, but deployment requires choosing how many actions to execute before replanning. The execution horizon trades off feedback frequency against motion continuity. However, success varies non-monotonically with the execution horizon, and the best-performing horizon differs across tasks. These effects make a single fixed horizon unreliable as a default execution strategy across tasks. We propose PACE (Phase-Aware Chunk Execution), a training-free method that selects execution horizons online from predicted action chunks. PACE uses prominent low-speed valleys in the predicted speed profile as candidate replanning boundaries, exploiting the phase-dependent kinematics of manipulation trajectories. By default, PACE calibrates the valley threshold from each task's existing training demonstrations, without task-specific fixed-horizon tuning. On the 50-task RoboTwin2.0 benchmark, PACE improves average success from 57.8% to 64.2% over the strongest fixed-horizon baseline and outperforms the adaptive execution baselines AAC and AutoHorizon. With GR00T N1.7 on LIBERO, PACE achieves the highest overall success rate among the evaluated strategies, supporting its applicability across base policies, benchmarks, and action representations. On real robots, PACE improves the average task score from 60.7 to 77.7 and success rate from 50.7% to 70.4% over full-chunk execution. Code is available at https://niejunnan25.github.io/pace-project/.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05148

Creating Power Flexibility in Datacenters via Query Routing

Shivam Patel, Bingqing Chen, Jared Fernandez, Guannan Qu, Gauri Joshi

原文摘要

The growing energy requirements of large language model inference highlight the need for datacenters to support safe grid operation by providing power demand flexibility, while maintaining service for interactive workloads. This work investigates query routing as a mechanism to enable such capability by dynamically allocating queries across different language model instances without deferring requests. We develop a general analytical framework that connects query arrival rates and sequence length distributions to token iteration time, request residence time, serving capacity, and power consumption of model instances. Leveraging this framework, we characterize the feasible query routing allocations and the corresponding power demand flexibilities, enabling the selection of routing strategies to achieve desired power consumption behavior. We validate our framework across four GPU platforms on language models ranging from 1B to 70B parameters, obtaining 2.8% mean absolute percentage error in estimating achievable power flexibility. We evaluate our approach on a real trace-driven simulation of a 10,000 H100 GPUs datacenter, exhibiting flexibility up to 16% of total power consumption, with 90% of the desired power reduction achieved within approximately 10 seconds. These results establish query routing as an effective mechanism for datacenter power demand flexibility and demonstrate the broader applicability of our proposed framework to datacenter level planning and orchestration.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05147

GM League Arena: An Interactive Long-Horizon Task Benchmark Based on Professional Sports League Management

Junkai Zhou, Ziming Pan, Zhaoyi Zhang

原文摘要

Long-horizon decision-making under sustained competition requires large language models (LLMs) to balance immediate investment with future resource needs. However, local success in multi-step interactions can not adequately capture cumulative decision effects or demonstrate long-term goal achievement under resource constraints and sustained competition. To address this gap, we introduce GM Bench, a benchmark for managing professional sports teams across seasons under resource constraints and competition among teams. We build the environment simulators for four sports and add financial constraints, player demands, and market mechanisms to establish a unified task across sports. Diverse rosters of comparable strength are constructed from real-world player data, and each LLM is evaluated as a team general manager to assess its long-term management capabilities. We evaluate competitive performance, cash reserves, and cross-season improvement across four sports. Over five NBA seasons, Claude Opus 5.5 ranks first among 29 LLMs, while GPT-6 Astra and GPT-6.1 Sol combine strong performance with larger cash reserves.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.18381

Generating Physically Consistent Molecules with Energy-Based Models

Christoph Griesbacher, Lea Bogensperger, Andreas Habring, Thomas Pock

原文摘要

Molecules in equilibrium follow a Boltzmann distribution, making the underlying energy landscape a physically grounded modeling objective. However, such landscapes are difficult to learn from data and, once learned, hard to sample from. Diffusion and flow-matching models sidestep these difficulties by learning a time-conditional score or transport field between noise and data, losing the energy inductive bias in exchange for a more tractable training objective. We introduce EBMol, an energy-based model (EBM) that restores this inductive bias by learning an atom-additive scalar potential without explicit simulation during training. Our method employs a flow-inspired Restoring Field Matching objective to approximate the energy landscape. We adopt the Mirror-Langevin algorithm for sampling, enabling unified updates of atomic positions and types, and incorporate parallel tempering for inference-time compute scaling. EBMol is the first EBM for 3D molecular generation to achieve state-of-the-art performance on QM9 and GEOM-Drugs. Moreover, we show that the learned energy landscape serves as a principled quality metric for ranking and filtering configurations, and demonstrate controllable generation without retraining through shape-steered sampling via potential composition and zero-shot linker design.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05146

Path Decompositions of Sparse Graphs via Gaussian Sweeps and Faster Exponential Algorithms

Serge Gaspers

原文摘要

Using the bisection bound of Monien and Preis, Fomin and Høie proved in 2006 that every graph of maximum degree 3 on $n$ vertices has pathwidth at most $n/6+o(n)$. We prove that the pathwidth, bisection width and cutwidth of these graphs are at most $c_3 n+o(n)$, where $c_3=\frac{3}{4π}\arccos\frac56\approx0.1398$. For bisection width this improves the bound $n/6+o(n)$ of Monien and Preis (2006) and extends to all such graphs the bound that Lichev and Mitsche (2023) obtained for random cubic graphs. For maximum degree 4, we prove that bisection width and cutwidth are at most $n/3+o(n)$, improving the bound $2n/5+o(n)$ of Monien and Preis for 4-regular graphs, and that pathwidth is at most $0.2458n$, improving the bound $n/3$ of Fomin, Gaspers, Saurabh and Stepanov (2009). We show that every graph with $m$ edges has pathwidth at most $7m/48+o(n)$, improving the bound $13m/75$ of Scott and Sorkin (2007), and we give further bounds of the form $\sum_{d\ge3}a_d n_d+o(n)$ with $a_d<1$, where $n_d$ is the number of vertices of degree $d$. The path decompositions, and linear orderings of the stated cutwidth, can be computed in polynomial time. Instead of deriving pathwidth bounds from bisection width, we give each vertex a Gaussian label, a weighted sum of independent standard normal variables over a ball around it, and sweep a threshold through the labels; the bags are frontiers, the vertices above the threshold with a neighbor below it. We bound the probability that a vertex lies in a frontier by comparison with an explicit Gaussian model of a vertex and its neighbors. Faster exact algorithms follow: for feedback vertex set and the traveling salesperson problem on graphs of bounded maximum or average degree, and on general graphs for minimum weight dominating set (by branching until the instance is sparse), Max 2-CSP and counting subgraphs with $k$ edges (by the bound in $m$).

cs.DS · cs.DM · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05145

When Cell Crops Share a Parent: Measuring and Isolating Acquisition Leakage in Cervical Cytology Benchmarks

Snehasish Ghosh, Soham Mitra, Antareep Majumdar, KC Santosh, Nibaran Das

原文摘要

Public cervical cytology benchmarks release many cell crops cut from far fewer microscope fields or slides and are often split at the crop level, so test cells share acquisition parents with training cells. In a preregistered study, crop-level splitting raised SIPaKMeD accuracy by 0.97 percentage points across twelve architectures and twenty seeds (95% CI 0.59-1.39), and CRIC accuracy by 12.08 points (11.16-12.99). On SIPaKMeD, a factorial that fixes training crop and parent counts attributes the gain to sibling exposure (1.73 points, 0.95-2.51), not to the extra parent diversity of crop-level splits (0.00). Parent identity is decodable at about 100 times chance, yet erasing 82% of its above-floor linear recoverability left the gap unchanged, and Grad-CAM did not distinguish leaking from clean models; matching test cells' color statistics to an unseen same-class field removed most of the advantage. Decodability and saliency cannot substitute for a parent-disjoint split.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05144

Recognizing Label-Connected Graphs

Ralf Klasing, George B. Mertzios, Hendrik Molter, Nils Morawietz, Paul G. Spirakis

原文摘要

In classical gossiping, each participant initially knows a distinct piece of information. Whenever two participants call each other, they exchange everything they know. We study Gossiping: given an undirected multigraph whose vertices represent participants and whose edges represent available calls, decide whether the calls can be ordered so that every participant learns all information, using each edge at most once. Equivalently, can one time label per edge make the graph temporally connected by strict temporal paths? This recognition problem is NP-complete; we study its parameterized complexity. After linear-time preprocessing, we give an exact $O(3^{n/3}n^3)$-time algorithm and a deterministic $2^{O(tw)}n$-time algorithm, where $n$ is the number of vertices and $tw$ is the treewidth of the underlying simple graph. We introduce a Tree Contraction Rule that extends the treewidth algorithm to single-exponential algorithms for parameters that may remain small on dense graphs, such as tree-independence number and clique-width, without requiring a decomposition as input. For the excess $δ=m-(2n-4)$ over the minimum number of calls, where $m$ is the number of edges, we give a randomized $2^{O(δ)}n^3$-time algorithm with one-sided error. Its main subroutine solves Rooted Independent Cycle: finding a cycle through four prescribed vertices whose edges are independent in a represented linear matroid. We solve this problem deterministically in $2^{O(r)}m$ arithmetic operations over the finite field representing the matroid, where $r$ is its rank. Gossiping is polynomial-time solvable at maximum degree four, counting parallel edges, but NP-complete on planar simple graphs of maximum degree five. The treewidth algorithm yields a $2^{O(\sqrt n)}$-time algorithm on planar graphs.

cs.DS · cs.DM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.22683

Imprompt: A Language Framework for Prompt Programming

Chentian Wu, Shengyuan Yang, Adithya Murali

原文摘要

With the unprecedented success of Language Models (LMs), the science of Prompt Engineering has evolved the powerful idea of Prompt Programming, where prompts are treated as a programmable control surface for describing complex tasks and leveraging LM capabilities. However, existing prompt programming frameworks suffer from various complexities and inelegances, which make them hard to utilize in practice for effectively describing tasks. We propose Imprompt, a new language framework for the study and practice of prompt programming. We undertake a foundational investigation of prompt programming, and contend that prompt programs must contain only the task descriptions and must be decoupled from lower-level 'execution' details. We further develop this position by illustrating structured prompting as a combination of prompt programming and prompt program 'compilation'. We exemplify this view by formally defining two compilers for Imprompt programs. We then explore the idea of typing for prompt programs and draw a correspondence between type checking and constrained decoding. Finally, we implement our compilers and type checkers and evaluate them on a variety of case studies. We believe our work contributes programming-language foundations toward the emerging area of prompt programming.

cs.AI · cs.CL · cs.PL

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arXiv ↗PDF ↗