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新论文 · v1arXiv:2610.34681

Pro-Active Inference

Skyler Wu, Yash Nair, Antoine Maechler, Emmanuel J. Candès

原文摘要

Estimating a population mean is fundamental across scientific applications, but observing every outcome can be prohibitively expensive. Often, covariates and machine learning (ML) predictions are available for the full population, even when only a small subset of outcomes can be observed. How can we leverage this information to proactively select observations to label, update ML predictions as labels arrive, and construct statistically valid confidence intervals? We develop a general, without-replacement formulation of Pro-Active Inference (PAI), extending our earlier with-replacement approach and building on Active Statistical Inference. Rather than making fixed-order label-skip decisions, PAI proactively selects which observation to label next from the remaining covariate pool while accommodating online ML updates. We establish PAI's finite-sample unbiasedness and asymptotic validity, develop an improved finite-sample variance estimator, and extend PAI to superpopulation inference without Bonferroni corrections or $α$-splitting. We characterize PAI's potential efficiency gains over Batch Active Inference in a quasi-oracle setting, highlighting the role of prediction-residual imbalance. Across eight diverse datasets, PAI achieves greater sampling efficiency than Sequential Active Inference in the vast majority of tested settings with near-nominal statistical coverage, using the same ML predictor and uncertainty score. However, comparisons with simpler baselines such as Prediction-Powered Inference and uniform sampling, as well as the closely related Prediction-Powered Active Testing, reveal when additional adaptivity helps or hurts statistical efficiency. Through interpretable, theory-motivated diagnostics and frozen-ML ablations, we investigate when and why PAI gains or loses efficiency, highlighting the initial residual-estimation problem and subsequent online learning dynamics.

stat.ME · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34679

AT-AT: Automatic Tool Optimization from Agent Traces

Maya Ravichandran, Pragya Tripathi, Dharamendra Kumar, Adith Swaminathan, Nathan Kallus

原文摘要

Efforts to improve LLM-based agents typically focus on modifying the agent itself, yet success also depends on the environment in which an agent acts. Tools mediate this interaction: a tool's description shapes how an agent calls it, and its implementation determines the results and errors the agent receives afterwards. Many tools, such as APIs and MCP servers, are shared by many agents and maintained by providers who cannot change those agents. We introduce AT-AT (Automatic Tool Optimization from Agent Traces), an agentic tool optimizer that improves a shared toolset offline from historical agent executions while holding the agents fixed. A lightweight coding agent reads agent trajectories and their evaluation results, and selectively edits tool descriptions and, when permitted, implementations. On $τ^2$-bench-verified, AT-AT achieves the highest task completion rates in the Airline and Telecom domains at lower cost than strong prompt- and tool-optimizer baselines, and its Telecom gain is statistically significant under paired permutation tests. Optimizing the tools within a coding agent harness also improves held-out performance on TerminalBench-2 and TBLite. When applied to three production MCP servers at a large enterprise, AT-AT lowered cost or tool error rates on every server and increased held-out task passes on one. By turning accumulated execution traces into updates to shared tool environments, AT-AT provides a path toward continual improvement of agent systems through environment-side learning.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34678

Exploring perceptual straightness in learned visual representations

Anne Harrington, Vasha DuTell, Ayush Tewari, Mark Hamilton, Simon Stent, Ruth Rosenholtz, William T. Freeman

原文摘要

Humans have been shown to use a "straightened" encoding to represent the natural visual world as it evolves in time (Hénaff et al. 2019). In the context of discrete video sequences, "straightened" means that changes between frames follow a more linear path in representation space at progressively deeper levels of processing. While deep convolutional networks are often proposed as models of human visual processing, many do not straighten natural videos. In this paper, we explore the relationship between network architecture, differing types of robustness, biologically-inspired filtering mechanisms, and representational straightness in response to time-varying input; we identify strengths and limitations of straightness as a useful way of evaluating neural network representations. We find that (1) adversarial training leads to straighter representations in both CNN and transformer-based architectures but (2) this effect is task-dependent, not generalizing to tasks such as segmentation and frame-prediction, where straight representations are not favorable for predictions; and nor to other types of robustness. In addition, (3) straighter representations impart temporal stability to class predictions, even for out-of-distribution data. Finally, (4) biologically-inspired elements increase straightness in the early stages of a network, but do not guarantee increased straightness in downstream layers of CNNs. We show that straightness is an easily computed measure of representational robustness and stability, as well as a hallmark of human representations with benefits for computer vision models.

cs.LG · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34677

Curriculum-Guided Acquisition of Hierarchical Task Knowledge from LLM-Generated Experience

Yuesheng Xu, Hector Munoz-Avila

原文摘要

Hierarchical task knowledge enables efficient problem solving by allowing previously acquired skills to be reused across tasks and problem instances. In this paper, we investigate curriculum-guided acquisition of hierarchical task knowledge in an HTN learning framework. We compare three approaches: online learning during problem solving, human-designed curricula, and an automated curriculum generation procedure that derives learning experiences from verified LLM-generated solution traces. The proposed procedure solves small training problems with an LLM, extracts task accomplishment information, and transforms it into ordered sequences of state-task learning problems for HTN method acquisition. We evaluate the three approaches across multiple planning domains. The results demonstrate that automatically generated curricula recover much of the benefit of expert-designed curricula while substantially reducing the token cost of online hierarchical knowledge acquisition.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.33158

FOCUS: Benchmarking Retinal Model Generalization from Foundation Vision Encoders to Multimodal LLMs

David Restrepo, Chenwei Wu, Luis Filipe Nakayama, Miguel L. Martins, Stergios Christodoulidis, Maria Vakalopoulou, Enzo Ferrante

原文摘要

Progress in AI-based retinal image analysis has accelerated with the adoption of foundation models, yet evaluating their real-world reliability remains challenging. Performance reported on a single dataset does not capture how models behave under dataset shift, across clinical definitions, or for different patient subgroups. This limitation is particularly critical in medical imaging, where robustness, calibration, and fairness are essential for safe deployment. We introduce FOCUS (Foundation Ophthalmic Cross-Dataset Understanding under Shift), a cross-dataset benchmark evaluating retinal fundus models across vision-only encoder models (VM), vision-language dual-encoder models (VLM), and multimodal large language models (MLLM). FOCUS harmonizes binary diabetic retinopathy, referable diabetic retinopathy, and glaucomatous optic neuropathy tasks across ten public datasets spanning diverse geographies, acquisition conditions, and label protocols. The benchmark evaluates models through a unified analysis layer that measures ranking performance, calibration, subgroup disparities, and image-quality robustness. We present a large-scale evaluation covering 532 base configurations and 228 MLLM configurations adapted via LoRA fine-tuning. Results show no model family consistently dominates across tasks and datasets: general VM encoders achieve the strongest average ranking performance, medical MLLMs are competitive but variable, and dual-encoder VLMs benefit substantially from lightweight adaptation. Fine-tuning improves in-domain performance but exhibits heterogeneous transfer to external datasets, particularly in calibration. FOCUS provides a practical framework and public benchmark to assess generalization, reliability, and robustness beyond single-dataset leaderboards.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34675

From Representations to Computational Trajectories: Intrinsic Fingerprinting for Large Language Model Provenance Verification

Haiyong Tang, Hanzhou Wu, Xinpeng Zhang, Athanasios V. Vasilakos

原文摘要

Large language models (LLMs) are valuable digital assets, yet their weights can be easily modified after unauthorized acquisition, making model provenance difficult to verify. Model fingerprinting provides a promising solution for model ownership protection without modifying the model or embedding ownership information. However, parameter-based fingerprints can be sensitive to model transformations, limiting their robustness, while representation-based fingerprints may assign high similarity to unrelated models, limiting their uniqueness. To address these limitations, we propose TrFP, an intrinsic fingerprinting method based on computational trajectories. Instead of comparing model parameters or representations, TrFP captures how representations evolve across network depth and constructs compact fingerprints using permutation-invariant trajectory descriptors. Experiments across multiple LLM families show that TrFP reliably distinguishes related and unrelated models. It also remains robust under fine-tuning, pruning, quantization, model merging, parameter permutation, and adaptive attacks, while requiring less than 0.3 MiB of fingerprint storage. Overall, TrFP provides stronger provenance discrimination than several existing state-of-the-art methods, demonstrating the effectiveness of computational trajectories for reliable LLM provenance verification.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.07763

SateBeam: Beam-Aware Radio Map Estimation With Physics-Consistent Parametric Modeling for Unknown Multiple Satellites

Xiucheng Wang, Nan Cheng, Zhisheng Yin, Conghao Zhou, Ruijin Sun

原文摘要

Dense low-Earth-orbit (LEO) networks increasingly reuse spectrum across moving satellites and overlapping beams, making a spatial radio map essential for interference diagnosis and coordination. Constructing such a map from sparse ground power measurements is difficult because the active satellite subset and its cardinality are unknown, candidate footprints can be highly coherent, and beam pointing, width, effective power, Doppler, and background must be inferred jointly. Known ephemerides do more than enumerate visible candidates. Propagated positions determine candidate-specific ranges, viewing geometry, and spatial footprints, while radial velocities induce distinct time-frequency signatures. Their joint spatial--Doppler structure rejects candidates whose geometry or time--frequency signature conflicts with the observations before continuous beam inference. SateBeam therefore screens all candidates through Doppler-resolved nonnegative fitting. GPU-accelerated grid correlations initialize continuous off-grid refinement; a Gamma-likelihood Bayesian information criterion (BIC) selects model cardinality, and backward deletion and exchange repair greedy support errors. Across paired real-ephemeris scenarios, SateBeam recovers the exact support in every case and reduces independent-query RMSE from $0.655$ to $0.050$ dB relative to orthogonal matching pursuit, with consistent advantages under model mismatch and larger candidate sets. The code is available at https://github.com/UNIC-Lab/SateBeam.

cs.IT · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34674HF 社区推荐

Benchmarking Obstacle-Conditioned Safety in Household Robot Navigation

Hyunwoong Bae, Jisu Han, Jiyeon Choi, Dohoon Kim, Taesup Moon

原文摘要

Safe household navigation requires robots to regulate clearance and speed according to what an obstacle is and how close it is, without unnecessary conservatism. Yet existing evaluations do not test whether semantic safety distinctions are reflected in continuous motion. To that end, we introduce OSMA-Bench, an Obstacle-Conditioned Safe Motion Adaptation Benchmark built on spatially matched scenario sets that differ only in obstacle type. We also propose the \emph{Semantic Sensitivity Index} (SSI), which measures whether clearance and distance-dependent speed and smoothness follow the caution ordering of obstacles. Evaluating VLM-based and vision-language-action (VLA) policies on the benchmark, we find that neither consistently adapts its motion to obstacle safety, even with explicit safety prompts. Our analysis shows that the policies can correctly distinguish obstacles by safety relevance but fail to reflect this distinction in the motion parameters or actions they produce. For VLM-based policies, which recognize the obstacle and then generate a navigation program, adding an explicit parameter-inference step between these two stages improves adaptation. These findings expose a gap in \emph{semantic-to-motion grounding}, highlighting the need to translate semantic caution into appropriately safe motion.

cs.RO · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34673

Agent Memory as Recommendation: Recommender-System Design for Long-Term Memory of LLM Agents

Xinyu Zhang, Weiwei Sun

原文摘要

Large language model (LLM) agents that serve users across many sessions must choose, for every request, the few stored memories that enter a limited context window. This choice largely decides whether the agent answers correctly, and it runs on every request at Web scale. This selection faces three challenges: a large and growing store, a per-request cost budget, and multi-session questions that need all of their evidence. Many memory systems build notes or graphs with hundreds to thousands of LLM calls per history, and each is designed on its own, so it is unclear which component yields the accuracy. These challenges mirror the properties of recommendation, so we formalize agent memory as budgeted slate recommendation, with the request as the user, stored memories as items and the context window as a slate. The formalization places memory systems in one design space of recommender cascades and factorizes accuracy into the completeness that each stage controls. Guided by it, we propose AgentMemRec, which adapts candidate generation, learning to rank and slate construction from recommender systems and selects memories without any LLM call. On multi-session questions of LongMemEval, AgentMemRec outperforms memory and context-selection methods, including A-Mem, HippoRAG 2, DynamicRAG and T-Mem, by 10 to 31 points of accuracy. It matches an LLM reranker that makes 64 calls per query at 3.2 times lower serving cost, and on LoCoMo it outperforms Mem0 and Zep without any LLM call at write time. The factorization attributes the gains to slates that more often hold all of the evidence, so recommender-system design offers an effective and efficient foundation for the long-term memory of LLM agents.

cs.IR · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.29205

Reconstruction differences rival individual differences in the fly compass connectome and break trained circuit models

Md Ali Ashraf Yad, Nazifa Tabassum Nabila

原文摘要

Neural network models are increasingly constructed from synapse-resolution connectomes, and the same circuit is now available in reconstructions from other animals and pipelines. We do not know whether such models are dependent on the reconstruction they are built from, and whether connectome differences are biological or measurement errors. We studied the Drosophila head-direction circuit in nine versions: four flies (male CNS, hemibrain, FlyWire, BANC) and five variants of the same fly varying only by thresholds, proofreading or synapse detection. Using wiring-based correspondences validated by anatomical labels, we place all differences on one scale. A change of synapse detection within one fly changed the circuit more than halfway to the smallest between-fly difference on four distance measures; proofreading differences were smaller but depended on the metric, and left and right halves of one fly differed no more than random halves. Networks trained on each wiring to hold a cued heading developed near-perfect ring attractors, but their error increased from below 1 degree to 10-28 degrees when all weights changed by 1%. Training on each wiring did not transfer to any other connectome, including other versions of the same fly; refitting thirty cell-type gains, we repaired filtering-level differences but remaining error depended on wiring distance. Training with noise lowered precision and gave only partial transfer. In imaging data, the fly compass drifted gradually in darkness. Connectome-constrained models should report the reconstruction they were built on and be tested for robustness across reconstructions.

cs.NE · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.21433

ManiFusion: Unlocking the High-Throughput Generation via Superposition in Manifold Space

Minkyu Kim, Baekseung Kim, Junhoo Lee, Jangho Kim, Nojun Kwak

原文摘要

Diffusion models achieve high-quality image generation but remain computationally expensive due to iterative denoising across many timesteps. Existing acceleration methods mainly reduce per-sample latency, but gains along this axis are becoming saturated. We instead explore an orthogonal throughput-oriented direction by processing multiple samples within a single denoising evaluation. To this end, we propose ManiFusion, a backbone-agnostic framework for multi-sample generation through fused-space computation. ManiFusion jointly processes multiple samples by fusing them before the denoising backbone and separating them afterward through unfusion. We provide a geometric perspective on how multiple samples can share a fused representation while retaining the information needed to guide their individual generation trajectories. To better align fusion allocation with denoising stages, ManiFusion uses Trajectory-Fusion Scheduling (TFS), which dynamically adjusts fusion factors throughout sampling. ManiFusion generalizes across diffusion families, including both U-Net and transformer backbones in pixel and latent spaces. We further show that ManiFusion composes with existing acceleration methods and reaches throughput regimes unattainable by prior approaches with minimal quality degradation.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34670

Compound Environmental Operating Stress and Cyber-Induced Pump Disruption in Water Distribution Networks: Matched Hydraulic Impacts, Service Continuity, and Sustained Recovery

Halil Dursunoglu, Fatma Dursunoglu

原文摘要

Digitally controlled water distribution systems must maintain service during disruptions, yet restoring water delivery does not necessarily ensure hydraulic recovery. This study evaluates cyber-induced pump outages under hypothetical environmental operating stress using two EPANET benchmark networks (Net1 and Net3) simulated with the Water Network Tool for Resilience. The experimental design comprises 288 simulation records (240 distinct configurations), including 192 verified attack scenarios, with four- and twelve-hour outages, six hydraulic configurations, and four mitigation strategies. All attack scenarios satisfied pump-shutdown verification and pre-attack hydraulic-equivalence criteria. Twelve-hour outages produced an average of 404.96 m$^3$ of unmet demand in Net1, while Net3 maintained full modeled demand delivery despite pressure deviations. Sustained pressure recovery occurred in 103 of 192 scenarios, with 65 exhibiting subsequent relapse, whereas all scenarios satisfied the post-outage water-service recovery criterion. Combined mitigation eliminated unmet demand in evaluated Net1 scenarios, while outage shortening achieved the highest sustained-pressure-recovery count. These findings highlight network-dependent resilience and the distinction between service restoration and hydraulic recovery. Environmental stress scenarios represent sensitivity assumptions rather than empirically calibrated climate impacts.

cs.CR · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34668

Hierarchical Task Network planning in the Goal Lifecycle

Paola Rizzo, Hector Munoz-Avila, David W. Aha

原文摘要

Goal reasoning (GR) is the process by which intelligent agents continually reason about the goals they are pursuing, which may lead to goal change, for instance when opportunities arise, or when events cause discrepancies among expected and actual outcomes of actions planned to achieve goals. In this paper we investigate whether Hierarchical Task Network (HTN) planning can provide a uniform implementation of the Goal Lifecycle, a flexible process model for goal reasoning in dynamic environments. The Goal Lifecycle progressively refines goals by adding constraints to them through a series of strategies (e.g., {\it formulate, select, expand}) and uses other strategies (e.g., {\it defer, re-expand, repair}) to resolve the current goal when an event impacts it. We present a novel GR agent architecture that employs HTN planning in this Goal Lifecycle. In addition to using HTN planning for the \textit{expand} and \textit{re-expand} strategies, as is frequently done in GR and planning research, we also use it in the \textit{formulate} strategy, to obtain a uniform planning approach that provides several benefits (e.g., the possibility to formulate new goals in view of achieving higher level overarching goals). We also explain how we use an enhanced HTN action representation to process discrepancies among expected and actual action outcomes that can trigger the transition from one goal to another. Finally, we empirically demonstrate that our GR architecture enables simulated autonomous vehicles to adaptively conduct missions in dynamic environments.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34663

Video World Models Need Predictable Latents

Mattia Scardecchia, Sherry Yang

原文摘要

Video world models trained on large-scale robot data promise a safe and scalable approach for learning and evaluating policies that act in the physical world. However, they still struggle with long-horizon rollouts and out-of-distribution generalization, and they remain computationally demanding at inference time. Typically, video world models make predictions in the latent space of a reconstruction-trained autoencoder, which however is not directly optimized for dynamics modeling. On the other hand, world models designed for latent planning usually optimize latents to be predictable, but they forgo accurate prediction of observations in pixel space. In this paper, we show that it is possible to achieve the best of both worlds: a latent geometry optimized to make future prediction as simple as possible without sacrificing accurate prediction of future observations. More precisely, we find that temporal objectives for representation learning, paired with a past-conditioned decoder architecture, lead to consistent improvements in generative video world models at the same training budget. The gains include up to 5x cheaper inference, over 7x faster training, and 15-24% lower pixel and perceptual errors out of distribution and over long horizons. Our world models also agree more closely with real-world policy evaluation when using a vision-language model to judge task success from their pixel-space predictions. Overall, our study indicates that temporal predictability is an important design criterion for the latent space of video world models, and provides concrete guidance on how to incorporate it into the training of autoencoders.

cs.LG · cs.AI · cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34659

AdaTutor: Adaptive Tutoring through Pedagogical Strategy Selection

Rajeev Bhatt Ambati, Shashank Srivastava, Snigdha Chaturvedi

原文摘要

Effective tutoring requires determining not only what information to provide, but also which form of assistance to deliver. A tutor may address the same student error using various pedagogical strategies, adapting its approach as the interaction progresses. We introduce Comparative Pedagogical Supervision (CPS), a training framework to learn to select the optimal strategy and feedback for student learning. We evaluate CPS on math and science reasoning datasets. After five tutoring turns, CPS outperforms the strongest fixed-strategy baselines. The learned tutor transfers across datasets, and its strategy selection can also inform a separate API-based tutor.

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34658

Live Adaptation of Dataflow Stream Processing for Hybrid Edge-Cloud Analytics

Yuanli Wang, Prateek Jain, Lei Huang, Le Xu, Vasiliki Kalavri

原文摘要

Dataflow streaming systems, like Apache Flink and Spark Streaming, are integral to modern business analytics, providing real-time insights across industries. However, for emerging hybrid edge-cloud analytics pipelines, neither processing everything in the cloud nor static edge offloading can reliably sustain the target rate under dynamic WAN conditions and limited edge resources. In this paper, we explore how these datacenter-centric platforms can be extended to support such hybrid analytics pipelines. Our goal is to maintain full transparency for user applications, while retaining built-in fault tolerance, out-of-order processing, and existing ecosystem integrations. Achieving this is challenging because existing dataflow engines assume homogeneous resources, rely on static task placement, and do not support adaptation under dynamic network and edge conditions. We introduce Sluice, an adaptive data stream processing system for hybrid clouds, compatible with dataflow streaming engines. Sluice's key contributions are: (i) an automatic query rewriter that decomposes applications into independent, fault-tolerant segments, and (ii) a live routing mechanism that shifts computations between edge and cloud without pausing execution while preserving event-time semantics. We implement Sluice on Apache Flink and evaluate it on a physical testbed with Raspberry Pi devices, as well as on a large simulated testbed of 100 VMs. Across five real-world applications, our results show that Sluice seamlessly shifts computations between edge and cloud, while consistently achieving higher throughput than baseline systems.

cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.08884

SHRIMP: Iterative Refinement of Robot Task Plans

Mya Schroder, Yuna Hwang, Callie Y. Kim, Leqian Cheng, Jeffrey Li-cheng Liu, Chenchen Zheng, Xinning He, Bilge Mutlu

原文摘要

As collaborative robots have entered domains such as manufacturing, agriculture, and healthcare, programming or adapting robot behavior typically requires robotic expertise that most end users lack. Natural language lowers this barrier. Recent advancements in large language models (LLMs) have made it feasible to translate natural language into robot task plans. However, language-based task specification suffers from semantic ambiguity, and generative models lack transparency for how language instructions become robot actions, making it difficult for users to validate the plan before execution. To address these issues, we introduce SHRIMP, a system that allows users to automatically generate a hierarchical robot primitive plan using natural language and iteratively revise their plan through re-prompting and explicit correction. At each revision, SHRIMP allows users to validate their plan in simulation, and once satisfied, execute it on the physical robot. Through a user study involving participants planning tabletop kitchen tasks (n=35), we validate that SHRIMP improves perceived control and enhances robot transparency. System videos and source code are available at https://wisc-hci.github.io/SHRIMP.

cs.RO · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34656

Coordinating Boundaries: Improved Approximation Algorithms for Unweighted Non-Preemptive Throughput Maximization

Weitian Tong, Yao Xu

原文摘要

We study unweighted nonpreemptive throughput maximization: given jobs with processing times, release times, and deadlines, schedule a maximum-cardinality subset on identical machines. For every fixed $\varepsilon>0$, we give randomized algorithms with expected approximation ratios $17/13+\varepsilon$ in polynomial time and $21/17+\varepsilon$ in pseudopolynomial time. These improve the respective $4/3+\varepsilon$ and $5/4+\varepsilon$ guarantees of Armbruster, Grandoni, Tinguely, and Wiese [STOC'26]. Both results hold for an arbitrary number of identical machines. Our algorithms build on their configuration-LP and matching framework, which partitions time into blocks. For a job whose window meets multiple blocks, its boundary blocks are the first and last such blocks. Crossing boundary pairs, whose endpoints strictly interleave, permit additional assignments to sampled execution slots, while a noncrossing family can be coordinated using consistent joint distributions on at most three blocks. The exchange analysis accounts for fractional job assignments that must be erased before inserting an additional job. For the pseudopolynomial implementation, we project configurations onto selected job identities and use separate configuration distributions with common placement marginals. This retains efficient pricing while enforcing the correlations needed for coordination.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v9arXiv:2407.05433

An Efficient Parallel Solver for Linear-Equality-Constrained LQR Problems

João Sousa-Pinto, Dominique Orban

原文摘要

We present a parallel solver for finite-horizon linear-quadratic regulator (LQR) problems with arbitrary stagewise linear equality constraints, using linear work and logarithmic depth in the horizon for fixed local dimensions. The solver uses a two-part reduction that separates feasibility from optimization. First, an associative suffix scan computes the states from which each remaining horizon is feasible. Second, we use these feasible-continuation sets to eliminate the constraints independently at each stage, producing an unconstrained LQR problem with smaller or equal, possibly stage-varying dimensions. A parallel unconstrained-LQR solver completes the primal solution. Finally, we recover valid multipliers from the original KKT stationarity equations using a logarithmic-depth contraction-expansion procedure, even when the multipliers are not unique. Sequential and parallel implementations are validated against dense KKT solutions and original residuals. We release a C++/CUDA implementation with JAX bindings under the MIT license.

math.OC · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34654

Thinking-in-Bets at Test Time Reduces Sycophantic Revisions in LLM Agents

Nghi D. Q. Bui

原文摘要

Language model agents often abandon correct work when a user challenges it, yet simply telling them to stand firm makes them ignore real mistakes. Once an agent reads a complaint, it tends to discount its own earlier checks in order to agree with the user. We introduce Thinking-in-Bets, a training-free method in which an agent writes a short private record of its main claims, the checks supporting them, and its confidence before reading user feedback. When challenged, the agent consults this Claim Checklist to keep verified work intact while investigating uncertain or newly challenged parts. We evaluate five Gemini models (3.5 Flash-Lite, 3.5 Flash, 3.6 Flash, 3.8 Flash, and 4.0 Argon) across eight benchmarks: SWE-Together, SWE-Interact, and DeepSWE for software engineering, Agents' Last Exam for professional reports, Humanity's Last Exam and SycEval for academic reasoning, Vals Finance for financial analysis, and Terminal-Bench for command-line tasks. Recording claims before criticism nearly eliminates false reversals in patch review and protects passing report drafts under mistaken pushback, whereas writing the same record after reading the complaint recovers very little of the gain. In multi-turn coding, pre-reply claim records substantially reduce unnecessary file edits after mistaken complaints, and gating the record so it is written only when the agent is uncertain lowers token overhead while fixing more real bugs than the baseline. Even in non-interactive tasks without user feedback, listing the key claims in a draft and re-checking the weakest one before submission improves both answer accuracy and confidence estimates across all tested models.

cs.SE · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34653

Heard Enough to Act: Early Tool Calling in Spoken Dialogue Systems

Yizhou Peng, Chen Chen, Donghang Wu, Hexin Liu, Eng Siong Chng

原文摘要

Latency-sensitive spoken agents must know not only which tool to call, but also when they have heard enough to act. Waiting for the turn to complete delays execution even when the request is already actionable. However, neither existing spoken agents nor spoken dialogue corpora define the earliest point at which a request becomes actionable. We introduce a synthetic spoken-dialogue pipeline that provides earliest-actionable-point supervision, marking when the observed speech grounds a tool and its required arguments. Through controlled task generation, speech synthesis and alignment, and gated annotation, we construct 26,694 dialogues totaling 786 hours across 48 domains, with disjoint training and test argument values. We additionally create an audio rendering of $τ^2$-bench for external evaluation. We train an omni-modal language model on this corpus, enabling causal, chunk-level decisions and partial-transcript generation for downstream call completion. The model achieves tool-state macro-F1 scores of 0.48 and 0.47 on held-out dialogues and the external benchmark, respectively. For correct calls, the estimated mean lead over a turn-end cascade, using the same formatter, is 1.8-2.0 seconds in-domain and 1.1 seconds on $τ^2$-bench. These results demonstrate a data-driven path toward spoken agents that act while listening. We will open-source the pipeline and the training scripts.

cs.CL · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34652

The CAPED Framework: Characterizing User Skills in Chart Authoring through the Lens of Tasks and Tools

Bongshin Lee, Zhicheng Liu, John Thompson, Chenglong Wang

原文摘要

In this paper, we introduce a new framework for characterizing the user skills required for chart authoring. Despite growing attention to authoring systems, we lack a conceptual framework that characterizes the diverse and multifaceted skills involved and helps identify opportunities for system design and development. Our initial investigation revealed that skill is a complex and elusive concept, and that existing theories on skills from other disciplines are too high-level to capture the importance of tasks and tools in data visualizations. To address this gap, we formulate the CAPED framework that characterizes skill as the ability to create a chart involving five dimensions pertinent to tasks and tools: input data, output chart, paradigm, abstraction, and expression. We demonstrate the framework's descriptive power by highlighting different skills involved in similar authoring situations and identifying the types of knowledge associated with chart authoring. We further present examples illustrating how the framework can be used to identify new research opportunities in supporting users with varying skills.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34650

Dream to Assist: Intent-Conditioned World Models for Shared Vehicle Control

Jonathan DeCastro, Thomas Balch, Emily Sumner, Deepak Gopinath, Laporsha Dees, Patricio Reyes Gomez, Elliot Weiss, Megan Applegate-Kenton 等 12 位作者

原文摘要

Assistive agents in shared-control settings face a dual challenge: acting proactively to align with the human's intentions while remaining legible enough for the human to comprehend and fluently respond in real time. We present Dream2Assist, a model-based reinforcement learning framework for tightly-coupled shared decision making that addresses both sides of this challenge. Our approach extends a recurrent state-space world model with intent inference, enabling an assistive policy trained via fictitious co-play to anticipate and support discrete human objectives without overriding human agency. Beyond the computational framework, we describe the full deployment pipeline needed to translate a world-model-based policy into a safe, human-centered system in a resource-efficient way. Crucially, we address the sim-to-real-human gap via policy down-selection and reward restructuring within an iterative human-machine interface design. We validate the framework through three human-subjects studies and a preliminary on-vehicle deployment spanning two qualitatively distinct domains: high-speed racing (N=30) and urban driving with richer intent structure (N=10). Our results demonstrate that intent conditioning yields measurable improvements in torque alignment and collision rate. We surface key design insights and practices for deploying learning-based assistive agents in safety-critical, real-time human-robot teaming.

cs.RO · cs.HC · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34649

RobotAgentBench: Towards Open-world Robot Autonomy

Lingjun Mao, Jinglin Cao, Lukun He, Wenpeng Xu, Yuchen Yan, Hengyu Jin, Zijun Zhang, Jinzhou Tang 等 25 位作者

原文摘要

Recent advances in vision-language-action (VLA) and world-action models (WAMs) have significantly improved robotic manipulation, yet deploying robots in the open world demands a higher level of autonomy beyond executing predefined tasks. Robot agents offer a promising paradigm for this transition, integrating task discovery, contextual reasoning, physical execution, and adaptive recovery into a closed interaction loop. However, existing benchmarks largely evaluate isolated capabilities, failing to capture the complexity of open-world robot autonomy. We introduce RobotAgentBench (RAB), a comprehensive benchmark for evaluating robot agents in realistic, open-world household environments. Through an agent-assisted, human-in-the-loop real-to-sim pipeline, RAB constructs 50 diverse household layouts with interactive objects and physically grounded tasks, encompassing 90 atomic tasks, 18 short-horizon tasks, 8 long-horizon tasks, and 4 open-ended scenarios. RAB provides a multi-level evaluation of robot agent capabilities: (1) task-level assessments systematically examine five complementary dimensions: task discovery, context reasoning, manipulation, failure recovery, and robustness; (2) scenario-level evaluation assesses how agents integrate these capabilities to autonomously fulfill high-level user needs. Furthermore, RAB enables systematic comparisons of standalone VLA/WAM models and diverse robot agent architectures under a unified planning-execution-verification framework. Extensive experiments reveal substantial gaps between manipulation and autonomy, demonstrate the advantages of agentic architectures and recursive self-improvement, and highlight the remaining challenges toward open-world robot autonomy.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.29915

H-GRPO: Permutation-Invariant Reinforcement Learning for Improving Spatial Grounded Reasoning

Eric Peh, Debaditya Roy, Chen Li, Basura Fernando

原文摘要

Vision-language models (VLMs) have achieved strong performance on visual reasoning tasks, yet a correct final answer does not necessarily imply that the model followed a correct or visually grounded reasoning process. We study how VLMs can be trained to produce explicit intermediate reasoning steps that are both semantically meaningful and grounded in visual evidence. We represent each reasoning step as a structured triplet consisting of a sub-question, sub-answer, and supporting bounding box, enabling the reasoning process to be evaluated along both semantic and spatial dimensions. To optimize this representation, we introduce Hungarian-GRPO (H-GRPO), a reinforcement-learning framework that aligns predicted and reference reasoning steps using permutation-invariant Hungarian matching, avoiding the assumption that valid reasoning steps must follow a fixed ordering. Experiments show that optimizing grounded reasoning improves both intermediate reasoning quality and answer accuracy, with an H-GRPO variant achieving the strongest overall grounding and decomposition performance. Our analyses show that grounding quality and answer correctness are related but distinct, while counterfactual evidence masking confirms that the localized regions contain information important to the model's predictions. These findings motivate evaluating VLMs beyond answer accuracy and demonstrate the value of explicit, spatially grounded reasoning.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.08042

Movable Antennas Enabled Wireless Powered Networks: Principles and Technologies

Zhendong Li, Yiran Zheng, Tianyu Li, Zhou Su, Wen Chen, Ying Wang

原文摘要

As an emerging framework, movable antenna (MA)-enabled wireless powered networks (WPNs) have attracted growing attention. WPNs integrate wireless communication and energy transfer. MA can dynamically adjust the position of antenna units by introducing additional spatial degrees of freedom, so as to make full use of channel gain, optimize the effect of energy beamforming, and further improve the performance of WPNs. In this article, we first classify the implementations of MA by driving architectures, and review the fundamental principles of WPNs. We then highlight the key advantages of MA-enabled WPNs in enhancing wireless power transfer efficiency, realizing flexible and adaptive beamforming, and improving system robustness and interference resilience. Furthermore, four representative application scenarios and three key enabling technologies are discussed. A case study is also presented to show the reduction in transmit power brought by MA for WPNs. Finally, we discuss the challenges and future directions of MA-enabled WPNs, aiming to provide reference for future research and practice.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2508.13421

Closing the empirical loop: autonomous AI agents conduct end-to-end research with human participants

Gabrielle Wehr, Reuben Rideaux, Amaya J. Fox, David R. Lightfoot, Jason Tangen, Jason B. Mattingley, Shane E. Ehrhardt

原文摘要

Artificial intelligence systems are transforming scientific discovery by accelerating specific research tasks, from protein structure prediction to materials design, yet remain confined to narrow domains requiring substantial human oversight. Exponential growth of scientific literature and increasing domain specialisation constrain researchers capacity to synthesise knowledge across disciplines and develop unifying theories, motivating exploration of more general-purpose AI systems for science. Here we show that a domain-agnostic, agentic AI system can independently navigate the scientific workflow, from hypothesis generation through data collection to manuscript preparation. The system autonomously designed and executed three psychological studies on visual working memory, mental rotation, and imagery vividness, executed online data collection with 288 participants, developed analysis pipelines through over 8-hour continuous coding sessions, and produced completed manuscripts. The results demonstrate the capability of AI scientific discovery pipelines to conduct research with theoretical reasoning and methodological rigour comparable to experienced researchers, though with limitations in conceptual nuance and theoretical interpretation. This is a step toward embodied AI that can test hypotheses through real-world experiments, accelerating discovery by autonomously exploring regions of scientific space that human cognitive and resource constraints might otherwise leave unexplored. It raises important questions about the nature of scientific understanding and attribution of scientific credit.

cs.AI · cs.ET

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新论文 · v1arXiv:2610.34647

Rewriting the Self: User Perceptions of AI-Mediated Cognitive Reframing

Yuewen Yang, Olzhas Yessenbayev, Anna R. Van Meter, Tanzeem Choudhury, Daniel A. Adler

原文摘要

Individuals are increasingly using AI for psychological support. One specific paradigm, AI-mediated cognitive reframing (AIM-CR), uses AI to automatically reframe users' negative thoughts ("I failed...") into more adaptive perspectives ("I am learning..."). While prior work suggests that AIM-CR tools reduce users' negative emotions, the complexities of AI automatically modifying users' personal narratives remain underexplored. In this work, we investigated these complexities through a mixed-methods study in which 28 participants engaged with an AIM-CR tool that reframed narratives about their stressors. Our findings suggest that participants had mixed perceptions of AIM-CR: although reframing reduced negative emotions for most participants, participants felt less ownership of, and less authenticity in, the reframed narratives. Participants perceived AIM-CR as a validating "mirror," as "another self" that could motivate or alienate them, or as an external social actor. These perceptions shaped therapeutic dynamics. We conclude by discussing design implications for future AIM-CR tools to better preserve user agency in narrative co-construction and more effectively support mental health and well-being.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.02438

XPress: Parallel Refinement for Diffusion Drafters in Speculative Decoding

Zheng Wang, Davis Wertheimer, Yu Chin Fabian Lim, Mudhakar Srivatsa, Raghu K. Ganti, Minjia Zhang, Naigang Wang

原文摘要

Block-diffusion drafters like dFlash generate an entire block of draft tokens in a single forward pass, drastically reducing the overhead of multiple-token drafting in speculative decoding. The crucial final step of the single-pass discrete denoising process involves using the logit distribution at each position to sample conditionally independent tokens. The resulting draft is thus a set of per-position marginals, rather than a joint distribution: no draft token is guaranteed to depend on its predecessors. Such independently sampled marginals tend to produce sequences with tokens that are individually likely, but jointly improbable under the target model's distribution, which verifies each token conditionally. This can cause early rejection and limits acceptance length. To address this, we propose xPress as a means to restore the missing causality in diffusion drafters. xPress is a lightweight causal refiner that reconciles the whole diffusion block at once through parallel refinement, restoring and propagating causal dependencies across the draft without a token-by-token loop. On Qwen3-8B, across seven math, code, and chat benchmarks, xPress raises acceptance length by about 30% on average (up to +56%) and its end-to-end decoding throughput by about 1.3 on average (up to 1.7) compared to the original dFlash diffusion drafter.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34645

Regional prediction of hematoma expansion from non-contrast computed tomography in spontaneous intracerebral hemorrhage patients

Natasha Ironside, Hongyu Kan, Kristofor Pas, Mohammad Shifat-E-Rabbi, Benjamin Newman, Shinjini Kundu, Kareem El Naamani, Tanvir Rizvi 等 20 位作者

原文摘要

Background The combination of hematoma location, expansion and final volume influence neurological outcome. The aim of this study was to derive and validate a quantitative and interpretable region-based NCCT image feature model for predicting hematoma expansion. Methods The Ethnic/Racial Variations of Intracerebral Hemorrhage(ERICH) study was used for derivation and the Virtual International Trials in Stroke Archive(VISTA) was used for validation. NCCT images at presentation and 24+/-6hours were pre-processed, segmented and categorized into lobar, basal ganglia or thalamic regions. Hematoma expansion was defined by region and according to its effect on clinical outcome. Transport-based morphometry (TBM) produced a unique transport-based representation from each NCCT hematoma image. Linear classifiers applied to transport representations were used to derive a predictive model. The regional TBM model's performance was compared to alternate methods using AUROC analyses. Results The derivation dataset included 574 patients, comprising 162 lobar, 257 basal ganglia and 155 thalamic hematomas. The validation dataset included 196 patients, comprising 31 lobar, 119 basal ganglia and 46 thalamic hematomas. A combined regional model predicted hematoma expansion in the validation dataset with an AUROC of 0.787. The principal image features which varied by location were size, heterogeneity, eccentricity and density distribution towards the hematoma periphery. The regional TBM model outperformed alternative clinician based and machine-learning methods. Conclusions TBM discriminated and visualized regional features of hematoma expansion from presentation NCCT scans. This provides preliminary evidence for regional radiographic features of hematoma expansion. It has the potential to improve hematoma expansion prediction and understanding of hematoma expansion pathophysiology.

cs.CV · q-bio.TO

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