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版本更新 · v3arXiv:2406.14234

Feed-forward active magnetic shielding

Alain de Cheveigné

原文摘要

Measurement of small magnetic fields requires shielding. For example, magnetoencephalography (MEG) measures small fields arising within the brain, in the presence of environmental fields several orders of magnitude larger. A common solution is passive shielding, but its cost, bulk and limited effectiveness motivate the alternative approach of active shielding, which comes in two flavours: feed-back and feed-forward. In feed-back shielding (the most common design), coils are driven from sensors that measure the corrected field. In feed-forward shielding (less common), coils are driven from sensors that measure the uncorrected field. The latter has the potential of providing better performance, but its implementation is hobbled by an inevitable coupling between coils and reference sensors. This paper describes an approach that employs a data-driven ``decoupling matrix" to suppress the coupling, enabling greater flexibility to achieve effective shielding across a wide frequency range. The method is evaluated by simulation, first using a simplified "flat world" in which the principle is clearly visualised, and then based on the more realistic geometry of a standard MEG system. Performance appears to be limited only by spatial sampling and numerical resolution. The method, which is data-driven and does not require geometric calculations, tight-tolerance fabrication, or manual calibration, is complementary with standard feed-back shielding methods. Active shielding is of particular interest in the context of a new generation of optically-pumped magnetometers (OPM), as the lower cost and reduced constraints of these new systems relative to SQUID-based systems provide an incentive to likewise reduce the cost and constraints of shielding.

physics.med-ph · cs.HC · eess.SP · physics.ins-det · q-bio.NC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.22538

STAGE: Stateful Translation to Agentic Graph Execution with Policy-Scoped Context and Deterministic Control

Mengxi Luo, Changjia Chen, An Cao, Wanyi Dai, Zirong Huang, Shixiong Gao, Ruoqi Shu, John Chen 等 9 位作者

原文摘要

Enterprise agents are expected to deliver correct and consistent outcomes while executing complex procedures, adhering to governing policies, and operating within practical resource budgets. We present \textsc{Stage}, an integrated execution framework combining typed task nodes, shared execution memory, document tools, and deterministic orchestration. We evaluate \textsc{Stage} on SOP-Bench Referral Abuse, the Retail and Telecom domains of $τ^2$-bench, and two proprietary banking workflows, Smart Dispute and Paystub Fraud Detection. Using the same underlying model in each comparison, \textsc{Stage} improves single-run success and repeated-run reliability over the baseline in all 18 benchmark--model pairs. Success across all three runs (\(\mathrm{Pass}^{3}\)) increases by 7.5--66.0 percentage points. Fixed-graph ablations demonstrate the contributions of localized policy context and coordinator-owned routing. Further analysis of Smart Dispute shows improved coverage of required operations across procedure lengths. These findings show that integrating local reasoning, persistent state, and procedural control can improve reliable enterprise workflow completion without changing the underlying model.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02087

The Vibe Paradox: What EEG Reveals About Your Brain When Vibe Coding

Olivia Schutz, Rudrajit Choudhuri, Yunhan Qiao, Fatima A. Moussaoui, Julia Russo, Zoe Steine-Hanson, Anita Sarma

原文摘要

Background: Much of the enthusiasm for vibe coding rests on two intuitive beliefs: (1) that developers stay invested even when they no longer write the code, and (2) that delegating more of the work to AI lowers their cognitive load. Objective: Our goal was to investigate whether developers' neurophysiological states and interactions with AI are consistent with these beliefs when they work with agentic vs. non-agentic AI on their own project vs. a legacy codebase. Method: We conducted an exploratory within-subjects crossover study with 11 participants across 22 sessions. Each participant used GitHub Copilot Chat in one session and Cursor Agents in the other, working on their own project and a legacy codebase, with tool-codebase pairings randomly assigned and session order counterbalanced. We recorded participants' EEG, on-screen activity, and prompts, and analyzed 7,924 EEG data points, 984 activities, and 141 prompts. Results: Our findings contradict both beliefs. With the agent, participants remained indifferent to task outcomes, even on their own projects, and cognitive load was higher than with non-agentic AI. Furthermore, their ways of interacting shifted: with non-agentic AI, participants asked how the code worked and read it to verify changes; with the agent, they described desired app behavior and checked results in the running app. Conclusion: Our study reveals a vibe paradox: delegating to an agent left participants motivationally disinterested, even on their own projects, without relieving cognitive load, while coinciding with increased trust in AI. The community should treat this paradox as a first-class concern if developers and their AI agents are to go far together, and not just fast.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.25421

Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making

Guangfeng Cai, Kaibing Yang, Shuo He, Yu Li, Shengtian Yang, Jiaqi Lv, Lei Feng

原文摘要

Recent studies on world modeling for Large Language Model (LLM) agents typically formulate the learning objective as next-observation prediction. However, this objective ties supervision to what a transition happens to reveal, which may omit the dynamics most relevant to the agent's current decision. To bridge this gap, we propose Agent-Authored World Modeling (AAWM), a training procedure that constructs supervision from the policy's own decision needs. Specifically, at each state, the agent identifies what it needs to understand about the environment before acting. These needs drive the retrieval of relevant transition evidence across trajectories, which is then synthesized into training targets that capture decision-oriented dynamics instead of reconstructing the next observation. This aligns the training objective with the dynamics the policy needs before acting, not with the contents of the next observation. Experimental results validate the effectiveness of AAWM across multiple environments and training settings. These results show that decision-aware world-model targets provide a more effective learning signal than next-observation prediction.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02086

Reduced Cost Influence Functions for Predict-then-Optimize under Noisy Data

William Zhang, Saurabh Amin, Georgia Perakis

原文摘要

Machine learning models deployed in decision-making pipelines, from vehicle routing to dynamic pricing to clinical decision support, must remain reliable in the face of low-quality training data. We study a predict-then-optimize setting in which an upstream model predicts the cost vector of a downstream linear program, with a large pool of noisy training data supplemented by a small curated set of clean data. We introduce Reduced Cost Influence Functions (RCIFs), which combine influence functions from robust statistics with the geometry of linear optimization to estimate how each training point shifts downstream decisions. Building on this, we propose a reweighting algorithm that uses RCIFs to downweight harmful training points and reduce downstream regret. Experiments on random linear programs and shortest-path problems show that our approach reduces regret, and accompanying theory characterizes properties of RCIFs and identifies regimes favorable to our method relative to baselines.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.19098

Global Parameter Identification and Uncertainty Quantification of reduced-order ADM1 using Maximum Likelihood Estimation and Bayesian Inference

Simon Hellmann, Leander Lerch, Terrance Wilms, Moritz Reumschüssel, Alberto Meola, Félix Delory, Steffi Knorn, Stefan Streif 等 9 位作者

原文摘要

Anaerobic digestion (AD) models can be challenging to calibrate against real data due to limited practical identifiability, data scarcity, and model complexity. Weighted least squares objective functions and gradient-based solvers using finite differences are often used in literature, but result in suboptimal parameter estimates and large uncertainty bands. This paper identifies the parameters of a reduced-order ADM1 by comparing the frameworks of maximum likelihood estimation (MLE) and Bayesian inference (BI), and quantifies uncertainties of parameters and outputs. Multirate experimental data are taken from a lab-scale AD reactor fed with agricultural substrates and operated at organic loading rates of 3$\,$g VS $\mathrm{m^{-3}d^{-1}}$. Global optima are found via global and gradient-based local search algorithms using forward sensitivities. Enhanced scatter search provides the best global search results. These are refined by the Gauss-Newton algorithm as the best local search, which delivers $R^2$ values of 0.95 for acetic acid and 0.77 for biogas production. BI yields substantially narrower uncertainty bands than MLE and avoids the need for a global search by incorporating prior knowledge about parameter distributions. The search space is captured efficiently by log10 scaling of parameters. Algorithmic details on its implications are provided. Despite a high-resolution lab-scale dataset, the ADM1-specific inhibition parameter $K_\mathrm{I,nh3}$ is practically not identifiable with MLE, and shows a low output sensitivity, whereas a parameter for the effective ion concentration shows superb practical identifiabilty at high confidence with both MLE and BI. The present work lays a scalable base for calibration of nonlinear AD models by providing reliable simulations. It thus advances model-based monitoring and control applications of multirate processes such as agricultural AD plants.

eess.SY · cs.CE · stat.AP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02084

Black Hole Love Numbers with Physics-Informed Neural Networks

Maria J. Rodriguez, Luis Fernando Temoche, Pavlos Protopapas

原文摘要

We develop and validate a physics-informed neural network (PINN) framework for computing the tidal Love numbers of black holes directly from their perturbation equations. We consider static perturbations of the Kerr black hole, whose exact hypergeometric solutions provide a controlled benchmark for both the numerical solver and the extraction of the physical response. The framework combines exact enforcement of the horizon boundary conditions with an $H^2$-Sobolev loss that incorporates derivatives of the differential-equation residual. The Love number is then obtained by extracting the asymptotic coefficients of the learned solution, without using the analytic connection formula. Across the tested scalar, vector, and tensor perturbations, the method recovers the finite dissipative response with relative errors below $10^{-5}$. Controlled comparisons show that the higher-derivative loss terms improve solution accuracy by orders of magnitude, while independent tests of the extraction procedure distinguish solver errors from errors in recovering the response coefficients. The conservative response, which vanishes analytically, is constrained by the numerical error floor. These results demonstrate that PINNs can accurately compute black-hole tidal response and establish a validated framework for future extensions to frequency-dependent and non-separable perturbation problems.

gr-qc · cs.LG · hep-th · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02082

Fairway: Load-Aware KV Cache Reuse for LLM Prefill Scheduling

Hamidreza Alikhani, Nazmul Takbir, Matin Khajavi, Rajat Bhattacharjya, Sangeetha Abdu Jyothi, Nikil Dutt

原文摘要

LLM-based applications often employ agentic workflows and retrieval-augmented generation (RAG), which build long prompts from shared context. When prefixes are shared across requests, serving engines reuse the key-value (KV) cache to skip parts of the prefill computation. Reuse is typically graded: the uncached work of requests in a queue can differ by up to 100x. In this setting, the order of scheduling determines the time-to-first-token (TTFT) for the queued requests. KV reuse-first scheduling, which prioritizes the requests with the longest KV cache hit, gives the lowest median TTFT but starves expensive requests under load spikes; first-come-first-served (FCFS) scheduling bounds the tail but raises the median TTFT. We present Fairway, a prefill scheduler built on adaptive reuse prioritization: it favors KV cache reuse while queues are short, and shifts toward fair allocation as requests wait longer relative to their own prefill time. Fairway employs a virtual dual lane model: a KV reuse-first Fast lane and a Fair lane atop the serving engine's waiting queue that enables speeding up of cheap reuse requests without starving expensive ones. This model automatically adapts to the workload without any per-workload tuning. Fairway lowers the tail TTFT at no cost to the median in transient overload. Across workloads and load levels, Fairway matches or beats the best baseline, with a p99 TTFT up to 8.6x lower than prior prefix-aware schedulers. During load spikes, it keeps the tail of FCFS at a 6x lower median TTFT.

cs.DC

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2511.21524

$k$-path graphs: experiments and conjectures about algebraic connectivity and $α$-index

Rafael L. de Paula, Claudia M. Justel, Carla S. Oliveira, Milena S. Carauba

原文摘要

This work presents conjectures about eigenvalues of matrices associated with $k$-path graphs, the algebraic connectivity, defined as the second smallest eigenvalue of the Laplacian matrix, and the $α$-index, as the largest eigenvalue of the $A_α$-matrix. For this purpose, a process based on [Discrete Applied Mathematics 164 (2014) 297-303] is presented to generate lists of $k$-path graphs containing all non-isomorphic 2-paths, 3-paths, and 4-paths of order $n$, for $6 \leq n \leq 26, 8 \leq n \leq 19$, and $10 \leq n \leq 18$, respectively. Using these lists, exhaustive searches for extremal graphs of fixed order for the mentioned eigenvalues were performed. Based on the empirical results, conjectures are suggested about the structure of extremal $k$-path graphs for these eigenvalues.

cs.DM · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02081

Understanding the relationship between retrospective soundscape attributes and continuous pleasantness and eventfulness ratings

Yuanbo Hou, Kening Guo, Francesco Aletta, Woon-Seng Gan, Dick Botteldooren, Jian Kang

原文摘要

Soundscape studies ask listeners to rate perceived affective quality (PAQ) attributes, such as pleasant and vibrant, after listening to recordings or experiencing acoustic environments. These retrospective ratings summarise an experience during which impressions may change. International Organization for Standardization (ISO) procedures convert these PAQ ratings into Pleasantness and Eventfulness scores. Pleasantness and Eventfulness (P/E) can also be rated directly and continuously during listening, capturing changes in reported perception over time. Retrospective PAQ ratings and continuous direct P/E describe the same soundscape experience from different perspectives. We examine how PAQs relate to direct P/E and how ISO-derived P/E corresponds to direct judgements, using 1,782 paired 30-s trials involving audiovisual recordings from 18 streets. The results show that PAQs retain a predominantly two-dimensional structure. Vibrant and Monotonous lie closer to the Eventfulness-Uneventfulness axis in the direct P/E plane than in the ISO 2025 reference geometry. The full 30-s direct P/E mean has lower calibrated error against ISO-derived P/E than three 10-s means. An empirical mapping using the same 8 PAQs reduces held-out joint root-mean-square error by 4.9% relative to affine-calibrated ISO 2025 scoring. The findings show that retrospective and continuous assessments offer related but distinct soundscape descriptions.

eess.AS · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02079

ParetoSaddler: Pareto-Guided Optimization of Agent Harnesses for Quality and Cost

Xingzhi Qian, Sungho Park, Rongyuan Tan, Jianbo Yu, Jue Zhang, Lorenzo Cavallaro, Menglin Xia, Yanzhi Chen 等 16 位作者

原文摘要

Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality. Extending them to joint quality--cost optimization introduces a challenge: applying the deployment preference during intermediate updates can prematurely reject candidates that are valuable for later improvements. We introduce ParetoSaddler, which addresses this mismatch by separating intermediate candidate promotion from final deployment selection. At each iteration, an optimizer updates a parent harness to produce a candidate, and then compares the two over repeated executions and uses Bayesian bootstrap Pareto support to promote candidates showing credible progress in either quality or cost. Promoted candidates receive broader development evaluation and remain available as parents for later optimization steps. We evaluate ParetoSaddler on ClawMark and GAIA2 using domain-disjoint splits. On ClawMark, ParetoSaddler improves quality from 70.86\% to 81.35\% while reducing cost from \$1.17 to \$1.01 per task; on GAIA2, it improves quality from 56.22\% to 62.89\% while reducing cost from \$0.78 to \$0.45 per task. The Pareto-selected harnesses also achieve higher quality at lower cost than quality-only harness optimization. Analysis shows that Pareto-aware optimization retains intermediate candidates that would be discarded by deployment preference but can support later improvements, while Bayesian bootstrap screening filters out candidates whose gains are weakly supported across runs.

cs.AI · cs.CL · cs.LG · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02076

Scene-Adaptive Readout and Decoding in Diffusion Driving VLAs

Seungjun Yu, Jiho Choi, Inha Kang, Hojun Choi, Seungwook Kim, Hyunjung Shim

原文摘要

Diffusion driving vision-language-action models repeatedly execute the backbone at every denoising stage, incurring substantial inference cost, while it remains unclear whether all scenes require the same decoding computation. To distinguish when a trajectory becomes readable from how much further refinement it requires, we analyze trajectory readout across network depth and decoding progress. We identify a depth-decoding dissociation in which late-layer representations already support useful trajectory readout after the first forward sweep, whereas early-layer representations benefit more from continued decoding. We further find that both the preferred readout layer and decoding depth vary across scenes. Based on these observations, we propose a scene-adaptive readout and decoding framework. A scene-conditioned readout MoE adaptively weights and combines trajectory predictions from layer-specific late-layer representations after a single backbone sweep, while an adaptive decoding policy applies additional native refinement only when required by the scene. We evaluate our framework on two diffusion driving VLAs across the Waymo E2E and nuScenes benchmarks. Our method improves validation RFS by up to 0.56 while also improving test performance, and achieves an end-to-end speedup of up to $6.0\times$ on Waymo E2E, while consistently reducing trajectory error on nuScenes. These results demonstrate that scene-level readout and decoding allocation improves planning quality while avoiding unnecessary decoding. Our code is available at https://github.com/sjyu001/SARD

cs.CV · cs.AI · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02075

Decoupled Predictive Decision Making: A Shared Conditional Predictor for Agents

Lang Mo, Xingxuan Zhang, Qi Wang, Mingchao Hao, Hao Yuan, Chunjing Gan, Wang Lu, Peng Cui

原文摘要

Agent decisions depend on empirical relationships that deployment observations reveal and task descriptions alone may leave unresolved. We introduce Decoupled Predictive Decision Making (DPDM), an architecture that formulates heterogeneous agent decisions as conditional prediction with a shared pretrained learner. Language models provide semantic representations of current and historical records, while a general-purpose conditional predictor predicts task-defined targets from the represented evidence. Pretraining supplies reusable inference capability; deployment evidence instantiates problem-specific predictions without updating either model's neural parameters. Tool judgments, candidate preferences, and reasoning assessments share an evidence-and-query interface, with predictions mapped to choices by a decision rule. This architecture establishes complementary sources of agent capability: semantic knowledge acquired through language pretraining and empirical inference acquired through structured-data pretraining. A single frozen LimiX-2 predictor serves all five task categories. With Qwen3.5-27B as the semantic encoder, DPDM raises mean accuracy across 50 benchmarks from 78.80% with few-shot prompting to 84.92% under their respective evaluated history budgets, and reaches 82.33% with identical histories and queries. On ToolPRMBench's BFCL and ToolSandbox tasks, absolute accuracy gains are 34.26% and 28.87%, respectively, at the methods' respective evaluated history budgets. Accuracy improves as history grows. With identical imbalanced histories, mean balanced accuracy improves by an absolute 14.51% across five reasoning-step assessment tasks and four encoders. DPDM makes learning from experience an independently improvable foundation-model capability, creating a shared route for advances in conditional prediction to improve decisions across agent tasks.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02074

SWE-Trail: History-Guided Trajectory Collection for Coding Agent Training

Ziming Li, Lu Yu, Jiatan Huang, Qing Cui, Jun Zhou, Chuxu Zhang

原文摘要

Coding agents solve repository issues through long sequences of exploration, editing, and validation. These trajectories are increasingly used as post-training data, so how they are collected shapes what the trained model learns. A common approach is to sample multiple trajectories independently for each issue and then select or curate them. However, as more trajectories are sampled independently, later trajectories become increasingly redundant, even though earlier trajectories could guide where subsequent collection effort should be spent. A natural way to use this history is to expose it to the coding agent as advice. But training on such trajectories can make the model rely on advice that is unavailable at test time, creating a train-test mismatch. Instead, we argue that history should guide where collection effort is spent, while the coding agent decides what to generate. We introduce SWE-Trail, a trajectory collector that uses earlier trajectories to guide subsequent collection while keeping that history outside the coding agent's context. During a new rollout, SWE-Trail uses history to identify a state where additional collection effort may be useful. There, the coding agent proposes candidate next actions from its ordinary context, and SWE-Trail briefly executes them from the same checkpoint to decide which route continues. We evaluate collection approaches by training student models on the trajectories they produce. For a 124B student initialized from Ling-3.0-flash-base, SWE-Trail improves mean resolution over independent sampling by 2.33 and 1.60 percentage points on SWE-bench Multilingual and SWE-bench Pro, respectively. Without history, the same pipeline produces more resolved trajectories than independent sampling, but improves by only 0.11 points on Multilingual. These results show the value of using history to guide collection without exposing it to the coding agent.

cs.AI · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02073

Back to the Future: Predicting Long-Horizon Dynamics with Retrospective Correction for Disturbance-Aware Retrieval

Jiwoong Choi, Haoyu Li, Yiwei Lyu

原文摘要

Retrieving objects from densely packed storage requires anticipating how contact disturbs surrounding objects when limited clearance prevents lifting the target over clutter. However, contact can propagate beyond the target's immediate neighbors, while accumulated prediction errors make learned dynamics unreliable over long planning horizons. We introduce DARE (Disturbance-Aware REtrieval), a geometry-aware relational dynamics framework that uses retrospective correction to improve long-horizon prediction for disturbance-aware retrieval. DARE encodes compact object descriptors and predicts action-conditioned disturbance propagation through object-level message passing. When a sparse observation becomes available, a learned corrector uses the prediction discrepancy to amend an earlier predicted state and reruns the intervening dynamics, propagating the correction through the predicted contact chain. These predictions guide a receding-horizon planner that balances retrieval progress against displacement and rotation of surrounding objects, permitting necessary contact while discouraging harmful disturbance. Experiments in planar and RoboCasa environments evaluate long-horizon prediction, generalization, and robustness for disturbance-aware retrieval in dense clutter. See our project website: https://the-back-to-the-future.github.io

cs.RO · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2512.01175

Structural and Spectral Properties of Strictly Interval Graphs

Claudia Justel, Lilian Markenzon

原文摘要

In this paper we deal with a subclass of chordal graphs, which are simultaneously strictly chordal and interval, the strictly interval graphs. We present a new characterization of the class that leads to a simple linear recognition algorithm. Next we introduce a new subclass of strictly interval graphs, the $SI$-core graphs, that are non-split and non-cograph graphs and show that several elements of the new class are Laplacian integral

cs.DM · math.CO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2511.09394

EyeAgent: An Agentic AI System for Multimodal Clinical Decision Support in Ophthalmology

Danli Shi, Xiaolan Chen, Bingjie Yan, Weiyi Zhang, Jiancheng Yang, Ruoyu Chen, Xinyuan Wu, Siyu Huang 等 26 位作者

原文摘要

Current medical artificial intelligence (AI) systems are often task-specific, poorly integrated, and difficult to interpret, limiting support for multimodal clinical workflows. We developed and evaluated EyeAgent for ophthalmic decision support. EyeAgent comprises three layers including 54 task-specific EyeTools, 32 clinical skills, and a modular large language model reasoning core with interchangeable backbones. A multi-level evaluation distinguished this scope from empirical system-level validation. Component-level evaluation assessed newly developed models on held-out splits of datasets totaling 871,862 samples. System-level evaluation included illustrative cases of workflow breadth, a 200-case expert-rating study of output quality, and an exploratory multicenter reader study comparing unassisted and EyeAgent-assisted performance among 27 ophthalmologists from 11 centers. Skill-level and backbone ablations further assessed orchestration and robustness. In illustrative cases, EyeAgent showed task-specific advantages over GPT-5.5 in lesion quantitation, cross-modality synthesis, and uncertainty-aware reporting. In the expert-rating study, EyeAgent achieved an F1 score of 0.84 for skill selection, with 82.7%-92.0% of outputs rated acceptable or better across six clinical dimensions. In the exploratory multicenter reader study, diagnostic accuracy increased from 72.8% unassisted to 91.9% assisted (difference, 19.1 percentage points (pp); 95% CI, 13.6-24.7; Holm-adjusted P<0.001). Report completeness increased from 55.8% to 75.6% (difference, 19.8 pp; 95% CI, 14.8-24.7; Holm-adjusted P<0.001). Across the evaluated settings, EyeAgent supported interpretable, evidence-guided reasoning and showed potential for human-AI collaboration. Broader real-world effectiveness and generalizability require prospective and cross-population validation.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02067

State-Aware Virtual Patient Simulation for Training Psychiatrists in Challenging Clinical Scenarios

Harsh Mohta, C Chandrahas Reddy, Sunhith Reddy Bojja, Manik Inder Singh Sethi, Vinesh Gupta, Raghuram Bharadwaj Diddigi

原文摘要

Psychiatrists must be prepared to manage patients exhibiting challenging psychological states, including suicidal ideation, hallucinations, and other high-risk behaviors. However, opportunities to safely practice such interactions are limited. While recent conversational agents can simulate patients, existing approaches largely rely on large language models (LLMs) to determine patient responses, making it difficult to consistently maintain psychological state, adapt to the clinician's responses, and generate clinically plausible challenges that meaningfully test the clinician. We present a state-aware virtual patient simulation pipeline for training psychiatrists in challenging clinical scenarios. The proposed system maintains an evolving patient state throughout the interaction and orchestrates patient behavior rather than leaving response generation entirely to an LLM. At each interaction, a learning-based behavioral strategy selector determines the type and level of challenge appropriate to the current patient and clinician context. The selected strategy, patient state, clinician response, interaction history, and relevant clinical knowledge are then provided as grounded context to an LLM to generate a semantically relevant and psychologically consistent patient response. Following each session, the system analyzes the interaction and provides feedback to the clinician on their communication and management of the virtual patient.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02066

Large Geometry Models Are Priors For Fast Physics Pretraining

Maggie Chlon, Leon Chlon

原文摘要

Language and vision models became data-efficient by pretraining on plentiful unlabelled data; physical field models have such data, over ten million 3D shapes, but geometry pretraining designs its targets rather than deriving them from the physics. Euler-Lagrange geometry pretraining derives them: for attached flow, a shape enters the flow's Euler-Lagrange equation only through coefficients that distance and normal line (the normal up to sign) fix to first order. Their reconstruction error therefore bounds what a representation loses about any field, and fine-tuning on costly simulations need only assemble the global field. We realise it in AREALS, a masked surface autoencoder whose areal tokens carry position, normal and area, pretrained self-supervised and sign-free on 61,822 raw meshes. Because the operating condition enters only through the far field, sensors can supply it: AREALS completes a hidden field in one forward pass without condition labels. It matches the accuracy of the same architecture trained from scratch with half the simulations on DrivAerML and DrivAerNet++ and about a quarter on WindsorML. On DoubleDelta wings, 20 sensors give $3.9\times$ and $4.0\times$ lower field and force error than GABI, a Bayesian inversion method with a learned prior, and beyond the training angles pretraining halves the error.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02065

Fragility Is Not Reducible to Hardness: Conditional Causal Difficulty for LLMs

Mohammad Reza Rezaei, Dingxian Wang, Nikhil Verma, Sajjad Pakdamansavoji, Andrew Rabinovich, Zhao Chen

原文摘要

Large language models show large and seemingly arbitrary performance drops when a task is rewritten in ways that leave its requirements untouched. We recast task difficulty as a causal quantity: the treatment effect of meaning-preserving perturbations on a model. A meaning-preserving rewrite can only be observed to hurt a task the model can already solve, so we place the estimand on the principal stratum where the degradation occurs: the conditional causal difficulty (CCD), the probability that a randomly drawn twin breaks a solvable task. Because harder tasks tend to be more phrasing-sensitive, complexity enters as an effect modifier rather than a confounder, and we residualize it out to obtain complexity-residualized difficulty (CRD); zero for a task exactly as fragile as its complexity predicts, positive for anomalously fragile ones. Current benchmarks do not supply the data CCD requires, so we introduce GlassGSM and GlassMATH, augmenting GSM8K and MATH with twins built from perturbation families, raising fragility on GlassGSM by $\approx 12\times$ over GSM-IC-style distractors on the same problems ($3.7\%\to44.9\%$) while keeping the answer key unchanged. Our predictor Frail learns the per-perturbation break probability and reaches $0.613{\pm}0.023$ of the suite reliability ceiling on GlassGSM and $0.557{\pm}0.023$ on GlassMATH, against $0.171$/$0.401$ for a surface-feature baseline and $0.444$/$0.463$ for supervised baselines such as VAP3, and, at a $5\%$ curation budget on GlassGSM, doubles random recall of fragile items, matching an oracle ranking. Additionally, cross-fitted residualization leaves CRD uncorrelated with the declared proxy in practice. Code is available at https://github.com/upwork/Conditional_Causal_Difficulty_for_LLMs

cs.LG

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新论文 · v1arXiv:2611.02057

Paying for Fractional Error: Tight Constant-Round Approximation of Covering Problems with Short Broadcasts

Saeed Amiri, Sebastian Siebertz, Wojciech Wawrzyniak

原文摘要

We present a general framework for deterministic constant-round approximation of covering problems in sparse networks in the broadcast CONGEST model, where every vertex broadcasts the same $O(\log n)$-bit message to all its neighbors. The framework combines two independent tools. The first is a solver that computes a $(1+δ)$-approximate solution of any local covering linear program with bounded positive integer costs on every class that excludes a fixed minor. The second is a rounding principle: a rounding rule that saves a fixed fraction of its own output, for every feasible input, pays for a solver error fixed in advance, so that an approximate fractional solution yields an exact coefficient. For ordinary domination, we achieve a $2a+1$ approximation against the fractional optimum $γ_f(G)$ for every fixed rational $a\ge1$, whenever every minor has a ratio of edges to vertices of at most $a$. On planar graphs this is seven, matching the lower bound of Hilke, Lenzen, and Suomela. The coefficients are also optimal at bounded treewidth and pathwidth, for $K_t$-minor-free graphs with $3\le t\le9$, on triangle-free planar graphs (five), and on planar graphs of large girth, where it approaches the optimal three. On every fixed surface the exact bound is $7γ(G)$, and we show that a bound of $7γ_f(G)$ is impossible on some fixed surfaces even sequentially. For total domination, on graphs without isolated vertices and under the same minor-density assumption, the exact coefficient is $2a$ against the fractional optimum, including the optimal six on planar graphs. Red-blue domination, and hence set cover and hitting set on planar incidence graphs, has the optimal limiting coefficient four. Our method also gives randomized constant-round and deterministic $O(\log^* n)$-round $(1+\varepsilon)$-approximation schemes, with the randomized cost guarantee holding in expectation.

cs.DS · cs.DC

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新论文 · v1arXiv:2611.02056

Do Embodied VLMs Actually Use Their Scene-Graph Memory?

Shubh Garg, Divyansh Pandey, Vedant Khandelwal, Niyati Rawal, Amit Sheth

原文摘要

Embodied agents that answer questions about their route need a memory of what they saw, and scene graphs are a natural form for it. A vision-language model (VLM) can receive such a graph as text or through a graph encoder, but accuracy alone cannot show whether the graph holds the episode's facts, reaches the model, or is actually relied on. In this diagnostic study, we introduce a graph-swap test for the last of these: each episode is given another episode's graph, and we count how many answers change. On an embodied question-answering benchmark with routes through two to seven rooms, we use it to compare ways of delivering scene-graph memory to a fine-tuned VLM. As text, the memory is used strongly, adding 30 points on route questions. As a graph, it is not, and we trace why: the encoder preserves the information, but standard pooled and late fusion discard it. Giving each node its own input token recovers part of it, a gain of 8.3 points on route questions that the swap test confirms comes from the graph. The test also exposes a shortcut that accuracy hides: when the same facts appear in the text, the model ignores its graph. The test thus offers a cheap check of whether a VLM uses its scene-graph memory, and our analysis points to how that memory should be delivered.

cs.RO · cs.AI · cs.CV

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新论文 · v1arXiv:2611.02055

Rare Event Constrained Reinforcement Learning with Large Deviations

Rahul Misra

原文摘要

We study finite-horizon reinforcement learning with a safety constraint on the probability of reaching a failure set. In contrast to the well-studied safe reinforcement learning literature, we consider problems where the probability of reaching the failure set is vanishingly small, but the consequences of reaching the failure set are catastrophic. Direct simulation and estimation of the optimal policy is naturally ineffective when the failure probability is extremely small. Moreover, on a real system the controller can only choose actions, and this is not enough to make failures frequent. In contrast to the rare event simulation literature, our goal is not to estimate the probability of the rare event occurrence, but to upper bound it such that the failure constraint is satisfied. Thus, instead of sampling failures, we use the large deviations principle. We define a damage variable that on exceeding a certain threshold indicates failure. We study the cumulant generating function (CGF) as its positive root provides an upper bound on the failure probability via Cramer's theorem. We extend this approach to Markov decision processes with hitting times through state-action CGFs, and give finite-horizon and state-dependent versions of the bound. The state-action CGFs do not depend on the policy and can be estimated from regular transitions, so the bound can be checked for any policy a priori. This gives a policy improvement algorithm in which every policy satisfies the failure constraint with high probability.

stat.ML · cs.LG · eess.SY · math.OC · math.PR · math.ST

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.21633

HERALD: High-Throughput Block Diffusion LLM Inference via CPU-GPU Cooperative KV Cache Retrieval

Omin Kwon, Doyeon Kim, Jongseok Park, Seung Yul Lee, Sehoon Kim, Ion Stoica, Jae W. Lee

原文摘要

Block diffusion LLMs (block dLLMs) improve decode throughput by generating tokens in parallel, and are emerging as a next-generation alternative to autoregressive LLMs. At long contexts, however, the KV cache remains a major throughput bottleneck in both memory bandwidth and capacity. For autoregressive LLMs, dynamic sparse attention is often combined with KV offloading to alleviate both bottlenecks, keeping the full KV cache in CPU DRAM and fetching only a critical subset to the GPU. However, KV subset selection and retrieval over bandwidth-limited PCIe incur substantial offloading overheads that often stall the GPU. We present HERALD, the first dynamic sparse attention method for block dLLMs that supports KV offloading. HERALD exploits a property of block dLLMs: a critical KV subset selected once per block is reused across all of its denoising steps, so the next block's selection can be hidden behind the current block's denoising. HERALD realizes this with a two-stage selection algorithm co-designed with its execution system. While the GPU denoises the current block, the CPU speculatively searches the full KV cache for the next block with a single query merged from the current block's B queries, cutting the selection compute by Bx; the GPU then refines the candidates into the final top-K subset with the next block's real queries. At K=1024, HERALD achieves near-lossless accuracy relative to Dense on LongBench and reasoning tasks across four block dLLMs, and improves decode throughput by up to 3.70x over Dense GPU-only inference, 2.59x over MAGE, a state-of-the-art block-dLLM sparse attention method, and 8.38x over MAGE-Offload, its direct KV-offloading extension.

cs.LG

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新论文 · v1arXiv:2611.03735

HyVox: Rethinking Low-Power Voice Intelligence towards Neuromorphic-von Neumann Edge Computing

Snehasis Addy, Eduardo Calle Ortiz, Prashant Shenoy, Phuc Nguyen

原文摘要

We present HyVox, a hybrid neuromorphic--von Neumann system for low-energy open-set speaker identification under tight energy budgets. HyVox splits the workload at the speaker embedding. A neuromorphic processor embeds the speech with sparse, event-driven computation, and a von Neumann microcontroller holds the speaker gallery and makes every decision: it scores queries by cosine similarity, rejects unknown speakers, and enrolls new users by writing a prototype, with no weight update. On the neuromorphic processor, a hierarchical temporal abstraction (HTA) SNN backbone learns a synaptic time constant per leaky integrate-and-fire (LIF) neuron, allocating 832 neurons across fast, medium, and slow timescales. A 32-neuron spiking embedding extractor (SEE), pretrained on episodes that each enroll new speakers and reject outsiders, turns the backbone output into an embedding for speakers unseen in training; its 32 spike counts are the only interface between the two processors. To fit the chip's synapse and integer budgets, we compress the backbone 6.5x to 29.0k synapses, train it for integer bit-shift dynamics, and fine-tune it with chip-accurate spiking training (CAST). CAST trains through a differentiable replica of the chip's integer neuron update, derived from its cycle-accurate simulator, so that gradient descent optimizes the exact weights and thresholds the chip runs; it doubles the top-1 accuracy of the mapped backbone on unseen speakers. On 20 Common Voice households of unseen speakers, HyVox reaches 99.6\% top-1 and 5.5\% rejection EER with 2 members and 94.9\% and 13.2\% with 20, and the network deployed on the neuromorphic chip matches its trained model. Each inference on a 3-s window takes 0.96 s and 1.63 mJ of dynamic energy, 42\% lower latency than the fastest embedded ANN baseline and 13.5x less dynamic and 5.8x less total energy than the most energy-efficient one.

cs.NE · cs.AI · cs.LG · cs.SD

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新论文 · v1arXiv:2611.02053

Learning to Look: A Foundation for Visual Understanding

Wenshuai Zhao, Yihao Wang, Benjamin Robson, Juho Kannala, Arno Solin

原文摘要

Vision-language models (VLMs) suffer from visual hallucination, such as saying an object that is not present in an image. We ask a more fundamental question: whether a VLM is even aware of where it is looking before understanding the semantic meaning in the image. We formulate this question as a LOOK task: identifying which parts of its visual input were available to the model. We construct the task in a self-supervised way without manually labeling the available region to the model. Our experiments show that existing VLMs fail to localize the visible region. We further show that fine-tuning VLMs on the proposed LOOK task improves a model's awareness of visual input.

cs.CV

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新论文 · v1arXiv:2611.02052

Same Function, Different Gradients: Evaluating Gradient Reliability under Reparameterization

Bum Jun Kim

原文摘要

Gradients are no longer used only to train models: Gradient magnitudes are now measurement instruments, used to rank attention heads for pruning, to decide per-example clipping in private training, to allocate adapter rank budgets, and to debug models. Across these workflows, larger gradient magnitudes are taken to mean greater importance. Even for a fixed input--output function, these gradients are computed in one of many possible parameter coordinate systems. For example, attention scores depend on the query and key projections only through the paired product $W_QW_K^\top$. Scaling a head's query projection by $c$ and the corresponding key projection by $1/c$ therefore leaves every output logit unchanged. This change in parameter coordinates rescales the corresponding query and key gradient norms by $1/c$ and $c$, respectively. To test whether gradient-based decisions remain unchanged under these coordinate changes, we introduce Gradient Reliability under Reparameterization (GRR). GRR rewrites a trained model in new parameter coordinates that compute exactly the same function, verifies that predictions are unchanged, and then tests whether the decisions derived from the gradients are unchanged too. Our audits span GLUE classifiers, a ViT on CIFAR-10, GPT-2-medium and GPT-2-large, a 1.1B-parameter Llama-style model on WikiText-2, and six trained LoRA adapters. Empirically, predictions are preserved throughout, but both the gradients and the gradient-based decisions change. Across 66 GLUE attention audits, the median top-3 overlap of raw head-gradient rankings is only $1/3$, and rankings and pruning masks change even under the mildest rescalings we test. In the largest of 14 global-pruning audits, the shifted ranking selects head masks that differ in four of 22 pruned heads and reach $50.92\%$ and $94.84\%$ validation accuracy on one ELECTRA SST-2 model.

cs.LG

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新论文 · v1arXiv:2611.02051

UniGS-SLAM: Monocular Gaussian SLAM with Enhanced Geometry and Open-Vocabulary Semantics

Xingyuan Ding, Sicheng Yu, Kaiyong Zhao, Hao Wang

原文摘要

Monocular semantic SLAM aims to reconstruct scene geometry and build semantic maps from RGB observations. Existing Gaussian-based methods often attach semantic labels or features to evolving Gaussian primitives, making semantic fusion difficult to maintain and increasing reconstruction overhead. Hybrid Gaussian--voxel representations provide more stable semantic support, but often rely on RGB-D input or known poses and do not revise historical semantic evidence after geometry updates. We present UniGS-SLAM, an RGB-only framework that decouples open-vocabulary semantics from Gaussian reconstruction while preserving bidirectional interaction. Foundation priors enhance monocular depth and pose estimation, while semantic observations are fused into an independent sparse voxel map with observation provenance, enabling historical contributions to be revised as reconstruction evolves. The fused semantics further guide Gaussian filtering and local surface refinement. Experiments on Replica, ScanNet, and KITTI-360 show leading tracking, rendering, and geometric reconstruction performance among representative RGB-only methods, together with strong open-set semantic mapping on Replica and ScanNet. We also achieve a 2.37-fold reconstruction speedup over the fastest compared semantic SLAM baseline. Project page: https://3dagentworld.github.io/UniGS-SLAM/.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02049

Reinforcement Learning in High-Energy Physics: A Survey from Accelerator Control to Theory Exploration

Luca Anzalone

原文摘要

Reinforcement learning (RL) is a machine learning paradigm in which an agent learns a decision-making policy by interacting with an environment, guided by a scalar reward rather than explicit supervision. Supervised and unsupervised learning have become indispensable tools in high-energy physics (HEP), but they learn from fixed datasets, whereas many HEP tasks are closed-loop decisions: the data an algorithm observes depend on its choices, the feedback is sparse or delayed, and the objective is often measured on a real machine or in a stochastic simulation, not by a differentiable model. This survey reviews the application of RL to such tasks: accelerator control and feedback, data quality monitoring, jet grooming, clustering, and representation, charged particle tracking, beyond-the-Standard-Model (BSM) and string model building, detector design, trigger control, analysis, and formal quantum field theory computation. It provides a self-contained introduction to the underlying RL methods, from model-free and model-based to multi-agent, offline, inverse, and bandit approaches, reviews each domain against its traditional methods, and summarizes the head-to-head comparisons. The applications range from policies deployed on real accelerators and evaluated on recorded collision data to exploratory studies in formal theory. RL is most advantageous for repeated tasks with a fast simulator, decisions inside fast control loops, operation under faults and corrupted measurements, physics-based rewards in place of labels, and sequential discrete constructions with a reusable policy, while Bayesian optimization, genetic algorithms, and differentiable methods remain competitive elsewhere. We conclude with the open challenges that recur across domains, including reward design, sim-to-real transfer, safety, the lack of shared benchmarks, and the statistical validity of policy-driven decisions.

hep-ex · cs.LG · hep-ph

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2403.10232

Over-fitting Control in Deep Nonlinear Matrix Completion via Gradual Nonsmooth Regularization

Sajad Faramarzi, Farzan Haddadi, Sajjad Amini, Masoud Ahookhosh, Symeon Chatzinotas

原文摘要

Conventional matrix completion methods exploit the underlying low-rank structure of a target matrix to recover its missing entries, but may be inadequate for capturing complex nonlinear dependencies. Deep neural networks have therefore been increasingly adopted for nonlinear matrix completion (NLMC). However, their high representational capacity, particularly in deep fully connected neural networks (FCNNs), can lead to over-fitting when training is restricted to incomplete observations. In this paper, we propose a Deep Neural Network with Nonsmooth Regularization (DNN-NSR) framework to address this issue by incorporating two complementary structural priors: sparsity of intermediate representations and low-rankness of layer-wise weight matrices. Specifically, the $\ell_1$-norm of hidden representations promotes sparse latent features, while the nuclear norm of weight matrices encourages low-rank layer-wise transformations. To optimize the resulting nonsmooth and nonconvex objective, we develop an extrapolated proximal gradient algorithm that employs soft-thresholding and singular-value thresholding to handle the two regularization terms, respectively. We further introduce a gradual regularization strategy that progressively increases the influence of these structural penalties during training. Under suitable assumptions, we establish convergence properties of the proposed algorithm toward a critical point of the training objective. The effectiveness of DNN-NSR is evaluated through extensive experiments on synthetic matrix recovery, single-image inpainting, and recommender systems using the MovieLens 100K and MovieLens 1M datasets. Comparisons with representative linear and nonlinear matrix completion methods demonstrate the potential of the proposed framework to improve recovery performance by jointly exploiting sparse latent representations and low-rank network transformations.

cs.IT · cs.LG

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